diff --git a/.gitignore b/.gitignore
index 501d3d8..0d5817b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,9 @@ __pycache__/
 # C extensions
 *.so
 
+# IntelliJ Idea
+*.idea
+
 # Distribution / packaging
 .Python
 env/
diff --git a/.idea/Vision-Rat-Brain.iml b/.idea/Vision-Rat-Brain.iml
deleted file mode 100644
index e8baac3..0000000
--- a/.idea/Vision-Rat-Brain.iml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.5.4 (C:\Users\xeroj\Anaconda3\envs\myWindowsCV\python.exe)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 0eefe32..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,5 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="PROJECT_PROFILE" />
-  </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index 28cad29..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6.0 (C:\Users\xeroj\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index e61280b..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Vision-Rat-Brain.iml" filepath="$PROJECT_DIR$/.idea/Vision-Rat-Brain.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index d26e51d..0000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,33 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ChangeListManager">
-    <list default="true" id="3f997c73-b6c9-4fd1-a872-a7ceaf411957" name="Default" comment="" />
-    <option name="TRACKING_ENABLED" value="true" />
-    <option name="SHOW_DIALOG" value="false" />
-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
-  </component>
-  <component name="PropertiesComponent">
-    <property name="last_opened_file_path" value="$USER_HOME$/Anaconda3/envs/py36/python.exe" />
-  </component>
-  <component name="ShelveChangesManager" show_recycled="false">
-    <option name="remove_strategy" value="false" />
-  </component>
-  <component name="TaskManager">
-    <task active="true" id="Default" summary="Default task">
-      <created>1509931484125</created>
-      <option name="number" value="Default" />
-      <option name="presentableId" value="Default" />
-      <updated>1509931484125</updated>
-    </task>
-    <servers />
-  </component>
-  <component name="VcsContentAnnotationSettings">
-    <option name="myLimit" value="2678400000" />
-  </component>
-  <component name="XDebuggerManager">
-    <breakpoint-manager />
-    <watches-manager />
-  </component>
-</project>
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..f9cb6a8
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,92 @@
+# Contributing
+
+When contributing to this repository, please first discuss the change you wish to make via issue,
+email, or any other method with the owners of this repository before making a change. 
+
+Please note we have a code of conduct, please follow it in all your interactions with the project.
+
+## Pull Request Process
+
+1. Ensure any install or build dependencies are removed before the end of the layer when doing a 
+   build.
+2. Update the README.md with details of changes to the interface, this includes new environment 
+   variables, exposed ports, useful file locations and container parameters.
+3. Increase the version numbers in any examples files and the README.md to the new version that this
+   Pull Request would represent. The versioning scheme we use is [SemVer](http://semver.org/).
+4. You may merge the Pull Request in once you have the sign-off of two other developers, or if you 
+   do not have permission to do that, you may request the second reviewer to merge it for you.
+
+## Code of Conduct
+
+### Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, gender identity and expression, level of experience,
+nationality, personal appearance, race, religion, or sexual identity and
+orientation.
+
+### Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+### Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+### Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+### Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at [INSERT EMAIL ADDRESS]. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+### Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at [http://contributor-covenant.org/version/1/4][version]
+
+[homepage]: http://contributor-covenant.org
+[version]: http://contributor-covenant.org/version/1/4/
\ No newline at end of file
diff --git a/LICENSE b/LICENSE.md
similarity index 96%
rename from LICENSE
rename to LICENSE.md
index e5cc1f6..4f5f882 100644
--- a/LICENSE
+++ b/LICENSE.md
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2016 Aditya Phan
+Copyright (c) 2018 Jose G. Perez
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..099ded1
--- /dev/null
+++ b/README.md
@@ -0,0 +1,49 @@
+# Vision Rat Brain
+This repository contains the supporting code for publications and projects done by Jose G. Perez
+
+It is not ready for public use yet.
+Expect it to be complete by the end of May 2018 with an installation guide and explanations.
+
+Any questions may be directed to me via pull requests.
+
+## Getting Started
+
+Here's a breakdown of all the projects included in this repository
+
+### Main projects 
+* dataset/
+    * Used to preprocess (crop, resize, compress) atlas images into NPZ files for use in other projects 
+* feature_matching_v1/
+    * Proof of concept used for region matching using SIFT and RANSAC
+    * http://www.abstractsonline.com/pp8/#!/4376/presentation/4332
+* feature_matching_v2/
+    * Region matching improved with custom RANSAC algorithm
+* feature_matching_v3/
+    * Whole image matching using SIFT and dynamic programming to derive plate correspondences
+    * https://www.frontiersin.org/articles/10.3389/fnsys.2018.00007/abstract
+### Side projects
+* auto_encoder/
+    * Deriving atlas correspondences using AE 
+* matching_networks/
+    * Deriving atlas correspondences through image similarities using matching networks
+* siamese_networks/
+    * Deriving atlas correspondences through image similarities using matching networks
+
+### Prerequisites
+
+### Installing
+
+## Contributing
+
+Please read [CONTRIBUTING.md](http://github.com/DeveloperJose/Vision-Rat-Brain) for details on our code of conduct, and the process for submitting pull requests to us.
+
+## Versioning
+
+We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/DeveloperJose/Vision-Rat-Brain/tags). 
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
+
+## Acknowledgments
+
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diff --git a/Vision-Rat-Brain-F/data/filmstrip.png b/Vision-Rat-Brain-F/data/filmstrip.png
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diff --git a/Vision-Rat-Brain-F/data/qrangeslider.ui b/Vision-Rat-Brain-F/data/qrangeslider.ui
deleted file mode 100644
index a449a51..0000000
--- a/Vision-Rat-Brain-F/data/qrangeslider.ui
+++ /dev/null
@@ -1,84 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<ui version="4.0">
- <class>Form</class>
- <widget class="QWidget" name="Form">
-  <property name="geometry">
-   <rect>
-    <x>0</x>
-    <y>0</y>
-    <width>500</width>
-    <height>16</height>
-   </rect>
-  </property>
-  <property name="windowTitle">
-   <string>Form</string>
-  </property>
-  <property name="styleSheet">
-   <string notr="true">QGroupBox {
-    background: #111; /*qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #111, stop:1 #333);*/
-    border:  0px;
-    padding: 0px;
-}
-#_handle {
-     border: 0px;
-     background: #393; /*qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #4a4, stop:1 #151);*/
-}
-QSplitter::handle {
-     border: 0px solid #5c5c5c;
-     background: #393; /*qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #4a4, stop:1 #151);*/
-}
-QSplitter::handle:vertical {
-     height: 4px;
-}
-QSplitter::handle:pressed {
-     background: qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #ccaa45, stop:1 #886623);
-}
-
-</string>
-  </property>
-  <layout class="QGridLayout" name="gridLayout">
-   <property name="margin">
-    <number>0</number>
-   </property>
-   <property name="spacing">
-    <number>0</number>
-   </property>
-   <item row="0" column="0">
-    <widget class="QSplitter" name="splitter">
-     <property name="minimumSize">
-      <size>
-       <width>0</width>
-       <height>0</height>
-      </size>
-     </property>
-     <property name="maximumSize">
-      <size>
-       <width>16777215</width>
-       <height>16777215</height>
-      </size>
-     </property>
-     <property name="orientation">
-      <enum>Qt::Horizontal</enum>
-     </property>
-     <widget class="QGroupBox" name="_head">
-      <property name="title">
-       <string/>
-      </property>
-     </widget>
-     <widget class="QGroupBox" name="_handle">
-      <property name="title">
-       <string/>
-      </property>
-     </widget>
-     <widget class="QGroupBox" name="_tail">
-      <property name="title">
-       <string/>
-      </property>
-     </widget>
-    </widget>
-   </item>
-  </layout>
- </widget>
- <resources/>
- <connections/>
-</ui>
diff --git a/Vision-Rat-Brain-F/data/sin.png b/Vision-Rat-Brain-F/data/sin.png
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diff --git a/Vision-Rat-Brain-F/results/Warp_Results.xlsx b/Vision-Rat-Brain-F/results/Warp_Results.xlsx
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diff --git a/Vision-Rat-Brain-F/results/csv/Level-34-range2-30pts.csv b/Vision-Rat-Brain-F/results/csv/Level-34-range2-30pts.csv
deleted file mode 100644
index 057f700..0000000
--- a/Vision-Rat-Brain-F/results/csv/Level-34-range2-30pts.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-warp_points,warp_disp,plate,matches,inliers
-30,30,34,83,35
-30,31,34,44,14
-30,32,34,26,11
-30,33,69,34,12
-30,34,34,27,10
-30,35,34,26,12
-30,36,34,59,18
-30,37,34,66,21
-30,38,34,52,24
-30,39,34,60,22
-30,40,34,33,12
-30,41,34,34,10
-30,42,34,35,14
-30,43,13,20,14
-30,44,34,36,10
-30,45,20,22,11
-30,46,49,22,10
-30,47,2,24,12
-30,48,58,23,13
-30,49,72,16,9
diff --git a/Vision-Rat-Brain-F/results/csv/level-34-15pts.csv b/Vision-Rat-Brain-F/results/csv/level-34-15pts.csv
deleted file mode 100644
index 7cb7356..0000000
--- a/Vision-Rat-Brain-F/results/csv/level-34-15pts.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-warp_points,warp_disp,plate,matches,inliers
-15,0,34,587,577
-15,1,34,422,410
-15,2,34,456,417
-15,3,34,405,392
-15,4,34,369,343
-15,5,34,333,269
-15,6,34,349,293
-15,7,34,337,271
-15,8,34,272,172
-15,9,34,306,155
-15,10,34,232,140
-15,11,34,265,167
-15,12,34,204,101
-15,13,34,154,82
-15,14,34,125,54
-15,15,34,193,93
-15,16,34,139,72
-15,17,34,160,98
-15,18,34,167,88
-15,19,34,109,38
-15,20,34,154,72
-15,21,34,105,65
-15,22,34,96,48
-15,23,34,132,47
-15,24,34,150,87
-15,25,34,58,28
-15,26,34,33,12
-15,27,34,52,15
-15,28,34,82,31
-15,29,34,92,33
diff --git a/Vision-Rat-Brain-F/results/csv/level-34-30pts.csv b/Vision-Rat-Brain-F/results/csv/level-34-30pts.csv
deleted file mode 100644
index 0f85fee..0000000
--- a/Vision-Rat-Brain-F/results/csv/level-34-30pts.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-warp_points,warp_disp,plate,matches,inliers
-30,0,34,575,558
-30,1,34,459,431
-30,2,34,398,388
-30,3,34,378,345
-30,4,34,365,253
-30,5,34,296,243
-30,6,34,279,195
-30,7,34,229,135
-30,8,34,217,124
-30,9,34,240,122
-30,10,34,224,121
-30,11,34,109,70
-30,12,34,158,95
-30,13,34,86,36
-30,14,34,131,73
-30,15,34,116,52
-30,16,34,75,33
-30,17,34,68,30
-30,18,34,81,27
-30,19,34,90,41
-30,20,34,48,16
-30,21,34,119,80
-30,22,34,42,23
-30,23,34,68,27
-30,24,3,31,13
-30,25,34,63,43
-30,26,34,25,13
-30,27,34,44,15
-30,28,34,41,15
-30,29,34,54,15
diff --git a/Vision-Rat-Brain-F/sm_regular.npz b/Vision-Rat-Brain-F/sm_regular.npz
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diff --git a/atlas_pw.npz b/atlas_pw.npz
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diff --git a/auto_encoder/README.md b/auto_encoder/README.md
deleted file mode 100644
index 7ff29ef..0000000
--- a/auto_encoder/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Unsupervised image retrieval
-
-This code is related to this [blog post](https://medium.com/p/867a671b7e65/edit) on using convolutional denoising 
-autoencoder for content based image retrieval.
-
-We use [keras]() machine learning python library and [mnist dataset](http://yann.lecun.com/exdb/mnist/).
-
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-bleach==1.5.0
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diff --git a/dataset/.gitignore b/dataset/.gitignore
new file mode 100644
index 0000000..fca3e5c
--- /dev/null
+++ b/dataset/.gitignore
@@ -0,0 +1,109 @@
+# Raw images
+atlas_sw/
+atlas_s/
+atlas_pw/
+atlas_testing/
+*.png
+*.jpg
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
diff --git a/dataset/bounding.py b/dataset/bounding.py
new file mode 100644
index 0000000..5207732
--- /dev/null
+++ b/dataset/bounding.py
@@ -0,0 +1,80 @@
+# Author: Jose G Perez
+# Version: 1.2
+# Last Modified: Jan 31st, 2018
+from timeit import default_timer as timer
+from PIL import Image
+import numpy as np
+import os
+
+WIDTH = 240
+HEIGHT = 300
+WHITE_THRESHOLD = 235
+
+
+def process_plate(filename, split=False):
+    im = Image.open(filename).convert("L")
+
+    # Split in half if required
+    if split:
+        box = (0, 0, im.width // 2, im.height)
+        crop = im.crop(box)
+        crop.load()
+        im = crop
+
+    im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)
+    im = np.array(im, dtype=np.uint8)
+
+    # Convert values very close to white to white for cropping
+    im[im >= WHITE_THRESHOLD] = 255
+
+    # Bounding box cropping
+    # https://stackoverflow.com/questions/9396312/use-python-pil-or-similar-to-shrink-whitespace
+    idx = np.where(im - 255)
+    box = list(map(min, idx))[::-1] + list(map(max, idx))[::-1]
+    region = Image.fromarray(im).crop(box)
+    region = region.resize((WIDTH, HEIGHT), Image.LANCZOS)
+    im_cropped = np.array(region, dtype=np.uint8)
+
+    return im, im_cropped
+
+
+def process_atlas(folder, prefix, ext, zfill, plate_min, plate_max, split):
+    atlas_im = []
+    atlas_label = []
+    atlas_original = []
+    print('[', end='', flush=True)
+    for plate in range(plate_min, plate_max+1):
+        filename = prefix + str(plate).zfill(zfill) + ext
+        filename = os.path.join(folder, filename)
+
+        print(plate, ',', end='', flush=True)
+        if not os.path.exists(filename):
+            print("Couldn't find ", filename, ", skipping")
+            continue
+
+        im, im_cropped = process_plate(filename, split)
+
+        atlas_im.append(im_cropped)
+        atlas_label.append(plate)
+        atlas_original.append(im)
+
+    print(']\n', end='', flush=True)
+    return np.asarray(atlas_im), np.asarray(atlas_label), np.asarray(atlas_original)
+
+
+if __name__ == '__main__':
+    print("===== Starting timer")
+    time_start = timer()
+
+    print("Processing S...")
+    s_im, s_label, s_original = process_atlas('atlas_s', 'Level-', '.jpg', 2, 1, 73, False)
+
+    print("Processing PW...")
+    pw_im, pw_label, pw_original = process_atlas('atlas_pw', 'RBSC7-', '.jpg', 3, 1, 161, True)
+
+    print("Saving...")
+    np.savez_compressed('S_BB_V', images=s_im, labels=s_label, originals=s_original)
+    np.savez_compressed('PW_BB_V', images=pw_im, labels=pw_label, originals=pw_original)
+
+    duration = timer() - time_start
+    print("Program took %.3fs" % duration)
\ No newline at end of file
diff --git a/dataset/fetch_swanson_atlas.py b/dataset/fetch_swanson_atlas.py
new file mode 100644
index 0000000..49ba05a
--- /dev/null
+++ b/dataset/fetch_swanson_atlas.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+import urllib2
+
+# Swanson's 3rd edition brain map atlas
+complete_url = "http://larrywswanson.com/wp-content/uploads/2015/03/Complete-Atlas-Level-"
+map_url = "http://larrywswanson.com/wp-content/uploads/2015/03/Map-only-Atlas-Level-"
+ext = ".pdf"
+
+def fetch_atlas_level(level):
+    fetch_website(complete_url + str(level).zfill(2) + ext)
+    #fetch_website(map_url + str(level).zfill(2) + ext)
+    
+def fetch_website(url):
+    file_name = url.split('/')[-1]
+    u = urllib2.urlopen(url)
+    f = open(file_name, 'wb')
+    meta = u.info()
+    file_size = int(meta.getheaders("Content-Length")[0])
+    print("Downloading: %s Bytes: %s" % (file_name, file_size))
+    
+    file_size_dl = 0
+    block_sz = 8192
+    while True:
+        buffer = u.read(block_sz)
+        if not buffer:
+            break
+    
+        file_size_dl += len(buffer)
+        f.write(buffer)
+        status = r"%10d  [%3.2f%%]" % (file_size_dl, file_size_dl * 100. / file_size)
+        status = status + chr(8)*(len(status)+1)
+        print(status)
+    
+    print("\n")
+    f.close()
+
+# ---=== Main
+print("Starting fetching script")
+
+for i in range(1, 74):
+    fetch_atlas_level(i)
\ No newline at end of file
diff --git a/dataset/viewer.py b/dataset/viewer.py
new file mode 100644
index 0000000..acf46ac
--- /dev/null
+++ b/dataset/viewer.py
@@ -0,0 +1,13 @@
+import numpy as np
+import pylab as plt
+
+s_data = np.load('S_BB_V.npz')
+s_im = s_data['images']
+s_label = s_data['labels']
+s_orig = s_data['originals']
+
+pw_data = np.load('PW_BB_V.npz')
+pw_im = pw_data['images']
+pw_label = pw_data['labels']
+pw_orig = pw_data['originals']
+
diff --git a/Vision-Rat-Brain-F/.gitignore b/feature_matching_v1/.gitignore
similarity index 97%
rename from Vision-Rat-Brain-F/.gitignore
rename to feature_matching_v1/.gitignore
index 058e3f3..c200ba2 100644
--- a/Vision-Rat-Brain-F/.gitignore
+++ b/feature_matching_v1/.gitignore
@@ -10,6 +10,9 @@ atlas_swanson_pdf/
 # Testing images
 part.*
 
+# Similarity matrix npz
+*-M.npz
+
 # Spyder
 *.spyproject/
 
diff --git a/Vision-Rat-Brain-F/LICENSE.txt b/feature_matching_v1/LICENSE.txt
similarity index 100%
rename from Vision-Rat-Brain-F/LICENSE.txt
rename to feature_matching_v1/LICENSE.txt
diff --git a/Vision-Rat-Brain-F/clean.sh b/feature_matching_v1/clean.sh
similarity index 100%
rename from Vision-Rat-Brain-F/clean.sh
rename to feature_matching_v1/clean.sh
diff --git a/Vision-Rat-Brain-F/config.py b/feature_matching_v1/config.py
similarity index 100%
rename from Vision-Rat-Brain-F/config.py
rename to feature_matching_v1/config.py
diff --git a/Vision-Rat-Brain-F/dialog.py b/feature_matching_v1/dialog.py
similarity index 100%
rename from Vision-Rat-Brain-F/dialog.py
rename to feature_matching_v1/dialog.py
diff --git a/Vision-Rat-Brain-F/feature.py b/feature_matching_v1/feature.py
similarity index 100%
rename from Vision-Rat-Brain-F/feature.py
rename to feature_matching_v1/feature.py
diff --git a/Vision-Rat-Brain-F/graph.py b/feature_matching_v1/graph.py
similarity index 100%
rename from Vision-Rat-Brain-F/graph.py
rename to feature_matching_v1/graph.py
diff --git a/Vision-Rat-Brain-F/prototype.py b/feature_matching_v1/prototype.py
similarity index 100%
rename from Vision-Rat-Brain-F/prototype.py
rename to feature_matching_v1/prototype.py
diff --git a/Vision-Rat-Brain-F/pw.py b/feature_matching_v1/pw.py
similarity index 100%
rename from Vision-Rat-Brain-F/pw.py
rename to feature_matching_v1/pw.py
diff --git a/Vision-Rat-Brain-F/qrangeslider.py b/feature_matching_v1/qrangeslider.py
similarity index 100%
rename from Vision-Rat-Brain-F/qrangeslider.py
rename to feature_matching_v1/qrangeslider.py
diff --git a/Vision-Rat-Brain-F/scripts_batch_processing/batch_warp.py b/feature_matching_v1/scripts_batch_processing/batch_warp.py
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_batch_processing/batch_warp.py
rename to feature_matching_v1/scripts_batch_processing/batch_warp.py
diff --git a/Vision-Rat-Brain-F/scripts_batch_processing/fetch_atlas.py b/feature_matching_v1/scripts_batch_processing/fetch_atlas.py
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_batch_processing/fetch_atlas.py
rename to feature_matching_v1/scripts_batch_processing/fetch_atlas.py
diff --git a/Vision-Rat-Brain-F/scripts_testing/plate-34.jpg b/feature_matching_v1/scripts_testing/plate-34.jpg
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_testing/plate-34.jpg
rename to feature_matching_v1/scripts_testing/plate-34.jpg
diff --git a/Vision-Rat-Brain-F/scripts_testing/plate-70.jpg b/feature_matching_v1/scripts_testing/plate-70.jpg
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_testing/plate-70.jpg
rename to feature_matching_v1/scripts_testing/plate-70.jpg
diff --git a/Vision-Rat-Brain-F/scripts_testing/region-34.jpg b/feature_matching_v1/scripts_testing/region-34.jpg
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_testing/region-34.jpg
rename to feature_matching_v1/scripts_testing/region-34.jpg
diff --git a/Vision-Rat-Brain-F/scripts_testing/region-70-hq.jpg b/feature_matching_v1/scripts_testing/region-70-hq.jpg
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_testing/region-70-hq.jpg
rename to feature_matching_v1/scripts_testing/region-70-hq.jpg
diff --git a/Vision-Rat-Brain-F/scripts_testing/region-70.jpg b/feature_matching_v1/scripts_testing/region-70.jpg
similarity index 100%
rename from Vision-Rat-Brain-F/scripts_testing/region-70.jpg
rename to feature_matching_v1/scripts_testing/region-70.jpg
diff --git a/Vision-Rat-Brain-F/similarity.py b/feature_matching_v1/similarity.py
similarity index 100%
rename from Vision-Rat-Brain-F/similarity.py
rename to feature_matching_v1/similarity.py
diff --git a/Vision-Rat-Brain-F/sm.npz b/feature_matching_v1/sm.npz
similarity index 100%
rename from Vision-Rat-Brain-F/sm.npz
rename to feature_matching_v1/sm.npz
diff --git a/Vision-Rat-Brain-F/testing.py b/feature_matching_v1/testing.py
similarity index 100%
rename from Vision-Rat-Brain-F/testing.py
rename to feature_matching_v1/testing.py
diff --git a/Vision-Rat-Brain-F/thread.py b/feature_matching_v1/thread.py
similarity index 100%
rename from Vision-Rat-Brain-F/thread.py
rename to feature_matching_v1/thread.py
diff --git a/Vision-Rat-Brain-F/timing.py b/feature_matching_v1/timing.py
similarity index 100%
rename from Vision-Rat-Brain-F/timing.py
rename to feature_matching_v1/timing.py
diff --git a/feature_matching/LICENSE.txt b/feature_matching_v2/LICENSE.txt
similarity index 100%
rename from feature_matching/LICENSE.txt
rename to feature_matching_v2/LICENSE.txt
diff --git a/feature_matching/README.md b/feature_matching_v2/README.md
similarity index 100%
rename from feature_matching/README.md
rename to feature_matching_v2/README.md
diff --git a/feature_matching/asift.py b/feature_matching_v2/asift.py
similarity index 100%
rename from feature_matching/asift.py
rename to feature_matching_v2/asift.py
diff --git a/feature_matching/atlas.py b/feature_matching_v2/atlas.py
similarity index 100%
rename from feature_matching/atlas.py
rename to feature_matching_v2/atlas.py
diff --git a/feature_matching/clean.sh b/feature_matching_v2/clean.sh
similarity index 100%
rename from feature_matching/clean.sh
rename to feature_matching_v2/clean.sh
diff --git a/feature_matching/config.py b/feature_matching_v2/config.py
similarity index 100%
rename from feature_matching/config.py
rename to feature_matching_v2/config.py
diff --git a/feature_matching/dialog.py b/feature_matching_v2/dialog.py
similarity index 100%
rename from feature_matching/dialog.py
rename to feature_matching_v2/dialog.py
diff --git a/feature_matching/graph.py b/feature_matching_v2/graph.py
similarity index 100%
rename from feature_matching/graph.py
rename to feature_matching_v2/graph.py
diff --git a/feature_matching/matching.py b/feature_matching_v2/matching.py
similarity index 100%
rename from feature_matching/matching.py
rename to feature_matching_v2/matching.py
diff --git a/feature_matching/prototype.py b/feature_matching_v2/prototype.py
similarity index 100%
rename from feature_matching/prototype.py
rename to feature_matching_v2/prototype.py
diff --git a/feature_matching/qrangeslider.py b/feature_matching_v2/qrangeslider.py
similarity index 100%
rename from feature_matching/qrangeslider.py
rename to feature_matching_v2/qrangeslider.py
diff --git a/feature_matching/ransac.py b/feature_matching_v2/ransac.py
similarity index 100%
rename from feature_matching/ransac.py
rename to feature_matching_v2/ransac.py
diff --git a/feature_matching/sift.py b/feature_matching_v2/sift.py
similarity index 100%
rename from feature_matching/sift.py
rename to feature_matching_v2/sift.py
diff --git a/feature_matching/testing.py b/feature_matching_v2/testing.py
similarity index 100%
rename from feature_matching/testing.py
rename to feature_matching_v2/testing.py
diff --git a/feature_matching/thread.py b/feature_matching_v2/thread.py
similarity index 100%
rename from feature_matching/thread.py
rename to feature_matching_v2/thread.py
diff --git a/feature_matching/timing.py b/feature_matching_v2/timing.py
similarity index 100%
rename from feature_matching/timing.py
rename to feature_matching_v2/timing.py
diff --git a/feature_matching/util.py b/feature_matching_v2/util.py
similarity index 100%
rename from feature_matching/util.py
rename to feature_matching_v2/util.py
diff --git a/feature_matching/warping.py b/feature_matching_v2/warping.py
similarity index 100%
rename from feature_matching/warping.py
rename to feature_matching_v2/warping.py
diff --git a/auto_encoder/computed_data/.gitkeep b/feature_matching_v3/.gitignore
similarity index 100%
rename from auto_encoder/computed_data/.gitkeep
rename to feature_matching_v3/.gitignore
diff --git a/feature_matching_v3/Bregma_PW1.csv b/feature_matching_v3/Bregma_PW1.csv
new file mode 100644
index 0000000..2fe6b2b
--- /dev/null
+++ b/feature_matching_v3/Bregma_PW1.csv
@@ -0,0 +1 @@
+6.7,6.2,5.7,5.2,4.7,4.2,3.7,3.2,2.7,2.2,1.7,1.6,1.2,1,0.7,0.48,0.2,-0.26,-0.3,-0.4,-0.8,-0.92,-1.3,-1.4,-1.8,-2.12,-2.3,-2.56,-2.8,-3.14,-3.3,-3.6,-3.8,-4.16,-4.3,-4.48,-4.8,-5.2,-5.3,-5.6,-5.8,-6.04,-6.3,-6.72,-6.8,-7.04,-7.3,-7.64,-7.8,-8,-8.3,-8.72,-8.8,-9.16,-9.3,-9.68,-9.8,-10.04,-10.3,-10.52,-10.8,-11,-11.3,-11.6,-11.8,-11.96,-12.3,-12.72,-12.8,-13.24,-13.3,-13.68,-13.8,-14.08,-14.3,-14.6
\ No newline at end of file
diff --git a/feature_matching_v3/Bregma_PW2.csv b/feature_matching_v3/Bregma_PW2.csv
new file mode 100644
index 0000000..d2f4e64
--- /dev/null
+++ b/feature_matching_v3/Bregma_PW2.csv
@@ -0,0 +1 @@
+6.7,6.2,5.7,5.2,4.7,4.2,3.7,3.2,2.7,2.2,1.7,1.6,1.2,1,0.7,0.48,0.2,-0.26,-0.3,-0.4,-0.8,-0.92,-1.3,-1.4,-1.6,-1.8,-1.88,-2.12,-2.3,-2.56,-2.8,-3.14,-3.3,-3.6,-3.8,-4.16,-4.3,-4.48,-4.8,-5.2,-5.3,-5.6,-5.8,-6.04,-6.3,-6.72,-6.8,-7.04,-7.3,-7.64,-7.8,-8,-8.3,-8.72,-8.8,-9.16,-9.3,-9.68,-9.8,-10.04,-10.3,-10.52,-10.8,-11,-11.3,-11.6,-11.8,-11.96,-12.3,-12.72,-12.8,-13.24,-13.3,-13.68,-13.8,-14.08,-14.3,-14.6
diff --git a/feature_matching_v3/Bregma_PW3.csv b/feature_matching_v3/Bregma_PW3.csv
new file mode 100644
index 0000000..cd997f0
--- /dev/null
+++ b/feature_matching_v3/Bregma_PW3.csv
@@ -0,0 +1 @@
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diff --git a/feature_matching_v3/Bregma_PW3_M.csv b/feature_matching_v3/Bregma_PW3_M.csv
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+++ b/feature_matching_v3/deep_style.py
@@ -0,0 +1,1225 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Neural Style Transfer with Eager Execution",
+      "version": "0.3.2",
+      "provenance": [],
+      "private_outputs": true,
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "jo5PziEC4hWs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Neural Style Transfer with tf.keras\n",
+        "\n",
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/research/nst_blogpost/4_Neural_Style_Transfer_with_Eager_Execution.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/research/nst_blogpost/4_Neural_Style_Transfer_with_Eager_Execution.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aDyGj8DmXCJI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this tutorial, we will learn how to use deep learning to compose images in the style of another image (ever wish you could paint like Picasso or Van Gogh?). This is known as **neural style transfer**! This is a technique outlined in [Leon A. Gatys' paper, A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576), which is a great read, and you should definitely check it out. \n",
+        "\n",
+        "But, what is neural style transfer?\n",
+        "\n",
+        "Neural style transfer is an optimization technique used to take three images, a **content** image, a **style reference** image (such as an artwork by a famous painter), and the **input** image you want to style -- and blend them together such that the input image is transformed to look like the content image, but “painted” in the style of the style image.\n",
+        "\n",
+        "\n",
+        "For example, let’s take an image of this turtle and Katsushika Hokusai's *The Great Wave off Kanagawa*:\n",
+        "\n",
+        "<img src=\"https://github.com/tensorflow/models/blob/master/research/nst_blogpost/Green_Sea_Turtle_grazing_seagrass.jpg?raw=1\" alt=\"Drawing\" style=\"width: 200px;\"/>\n",
+        "<img src=\"https://github.com/tensorflow/models/blob/master/research/nst_blogpost/The_Great_Wave_off_Kanagawa.jpg?raw=1\" alt=\"Drawing\" style=\"width: 200px;\"/>\n",
+        "\n",
+        "[Image of Green Sea Turtle](https://commons.wikimedia.org/wiki/File:Green_Sea_Turtle_grazing_seagrass.jpg)\n",
+        "-By P.Lindgren [CC BY-SA 3.0  (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Common\n",
+        "\n",
+        "\n",
+        "Now how would it look like if Hokusai decided to paint the picture of this Turtle exclusively with this style? Something like this?\n",
+        "\n",
+        "<img src=\"https://github.com/tensorflow/models/blob/master/research/nst_blogpost/wave_turtle.png?raw=1\" alt=\"Drawing\" style=\"width: 500px;\"/>\n",
+        "\n",
+        "Is this magic or just deep learning? Fortunately, this doesn’t involve any witchcraft: style transfer is a fun and interesting technique that showcases the capabilities and internal representations of neural networks.  \n",
+        "\n",
+        "The principle of neural style transfer is to define two distance functions, one that describes how different the content of two images are , $L_{content}$, and one that describes the difference between two images in terms of their style, $L_{style}$. Then, given three images, a desired style image, a desired content image, and the input image (initialized with the content image), we try to transform the input image to minimize the content distance with the content image and its style distance with the style image. \n",
+        "In summary, we’ll take the base input image, a content image that we want to match, and the style image that we want to match. We’ll transform the base input image by minimizing the content and style distances (losses) with backpropagation, creating an image that matches the content of the content image and the style of the style image. \n",
+        "\n",
+        "### Specific concepts that will be covered:\n",
+        "In the process, we will build practical experience and develop intuition around the following concepts\n",
+        "\n",
+        "* **Eager Execution** - use TensorFlow's imperative programming environment that evaluates operations immediately \n",
+        "  * [Learn more about eager execution](https://www.tensorflow.org/programmers_guide/eager)\n",
+        "  * [See it in action](https://www.tensorflow.org/get_started/eager)\n",
+        "* ** Using [Functional API](https://keras.io/getting-started/functional-api-guide/) to define a model** - we'll build a subset of our model that will give us access to the necessary intermediate activations using the Functional API \n",
+        "* **Leveraging feature maps of a pretrained model** - Learn how to use pretrained models and their feature maps \n",
+        "* **Create custom training loops** - we'll examine how to set up an optimizer to minimize a given loss with respect to input parameters\n",
+        "\n",
+        "### We will follow the general steps to perform style transfer:\n",
+        "\n",
+        "1. Visualize data\n",
+        "2. Basic Preprocessing/preparing our data\n",
+        "3. Set up loss functions \n",
+        "4. Create model\n",
+        "5. Optimize for loss function\n",
+        "\n",
+        "**Audience:** This post is geared towards intermediate users who are comfortable with basic machine learning concepts. To get the most out of this post, you should: \n",
+        "* Read [Gatys' paper](https://arxiv.org/abs/1508.06576) - we'll explain along the way, but the paper will provide a more thorough understanding of the task\n",
+        "* [Understand reducing loss with gradient descent](https://developers.google.com/machine-learning/crash-course/reducing-loss/gradient-descent)\n",
+        "\n",
+        "**Time Estimated**: 30 min\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "U8ajP_u73s6m",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "### Download Images"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "riWE_b8k3s6o",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "img_dir = '/tmp/nst'\n",
+        "if not os.path.exists(img_dir):\n",
+        "    os.makedirs(img_dir)\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/d/d7/Green_Sea_Turtle_grazing_seagrass.jpg\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/b/b4/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/0/00/Tuebingen_Neckarfront.jpg\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/6/68/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg\n",
+        "!wget --quiet -P /tmp/nst/ https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1024px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eqxUicSPUOP6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Import and configure modules"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sc1OLbOWhPCO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib as mpl\n",
+        "mpl.rcParams['figure.figsize'] = (10,10)\n",
+        "mpl.rcParams['axes.grid'] = False\n",
+        "\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "import time\n",
+        "import functools"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RYEjlrYk3s6w",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow.contrib.eager as tfe\n",
+        "\n",
+        "from tensorflow.python.keras.preprocessing import image as kp_image\n",
+        "from tensorflow.python.keras import models \n",
+        "from tensorflow.python.keras import losses\n",
+        "from tensorflow.python.keras import layers\n",
+        "from tensorflow.python.keras import backend as K"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "L7sjDODq67HQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We’ll begin by enabling [eager execution](https://www.tensorflow.org/guide/eager). Eager execution allows us to work through this technique in the clearest and most readable way. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sfjsSAtNrqQx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "tf.enable_eager_execution()\n",
+        "print(\"Eager execution: {}\".format(tf.executing_eagerly()))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "IOiGrIV1iERH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Set up some global values here\n",
+        "content_path = '/tmp/nst/Green_Sea_Turtle_grazing_seagrass.jpg'\n",
+        "style_path = '/tmp/nst/The_Great_Wave_off_Kanagawa.jpg'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xE4Yt8nArTeR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Visualize the input"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3TLljcwv5qZs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def load_img(path_to_img):\n",
+        "  max_dim = 512\n",
+        "  img = Image.open(path_to_img)\n",
+        "  long = max(img.size)\n",
+        "  scale = max_dim/long\n",
+        "  img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)\n",
+        "  \n",
+        "  img = kp_image.img_to_array(img)\n",
+        "  \n",
+        "  # We need to broadcast the image array such that it has a batch dimension \n",
+        "  img = np.expand_dims(img, axis=0)\n",
+        "  return img"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vupl0CI18aAG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def imshow(img, title=None):\n",
+        "  # Remove the batch dimension\n",
+        "  out = np.squeeze(img, axis=0)\n",
+        "  # Normalize for display \n",
+        "  out = out.astype('uint8')\n",
+        "  plt.imshow(out)\n",
+        "  if title is not None:\n",
+        "    plt.title(title)\n",
+        "  plt.imshow(out)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2yAlRzJZrWM3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are input content and style images. We hope to \"create\" an image with the content of our content image, but with the style of the style image. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_UWQmeEaiKkP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,10))\n",
+        "\n",
+        "content = load_img(content_path).astype('uint8')\n",
+        "style = load_img(style_path).astype('uint8')\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "imshow(content, 'Content Image')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "imshow(style, 'Style Image')\n",
+        "plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7qMVNvEsK-_D",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Prepare the data\n",
+        "Let's create methods that will allow us to load and preprocess our images easily. We perform the same preprocessing process as are expected according to the VGG training process. VGG networks are trained on image with each channel normalized by `mean = [103.939, 116.779, 123.68]`and with channels BGR."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hGwmTwJNmv2a",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def load_and_process_img(path_to_img):\n",
+        "  img = load_img(path_to_img)\n",
+        "  img = tf.keras.applications.vgg19.preprocess_input(img)\n",
+        "  return img"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xCgooqs6tAka",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to view the outputs of our optimization, we are required to perform the inverse preprocessing step. Furthermore, since our optimized image may take its values anywhere between $- \\infty$ and $\\infty$, we must clip to maintain our values from within the 0-255 range.   "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mjzlKRQRs_y2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def deprocess_img(processed_img):\n",
+        "  x = processed_img.copy()\n",
+        "  if len(x.shape) == 4:\n",
+        "    x = np.squeeze(x, 0)\n",
+        "  assert len(x.shape) == 3, (\"Input to deprocess image must be an image of \"\n",
+        "                             \"dimension [1, height, width, channel] or [height, width, channel]\")\n",
+        "  if len(x.shape) != 3:\n",
+        "    raise ValueError(\"Invalid input to deprocessing image\")\n",
+        "  \n",
+        "  # perform the inverse of the preprocessiing step\n",
+        "  x[:, :, 0] += 103.939\n",
+        "  x[:, :, 1] += 116.779\n",
+        "  x[:, :, 2] += 123.68\n",
+        "  x = x[:, :, ::-1]\n",
+        "\n",
+        "  x = np.clip(x, 0, 255).astype('uint8')\n",
+        "  return x"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "GEwZ7FlwrjoZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Define content and style representations\n",
+        "In order to get both the content and style representations of our image, we will look at some intermediate layers within our model. As we go deeper into the model, these intermediate layers represent higher and higher order features. In this case, we are using the network architecture VGG19, a pretrained image classification network. These intermediate layers are necessary to define the representation of content and style from our images. For an input image, we will try to match the corresponding style and content target representations at these intermediate layers. \n",
+        "\n",
+        "#### Why intermediate layers?\n",
+        "\n",
+        "You may be wondering why these intermediate outputs within our pretrained image classification network allow us to define style and content representations. At a high level, this phenomenon can be explained by the fact that in order for a network to perform image classification (which our network has been trained to do), it must understand the image. This involves taking the raw image as input pixels and building an internal representation through transformations that turn the raw image pixels into a complex understanding of the features present within the image. This is also partly why convolutional neural networks are able to generalize well: they’re able to capture the invariances and defining features within classes (e.g., cats vs. dogs) that are agnostic to background noise and other nuisances. Thus, somewhere between where the raw image is fed in and the classification label is output, the model serves as a complex feature extractor; hence by accessing intermediate layers, we’re able to describe the content and style of input images. \n",
+        "\n",
+        "\n",
+        "Specifically we’ll pull out these intermediate layers from our network: \n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N4-8eUp_Kc-j",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Content layer where will pull our feature maps\n",
+        "content_layers = ['block5_conv2'] \n",
+        "\n",
+        "# Style layer we are interested in\n",
+        "style_layers = ['block1_conv1',\n",
+        "                'block2_conv1',\n",
+        "                'block3_conv1', \n",
+        "                'block4_conv1', \n",
+        "                'block5_conv1'\n",
+        "               ]\n",
+        "\n",
+        "num_content_layers = len(content_layers)\n",
+        "num_style_layers = len(style_layers)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Jt3i3RRrJiOX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the Model \n",
+        "In this case, we load [VGG19](https://keras.io/applications/#vgg19), and feed in our input tensor to the model. This will allow us to extract the feature maps (and subsequently the content and style representations) of the content, style, and generated images.\n",
+        "\n",
+        "We use VGG19, as suggested in the paper. In addition, since VGG19 is a relatively simple model (compared with ResNet, Inception, etc) the feature maps actually work better for style transfer. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "v9AnzEUU6hhx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to access the intermediate layers corresponding to our style and content feature maps, we get the corresponding outputs and using the Keras [**Functional API**](https://keras.io/getting-started/functional-api-guide/), we define our model with the desired output activations. \n",
+        "\n",
+        "With the Functional API defining a model simply involves defining the input and output: \n",
+        "\n",
+        "`model = Model(inputs, outputs)`"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "nfec6MuMAbPx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_model():\n",
+        "  \"\"\" Creates our model with access to intermediate layers. \n",
+        "  \n",
+        "  This function will load the VGG19 model and access the intermediate layers. \n",
+        "  These layers will then be used to create a new model that will take input image\n",
+        "  and return the outputs from these intermediate layers from the VGG model. \n",
+        "  \n",
+        "  Returns:\n",
+        "    returns a keras model that takes image inputs and outputs the style and \n",
+        "      content intermediate layers. \n",
+        "  \"\"\"\n",
+        "  # Load our model. We load pretrained VGG, trained on imagenet data\n",
+        "  vgg = tf.keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')\n",
+        "  vgg.trainable = False\n",
+        "  # Get output layers corresponding to style and content layers \n",
+        "  style_outputs = [vgg.get_layer(name).output for name in style_layers]\n",
+        "  content_outputs = [vgg.get_layer(name).output for name in content_layers]\n",
+        "  model_outputs = style_outputs + content_outputs\n",
+        "  # Build model \n",
+        "  return models.Model(vgg.input, model_outputs)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kl6eFGa7-OtV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In the above code snippet, we’ll load our pretrained image classification network. Then we grab the layers of interest as we defined earlier. Then we define a Model by setting the model’s inputs to an image and the outputs to the outputs of the style and content layers. In other words, we created a model that will take an input image and output the content and style intermediate layers! \n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vJdYvJTZ4bdS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Define and create our loss functions (content and style distances)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F2Hcepii7_qh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Content Loss"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1FvH-gwXi4nq",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our content loss definition is actually quite simple. We’ll pass the network both the desired content image and our base input image. This will return the intermediate layer outputs (from the layers defined above) from our model. Then we simply take the euclidean distance between the two intermediate representations of those images.  \n",
+        "\n",
+        "More formally, content loss is a function that describes the distance of content from our output image $x$ and our content image, $p$. Let $C_{nn}$ be a pre-trained deep convolutional neural network. Again, in this case we use [VGG19](https://keras.io/applications/#vgg19). Let $X$ be any image, then $C_{nn}(X)$ is the network fed by X. Let $F^l_{ij}(x) \\in C_{nn}(x)$ and $P^l_{ij}(p) \\in C_{nn}(p)$ describe the respective intermediate feature representation of the network with inputs $x$ and $p$ at layer $l$. Then we describe the content distance (loss) formally as: $$L^l_{content}(p, x) = \\sum_{i, j} (F^l_{ij}(x) - P^l_{ij}(p))^2$$\n",
+        "\n",
+        "We perform backpropagation in the usual way such that we minimize this content loss. We thus change the initial image until it generates a similar response in a certain layer (defined in content_layer) as the original content image.\n",
+        "\n",
+        "This can be implemented quite simply. Again it will take as input the feature maps at a layer L in a network fed by x, our input image, and p, our content image, and return the content distance.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6KsbqPA8J9DY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Computing content loss\n",
+        "We will actually add our content losses at each desired layer. This way, each iteration when we feed our input image through the model (which in eager is simply `model(input_image)`!) all the content losses through the model will be properly compute and because we are executing eagerly, all the gradients will be computed. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "d2mf7JwRMkCd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_content_loss(base_content, target):\n",
+        "  return tf.reduce_mean(tf.square(base_content - target))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lGUfttK9F8d5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Style Loss"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6XtkGK_YGD1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Computing style loss is a bit more involved, but follows the same principle, this time feeding our network the base input image and the style image. However, instead of comparing the raw intermediate outputs of the base input image and the style image, we instead compare the Gram matrices of the two outputs. \n",
+        "\n",
+        "Mathematically, we describe the style loss of the base input image, $x$, and the style image, $a$, as the distance between the style representation (the gram matrices) of these images. We describe the style representation of an image as the correlation between different filter responses given by the Gram matrix  $G^l$, where $G^l_{ij}$ is the inner product between the vectorized feature map $i$ and $j$ in layer $l$. We can see that $G^l_{ij}$ generated over the feature map for a given image represents the correlation between feature maps $i$ and $j$. \n",
+        "\n",
+        "To generate a style for our base input image, we perform gradient descent from the content image to transform it into an image that matches the style representation of the original image. We do so by minimizing the mean squared distance between the feature correlation map of the style image and the input image. The contribution of each layer to the total style loss is described by\n",
+        "$$E_l = \\frac{1}{4N_l^2M_l^2} \\sum_{i,j}(G^l_{ij} - A^l_{ij})^2$$\n",
+        "\n",
+        "where $G^l_{ij}$ and $A^l_{ij}$ are the respective style representation in layer $l$ of $x$ and $a$. $N_l$ describes the number of feature maps, each of size $M_l = height * width$. Thus, the total style loss across each layer is \n",
+        "$$L_{style}(a, x) = \\sum_{l \\in L} w_l E_l$$\n",
+        "where we weight the contribution of each layer's loss by some factor $w_l$. In our case, we weight each layer equally ($w_l =\\frac{1}{|L|}$)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F21Hm61yLKk5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Computing style loss\n",
+        "Again, we implement our loss as a distance metric . "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N7MOqwKLLke8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def gram_matrix(input_tensor):\n",
+        "  # We make the image channels first \n",
+        "  channels = int(input_tensor.shape[-1])\n",
+        "  a = tf.reshape(input_tensor, [-1, channels])\n",
+        "  n = tf.shape(a)[0]\n",
+        "  gram = tf.matmul(a, a, transpose_a=True)\n",
+        "  return gram / tf.cast(n, tf.float32)\n",
+        "\n",
+        "def get_style_loss(base_style, gram_target):\n",
+        "  \"\"\"Expects two images of dimension h, w, c\"\"\"\n",
+        "  # height, width, num filters of each layer\n",
+        "  # We scale the loss at a given layer by the size of the feature map and the number of filters\n",
+        "  height, width, channels = base_style.get_shape().as_list()\n",
+        "  gram_style = gram_matrix(base_style)\n",
+        "  \n",
+        "  return tf.reduce_mean(tf.square(gram_style - gram_target))# / (4. * (channels ** 2) * (width * height) ** 2)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pXIUX6czZABh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Apply style transfer to our images\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "y9r8Lyjb_m0u",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Run Gradient Descent \n",
+        "If you aren't familiar with gradient descent/backpropagation or need a refresher, you should definitely check out this [awesome resource](https://developers.google.com/machine-learning/crash-course/reducing-loss/gradient-descent).\n",
+        "\n",
+        "In this case, we use the [Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam)* optimizer in order to minimize our loss. We iteratively update our output image such that it minimizes our loss: we don't update the weights associated with our network, but instead we train our input image to minimize loss. In order to do this, we must know how we calculate our loss and gradients. \n",
+        "\n",
+        "\\* Note that L-BFGS, which if you are familiar with this algorithm is recommended, isn’t used in this tutorial because a primary motivation behind this tutorial was to illustrate best practices with eager execution, and, by using Adam, we can demonstrate the autograd/gradient tape functionality with custom training loops.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-kGzV6LTp4CU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We’ll define a little helper function that will load our content and style image, feed them forward through our network, which will then output the content and style feature representations from our model. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O-lj5LxgtmnI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_feature_representations(model, content_path, style_path):\n",
+        "  \"\"\"Helper function to compute our content and style feature representations.\n",
+        "\n",
+        "  This function will simply load and preprocess both the content and style \n",
+        "  images from their path. Then it will feed them through the network to obtain\n",
+        "  the outputs of the intermediate layers. \n",
+        "  \n",
+        "  Arguments:\n",
+        "    model: The model that we are using.\n",
+        "    content_path: The path to the content image.\n",
+        "    style_path: The path to the style image\n",
+        "    \n",
+        "  Returns:\n",
+        "    returns the style features and the content features. \n",
+        "  \"\"\"\n",
+        "  # Load our images in \n",
+        "  content_image = load_and_process_img(content_path)\n",
+        "  style_image = load_and_process_img(style_path)\n",
+        "  \n",
+        "  # batch compute content and style features\n",
+        "  style_outputs = model(style_image)\n",
+        "  content_outputs = model(content_image)\n",
+        "  \n",
+        "  \n",
+        "  # Get the style and content feature representations from our model  \n",
+        "  style_features = [style_layer[0] for style_layer in style_outputs[:num_style_layers]]\n",
+        "  content_features = [content_layer[0] for content_layer in content_outputs[num_style_layers:]]\n",
+        "  return style_features, content_features"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3DopXw7-lFHa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Computing the loss and gradients\n",
+        "Here we use [**tf.GradientTape**](https://www.tensorflow.org/programmers_guide/eager#computing_gradients) to compute the gradient. It allows us to take advantage of the automatic differentiation available by tracing operations for computing the gradient later. It records the operations during the forward pass and then is able to compute the gradient of our loss function with respect to our input image for the backwards pass."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVDhSo8iJunf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):\n",
+        "  \"\"\"This function will compute the loss total loss.\n",
+        "  \n",
+        "  Arguments:\n",
+        "    model: The model that will give us access to the intermediate layers\n",
+        "    loss_weights: The weights of each contribution of each loss function. \n",
+        "      (style weight, content weight, and total variation weight)\n",
+        "    init_image: Our initial base image. This image is what we are updating with \n",
+        "      our optimization process. We apply the gradients wrt the loss we are \n",
+        "      calculating to this image.\n",
+        "    gram_style_features: Precomputed gram matrices corresponding to the \n",
+        "      defined style layers of interest.\n",
+        "    content_features: Precomputed outputs from defined content layers of \n",
+        "      interest.\n",
+        "      \n",
+        "  Returns:\n",
+        "    returns the total loss, style loss, content loss, and total variational loss\n",
+        "  \"\"\"\n",
+        "  style_weight, content_weight = loss_weights\n",
+        "  \n",
+        "  # Feed our init image through our model. This will give us the content and \n",
+        "  # style representations at our desired layers. Since we're using eager\n",
+        "  # our model is callable just like any other function!\n",
+        "  model_outputs = model(init_image)\n",
+        "  \n",
+        "  style_output_features = model_outputs[:num_style_layers]\n",
+        "  content_output_features = model_outputs[num_style_layers:]\n",
+        "  \n",
+        "  style_score = 0\n",
+        "  content_score = 0\n",
+        "\n",
+        "  # Accumulate style losses from all layers\n",
+        "  # Here, we equally weight each contribution of each loss layer\n",
+        "  weight_per_style_layer = 1.0 / float(num_style_layers)\n",
+        "  for target_style, comb_style in zip(gram_style_features, style_output_features):\n",
+        "    style_score += weight_per_style_layer * get_style_loss(comb_style[0], target_style)\n",
+        "    \n",
+        "  # Accumulate content losses from all layers \n",
+        "  weight_per_content_layer = 1.0 / float(num_content_layers)\n",
+        "  for target_content, comb_content in zip(content_features, content_output_features):\n",
+        "    content_score += weight_per_content_layer* get_content_loss(comb_content[0], target_content)\n",
+        "  \n",
+        "  style_score *= style_weight\n",
+        "  content_score *= content_weight\n",
+        "\n",
+        "  # Get total loss\n",
+        "  loss = style_score + content_score \n",
+        "  return loss, style_score, content_score"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "r5XTvbP6nJQa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Then computing the gradients is easy:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fwzYeOqOUH9_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def compute_grads(cfg):\n",
+        "  with tf.GradientTape() as tape: \n",
+        "    all_loss = compute_loss(**cfg)\n",
+        "  # Compute gradients wrt input image\n",
+        "  total_loss = all_loss[0]\n",
+        "  return tape.gradient(total_loss, cfg['init_image']), all_loss"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "T9yKu2PLlBIE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Optimization loop"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pj_enNo6tACQ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import IPython.display\n",
+        "\n",
+        "def run_style_transfer(content_path, \n",
+        "                       style_path,\n",
+        "                       num_iterations=1000,\n",
+        "                       content_weight=1e3, \n",
+        "                       style_weight=1e-2): \n",
+        "  # We don't need to (or want to) train any layers of our model, so we set their\n",
+        "  # trainable to false. \n",
+        "  model = get_model() \n",
+        "  for layer in model.layers:\n",
+        "    layer.trainable = False\n",
+        "  \n",
+        "  # Get the style and content feature representations (from our specified intermediate layers) \n",
+        "  style_features, content_features = get_feature_representations(model, content_path, style_path)\n",
+        "  gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]\n",
+        "  \n",
+        "  # Set initial image\n",
+        "  init_image = load_and_process_img(content_path)\n",
+        "  init_image = tfe.Variable(init_image, dtype=tf.float32)\n",
+        "  # Create our optimizer\n",
+        "  opt = tf.train.AdamOptimizer(learning_rate=5, beta1=0.99, epsilon=1e-1)\n",
+        "\n",
+        "  # For displaying intermediate images \n",
+        "  iter_count = 1\n",
+        "  \n",
+        "  # Store our best result\n",
+        "  best_loss, best_img = float('inf'), None\n",
+        "  \n",
+        "  # Create a nice config \n",
+        "  loss_weights = (style_weight, content_weight)\n",
+        "  cfg = {\n",
+        "      'model': model,\n",
+        "      'loss_weights': loss_weights,\n",
+        "      'init_image': init_image,\n",
+        "      'gram_style_features': gram_style_features,\n",
+        "      'content_features': content_features\n",
+        "  }\n",
+        "    \n",
+        "  # For displaying\n",
+        "  num_rows = 2\n",
+        "  num_cols = 5\n",
+        "  display_interval = num_iterations/(num_rows*num_cols)\n",
+        "  start_time = time.time()\n",
+        "  global_start = time.time()\n",
+        "  \n",
+        "  norm_means = np.array([103.939, 116.779, 123.68])\n",
+        "  min_vals = -norm_means\n",
+        "  max_vals = 255 - norm_means   \n",
+        "  \n",
+        "  imgs = []\n",
+        "  for i in range(num_iterations):\n",
+        "    grads, all_loss = compute_grads(cfg)\n",
+        "    loss, style_score, content_score = all_loss\n",
+        "    opt.apply_gradients([(grads, init_image)])\n",
+        "    clipped = tf.clip_by_value(init_image, min_vals, max_vals)\n",
+        "    init_image.assign(clipped)\n",
+        "    end_time = time.time() \n",
+        "    \n",
+        "    if loss < best_loss:\n",
+        "      # Update best loss and best image from total loss. \n",
+        "      best_loss = loss\n",
+        "      best_img = deprocess_img(init_image.numpy())\n",
+        "\n",
+        "    if i % display_interval== 0:\n",
+        "      start_time = time.time()\n",
+        "      \n",
+        "      # Use the .numpy() method to get the concrete numpy array\n",
+        "      plot_img = init_image.numpy()\n",
+        "      plot_img = deprocess_img(plot_img)\n",
+        "      imgs.append(plot_img)\n",
+        "      IPython.display.clear_output(wait=True)\n",
+        "      IPython.display.display_png(Image.fromarray(plot_img))\n",
+        "      print('Iteration: {}'.format(i))        \n",
+        "      print('Total loss: {:.4e}, ' \n",
+        "            'style loss: {:.4e}, '\n",
+        "            'content loss: {:.4e}, '\n",
+        "            'time: {:.4f}s'.format(loss, style_score, content_score, time.time() - start_time))\n",
+        "  print('Total time: {:.4f}s'.format(time.time() - global_start))\n",
+        "  IPython.display.clear_output(wait=True)\n",
+        "  plt.figure(figsize=(14,4))\n",
+        "  for i,img in enumerate(imgs):\n",
+        "      plt.subplot(num_rows,num_cols,i+1)\n",
+        "      plt.imshow(img)\n",
+        "      plt.xticks([])\n",
+        "      plt.yticks([])\n",
+        "      \n",
+        "  return best_img, best_loss "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vSVMx4burydi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "best, best_loss = run_style_transfer(content_path, \n",
+        "                                     style_path, num_iterations=1000)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dzJTObpsO3TZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "Image.fromarray(best)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dCXQ9vSnQbDy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To download the image from Colab uncomment the following code:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SSH6OpyyQn7w",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#from google.colab import files\n",
+        "#files.download('wave_turtle.png')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "LwiZfCW0AZwt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Visualize outputs\n",
+        "We \"deprocess\" the output image in order to remove the processing that was applied to it. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lqTQN1PjulV9",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def show_results(best_img, content_path, style_path, show_large_final=True):\n",
+        "  plt.figure(figsize=(10, 5))\n",
+        "  content = load_img(content_path) \n",
+        "  style = load_img(style_path)\n",
+        "\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  imshow(content, 'Content Image')\n",
+        "\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  imshow(style, 'Style Image')\n",
+        "\n",
+        "  if show_large_final: \n",
+        "    plt.figure(figsize=(10, 10))\n",
+        "\n",
+        "    plt.imshow(best_img)\n",
+        "    plt.title('Output Image')\n",
+        "    plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i6d6O50Yvs6a",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "show_results(best, content_path, style_path)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "tyGMmWh2Pss8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Try it on other images\n",
+        "Image of Tuebingen \n",
+        "\n",
+        "Photo By: Andreas Praefcke [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0  (https://creativecommons.org/licenses/by/3.0)], from Wikimedia Commons"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "x2TePU39k9lb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Starry night + Tuebingen"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ES9dC6ZyJBD2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "best_starry_night, best_loss = run_style_transfer('/tmp/nst/Tuebingen_Neckarfront.jpg',\n",
+        "                                                  '/tmp/nst/1024px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "X8w8WLkKvzXu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "show_results(best_starry_night, '/tmp/nst/Tuebingen_Neckarfront.jpg',\n",
+        "             '/tmp/nst/1024px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "QcXwvViek4Br",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Pillars of Creation + Tuebingen"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vJ3u2U-gGmgP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "best_poc_tubingen, best_loss = run_style_transfer('/tmp/nst/Tuebingen_Neckarfront.jpg', \n",
+        "                                                  '/tmp/nst/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pQUq3KxpGv2O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "show_results(best_poc_tubingen, \n",
+        "             '/tmp/nst/Tuebingen_Neckarfront.jpg',\n",
+        "             '/tmp/nst/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bTZdTOdW3s8H",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Kandinsky Composition 7 + Tuebingen"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bt9mbQfl7exl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "best_kandinsky_tubingen, best_loss = run_style_transfer('/tmp/nst/Tuebingen_Neckarfront.jpg', \n",
+        "                                                  '/tmp/nst/Vassily_Kandinsky,_1913_-_Composition_7.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Qnz8HeXSXg6P",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "show_results(best_kandinsky_tubingen, \n",
+        "             '/tmp/nst/Tuebingen_Neckarfront.jpg',\n",
+        "             '/tmp/nst/Vassily_Kandinsky,_1913_-_Composition_7.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "cg68lW2A3s8N",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Pillars of Creation + Sea Turtle"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dl0DUot_bFST",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "best_poc_turtle, best_loss = run_style_transfer('/tmp/nst/Green_Sea_Turtle_grazing_seagrass.jpg', \n",
+        "                                                  '/tmp/nst/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UzJfE0I1bQn8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "show_results(best_poc_turtle, \n",
+        "             '/tmp/nst/Green_Sea_Turtle_grazing_seagrass.jpg',\n",
+        "             '/tmp/nst/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "sElaeNX-4Vnc",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Key Takeaways\n",
+        "\n",
+        "### What we covered:\n",
+        "\n",
+        "* We built several different loss functions and used backpropagation to transform our input image in order to minimize these losses\n",
+        "  * In order to do this we had to load in a **pretrained model** and use its learned feature maps to describe the content and style representation of our images.\n",
+        "    * Our main loss functions were primarily computing the distance in terms of these different representations\n",
+        "* We implemented this with a custom model and **eager execution**\n",
+        "  * We built our custom model with the Functional API \n",
+        "  * Eager execution allows us to dynamically work with tensors, using a natural python control flow\n",
+        "  * We manipulated tensors directly, which makes debugging and working with tensors easier. \n",
+        "* We iteratively updated our image by applying our optimizers update rules using **tf.gradient**. The optimizer minimized a given loss with respect to our input image. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "U-y02GWonqnD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "**[Image of Tuebingen](https://commons.wikimedia.org/wiki/File:Tuebingen_Neckarfront.jpg)** \n",
+        "Photo By: Andreas Praefcke [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0  (https://creativecommons.org/licenses/by/3.0)], from Wikimedia Commons\n",
+        "\n",
+        "**[Image of Green Sea Turtle](https://commons.wikimedia.org/wiki/File:Green_Sea_Turtle_grazing_seagrass.jpg)**\n",
+        "By P.Lindgren [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IpUD9W6ZkeyM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
diff --git a/feature_matching_v3/eval_similarity_matrix.py b/feature_matching_v3/eval_similarity_matrix.py
new file mode 100644
index 0000000..e4a8e7f
--- /dev/null
+++ b/feature_matching_v3/eval_similarity_matrix.py
@@ -0,0 +1,85 @@
+from util_sift import load_sift, array_to_kp, kp_to_array
+from util_matching import match
+from util_sm import load_sm
+from util_cv import match_to_cv
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import cv2
+
+def display(s_idx, pw_idx):
+    plt.figure()
+    (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])
+    (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])
+    matches = match(kp1, des1, kp2, des2)
+    # Convert to OpenCV objects for viewing
+    matches = match_to_cv(matches)
+    kp1 = array_to_kp(kp1)
+    kp2 = array_to_kp(kp2)
+    s_plate = s_label[s_idx]
+    pw_plate = pw_label[pw_idx]
+    im_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+    s = '[CV] Matches between S%s & PW%s = %s' % (s_plate, pw_plate, str(len(matches)))
+    print(s)
+    plt.title(s)
+    plt.imshow(im_matches)
+
+#%% Load data
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')
+pwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)
+
+#%% Only use Nissl PWs=[''=/=
+idxs = pw_label[pwi_label-1]-1
+pw_im = pw_im[idxs]
+pw_label = pw_label[idxs]
+pw_kp = pw_kp[idxs]
+pw_des = pw_des[idxs]
+sm_matches = sm_matches[:,idxs]
+sm_metric = sm_metric[:,idxs]
+
+sm_matches = sm_matches.astype(np.uint8)
+
+#%% Figure
+fig, ax = plt.subplots()
+ax.set_title('Similarity Matrix Visualization (v2)')
+ax.imshow(sm_matches, cmap=plt.get_cmap('hot'))
+
+fig2, ax2 = plt.subplots()
+while True:
+    plt.figure(1)
+    p = plt.ginput(n=1, timeout=0)
+
+    if len(p) == 0:
+        break
+
+    p = p[0]
+    (x, y) = (p[0], p[1])
+    rect = patches.Rectangle((np.floor(x)+0.5, np.floor(y)-0.5), 0.5, 0.5, color='blue')
+    ax.add_patch(rect)
+
+    s_idx = int(y)
+    pw_idx = int(x)
+    s_plate = s_label[s_idx]
+    pw_plate = pw_label[pw_idx]
+
+    s = 'Matches between S%s & PW%s = %s' % (s_plate, pw_plate, sm_matches[s_idx,pw_idx])
+    ax.set_title(s)
+    print(s, 'index', s_idx, pw_idx)
+
+    plt.figure(2)
+    (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])
+    (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])
+    matches = match(kp1, des1, kp2, des2)
+    # Convert to OpenCV objects for viewing
+    matches = match_to_cv(matches)
+    kp1 = array_to_kp(kp1)
+    kp2 = array_to_kp(kp2)
+    im_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+    s = '[CV] Matches between S%s & PW%s = %s' % (s_plate, pw_plate, str(len(matches)))
+    print(s)
+    ax2.set_title(s)
+    ax2.imshow(im_matches)
+
+plt.title("Finished")
\ No newline at end of file
diff --git a/feature_matching_v3/exp_baseline.py b/feature_matching_v3/exp_baseline.py
new file mode 100644
index 0000000..26dbb18
--- /dev/null
+++ b/feature_matching_v3/exp_baseline.py
@@ -0,0 +1,379 @@
+import numpy as np
+import os
+from PIL import Image
+import pylab as plt
+import cv2
+
+np.set_printoptions(threshold=np.nan)
+plt.gray()
+
+#%% Generation function
+def to_gray(P):
+    return P.mean(axis=2)
+
+def generate_color(P1, P2, rstrides, cstrides, threshold):
+    P3 = np.zeros_like(P1)
+    rows = P1.shape[0] // rstrides
+    cols = P1.shape[1] // cstrides
+
+    for ridx in range(rows):
+        rstart = ridx * rstrides
+        if ridx == rows - 1:
+            rend = P1.shape[0]
+        else:
+            rend = rstart + rstrides
+
+        for cidx in range(cols):
+            cstart = cidx * cstrides
+            if cidx == cols - 1:
+                cend = P1.shape[1]
+            else:
+                cend = cstart + cstrides
+
+            # Extract plate quadrants
+            q1 = P1[rstart:rend, cstart:cend, :]
+            q1 = q1.reshape(q1.shape[0] * q1.shape[1], 3)
+
+            q2 = P2[rstart:rend, cstart:cend, :]
+            q2 = q2.reshape(q2.shape[0] * q2.shape[1], 3)
+
+            # Calculate quadrant coverage
+            px_total = q1.shape[0]
+            z1 = np.sum(q1 < threshold)
+            z2 = np.sum(q2 < threshold)
+            coverage1 = z1 / px_total
+            coverage2 = z2 / px_total
+            coverage3 = (coverage1 + coverage2) / 2
+
+            # Create blank quadrant
+            q3 = np.zeros_like(q1)
+            q3[:, :] = 255
+
+            # Figure out how much we need to cover it
+            q3_pixels = int(px_total * coverage3)
+
+            # Randomly select pixels to paint
+            idxs = np.random.choice(q3.shape[0], q3_pixels, True)
+
+            # Paint by averaging the two image quadrants
+            q3[idxs, :] = (q1[idxs, :] + q2[idxs, :]) / 2
+            # q3[idxs, :] = 0
+
+            # Reshape back into original form, add to output image
+            q3 = q3.reshape(rend-rstart, cend-cstart, 3)
+            P3[rstart:rend, cstart:cend, :] = q3
+
+    return P3
+
+
+def generate_gray(P1, P2, rstrides, cstrides, threshold, w1=0.5):
+    P3 = np.zeros_like(P1)
+    rows = P1.shape[0] // rstrides
+    cols = P1.shape[1] // cstrides
+
+    for ridx in range(rows):
+        rstart = ridx * rstrides
+        if ridx == rows - 1:
+            rend = P1.shape[0]
+        else:
+            rend = rstart + rstrides
+
+        for cidx in range(cols):
+            cstart = cidx * cstrides
+            if cidx == cols - 1:
+                cend = P1.shape[1]
+            else:
+                cend = cstart + cstrides
+
+            # Extract plate quadrants
+            q1 = P1[rstart:rend, cstart:cend]
+            q1 = q1.reshape(q1.shape[0] * q1.shape[1])
+
+            q2 = P2[rstart:rend, cstart:cend]
+            q2 = q2.reshape(q2.shape[0] * q2.shape[1])
+
+            # Calculate quadrant coverage
+            px_total = q1.shape[0]
+            z1 = np.sum(q1 < threshold)
+            z2 = np.sum(q2 < threshold)
+            coverage1 = z1 / px_total
+            coverage2 = z2 / px_total
+            coverage3 = (coverage1 + coverage2) / 2
+
+            # Create blank quadrant
+            q3 = np.zeros_like(q1)
+            q3[:] = 255
+
+            # Figure out how much we need to cover it
+            q3_pixels = int(px_total * coverage3)
+
+            # Randomly select pixels to paint
+            idxs = np.random.choice(q3.shape[0], q3_pixels, True)
+
+            # Paint by averaging the two image quadrants
+            w2 = 1 - w1
+            # q3[idxs] = (w1 * q1[idxs] + w2 * q2[idxs]) / 2
+            q3[idxs] = 0
+
+            # Reshape back into original form, add to output image
+            q3 = q3.reshape(rend-rstart, cend-cstart)
+            P3[rstart:rend, cstart:cend] = q3
+
+    return P3
+#%% Load Image Files
+print("Loading images")
+WIDTH = 800
+HEIGHT = 400
+dir = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'
+
+filename1 = os.path.join(dir, 'RBSC7-068.jpg')
+filename2 = os.path.join(dir, 'RBSC7-070.jpg')
+
+im1 = Image.open(filename1)
+im1 = im1.resize((WIDTH, HEIGHT), Image.LANCZOS)
+P1 = np.array(im1, dtype=np.uint8)
+P1 = P1[:, 0:P1.shape[1]//2, :]
+
+im2 = Image.open(filename2)
+im2 = im2.resize((WIDTH, HEIGHT), Image.LANCZOS)
+P2 = np.array(im2, dtype=np.uint8)
+P2 = P2[:, 0:P2.shape[1]//2, :]
+
+P1_g = to_gray(P1)
+P2_g = to_gray(P2)
+
+#%% Control points
+print("Computing control points")
+LEFT_PAD = 0
+RIGHT_PAD = 0
+COL_INTERVALS = 5
+CTRL_THRESHOLD = 200
+def get_controls(P):
+    w = P.shape[0]
+    h = P.shape[1]
+    ctrl_pts = [[0, 0], [w, 0], [0, h], [w, h]]
+    # Top to bottom
+    for col in range(LEFT_PAD, P.shape[1]-RIGHT_PAD, COL_INTERVALS):
+        for row in range(0, P.shape[0], 1):
+            if P[row,col] <= CTRL_THRESHOLD:
+                ctrl_pts.append([row, col])
+                break
+
+    # Bottom to top
+    for col in range(LEFT_PAD, P.shape[1]-RIGHT_PAD, COL_INTERVALS):
+        for row in range(P.shape[0]-1, 0, -1):
+            if P[row,col] <= CTRL_THRESHOLD:
+                ctrl_pts.append([row, col])
+                break
+
+
+    return ctrl_pts
+
+src_pts = get_controls(P1_g)
+dst_pts = get_controls(P2_g)
+
+size = min(len(src_pts), len(dst_pts))
+src_pts = src_pts[:size]
+dst_pts = dst_pts[:size]
+
+#%% Visualize Control Points
+print("Visualizing control points")
+fig, ax = plt.subplots(nrows=1,ncols=2)
+title = 'Control Points'
+fig.suptitle(title, fontsize=22)
+
+ax[0].set_title('PW68')
+ax[0].imshow(P1)
+
+ax[1].set_title('PW72')
+ax[1].imshow(P2)
+
+for pt in src_pts:
+    ax[0].plot(pt[1], pt[0], marker='x', color='red', markersize=5)
+
+for pt in dst_pts:
+    ax[1].plot(pt[1], pt[0], marker='x', color='red', markersize=5)
+
+#%% SIFT computing
+print("Computing SIFT features")
+# SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.02, edgeThreshold=100, sigma=2)
+SIFT = cv2.xfeatures2d.SIFT_create()
+kp1, des1 = SIFT.detectAndCompute(P1, None)
+kp2, des2 = SIFT.detectAndCompute(P2, None)
+
+fig, ax = plt.subplots(nrows=1,ncols=2)
+title = 'SIFT KeyPoints'
+fig.suptitle(title, fontsize=22)
+
+ax[0].set_title('Plate 1')
+ax[0].imshow(cv2.drawKeypoints(P1, kp1, None))
+
+ax[1].set_title('Plate 2')
+ax[1].imshow(cv2.drawKeypoints(P2, kp2, None))
+
+#%% SIFT + Homography
+# bf = cv2.BFMatcher()
+# matches = bf.knnMatch(des1,des2, k=2)
+# matches = np.asarray([m for m in matches if m[0].distance < 0.8*m[1].distance])
+# if len(matches[:,0]) >= 4:
+#     src = np.float32([ kp1[m.queryIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)
+#     dst = np.float32([ kp2[m.trainIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)
+#     H, masked = cv2.findHomography(src, dst, cv2.RANSAC, 50.0)
+#     im_match = cv2.drawMatches(P1, kp1, P2, kp2, matches[:,0], None, flags=2)
+#     plt.figure(1)
+#     plt.imshow(im_match)
+#     # dst = cv2.warpPerspective(P1,H,(P1.shape[1] + P2.shape[1], P2.shape[0]))
+
+#%% SIFT for warping
+# for m in matches[:,0]:
+#     src_pts.append(kp1[m.queryIdx].pt)
+#     dst_pts.append(kp2[m.trainIdx].pt)
+#
+# src_pts = np.array(src_pts, dtype=np.float32)
+# dst_pts = np.array(dst_pts, dtype=np.float32)
+
+#%% My Method
+print("Performing my matching algorithm")
+import util_matching, util_cv, util_sift
+matches2 = util_matching.match(util_sift.kp_to_array(kp1), des1, util_sift.kp_to_array(kp2), des2)
+matches2 = util_cv.match_to_cv(matches2)
+im_match2 = cv2.drawMatches(P1, kp1, P1, kp2, matches2, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+# plt.imshow(im_match2)
+
+#%% Mine for warping
+print("Adding matching points for warping")
+for m in matches2:
+    pt1 = kp1[m.queryIdx].pt
+    pt2 = kp2[m.trainIdx].pt
+    src_pts.append([pt1[1], pt1[0]])
+    dst_pts.append([pt2[1], pt2[0]])
+
+src_pts = np.array(src_pts, dtype=np.float32)
+dst_pts = np.array(dst_pts, dtype=np.float32)
+
+#%% Warping
+print("Warping")
+from skimage.transform import warp, PiecewiseAffineTransform
+tform = PiecewiseAffineTransform()
+tform.estimate(src_pts, dst_pts)
+im_warp1 = warp(P1_g, tform)
+
+tform = PiecewiseAffineTransform()
+tform.estimate(dst_pts, src_pts)
+im_warp2 = warp(P2_g, tform)
+#
+# pts1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])
+# pts2 = np.float32([[0, 0], [300, 0], [0, 300], [300, 300]])
+# M = cv2.getPerspectiveTransform(src_pts, dst_pts)
+# wrp = cv2.warpAffine(P1.astype(np.int32), M, dsize=(P1.shape[0], P1.shape[1]), flags=cv2.INTER_LINEAR)
+
+#%% Warp Visualization
+# fig, ax = plt.subplots(nrows=3,ncols=2)
+# title = 'Warping'
+# fig.suptitle(title, fontsize=22)
+#
+# ax[0,0].set_title('PW68')
+# ax[0,0].imshow(cv2.drawKeypoints(P1, kp1, None))
+#
+# ax[0,1].set_title('PW72')
+# ax[0,1].imshow(cv2.drawKeypoints(P2, kp2, None))
+#
+# ax[1,0].set_title('Match (SIFT + RANSAC)')
+# ax[1,0].imshow(None)
+#
+# ax[1,1].set_title("Match (Mine)")
+# ax[1,1].imshow(im_match2)
+#
+# ax[2,0].set_title('WARP1')
+# ax[2,0].imshow(im_warp1)
+#
+# ax[2,1].set_title('WARP2')
+# ax[2,1].imshow(im_warp2)
+
+#%% Calculation of Baseline & Figure
+print("Generating P3 baseline")
+rstrides = 2
+cstrides = 2
+threshold = 200
+im_src = im_warp1
+im_dst = im_warp2
+P3 = generate_gray(im_src, im_dst, rstrides, cstrides, threshold, 0.5)
+plt.figure()
+plt.imshow(P3)
+
+#%% P3 Visualization
+print("Visualizing P3 results")
+fig, ax = plt.subplots(nrows=1,ncols=3)
+title = 'Intermediate Plate Generation Baseline'
+fig.suptitle(title, fontsize=22)
+
+ax[0].set_title('Source')
+ax[0].imshow(im_src)
+
+ax[1].set_title('Destination')
+ax[1].imshow(im_dst)
+
+ax[2].set_title(str(rstrides) + ' by ' + str(cstrides))
+ax[2].imshow(P3)
+
+#%%
+print("Blending")
+plt.figure()
+im_b1 = im_warp1
+im_b2 = im_warp2
+# im_b1 = to_gray((im_warp1 * 255).astype(np.uint8))
+# im_b2 = to_gray((im_warp2 * 255).astype(np.uint8))
+for w in np.linspace(0, 1, 10):
+    b1 = im_b1 * w
+    b2 = im_b2 * (1-w)
+    gen = b1 + b2
+    plt.suptitle("Im1 Weight " + str(w))
+    plt.imshow(gen)
+    plt.waitforbuttonpress()
+
+#%% Optical Flow
+print("Optical Flow")
+# params for ShiTomasi corner detection
+feature_params = dict( maxCorners = 500,
+                       qualityLevel = 0.3,
+                       minDistance = 7,
+                       blockSize = 7 )
+# Parameters for lucas kanade optical flow
+lk_params = dict( winSize  = (15,15),
+                  maxLevel = 3,
+                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 25, 0.03))
+
+P1_C = cv2.imread('P1.png')
+P2_C = cv2.imread('P2.png')
+
+P1_CG = cv2.cvtColor(P1_C, cv2.COLOR_BGR2GRAY)
+P2_CG = cv2.cvtColor(P2_C, cv2.COLOR_BGR2GRAY)
+
+p0 = cv2.goodFeaturesToTrack(P1_CG, mask = None, **feature_params)
+mask = np.zeros_like(P1)
+# Create some random colors
+color = np.random.randint(0,255,(100,3))
+
+# calculate optical flow
+p1, st, err = cv2.calcOpticalFlowPyrLK(P1_CG, P2_CG, p0, None, **lk_params)
+# Select good points
+good_new = p1[st == 1]
+good_old = p0[st == 1]
+# draw the tracks
+for i, (new, old) in enumerate(zip(good_new, good_old)):
+    a, b = new.ravel()
+    c, d = old.ravel()
+    mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2)
+    frame = cv2.circle(P2, (a, b), 5, color[i].tolist(), -1)
+img = cv2.add(frame, mask)
+cv2.imshow('frame', img)
+
+
+
+#%% Generate many intermediate baselines and save as files
+# t = 150
+# for r in range(1, 20, 1):
+#     for c in range(1, 20, 1):
+#         P = generate(P1, P2, r, c, t)
+#         filename = 'baseline/' + str(r) + ' by ' + str(c) + '.png'
+#         Image.fromarray(P).save(filename)
diff --git a/feature_matching_v3/exp_baseline_manual.py b/feature_matching_v3/exp_baseline_manual.py
new file mode 100644
index 0000000..cced869
--- /dev/null
+++ b/feature_matching_v3/exp_baseline_manual.py
@@ -0,0 +1,306 @@
+import numpy as np
+import os
+import pylab as plt
+import cv2
+from skimage.transform import warp, PiecewiseAffineTransform
+from PIL import Image
+from timeit import default_timer as timer
+
+USE_SIFT = False
+USE_OPTICALFLOW = False
+
+def load(filename):
+    WIDTH = 800
+    HEIGHT = 400
+    im = Image.open(filename)
+    im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)
+    P = np.array(im, dtype=np.uint8)
+    return P[:, 0:P.shape[1] // 2, :]
+
+def to_gray(P):
+    return P.mean(axis=2)
+
+def get_controls(P):
+    LEFT_PAD = 0
+    RIGHT_PAD = 0
+    COL_INTERVALS = 5
+    CTRL_THRESHOLD = 200
+    w = P.shape[0]
+    h = P.shape[1]
+    ctrl_pts = [[0, 0], [w, 0], [0, h], [w, h]]
+    # Top to bottom
+    for col in range(LEFT_PAD, P.shape[1] - RIGHT_PAD, COL_INTERVALS):
+        for row in range(0, P.shape[0], 1):
+            if P[row, col] <= CTRL_THRESHOLD:
+                ctrl_pts.append([row, col])
+                break
+
+    # Bottom to top
+    for col in range(LEFT_PAD, P.shape[1] - RIGHT_PAD, COL_INTERVALS):
+        for row in range(P.shape[0] - 1, 0, -1):
+            if P[row, col] <= CTRL_THRESHOLD:
+                ctrl_pts.append([row, col])
+                break
+
+    return ctrl_pts
+
+#%% Load images
+print("Loading images")
+dir1 = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'
+dir2 = 'C:/Users/xeroj/Downloads/Processed'
+
+P1 = load(os.path.join(dir1, 'RBSC7-068.jpg'))
+P2 = load(os.path.join(dir1, 'RBSC7-070.jpg'))
+
+PM1 = load(os.path.join(dir2, '18-016 LHA s4t2.tif'))
+PM2 = load(os.path.join(dir2, '18-016 LHA s4t3.tif'))
+PM3 = load(os.path.join(dir2, '18-016 LHA s4t4.tif'))
+
+#%% Plate selection
+S1 = P1
+S2 = P2
+
+#%% SIFT Stuff
+if USE_SIFT:
+    #%% SIFT Features
+    print("Computing SIFT features")
+    # SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.02, edgeThreshold=100, sigma=2)
+    SIFT = cv2.xfeatures2d.SIFT_create()
+    kp1, des1 = SIFT.detectAndCompute(S1, None)
+    kp2, des2 = SIFT.detectAndCompute(S2, None)
+
+    #%% Matching (SIFT + Homography)
+    # bf = cv2.BFMatcher()
+    # matches = bf.knnMatch(des1,des2, k=2)
+    # matches = np.asarray([m for m in matches if m[0].distance < 0.9*m[1].distance])
+    # if len(matches[:,0]) >= 4:
+    #     src = np.float32([ kp1[m.queryIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)
+    #     dst = np.float32([ kp2[m.trainIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)
+    #     H, masked = cv2.findHomography(src, dst, cv2.RANSAC, 50.0)
+    #     im_match = cv2.drawMatches(P1, kp1, P2, kp2, matches[:,0], None, flags=2)
+    #     plt.figure()
+    #     plt.imshow(im_match)
+    #
+    # for m in matches[:,0]:
+    #     pt1 = kp1[m.queryIdx].pt
+    #     pt2 = kp2[m.trainIdx].pt
+    #
+    #     (x1, y1) = (pt1[1], pt1[0])
+    #     (x2, y2) = (pt2[1], pt2[0])
+    #
+    #     x1 = (x1 + x2) / 2
+    #     y1 = (y1 + y2) / 2
+    #     # x2 = (x1 + x2) / 2
+    #     # y2 = (y1 + y2) / 2
+    #
+    #     src_pts.append([x1, y1])
+    #     dst_pts.append([x2, y2])
+    #
+    # src_pts = np.array(src_pts, dtype=np.float32)
+    # dst_pts = np.array(dst_pts, dtype=np.float32)
+
+    #%% Matching (Mine)
+    import util_matching, util_cv, util_sift
+    src_pts = [[0, 0], [S1.shape[0], 0], [0, S1.shape[1]], [S1.shape[0], S1.shape[1]]]
+    dst_pts = [[0, 0], [S2.shape[0], 0], [0, S2.shape[1]], [S2.shape[0], S2.shape[1]]]
+    print("Performing my matching algorithm")
+    matches2 = util_matching.match(util_sift.kp_to_array(kp1), des1, util_sift.kp_to_array(kp2), des2)
+    matches2 = util_cv.match_to_cv(matches2)
+    im_match2 = cv2.drawMatches(S1, kp1, S1, kp2, matches2, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+    plt.figure(6)
+    plt.imshow(im_match2)
+
+    print("Adding matching points for warping")
+    for m in matches2:
+        pt1 = kp1[m.queryIdx].pt
+        pt2 = kp2[m.trainIdx].pt
+
+        (x1, y1) = (pt1[1], pt1[0])
+        (x2, y2) = (pt2[1], pt2[0])
+
+        # x1 = (x1 + x2) / 2
+        # y1 = (y1 + y2) / 2
+        x2 = (x1 + x2) / 2
+        y2 = (y1 + y2) / 2
+
+        src_pts.append([x1, y1])
+        dst_pts.append([x2, y2])
+
+    src_pts = np.array(src_pts, dtype=np.float32)
+    dst_pts = np.array(dst_pts, dtype=np.float32)
+
+    #%% Warping
+    print("Warping")
+    tform = PiecewiseAffineTransform()
+    e1 = tform.estimate(dst_pts, src_pts)
+    im_warp1 = warp(S1, tform)
+    im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))
+
+    tform = PiecewiseAffineTransform()
+    e2 = tform.estimate(src_pts, dst_pts)
+    im_warp2 = warp(S2, tform)
+    im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))
+
+    print("Cross-Dissolve")
+    im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)
+    plt.figure(7)
+    plt.suptitle("SIFT Generated")
+    plt.imshow(im_gen, cmap='gray')
+
+#%% OF
+if USE_OPTICALFLOW:
+    print("Optical Flow")
+    # params for ShiTomasi corner detection
+    feature_params = dict(maxCorners=500,
+                          qualityLevel=0.3,
+                          minDistance=7,
+                          blockSize=7)
+    # Parameters for lucas kanade optical flow
+    lk_params = dict(winSize=(15, 15),
+                     maxLevel=3,
+                     criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 25, 0.03))
+
+    P1_C = cv2.imread('P1.png')
+    P2_C = cv2.imread('P2.png')
+
+    P1_CG = cv2.cvtColor(P1_C, cv2.COLOR_BGR2GRAY)
+    P2_CG = cv2.cvtColor(P2_C, cv2.COLOR_BGR2GRAY)
+
+    p0 = cv2.goodFeaturesToTrack(P1_CG, mask=None, **feature_params)
+    mask = np.zeros_like(P1)
+    # Create some random colors
+    color = np.random.randint(0, 255, (100, 3))
+
+    # calculate optical flow
+    p1, st, err = cv2.calcOpticalFlowPyrLK(P1_CG, P2_CG, p0, None, **lk_params)
+    # Select good points
+    good_new = p1[st == 1]
+    good_old = p0[st == 1]
+    # draw the tracks
+    for i, (new, old) in enumerate(zip(good_new, good_old)):
+        a, b = new.ravel()
+        c, d = old.ravel()
+        mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2)
+        frame = cv2.circle(P2, (a, b), 5, color[i].tolist(), -1)
+    img = cv2.add(frame, mask)
+    cv2.imshow('frame', img)
+
+    src_pts = good_old
+    dst_pts = good_new
+
+    #%% Warping
+    print("Warping")
+    tform = PiecewiseAffineTransform()
+    e1 = tform.estimate(dst_pts, src_pts)
+    im_warp1 = warp(P1, tform)
+    im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))
+
+    tform = PiecewiseAffineTransform()
+    e2 = tform.estimate(src_pts, dst_pts)
+    im_warp2 = warp(P2, tform)
+    im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))
+
+    print("Cross-Dissolve")
+    im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)
+    plt.figure(7)
+    plt.suptitle("SIFT Generated")
+    plt.imshow(im_gen, cmap='gray')
+
+
+#%% Stack
+
+S1 = np.array(Image.open('face.jpg'))
+S2 = S1
+plt.figure(8)
+plt.suptitle("Intermediate PM2")
+plt.imshow(PM2)
+
+S1_pts = [[0, 0], [S1.shape[0], 0], [0, S1.shape[1]], [S1.shape[0], S1.shape[1]]]
+S2_pts = [[0, 0], [S2.shape[0], 0], [0, S2.shape[1]], [S2.shape[0], S2.shape[1]]]
+
+HS1 = ((S1 * 0.8) + (S2 * 0.2)).astype(np.uint8)
+HS2 = ((S1 * 0.2) + (S2 * 0.8)).astype(np.uint8)
+
+plt.figure(1)
+plt.imshow(np.hstack((HS1, HS2)))
+while True:
+    plt.figure(1)
+    plt.suptitle('Select point on left plate or press enter to generate warp with current points')
+    p1 = plt.ginput(n=1, timeout=0)
+
+    if len(p1) == 0:
+        plt.suptitle("Generating warp with current points...")
+
+        tform = PiecewiseAffineTransform()
+        e1 = tform.estimate(np.array(S2_pts), np.array(S1_pts))
+        im_warp1 = warp(S1, tform)
+        im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))
+
+        tform = PiecewiseAffineTransform()
+        e2 = tform.estimate(np.array(S1_pts), np.array(S2_pts))
+        im_warp2 = warp(S2, tform)
+        im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))
+
+        # plt.figure(2)
+        # plt.suptitle("Warps")
+        # plt.imshow(np.hstack((im_warp1, im_warp2)), cmap='gray')
+
+        plt.figure(3)
+        plt.suptitle("Cross-dissolve")
+        im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)
+        plt.imshow(im_gen, cmap='gray')
+
+        continue
+
+    c = np.random.uniform(0, 1, 3)
+    (x1, y1) = (p1[0][0], p1[0][1])
+    l1 = plt.plot(x1, y1, marker='x', markersize=15, color=c)
+    plt.suptitle('Select point on right plate')
+
+    p2 = plt.ginput(n=1, timeout=0)
+    if len(p2) == 0:
+        l1.pop(0).remove()
+        plt.suptitle("Breaking out of infinite loop")
+        break
+
+    # Translate
+    (x2, y2) = (p2[0][0], p2[0][1])
+
+    # If you click on the right side
+    if x2 > S1.shape[0]:
+        x2 = x2 - S1.shape[0]
+        print("Right side click")
+    else:
+        print("Left side click")
+
+    x2 = (x2 + x1) / 2
+    y2 = (y2 + y1) / 2
+    plt.plot(x2, y2, marker='x', markersize=15, color=c)
+
+    S1_pts.append([x1, y1])
+    S2_pts.append([x2, y2])
+    print("Total points so far: ", len(S1_pts))
+    # plt.figure(2)
+    # plt.imshow(S1)
+    # plt.plot(x1, y1, marker='x', markersize=5, color='red')
+    #
+    # plt.figure(3)
+    # plt.imshow(S2)
+    # plt.plot(x2, y2, marker='x', markersize=5, color='red')
+
+
+#%% Z-Plane using built-in warping
+
+
+#%%
+# print("Blending")
+# plt.figure()
+# im_b1 = im_warp1
+# im_b2 = im_warp2
+# for w in np.linspace(0, 1, 20):
+#     b1 = im_b1 * w
+#     b2 = im_b2 * (1-w)
+#     gen = b1 + b2
+#     plt.suptitle("Im1 Weight " + str(w))
+#     plt.imshow(gen, cmap='gray')
+#     plt.waitforbuttonpress()
\ No newline at end of file
diff --git a/feature_matching_v3/exp_both.py b/feature_matching_v3/exp_both.py
new file mode 100644
index 0000000..647f67c
--- /dev/null
+++ b/feature_matching_v3/exp_both.py
@@ -0,0 +1,101 @@
+#%% Load Data
+import numpy as np
+import pylab as plt
+import sys
+from util_visualization import imshow_matches
+from util_sm import load_sm, norm_sm, norm_prob_sm
+from util_sift import precompute_sift, load_sift
+from skimage import color
+
+b_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')
+b_pw3 = np.loadtxt('Bregma_PW3_M.csv', dtype=np.float, delimiter=',')
+
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')
+pwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)
+
+# Only Nissl but using V2 dataset
+b_pw3_im = []
+b_pw3_label = []
+b_pw3_kp = []
+b_pw3_des = []
+nop = [12, 14, 56, 76, 15, 17, 19, 21, 22, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59,
+       61, 63, 65, 67, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,
+       121,123,125,127,129,131,133,135,137,139,141,143,144,145,147,149,151,153,155]
+for plate in pw_label:
+    if plate in nop:
+        continue
+    idx = plate - 1
+    b_pw3_im.append(pw_im[idx])
+    b_pw3_label.append(pw_label[idx])
+    b_pw3_kp.append(pw_kp[idx])
+    b_pw3_des.append(pw_des[idx])
+
+pw_im = np.array(b_pw3_im)
+pw_label = np.array(b_pw3_label)
+pw_kp = np.array(b_pw3_kp)
+pw_des = np.array(b_pw3_des)
+
+b_sm_matches = []
+b_sm_metric = []
+for plate in pw_label:
+    idx = plate - 1
+    b_sm_matches.append(sm_matches[:,idx])
+    b_sm_metric.append(sm_metric[:,idx])
+
+sm_matches = np.array(b_sm_matches)
+sm_metric = np.array(b_sm_metric)
+# norm = norm_sm(sm_metric, 100)
+norm = norm_prob_sm(sm_matches)
+
+#%% Bregma Algorithm
+atlas1 = b_s4
+atlas2 = b_pw3
+M = np.zeros((atlas1.shape[0]+1, atlas2.shape[0]))
+M[1:,:] = np.inf
+DIR = np.zeros_like(M)
+DIR[:,:] = 2
+for row in range(1, M.shape[0]):
+    for col in range(M.shape[1]):
+        if row > col:
+            continue
+
+        choices = [M[row,col-1], # Left
+                   M[row-1,col-1]+abs(atlas1[row-1] - atlas2[col])] # Diagonal
+        M[row][col] = min(choices)
+        DIR[row][col] = np.argmin(choices)
+
+M = M[1:,:]
+DIR = DIR[1:,:]
+#%% Path Backtracking
+im = np.zeros((DIR.shape[0], DIR.shape[1], 3), dtype=np.uint8)
+for row in range(DIR.shape[0]):
+    for col in range(DIR.shape[1]):
+        if row > col:
+            im[row, col] = [0, 100, 50] # Dark Green
+        elif DIR[row][col] == 0: # Left
+            im[row,col] = [200, 0, 0] # Red
+        elif DIR[row][col] == 1: # Diagonal
+            im[row, col] = [0, 255, 0] # Green
+        elif DIR[row][col] == 2: # Unused
+            im[row, col] = [0, 100, 50] # Dark Green
+        else:
+            im[row, col] = [0, 0, 0] # Black
+
+c = [148, 0, 211] # Purple
+im[6-1][8-1] = c
+im[11-1][11-1] = c
+im[23-1][42-1] = c
+im[33-1][68-1] = c
+for row in range(DIR.shape[0]):
+    col = np.argmin(M[row])
+    # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    if (row == 6-1 and col == 8-1) or (row == 11-1 and col == 11-1) or (row == 23-1 and col == 42-1) or (row == 33-1 and col == 68-1):
+        im[row][col] = [255, 255, 255] # White
+    else:
+        im[row][col] = [0, 0, 255] # Blue
+
+fig, ax = plt.subplots()
+ax.set_title("DIR - Best Path")
+ax.imshow(im)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_dynamic2.py b/feature_matching_v3/exp_dynamic2.py
new file mode 100644
index 0000000..c3bc820
--- /dev/null
+++ b/feature_matching_v3/exp_dynamic2.py
@@ -0,0 +1,441 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: April 9, 2018
+import numpy as np
+import pylab as plt
+import cv2
+import os
+from timeit import default_timer as timer
+from multiprocessing.pool import Pool
+from skimage import color
+from util_sm import load_sm, norm_sm, norm_prob_sm
+from util_sift import precompute_sift, load_sift, array_to_kp, kp_to_array
+from bounding import process_plate
+from util_matching import match
+
+#%% Load precomputed atlas information
+precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v4', s_kp, pw_kp)
+
+#%% Figure Set-Ups
+pw_ticks_idxs = [0]
+pw_ticks_vals = [pw_label[0]]
+for x in range(len(pw_label)):
+    try:
+        diff = pw_label[x + 1] - pw_label[x]
+        if diff > 1:
+            pw_ticks_idxs.append(x)
+            pw_ticks_vals.append(pw_label[x])
+            # print("IDX: ", x, "DIFF:", diff)
+    except:
+        continue
+
+pw_ticks_idxs.append(len(pw_label) - 1)
+pw_ticks_vals.append(pw_label[-1])
+
+def figure_reg():
+    fig, ax = plt.subplots()
+    ax.set_xlabel('PW Level')
+    ax.set_ylabel('S Level')
+    plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+    plt.xticks(pw_ticks_idxs, pw_ticks_vals)
+    plt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label) + 1))
+
+    for tick in ax.xaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    for tick in ax.yaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    return fig, ax
+
+def figure_exp():
+    fig, ax = plt.subplots()
+    ax.set_xlabel('PW Plate')
+    ax.set_ylabel('EXP Plate')
+    plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+    plt.xticks(pw_ticks_idxs, pw_ticks_vals)
+    plt.yticks([0, 1, 2, 3], ['s1t6', 's2t3', 's3t3', 's4t2'])
+    for tick in ax.xaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    return fig, ax
+
+def figure_synth():
+    fig, ax = plt.subplots()
+    ax.set_ylabel('PW Plate')
+    ax.set_xlabel('EXP Plate')
+    plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+    plt.yticks(pw_ticks_idxs, pw_ticks_vals)
+    plt.xticks([0, 1, 2, 3], ['s1t6', 's2t3', 's3t3', 's4t2'])
+    for tick in ax.xaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    return fig, ax
+
+#%% Functions
+def visualize_dir(dir):
+    color_mask = np.zeros((dir.shape[0], dir.shape[1], 3), dtype=np.uint8)
+    color_mask[:, :] = [255, 255, 255]
+    for row in range(dir.shape[0]):
+        for col in range(dir.shape[1]):
+            val = dir[row, col]
+            if val == 0: # Col-1
+                color = [255, 0, 0]
+            elif val == 1: # Diagonal
+                color = [0, 0, 255]
+            else: # Row-1
+                color = [0, 255, 0]
+
+            color_mask[row, col] = color
+
+    return color_mask
+
+def brute_force(sm):
+    bg = sm.astype(np.uint8)
+    color_mask = np.zeros((sm.shape[0], sm.shape[1], 3))
+
+    for row in range(sm.shape[0]):
+        best_col = sm[row].argmax()
+        color_mask[row, best_col] = [0, 0, 255]
+        bg[row, best_col] = 255
+
+    img_color = np.stack((bg,) * 3, axis=2)
+    img_hsv = color.rgb2hsv(img_color)
+    color_mask_hsv = color.rgb2hsv(color_mask)
+    img_hsv[..., 0] = color_mask_hsv[..., 0]
+    img_hsv[..., 1] = color_mask_hsv[..., 1]
+    im_overlay = color.hsv2rgb(img_hsv)
+    return im_overlay
+
+def brute_force_t(sm):
+    bg = sm.astype(np.uint8)
+    color_mask = np.zeros((sm.shape[0], sm.shape[1], 3))
+
+    for col in range(sm.shape[1]):
+        best_row = sm[:, col].argmax()
+        color_mask[best_row, col] = [0, 0, 255]
+        bg[best_row, col] = 255
+
+    img_color = np.stack((bg,) * 3, axis=2)
+    img_hsv = color.rgb2hsv(img_color)
+    color_mask_hsv = color.rgb2hsv(color_mask)
+    img_hsv[..., 0] = color_mask_hsv[..., 0]
+    img_hsv[..., 1] = color_mask_hsv[..., 1]
+    im_overlay = color.hsv2rgb(img_hsv)
+    return im_overlay
+
+def dynamic_prog(sm, col_penalty, row_penalty):
+    ed = np.zeros((sm.shape[0]+1, sm.shape[1]+1))
+    dir = np.zeros_like(ed)
+    ed[:,0] = np.arange(ed.shape[0]) * -row_penalty
+    ed[0,:] = np.arange(ed.shape[1]) * -col_penalty
+    # ed[:,0] = ed[0,:] = 0
+
+    for i in range(1,ed.shape[0]):
+        for j in range(1,ed.shape[1]):
+            choices = [ed[i,j-1] - col_penalty,  # 0 = top
+                       ed[i-1,j-1] + sm[i-1,j-1],  # 1 = diagonal
+                       ed[i-1,j] - row_penalty] # 2 = left
+            idx = np.argmax(choices)
+            dir[i,j]=idx
+            ed[i,j]=choices[idx]
+    return ed, dir.astype(np.uint8)
+
+def dynamic_prog_breg(sm, pw_penalty, s_penalty, b_s4, b_pw3):
+    ed = np.zeros((sm.shape[0]+1, sm.shape[1]+1))
+    dir = np.zeros_like(ed)
+
+    # Adjust bregma ranges so they are all negative
+    # b_s4 = b_s4 - b_s4.max()
+    # b_pw3 = b_pw3 - b_pw3.max()
+
+    # Append 0th bregma
+    # b_s4 = np.insert(b_s4, 0, 0.001)
+    # b_pw3 = np.insert(b_pw3, 0, 0.001)
+    alpha = 1
+
+    ed[:,0] = np.arange(ed.shape[0]) * -s_penalty
+    ed[0,:] = np.arange(ed.shape[1]) * -pw_penalty
+    # ed[:,0] = ed[0,:] = 0
+
+    for i in range(1,ed.shape[0]):
+        for j in range(1,ed.shape[1]):
+            diff = np.abs(b_s4[i-1] - b_pw3[i-1])
+            choices = [ed[i,j-1] - pw_penalty, # 0 = top
+                       ed[i-1,j-1] + (1 * sm[i-1,j-1]) - (alpha * diff) , # 1 = diagonal
+                       ed[i-1,j] - s_penalty] # 2 = left
+            idx = np.argmax(choices)
+            dir[i,j]=idx
+            ed[i,j]=choices[idx]
+    return ed, dir.astype(np.uint8)
+
+def bregma_space(dir, sm, b_s4, b_pw3):
+    fig, ax = plt.subplots()
+    ax.set_xlabel('S4 Bregma')
+    ax.set_ylabel('PW3 Bregma')
+    sidx = sm.shape[0] - 1
+    pwidx = sm.shape[1] - 1
+    while sidx >= 0 and pwidx >= 0:
+        s_breg = b_s4[sidx]
+        pw_breg = b_pw3[pwidx]
+
+        ax.plot(s_breg, pw_breg, marker='o', markerSize=5, color='red')
+        next_dir = dir[sidx, pwidx]
+        if next_dir == 0:  # Left
+            pwidx -= 1
+        elif next_dir == 1:  # Diagonal
+            sidx -= 1
+            pwidx -= 1
+        else:  # Up
+            sidx -= 1
+
+    return fig, ax
+
+#%% Overlay Function
+def overlay(dir, sm):
+    color_mask = np.zeros((dir.shape[0],dir.shape[1],3))
+    # bg = np.zeros((dir.shape[0],dir.shape[1]))
+    # bg[1:,1:] = sm.astype(np.uint8)
+    bg = sm.astype(np.uint8)
+
+    sidx = sm.shape[0]-1
+    pwidx = sm.shape[1]-1
+    count = 0
+    path = ['START']
+    pairs = []
+    while sidx >= 0 and pwidx >= 0:
+        count += 1
+        color_mask[sidx, pwidx] = [0, 0, 255]
+        # bg[sidx-1, pwidx-1] = 255
+        bg[sidx, pwidx] = 255
+        next_dir = dir[sidx, pwidx]
+        pairs.append([sidx, pwidx])
+        if next_dir == 0: # Left
+            pwidx -= 1
+            path.append('L')
+        elif next_dir == 1: # Diagonal
+            sidx -= 1
+            pwidx -= 1
+            path.append('D')
+        else: # Up
+            sidx -= 1
+            path.append('U')
+
+    # Remove penalty row/col
+    dir = dir[1:,1:]
+    color_mask = color_mask[1:,1:,:]
+
+    # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    if dir.shape[0] == 74:
+        color_mask[np.where(s_label == 6), np.where(pw_label == 8)] = [255, 0, 0]
+        bg[np.where(s_label == 6), np.where(pw_label == 8)] = 255
+
+        color_mask[np.where(s_label == 11), np.where(pw_label == 11)] = [255, 0, 0]
+        bg[np.where(s_label == 11), np.where(pw_label == 11)] = 255
+
+        color_mask[np.where(s_label == 23), np.where(pw_label == 42)] = [255, 0, 0]
+        bg[np.where(s_label == 23), np.where(pw_label == 42)] = 255
+
+        color_mask[np.where(s_label == 33), np.where(pw_label == 68)] = [255, 0, 0]
+        bg[np.where(s_label == 33), np.where(pw_label == 68)] = 255
+
+    print("path", count, path)
+    img_color = np.stack((bg,)*3,axis=2)
+    img_hsv = color.rgb2hsv(img_color)
+    color_mask_hsv = color.rgb2hsv(color_mask)
+    img_hsv[..., 0] = color_mask_hsv[..., 0]
+    img_hsv[..., 1] = color_mask_hsv[..., 1]
+    im_overlay = color.hsv2rgb(img_hsv)
+    return im_overlay, np.array(pairs)
+
+#%% Load Bregma data
+b_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')
+b_pw3 = np.loadtxt('Bregma_PW3_M.csv', dtype=np.float, delimiter=',')
+
+b_pw = []
+for i in range(len(pw_label)):
+    lbl = pw_label[i]
+    idx = lbl-1
+    b_pw.append(b_pw3[idx])
+
+b_pw3 = np.array(b_pw)
+
+#%% Brute Force Experiment
+im_bf_overlay = brute_force(sm_matches)
+fig, ax = figure_reg()
+ax.set_title('Brute Force Overlay')
+ax.imshow(im_bf_overlay)
+
+#%% Dynamic Programming (Bregma Distances)
+ed_b, dir_b = dynamic_prog_breg(sm_matches, pw_penalty=50, s_penalty=50, b_s4=b_s4, b_pw3=b_pw3)
+im_dpb_overlay, pairs_b = overlay(dir_b, sm_matches)
+im_dir_b = visualize_dir(dir_b)
+
+fig, ax = figure_reg()
+ax.set_title('Dynamic Prog (Bregma) - Overlay')
+ax.imshow(im_dpb_overlay)
+
+fig, ax = figure_reg()
+ax.set_title('Dynamic Prog (Bregma) - Dir [Red=Col-1, Blue=Diagonal, Green=Row-1]')
+ax.imshow(im_dir_b)
+
+#%% Dynamic Programming (Regular)
+mat = sm_matches
+pw_penalty = 50
+s_penalty = 50
+ed, dir = dynamic_prog(mat, col_penalty=pw_penalty, row_penalty=s_penalty)
+im_overlay, pairs = overlay(dir, mat)
+im_dir = visualize_dir(dir)
+
+fig, ax = figure_reg()
+ax.set_title('Dynamic Prog (Regular) - Overlay')
+ax.imshow(im_overlay)
+
+fig, ax = figure_reg()
+ax.set_title('Dynamic Prog (Regular) - Dir [Red=Col-1, Blue=Diagonal, Green=Row-1]')
+ax.imshow(im_dir)
+
+#%% Bregma Space
+fig, ax = bregma_space(dir, mat, b_s4, b_pw3)
+ax.set_title('Bregma Space (Regular DP)')
+
+fig, ax = bregma_space(dir_b, sm_matches, b_s4, b_pw3)
+ax.set_title('Bregma Space (Bregma Dist DP)')
+
+#%% Experimental data init
+e1, e1_c = process_plate('experimental/18-016 LHA s1t6.tif', split=True)
+e2, e2_c = process_plate('experimental/18-016 LHA s2t3.tif', split=True)
+e3, e3_c = process_plate('experimental/18-016 LHA s3t3.tif', split=True)
+e4, e4_c = process_plate('experimental/18-016 LHA s4t2.tif', split=True)
+
+exp = [e1_c, e2_c, e3_c, e4_c]
+exp_kp = []
+exp_des = []
+
+if not os.path.isfile('EXP_SIFT.npz'):
+    print("Precomputing and saving SIFT for experimental")
+    SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.05, edgeThreshold=100, sigma=2)
+    for im_exp in exp:
+        kp, des = SIFT.detectAndCompute(im_exp, None)
+        kp = kp_to_array(kp)
+
+        exp_kp.append(kp)
+        exp_des.append(des)
+
+    exp_kp = np.asarray(exp_kp)
+    exp_des = np.asarray(exp_des)
+
+    np.savez_compressed('EXP_SIFT', kp=exp_kp, des=exp_des)
+else:
+    print("Loading pre-saved SIFT for experimental")
+    data = np.load('EXP_SIFT.npz')
+    exp_kp = data['kp']
+    exp_des = data['des']
+
+#%% Prepare to match with PW
+def perform_match(idx):
+    global s_kp, s_des, pw_kp, pw_des, exp_kp, exp_des
+    matches = []
+    print('EXP IDX', idx)
+    for pw_idx in range(pw_kp.shape[0]):
+        matches.append(match(exp_kp[idx], exp_des[idx], pw_kp[pw_idx], pw_des[pw_idx]))
+
+    return np.array(matches)
+    # np.savez_compressed(str(idx) + '-EM', m=matches)
+
+#%% Match (Sequential)
+if not os.path.isfile('EXP_SM.npz'):
+    print("Creating EXP SM")
+    time_start = timer()
+    sm_exp = []
+    for i in range(len(exp)):
+        sm_exp.append(perform_match(i))
+
+    sm_exp = np.array(sm_exp)
+    duration = timer() - time_start
+    duration_m = duration / 60
+    print("Matching took %.3fs %.3fm" % (duration, duration_m))
+    np.savez_compressed('EXP_SM', sm=sm_exp)
+else:
+    print("Loading pre-saved SM for experimental")
+    data = np.load('EXP_SM.npz')
+    sm_exp = data['sm']
+
+sm_exp_count = np.zeros_like(sm_exp)
+for row in range(sm_exp.shape[0]):
+    count = []
+    for col in range(sm_exp.shape[1]):
+        matches = sm_exp[row,col]
+        count.append(len(matches))
+
+    sm_exp_count[row] = np.array(count)
+
+#%% Experimental matching with brute-force approach
+bf_exp_overlay = brute_force(sm_exp_count)
+fig, ax = plt.exp()
+ax.set_title('Brute-Force Matching (SM-Exp)')
+ax.imshow(bf_exp_overlay)
+
+#%% Experimental matching with dynamic programming
+ed_e, dir_e = dynamic_prog(sm_exp_count, col_penalty=10, row_penalty=10)
+im_overlay_e, pairs_e = overlay(dir_e, sm_exp_count)
+im_dir_e = visualize_dir(dir_e)
+
+fig, ax = figure_exp()
+ax.imshow(sm_exp_count.astype(np.uint8))
+ax.set_title('Similarity Matrix - Experimental')
+
+fig, ax = figure_exp()
+ax.set_title('Dynamic Prog (Experimental) - Overlay')
+ax.imshow(im_overlay_e)
+
+fig, ax = figure_exp()
+ax.set_title('Dynamic Prog (Experimental) - Dir [Red=Col-1, Blue=Diagonal, Green=Row-1]')
+ax.imshow(im_dir_e)
+
+#%% Synthetic
+sm_synth = sm_exp_count
+sm_synth[1,1] = 20
+
+fig, ax = figure_synth()
+ax.imshow(sm_synth.astype(np.uint8))
+ax.set_title('Similarity Matrix - Synthetic')
+
+#%% Synthetic Brute Force
+bf_exp_overlay = brute_force(sm_synth)
+fig, ax = figure_synth()
+ax.set_title('Brute-Force Matching (SM-Exp)')
+ax.imshow(bf_exp_overlay)
+
+#%% Synthetic Dynamic Programming
+ed_s, dir_s = dynamic_prog(sm_synth, col_penalty=10, row_penalty=10)
+im_overlay_s, pairs_s = overlay(dir_s, sm_synth)
+im_dir_s = visualize_dir(dir_s)
+
+fig, ax = figure_synth()
+ax.set_title('Dynamic Prog (Synthetic) - Overlay')
+ax.imshow(im_overlay_s)
+
+fig, ax = figure_synth()
+ax.set_title('Dynamic Prog (Synthetic) - Dir [Red=Col-1, Blue=Diagonal, Green=Row-1]')
+ax.imshow(im_dir_s)
+
+
+#%% Match (Threaded)
+# TODO: Required to be in main function
+# time_start = timer()
+#
+# pool = Pool()
+# idx = range(im_exp.shape[0])
+#
+# print('Beginning pool work: Matching experimental with atlas')
+# pool.map(perform_match, idx)
+# pool.close()
+# pool.join()
+#
+# duration = timer() - time_start
+# duration_m = duration / 60
+# print("Program took %.3fs %.3fm" % (duration, duration_m))
\ No newline at end of file
diff --git a/feature_matching_v3/exp_dynamic_bregma.py b/feature_matching_v3/exp_dynamic_bregma.py
new file mode 100644
index 0000000..d65735c
--- /dev/null
+++ b/feature_matching_v3/exp_dynamic_bregma.py
@@ -0,0 +1,116 @@
+#%% Load Bregma Data
+import numpy as np
+import pylab as plt
+from skimage import color
+import sys
+
+from util_visualization import imshow_matches
+from util_sm import load_sm, norm_sm, norm_prob_sm
+from util_sift import precompute_sift, load_sift
+
+BREG = True
+
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')
+pwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)
+
+def norm_sm(sm, max_value=255, min_value=0):
+    sm[sm == np.inf] = sys.maxsize
+    im_result = np.zeros_like(sm)
+    for idx in range(sm.shape[0]):
+        x = sm[idx]
+        norm = ((x - np.min(x))*(max_value-min_value)) / (np.max(x)-np.min(x)) + min_value
+        im_result[idx] = (norm).reshape(1, sm.shape[1])
+    return im_result
+
+np.set_printoptions(edgeitems=5)
+b_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')
+b_pw3 = np.loadtxt('Bregma_PW3.csv', dtype=np.float, delimiter=',')
+
+
+
+breg_1 = b_s4
+breg_2 = b_pw3
+# M = np.zeros((breg_1.shape[0]+1, breg_2.shape[0]))
+M = np.zeros((breg_1.shape[0]+1, breg_2.shape[0]))
+M[1:,:] = np.inf
+DIR = np.zeros_like(M)
+DIR[1:,:] = np.inf
+
+
+
+#%% Dynamic programming
+for row in range(1, M.shape[0]):
+    for col in range(M.shape[1]):
+        if row > col:
+            continue
+        if BREG:
+            choices = [M[row,col-1], # Left
+                       M[row-1,col-1]+abs(breg_1[row-1] - breg_2[col])] # Diagonal
+            M[row][col] = min(choices)
+            DIR[row][col] = np.argmin(choices)
+        else:
+            choices = [M[row,col-1], # Left
+                        M[row-1,col-1] + sm_matches[row-1][col-1], # Diagonal
+                        M[row-1][col-1] # Right
+                       ]
+            M[row][col] = max(choices)
+            DIR[row][col] = np.argmax(choices)
+
+
+
+#%% Overlap
+bg = norm_sm(M, 255).astype(np.uint8)
+color_mask = np.zeros((DIR.shape[0], DIR.shape[1], 3))
+for row in range(1, M.shape[0]):
+    for col in range(M.shape[1]):
+        if row > col:
+            color_mask[row][col] = [255, 0, 0]
+d_row = DIR.shape[0] - 1
+d_col = DIR.shape[1] - 1
+count = 0
+path = ['START']
+pairs = []
+if BREG:
+    while d_row != 0 and d_col != 0:
+        color_mask[d_row, d_col] = [0, 0, 255]
+        bg[d_row, d_col] = 255
+        next_dir = DIR[d_row, d_col]
+        pairs.append([d_row, d_col])
+        if next_dir == 0:
+            d_col -= 1
+            path.append('L')
+        elif next_dir == 1:
+            d_row -= 1
+            d_col -= 1
+            path.append('D')
+else:
+    while d_row != 0 and d_col != 0:
+        color_mask[d_row, d_col] = [0, 0, 255]
+        bg[d_row, d_col] = 255
+        next_dir = DIR[d_row, d_col]
+        pairs.append([d_row, d_col])
+        if next_dir == 0:
+            d_col -= 1
+            path.append('L')
+        elif next_dir == 1:
+            d_row -= 1
+            d_col -= 1
+            path.append('D')
+        else:
+            d_row -= 1
+            path.append("U")
+
+#%% Path Figure
+print("path", count, path)
+img_color = np.stack((bg,) * 3, axis=2)
+img_hsv = color.rgb2hsv(img_color)
+color_mask_hsv = color.rgb2hsv(color_mask)
+img_hsv[..., 0] = color_mask_hsv[..., 0]
+img_hsv[..., 1] = color_mask_hsv[..., 1]
+im_overlay = color.hsv2rgb(img_hsv)
+plt.figure()
+plt.title("Bregma Overlay: " + str(BREG))
+plt.imshow(im_overlay)
+plt.show()
\ No newline at end of file
diff --git a/feature_matching_v3/exp_dynamic_local_alignment.py b/feature_matching_v3/exp_dynamic_local_alignment.py
new file mode 100644
index 0000000..76ad7d4
--- /dev/null
+++ b/feature_matching_v3/exp_dynamic_local_alignment.py
@@ -0,0 +1,196 @@
+#%% Experiment: Dynamic programming to align a sequence of plates to an atlas
+# Author: Jose G. Perez
+# Last Modified: March 28, 2020
+import numpy as np
+import pylab as plt
+import cv2
+import os
+from timeit import default_timer as timer
+
+import util_sm
+import util_sift
+import util_matching
+import util_visualization
+import bounding
+
+PATH_CHARACTERS = ['', '↖', '↑', '←']
+
+#%% Load atlas data
+util_sift.precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = util_sift.load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = util_sift.load_sift('PW_BB_V4_SIFT.npz')
+sm_matches, sm_metric = util_sm.load_sm('sm_v4', s_kp, pw_kp)
+
+#%% Load local sequence data
+e1, e1_c = bounding.process_plate('experimental/18-016 LHA s1t6.tif', split=True)
+e2, e2_c = bounding.process_plate('experimental/18-016 LHA s2t3.tif', split=True)
+e3, e3_c = bounding.process_plate('experimental/18-016 LHA s3t3.tif', split=True)
+e4, e4_c = bounding.process_plate('experimental/18-016 LHA s4t2.tif', split=True)
+
+exp_filenames = ['s1t6', 's2t3', 's3t3', 's4t2']
+exp = [e1_c, e2_c, e3_c, e4_c]
+exp_kp = []
+exp_des = []
+
+if not os.path.isfile('EXP_SIFT.npz'):
+    print("Precomputing and saving SIFT for experimental dataset")
+    SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.05, edgeThreshold=100, sigma=2)
+    for im_exp in exp:
+        kp, des = SIFT.detectAndCompute(im_exp, None)
+        kp = util_sift.kp_to_array(kp)
+
+        exp_kp.append(kp)
+        exp_des.append(des)
+
+    exp_kp = np.asarray(exp_kp)
+    exp_des = np.asarray(exp_des)
+
+    np.savez_compressed('EXP_SIFT', kp=exp_kp, des=exp_des)
+else:
+    print("Loading pre-saved SIFT for experimental")
+    data = np.load('EXP_SIFT.npz')
+    exp_kp = data['kp']
+    exp_des = data['des']
+
+#%% Compute similarity matrix between atlas (PW) and experimental sequence
+def perform_match(idx):
+    global s_kp, s_des, pw_kp, pw_des, exp_kp, exp_des
+    matches = []
+    print('EXP IDX', idx)
+    for pw_idx in range(pw_kp.shape[0]):
+        matches.append(util_matching.match(exp_kp[idx], exp_des[idx], pw_kp[pw_idx], pw_des[pw_idx]))
+
+    return np.array(matches)
+
+
+if not os.path.isfile('EXP_SM.npz'):
+    print("Creating atlas-experimental similarity matrix")
+    time_start = timer()
+    sm_exp = []
+    for i in range(len(exp)):
+        sm_exp.append(perform_match(i))
+
+    sm_exp = np.array(sm_exp)
+    duration = timer() - time_start
+    duration_m = duration / 60
+    print("Matching took %.3fs %.3fm" % (duration, duration_m))
+    np.savez_compressed('EXP_SM', sm=sm_exp)
+else:
+    print("Loading pre-saved SM for atlas-experimental")
+    data = np.load('EXP_SM.npz')
+    sm_exp = data['sm']
+
+sm_atlas_experimental = np.zeros_like(sm_exp, dtype=np.uint8)
+for row in range(sm_exp.shape[0]):
+    count = []
+    for col in range(sm_exp.shape[1]):
+        matches = sm_exp[row,col]
+        count.append(len(matches))
+
+    sm_atlas_experimental[row] = np.array(count)
+
+
+#%% Dynamic programming algorithm (Sequence to Atlas / Local Alignment)
+def local_alignment(sm, col_penalty, row_penalty):
+    V = np.zeros((sm.shape[0]+1, sm.shape[1]+1))
+    paths = np.zeros_like(V)
+    V[:, 0] = V[0, :] = 0
+
+    for i in range(1, V.shape[0]):
+        for j in range(1, V.shape[1]):
+            # 0 = Finish
+            # 1 = Diagonal / Match
+            # 2 = Up / Deletion
+            # 3 = Left / Insertion
+            choices = [0,
+                       V[i-1, j-1] + sm[i-1, j-1],
+                       V[i-1, j] - row_penalty,
+                       V[i, j-1] - col_penalty,
+                       ]
+
+            idx = np.argmax(choices)
+            paths[i,j] = idx
+            V[i,j] = choices[idx]
+
+    return V, paths.astype(np.uint8)
+
+
+def reconstruct(sm, V, paths):
+    # Visualization
+    foreground = np.zeros((V.shape[0], V.shape[1], 3))
+    background = V.astype(np.uint8)
+
+    # For local alignment, pick the highest number in V
+    max_value = np.amax(V)
+    idxs = np.where(V == max_value)
+    start_row_idx = idxs[0][0]
+    start_col_idx = idxs[1][0]
+
+    # Then backtrack until we hit a 0
+    # We also keep backtracking until we reach the top-left corner
+    row_idx = start_row_idx
+    col_idx = start_col_idx
+    score = V[row_idx, col_idx]
+
+    while row_idx > 0 or col_idx > 0:
+        # Paint the pixel we traveled to in the visualization
+        value = paths[row_idx, col_idx]
+        foreground[row_idx, col_idx] = [0, 0, 255]
+        background[row_idx, col_idx] = 255
+
+        # Navigate the dynamic programming table
+        diagonal = (value == 1) or (value == 0 and col_idx >= 1 and row_idx >= 1)
+        up = (value == 2) or (value == 0 and row_idx >= 1)
+        left = (value == 3) or (value == 0 and col_idx >= 1)
+        if diagonal:
+            row_idx -= 1
+            col_idx -= 1
+        elif up:
+            row_idx -= 1
+        elif left:
+            col_idx -= 1
+
+    return util_visualization.overlay(background, foreground)
+
+
+#%% Actual Experiment!
+V, paths = local_alignment(sm_atlas_experimental, 1, 1)
+im_overlay = reconstruct(sm_atlas_experimental, V, paths)
+
+#%% Figure 1: Atlas-Experimental Similarity Matrix
+util_visualization.imshow_detailed(
+                np_arr=sm_atlas_experimental,
+                title='Atlas-Experimental Similarity Matrix',
+                axis_xlabel='PW Plate',
+                axis_ylabel='Experimental Plate',
+                xlabel_arr=['ε'] + pw_label,
+                ylabel_arr=['ε'] + exp_filenames)
+
+#%% Figure 2: DP Value Matrix (V)
+util_visualization.imshow_detailed(
+                np_arr=V,
+                title='Atlas-Experimental Dynamic Programming Value Matrix',
+                axis_xlabel='PW Plate',
+                axis_ylabel='Experimental Plate',
+                xlabel_arr=['ε'] + pw_label,
+                ylabel_arr=['ε'] + exp_filenames)
+
+#%% Figure 3: DP Paths Matrix (With arrows!)
+util_visualization.imshow_detailed(
+                np_arr=paths,
+                title='Atlas-Experimental Dynamic Programming Path',
+                axis_xlabel='PW Plate',
+                axis_ylabel='Experimental Plate',
+                xlabel_arr=['ε'] + pw_label,
+                ylabel_arr=['ε'] + exp_filenames,
+                value_to_str_func=lambda i, j, val: PATH_CHARACTERS[val])
+
+#%% Figure 4: DP Path Backtracking Overlay
+util_visualization.imshow_detailed(
+                np_arr=im_overlay,
+                title='Atlas-Experimental Dynamic Programming Overlay',
+                axis_xlabel='PW Plate',
+                axis_ylabel='Experimental Plate',
+                xlabel_arr=['ε'] + pw_label,
+                ylabel_arr=['ε'] + exp_filenames,
+                value_to_str_func=lambda i, j, val: int(V[i, j]))
\ No newline at end of file
diff --git a/feature_matching_v3/exp_dynamic_programming.py b/feature_matching_v3/exp_dynamic_programming.py
new file mode 100644
index 0000000..12aa34f
--- /dev/null
+++ b/feature_matching_v3/exp_dynamic_programming.py
@@ -0,0 +1,314 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import pylab as plt
+from skimage import color
+from util_visualization import imshow_matches
+from util_sm import load_sm, norm_sm, norm_prob_sm
+from util_sift import precompute_sift, load_sift
+
+precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v4', s_kp, pw_kp)
+
+def idx_to_plate(labels, plate):
+    return np.where(labels == plate)
+
+def dynamic_prog(sm, pw_penalty, s_penalty):
+    ed = np.zeros((sm.shape[0]+1, sm.shape[1]+1))
+    dir = np.zeros_like(ed)
+    ed[:,0] = np.arange(ed.shape[0]) * -s_penalty
+    ed[0,:] = np.arange(ed.shape[1]) * -pw_penalty
+    # ed[:,0] = ed[0,:] = 0
+
+    for i in range(1,ed.shape[0]):
+        for j in range(1,ed.shape[1]):
+            choices = [ed[i,j-1] - pw_penalty, # 0 = top
+                       ed[i-1,j-1] + sm[i-1,j-1], # 1 = diagonal
+                       ed[i-1,j] - s_penalty] # 2 = left
+            idx = np.argmax(choices)
+            dir[i,j]=idx
+            ed[i,j]=choices[idx]
+    return ed, dir.astype(np.uint8)
+    # return ed, dir.astype(np.uint8)
+
+def get_pairs(dir):
+    sidx = dir.shape[0]-1
+    pwidx = dir.shape[1]-1
+    pairs = []
+    while sidx > 0 and pwidx > 0:
+        next_dir = dir[sidx, pwidx]
+        pairs.append([sidx, pwidx])
+        if next_dir == 0:
+            sidx -= 1
+        elif next_dir == 1:
+            sidx -= 1
+            pwidx -= 1
+        else:
+            pwidx -= 1
+    return np.array(pairs)
+
+def pair_metric(sm_metric, pairs):
+    best6_pw = get_best_pw(sm_metric, pairs, 6)
+    best11_pw = get_best_pw(sm_metric, pairs, 11)
+    best23_pw = get_best_pw(sm_metric, pairs, 23)
+    best33_pw = get_best_pw(sm_metric, pairs, 33)
+
+    # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    # m += np.count_nonzero(best6_pw == np.where(pw_label == 8))
+    # m += np.count_nonzero(best11_pw == np.where(pw_label == 11))
+    # m += np.count_nonzero(best23_pw == np.where(pw_label == 42))
+    # m += np.count_nonzero(best33_pw == np.where(pw_label == 68))
+    return np.min(abs(best6_pw - np.where(pw_label == 8))) + \
+         np.min(abs(best11_pw - np.where(pw_label == 11))) + \
+         np.min(abs(best23_pw - np.where(pw_label == 42))) + \
+         np.min(abs(best33_pw - np.where(pw_label == 68)))
+
+def overlay(dir, sm):
+    # bg = norm_sm(sm, 255).astype(np.uint8)
+    bg = sm.astype(np.uint8)
+    color_mask = np.zeros((dir.shape[0],dir.shape[1],3))
+
+    sidx = sm.shape[0]-1
+    pwidx = sm.shape[1]-1
+    count = 0
+    path = ['START']
+    pairs = []
+    while sidx >= 0 and pwidx >= 0:
+        count += 1
+        color_mask[sidx, pwidx] = [0, 0, 255]
+        bg[sidx, pwidx] = 255
+        next_dir = dir[sidx, pwidx]
+        pairs.append([sidx, pwidx])
+        if next_dir == 0: # Left
+            pwidx -= 1
+            path.append('L')
+        elif next_dir == 1: # Diagonal
+            sidx -= 1
+            pwidx -= 1
+            path.append('D')
+        else: # Up
+            sidx -= 1
+            path.append('U')
+
+    # Remove penalty row/col
+    dir = dir[1:,1:]
+    color_mask = color_mask[1:,1:,:]
+
+    # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    color_mask[np.where(s_label == 6), np.where(pw_label == 8)] = [255, 0, 0]
+    bg[np.where(s_label == 6), np.where(pw_label == 8)] = 255
+
+    color_mask[np.where(s_label == 11), np.where(pw_label == 11)] = [255, 0, 0]
+    bg[np.where(s_label == 11), np.where(pw_label == 11)] = 255
+
+    color_mask[np.where(s_label == 23), np.where(pw_label == 42)] = [255, 0, 0]
+    bg[np.where(s_label == 23), np.where(pw_label == 42)] = 255
+
+    color_mask[np.where(s_label == 33), np.where(pw_label == 68)] = [255, 0, 0]
+    bg[np.where(s_label == 33), np.where(pw_label == 68)] = 255
+
+    print("path", count, path)
+    img_color = np.stack((bg,)*3,axis=2)
+    img_hsv = color.rgb2hsv(img_color)
+    color_mask_hsv = color.rgb2hsv(color_mask)
+    img_hsv[..., 0] = color_mask_hsv[..., 0]
+    img_hsv[..., 1] = color_mask_hsv[..., 1]
+    im_overlay = color.hsv2rgb(img_hsv)
+    return im_overlay, np.array(pairs)
+
+def error(best_pw, pw_plate, s_plate):
+    # s_idx = int(np.argwhere(s_label == s_plate))
+    pw_idx = int(np.argwhere(pw_label == pw_plate))
+    pred_sidx = best_pw[pw_idx]
+    pred_s = int(np.argwhere(s_label == pred_sidx))
+
+    return abs(pred_s - s_plate)
+
+def get_best_pw(sm_metric, pairs, s_plate):
+    # Indices start at 0, plates start at 1
+    sidx = s_plate-1
+
+    pidx = np.where(pairs[:, 0] == sidx)
+    matches = pairs[pidx, 1].flatten()
+    # return pw_label[matches] if len(matches >= 1) else -1
+    return pw_label[matches] if len(matches >= 1) else np.array([np.inf])
+    # if len(matches) > 1:
+    #     metrics = sm_metric[sidx,matches]
+    #     best_idx = np.argmax(metrics)
+    #     return int(pw_label[matches[best_idx]])
+    # elif len(matches) == 1:
+    #     # Convert from PW Indices to PW Labels
+    #     return int(pw_label[matches])
+    # else:
+    #     return -1
+
+if __name__ == '__main__':
+    # lowest_error = np.inf
+    # best_pw = -1
+    # best_s = -1
+    # for pw_penalty in np.arange(0.4, 0.5, 0.001):
+    #     for s_penalty in np.arange(0.4, 0.5, 0.001):
+    #         ed, dir = dynamic_prog(norm, pw_penalty=pw_penalty, s_penalty=s_penalty)
+    #         pairs = get_pairs(dir)
+    #         metric = pair_metric(sm_metric, pairs)
+    #         if metric < lowest_error:
+    #             print("New error", metric, pw_penalty, s_penalty)
+    #             lowest_error = metric
+    #             best_pw = pw_penalty
+    #             best_s = s_penalty
+    # ed, dir = dynamic_prog(norm, pw_penalty=best_pw, s_penalty=best_s)
+    # im_overlay, pairs = overlay(dir, sm_metric)
+    # best6_pw = get_best_pw(sm_metric,pairs,6)
+    # best11_pw = get_best_pw(sm_metric,pairs,11)
+    # best23_pw = get_best_pw(sm_metric,pairs,23)
+    # best33_pw = get_best_pw(sm_metric,pairs,33)
+    # print("[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]" % (best6_pw, best11_pw, best23_pw, best33_pw))
+    #
+    # imshow_matches(im_overlay, 'Dynamic Programming')
+
+    # import pylab as plt
+    # best_pw = 200
+    # best_s = 220
+    # ed, dir = dynamic_prog(norm, pw_penalty=best_pw, s_penalty=best_s)
+    # pairs = get_pairs(dir)
+    # metric = pair_metric(sm_metric, pairs)
+    # im_overlay, pairs = overlay(dir, sm_metric)
+    # best6_pw = get_best_pw(sm_metric,pairs,6)
+    # best11_pw = get_best_pw(sm_metric,pairs,11)
+    # best23_pw = get_best_pw(sm_metric,pairs,23)
+    # best33_pw = get_best_pw(sm_metric,pairs,33)
+    # print("[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]" % (best6_pw, best11_pw, best23_pw, best33_pw))
+    #
+    # imshow_matches(im_overlay, 'Dynamic Programming')
+    # plt.show()
+
+    # mat = sm_matches
+    #
+    # pw_penalty = 50
+    # s_penalty = 50
+    # ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)
+    # im_overlay, pairs = overlay(dir, mat)
+    # norm = norm_sm(mat)
+    #
+    # import pylab as plt
+    # fig, axes = plt.subplots(nrows=2, ncols=2)
+    # plt.subplots_adjust(left=0.25, bottom=0.25)
+    # plt.set_cmap(plt.get_cmap('hot'))
+    # # axes.set_title('Dynamic')
+    #
+    # axes[0,0].set_title('Similarity Matrix')
+    # axes[0,0].imshow(mat)
+    #
+    # axes[0,1].set_title('SM Norm')
+    # axes[0,1].imshow(norm_prob_sm(sm_matches))
+    #
+    # axes[1,0].set_title('ED')
+    # axes[1,1].set_title('Overlay')
+    #
+    # # Sliders
+    # axcolor = 'lightgoldenrodyellow'
+    # axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
+    # axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
+    # # s_pwp = plt.Slider(axfreq, 'PW Penalty', 0, 1, .0001, valfmt='%.8f')
+    # # s_sp = plt.Slider(axamp, 'S Penalty', 0, 1, .0001, valfmt='%.8f')
+    # s_pwp = plt.Slider(axfreq, 'PW Penalty', 0, 400, 10, valfmt='%.8f')
+    # s_sp = plt.Slider(axamp, 'S Penalty', 0, 400, 10, valfmt='%.8f')
+    #
+    # def update(val):
+    #     pw_penalty = s_pwp.val
+    #     s_penalty = s_sp.val
+    #
+    #     ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)
+    #     im_overlay, pairs = overlay(dir, mat)
+    #
+    #     best6_pw = get_best_pw(sm_metric,pairs,6)
+    #     best11_pw = get_best_pw(sm_metric,pairs,11)
+    #     best23_pw = get_best_pw(sm_metric,pairs,23)
+    #     best33_pw = get_best_pw(sm_metric,pairs,33)
+    #     print("[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]" % (best6_pw, best11_pw, best23_pw, best33_pw))
+    #
+    #     axes[1,0].imshow(ed)
+    #     axes[1,1].imshow(im_overlay)
+    #     fig.canvas.draw_idle()
+    #
+    # s_pwp.on_changed(update)
+    # s_sp.on_changed(update)
+    # plt.show()
+
+    #%% Runtime Experiments
+    mat = sm_matches
+    pw_penalty = 50
+    s_penalty = 50
+    ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)
+    im_overlay, pairs = overlay(dir, mat)
+
+    # Figure prep
+    pw_ticks_idxs = [0]
+    pw_ticks_vals = [pw_label[0]]
+    for x in range(len(pw_label)):
+        try:
+            diff = pw_label[x + 1] - pw_label[x]
+            if diff > 1:
+                pw_ticks_idxs.append(x)
+                pw_ticks_vals.append(pw_label[x])
+                # print("IDX: ", x, "DIFF:", diff)
+        except:
+            continue
+
+    pw_ticks_idxs.append(len(pw_label) - 1)
+    pw_ticks_vals.append(pw_label[-1])
+
+    # Figure
+    plt.figure()
+    ax = plt.gca()
+    ax.set_title('Dynamic Programming Back-Tracing')
+    plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+    plt.imshow(im_overlay)
+    plt.xticks(pw_ticks_idxs, pw_ticks_vals)
+    plt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label) + 1))
+
+    for tick in ax.xaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    for tick in ax.yaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    plt.xlabel('PW Level')
+    plt.ylabel('S Level')
+
+    # best_pwp = 0
+    # best_sps = 0
+    # best_total = np.inf
+    # for pw_penalty in range(0, 200):
+    #     for s_penalty in range(0, 200):
+    #         ed, ed2 = dynamic_prog(norm, pw_penalty=pw_penalty, s_penalty=s_penalty)
+    #         best_pw = s_label[np.argmin(ed, axis=0)]
+    #
+    #         # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    #         e = error(best_pw, 68, 33) + \
+    #             error(best_pw, 11, 11) + \
+    #             error(best_pw, 42, 23) + \
+    #             error(best_pw, 68, 33)
+    #
+    #         if e < best_total:
+    #             print("New best total", e)
+    #             best_total = e
+    #             best_pwp = pw_penalty
+    #             best_sps = s_penalty
+
+    # best_pwp = 200
+    # best_sps = 200
+    # ed, ed2 = dynamic_prog(norm, pw_penalty=best_pwp, s_penalty=best_sps)
+    # im_overlay = overlay(ed, norm)
+
+    # imshow_matches(dynamic_prog(norm, pw_penalty=1, s_penalty=1)[1], '')
+    # imshow_matches(overlay(dynamic_prog(sm_matches, 0.9, 0.1)[0], sm_matches), '')
+
+    # aoi = ed[32:35, 38:41]
+    # best_s = pw_label[np.argmin(ed,axis=1)]
+    # print("PW68 best match", best_pw[np.where(pw_label==68)])
+    # print("S33 best match", best_s[np.where(s_label==33)])
+
diff --git a/Vision-Rat-Brain-F/cmatching.py b/feature_matching_v3/exp_match_details.py
similarity index 68%
rename from Vision-Rat-Brain-F/cmatching.py
rename to feature_matching_v3/exp_match_details.py
index f683711..7b6dbe6 100644
--- a/Vision-Rat-Brain-F/cmatching.py
+++ b/feature_matching_v3/exp_match_details.py
@@ -1,10 +1,14 @@
-# Author: Jose G Perez <josegperez@mail.com>
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
 import cv2
 import numpy as np
 import pylab as plt
 from multiprocessing.pool import ThreadPool
 from timeit import default_timer as timer
 
+from util_sift import load_sift
+from util_matching import match_details
 INDIVIDUAL = False
 
 np.random.seed(1)
@@ -58,73 +62,8 @@ def gallery(array, ncols=3):
                   .reshape(height*nrows, width*ncols, intensity))
         return result
 
-def match(kp1, des1, kp2, des2):
-    matches = []
-    stat_s_diff = set()
-    stat_d_dist = set()
-    stat_resp_kp1 = set()
-    stat_resp_kp2 = set()
-    stat_rad = set()
-
-    for idx1 in range(len(kp1)):
-        kpa = kp1[idx1]
-        stat_resp_kp1.add(kpa.response)
-        if kpa.response < RESPONSE_THRESHOLD:
-            continue
-
-        dpa = des1[idx1]
-        best_distance = float('inf')
-        best_idx2 = -1
-
-        for idx2 in range(len(kp2)):
-            kpb = kp2[idx2]
-            stat_resp_kp2.add(kpb.response)
-            if kpb.response < RESPONSE_THRESHOLD:
-                continue
-            dpb = des2[idx2]
-
-            d_pt_squared = (kpb.pt[0] - kpa.pt[0]) ** 2 + (kpb.pt[1] - kpa.pt[1]) ** 2
-            stat_rad.add(np.linalg.norm(np.array(kpb.pt)-np.array(kpa.pt)))
-            if d_pt_squared >= RADIUS_SQUARED:
-                continue
-
-            scale_diff = abs(kpa.size - kpb.size)
-            stat_s_diff.add(scale_diff)
-            if scale_diff > SCALE_THRESHOLD:
-                continue
-
-            d_des = np.linalg.norm(dpb - dpa)
-            stat_d_dist.add(d_des)
-            if d_des < best_distance:
-                best_distance = d_des
-                best_idx2 = idx2
-
-        if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
-            continue
-
-        match = cv2.DMatch()
-        match.queryIdx = idx1
-        match.imgIdx = idx1
-        match.trainIdx = best_idx2
-        match.distance = best_distance
-        matches.append(match)
-
-    print("\t[ScaleD] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_s_diff) , min(stat_s_diff), sum(stat_s_diff) / len(stat_s_diff)))
-    print("\t[DescD] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_d_dist),  min(stat_d_dist), sum(stat_d_dist) / len(stat_d_dist)))
-    print("\t[RespKp1] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_resp_kp1), min(stat_resp_kp1), sum(stat_resp_kp1) / len(stat_resp_kp1)))
-    print("\t[RespKp2] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_resp_kp2), min(stat_resp_kp2), sum(stat_resp_kp2) / len(stat_resp_kp2)))
-    print("\t[Rad] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_rad), min(stat_rad), sum(stat_rad) / len(stat_rad)))
-
-    # print("Total Matches", len(matches), "/", len(kp1) * len(kp2))
-    return matches
-
-s_data = np.load('S_BB_V1.npz')
-s_im = s_data['images']
-s_label = s_data['labels']
-
-pw_data = np.load('PW_BB_V1.npz')
-pw_im = pw_data['images']
-pw_label = pw_data['labels']
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')
 
 time_start = timer()
 # ==== MATCHING SYSTEM BEGIN
@@ -141,7 +80,12 @@ def match(kp1, des1, kp2, des2):
     kp1, des1 = SIFT.detectAndCompute(im1, None)
     kp2, des2 = SIFT.detectAndCompute(im2, None)
 
-    matches = match(kp1, des1, kp2, des2)
+    m, matches, stat_s_diff, stat_d_dist, stat_resp_kp1, stat_resp_kp2, stat_rad  = match_details(kp1, des1, kp2, des2)
+    print("\t[ScaleD] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_s_diff) , min(stat_s_diff), sum(stat_s_diff) / len(stat_s_diff)))
+    print("\t[DescD] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_d_dist),  min(stat_d_dist), sum(stat_d_dist) / len(stat_d_dist)))
+    print("\t[RespKp1] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_resp_kp1), min(stat_resp_kp1), sum(stat_resp_kp1) / len(stat_resp_kp1)))
+    print("\t[RespKp2] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_resp_kp2), min(stat_resp_kp2), sum(stat_resp_kp2) / len(stat_resp_kp2)))
+    print("\t[Rad] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_rad), min(stat_rad), sum(stat_rad) / len(stat_rad)))
 
     # For comparison, the selected SIFT keypoints
     # BF = cv2.BFMatcher(normType=cv2.NORM_L2, crossCheck=True)
@@ -179,8 +123,10 @@ def match(kp1, des1, kp2, des2):
     imshow(im_matches, 'New Matching')
     imshow(im_ransac, 'RANSAC')
 else:
-    im2 = pw_im[np.where(pw_label == 68)[0][0]]
-    kp2, des2 = SIFT.detectAndCompute(im2, None)
+    # im2 = pw_im[np.where(pw_label == 68)[0][0]]
+    pw_idx = 33
+    im2 = pw_im[pw_idx]
+    (kp2, des2) = (pw_kp[pw_idx], pw_des[pw_idx])
 
     all_matches = []
     all_im = []
@@ -189,16 +135,26 @@ def match(kp1, des1, kp2, des2):
 
     cidx = 0
     for sidx in range(25, 40):
-        if sidx == 32:
-            print('=[P]', end='')
+        # if sidx == 32:
+        #     print('=[P]', end='')
         print("S", (sidx + 1), "CIDX", cidx)
 
-        im1 = s_im[sidx]
-        kp1, des1 = SIFT.detectAndCompute(im1, None)
-        matches = match(kp1, des1, kp2, des2)
+        (im1, kp1, des1) = (s_im[sidx], s_kp[sidx], s_des[sidx])
+        m, matches, stat_s_diff, stat_d_dist, stat_resp_kp1, stat_resp_kp2, stat_rad = match_details(kp1, des1, kp2,
+                                                                                                     des2)
+        # print("\t[ScaleD] Max %.3f, Min %.3f, Avg %.3f" % (
+        # max(stat_s_diff), min(stat_s_diff), sum(stat_s_diff) / len(stat_s_diff)))
+        # print("\t[DescD] Max %.3f, Min %.3f, Avg %.3f" % (
+        # max(stat_d_dist), min(stat_d_dist), sum(stat_d_dist) / len(stat_d_dist)))
+        # print("\t[RespKp1] Max %.3f, Min %.3f, Avg %.3f" % (
+        # max(stat_resp_kp1), min(stat_resp_kp1), sum(stat_resp_kp1) / len(stat_resp_kp1)))
+        # print("\t[RespKp2] Max %.3f, Min %.3f, Avg %.3f" % (
+        # max(stat_resp_kp2), min(stat_resp_kp2), sum(stat_resp_kp2) / len(stat_resp_kp2)))
+        # print("\t[Rad] Max %.3f, Min %.3f, Avg %.3f" % (max(stat_rad), min(stat_rad), sum(stat_rad) / len(stat_rad)))
+
         metric = len(matches) / (len(kp1) + len(kp2) - len(matches))
 
-        print("\tMetric w/PW68 %.3f, Matches %d" % (metric, len(matches)))
+        print("\tMetric w/PW %s is %.3f, Matches %d" % (pw_idx, metric, len(matches)))
 
         all_matches.append(matches)
         all_im.append(im1)
@@ -206,10 +162,10 @@ def match(kp1, des1, kp2, des2):
         all_des.append(des1)
         cidx += 1
 
-    index = 7
-    imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), '[P]SW33')
-    index = 8
-    imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), 'SW34')
+    # index = 7
+    # imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), '[P]SW33')
+    # index = 8
+    # imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), 'SW34')
 
 duration = timer() - time_start
 print("Program took %.3fs" % duration)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_match_individual.py b/feature_matching_v3/exp_match_individual.py
new file mode 100644
index 0000000..c98deac
--- /dev/null
+++ b/feature_matching_v3/exp_match_individual.py
@@ -0,0 +1,30 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import pylab as plt
+import cv2
+
+from util_sift import array_to_kp, precompute_sift, load_sift
+from util_matching import match
+from util_cv import match_to_cv
+from util_visualization import imshow
+
+precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')
+
+#%%
+s_idx = 32 # np.where(s_label == 33)[0][0]
+pw_idx = 36 # np.where(pw_label == 50)[0][0]
+(im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])
+(im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])
+matches = match(kp1,des1,kp2,des2)
+# Convert to OpenCV objects for viewing
+matches = match_to_cv(matches)
+kp1 = array_to_kp(kp1)
+kp2 = array_to_kp(kp2)
+im_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
+# plt.gray()
+str = '%s Matches Between S%s and PW%s' % (len(matches), s_label[s_idx], pw_label[pw_idx])
+imshow(im_matches, str)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_match_row.py b/feature_matching_v3/exp_match_row.py
new file mode 100644
index 0000000..cfd7c33
--- /dev/null
+++ b/feature_matching_v3/exp_match_row.py
@@ -0,0 +1,42 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import pylab as plt
+
+from util_matching import match
+from util_sift import precompute_sift, load_sift
+
+precompute_sift('S_BB_V1', 'PW_BB_V1')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')
+# =======******* Row Testing [S33 and all PW]
+# PW68 is IDX:39
+# matches = []
+# s_idx = np.where(s_label == 33)[0][0]
+# (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])
+# for pw_idx in range(pw_im.shape[0]):
+#     print(pw_idx, '/', pw_im.shape[0])
+#     (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])
+#     m = match(kp1, des1, kp2, des2)
+#     matches.append(m)
+#
+# count = []
+# for match in matches:
+#     count.append(len(match))
+matches = []
+pw_idx = 33
+(im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])
+for s_idx in range(s_im.shape[0]):
+    print(s_idx, '/', s_im.shape[0])
+    (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])
+    m = match(kp1, des1, kp2, des2)
+    matches.append(m)
+
+count = []
+for match in matches:
+    count.append(len(match))
+count_norm = np.array(count) / np.max(count)
+count_im = (count_norm * 255).reshape(1, 73)
+plt.gray()
+plt.imshow(count_im)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_match_thread.py b/feature_matching_v3/exp_match_thread.py
new file mode 100644
index 0000000..280b759
--- /dev/null
+++ b/feature_matching_v3/exp_match_thread.py
@@ -0,0 +1,41 @@
+import numpy as np
+from timeit import default_timer as timer
+from multiprocessing.pool import Pool
+
+from util_ransac import perform_ransac
+from util_matching import match
+from util_sift import precompute_sift, load_sift
+
+RADIUS = 25
+RADIUS_SQUARED = RADIUS ** 2
+SCALE_THRESHOLD = 3
+DISTANCE_THRESHOLD = 200
+RESPONSE_THRESHOLD = 0.01
+precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')
+
+def perform_match(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    matches = []
+    print('s_idx', s_idx)
+    for pw_idx in range(pw_kp.shape[0]):
+        matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))
+
+    np.savez_compressed(str(s_idx) + '-M', m=matches)
+
+if __name__ == '__main__':
+    time_start = timer()
+
+    pool = Pool()
+    s_idx = range(s_kp.shape[0])
+
+    print('Begin pool work')
+    pool.map(perform_match, s_idx)
+    # pool.map(perform_ransac, s_idx)
+    pool.close()
+    pool.join()
+
+    duration = timer() - time_start
+    duration_m = duration / 60
+    print("Program took %.3fs %.3fm" % (duration, duration_m))
\ No newline at end of file
diff --git a/feature_matching_v3/exp_matching_new.py b/feature_matching_v3/exp_matching_new.py
new file mode 100644
index 0000000..48e66b2
--- /dev/null
+++ b/feature_matching_v3/exp_matching_new.py
@@ -0,0 +1,75 @@
+#%% Load Data
+import numpy as np
+import pylab as plt
+import sys
+from util_visualization import imshow_matches
+from util_sm import load_sm, norm_sm, norm_prob_sm
+from util_sift import precompute_sift, load_sift
+from skimage import color
+
+b_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')
+b_pw3 = np.loadtxt('Bregma_PW3_M.csv', dtype=np.float, delimiter=',')
+
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')
+pwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')
+sm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)
+
+b_pw3_m = []
+for plate in pwi_label:
+    idx = plate - 1
+    b_pw3_m.append(b_pw3[idx])
+
+# Only Nissl bregmas
+b_pw3 = np.array(b_pw3_m)
+
+#%% Bregma Algorithm
+atlas1 = b_s4
+atlas2 = b_pw3
+M = np.zeros((atlas1.shape[0]+1, atlas2.shape[0]+1))
+M[1:,:] = np.inf
+DIR = np.zeros_like(M)
+DIR[:,:] = 2
+for row in range(1, M.shape[0]):
+    for col in range(1, M.shape[1]):
+        if row > col:
+            continue
+
+        choices = [M[row,col-1], # Left
+                   M[row-1,col-1]+abs(atlas1[row-1] - atlas2[col])] # Diagonal
+        M[row][col] = min(choices)
+        DIR[row][col] = np.argmin(choices)
+
+M = M[1:,:]
+DIR = DIR[1:,:]
+#%% Path Backtracking
+im = np.zeros((DIR.shape[0], DIR.shape[1], 3), dtype=np.uint8)
+for row in range(DIR.shape[0]):
+    for col in range(DIR.shape[1]):
+        if row > col:
+            im[row, col] = [0, 100, 50] # Dark Green
+        elif DIR[row][col] == 0: # Left
+            im[row,col] = [200, 0, 0] # Red
+        elif DIR[row][col] == 1: # Diagonal
+            im[row, col] = [0, 255, 0] # Green
+        elif DIR[row][col] == 2: # Unused
+            im[row, col] = [0, 100, 50] # Dark Green
+        else:
+            im[row, col] = [0, 0, 0] # Black
+
+c = [148, 0, 211] # Purple
+im[6-1][8-1] = c
+im[11-1][11-1] = c
+im[23-1][42-1] = c
+im[33-1][68-1] = c
+for row in range(DIR.shape[0]):
+    col = np.argmin(M[row])
+    # PW8 S6, PW11 S11, PW42 S23, PW68 S33,
+    if (row == 6-1 and col == 8-1) or (row == 11-1 and col == 11-1) or (row == 23-1 and col == 42-1) or (row == 33-1 and col == 68-1):
+        im[row][col] = [255, 255, 255] # White
+    else:
+        im[row][col] = [0, 0, 255] # Blue
+
+fig, ax = plt.subplots()
+ax.set_title("DIR - Best Path")
+ax.imshow(im)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_planes.py b/feature_matching_v3/exp_planes.py
new file mode 100644
index 0000000..445e88d
--- /dev/null
+++ b/feature_matching_v3/exp_planes.py
@@ -0,0 +1,69 @@
+import numpy as np
+import os
+import pylab as plt
+import cv2
+from skimage.transform import warp, PiecewiseAffineTransform
+from PIL import Image
+from timeit import default_timer as timer
+
+def load(filename):
+    WIDTH = 800
+    HEIGHT = 400
+    im = Image.open(filename)
+    im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)
+    P = np.array(im, dtype=np.uint8)
+    return P[:, 0:P.shape[1] // 2, :]
+
+def to_gray(P):
+    return P.mean(axis=2)
+
+
+#%% Load images
+print("Loading images")
+dir1 = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'
+dir2 = 'C:/Users/xeroj/Downloads/Processed'
+
+P1 = load(os.path.join(dir1, 'RBSC7-068.jpg'))
+P2 = load(os.path.join(dir1, 'RBSC7-070.jpg'))
+FACE = np.array(Image.open('face.jpg'))
+
+PM1 = load(os.path.join(dir2, '18-016 LHA s4t2.tif'))
+PM2 = load(os.path.join(dir2, '18-016 LHA s4t3.tif'))
+PM3 = load(os.path.join(dir2, '18-016 LHA s4t4.tif'))
+
+# data = np.load('P1P2.npz')
+# S1_pts = data['src']
+# S2_pts = data['dst']
+
+data = np.load('face.npz')
+S1_pts = data['src']
+S2_pts = data['dst']
+S1 = FACE
+S2 = FACE
+
+#%% Scipy Warping
+tform = PiecewiseAffineTransform()
+e1 = tform.estimate(np.array(S2_pts), np.array(S1_pts))
+im_warp1 = warp(S1, tform)
+im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))
+
+plt.figure()
+plt.suptitle('warp')
+plt.imshow(im_warp1)
+#%% Inverse Warping with KNN Interpolation
+src = S1
+dst = np.zeros_like(src)
+for x in range(dst.shape[0]):
+    for y in range(dst.shape[1]):
+        cp_idx = np.argmin(np.sum((S1_pts - [x,y]) ** 2, axis=1))
+        diff = [x,y] - S2_pts[cp_idx]
+        disp = S1_pts[cp_idx] - S2_pts[cp_idx]
+        dist = np.linalg.norm(diff)
+        w = np.exp(-dist/100)
+        (dx,dy) = (disp[0]*w, disp[1]*w)
+        u = min(int(round(x+dy)),dst.shape[0]-1)
+        v = min(int(round(y+dx)),dst.shape[1]-1)
+        dst[x,y] = src[u,v]
+
+plt.figure()
+plt.imshow(dst)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_profiling.py b/feature_matching_v3/exp_profiling.py
new file mode 100644
index 0000000..fc466f5
--- /dev/null
+++ b/feature_matching_v3/exp_profiling.py
@@ -0,0 +1,150 @@
+import cProfile
+import numpy as np
+from timeit import default_timer as timer
+import multiprocessing
+from multiprocessing.pool import Pool
+from util_sift import precompute_sift, load_sift
+import itertools
+
+RADIUS = 25
+RADIUS_SQUARED = RADIUS ** 2
+SCALE_THRESHOLD = 3
+DISTANCE_THRESHOLD = 200
+RESPONSE_THRESHOLD = 0.01
+precompute_sift('S_BB_V1', 'PW_BB_V1')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')
+
+(best_distance, best_idx2) = (float('inf'), -1)
+def match_v3_helper2(kpa, dpa, kp2, des2, idx2):
+    global best_distance
+    kpb = kp2[idx2]
+    # (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])
+    # (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])
+    if kpb[4] < RESPONSE_THRESHOLD \
+            or (kpb[0] - kpa[0]) ** 2 + (kpb[1] - kpa[1]) ** 2 >= RADIUS_SQUARED \
+            or abs(kpa[2] - kpb[2]) > SCALE_THRESHOLD:
+        return
+
+    # 3: Descriptor L2 Norm
+    d_des = np.linalg.norm(des2[idx2] - dpa)
+    if(d_des < best_distance):
+        best_distance = d_des
+        best_idx2 = idx2
+
+def match_v3_helper(kp1, des1, kp2, des2, idx1):
+    global best_distance, best_idx2
+    kpa = kp1[idx1]
+    if kpa[4] < RESPONSE_THRESHOLD:
+        return
+
+    [match_v3_helper2(kpa, des1[idx1], kp2, des2, idx2) for idx2 in range(len(kp2))]
+
+    if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
+        return
+
+    return np.array([idx1, best_idx2, best_distance], dtype=np.int32)
+
+def match_v3(kp1, des1, kp2, des2):
+    matches = [match_v3_helper(kp1,des1,kp2,des2,idx1) for idx1 in range(len(kp1))]
+
+def match_v2_helper(kp1, des1, kp2, des2, idx1):
+    kpa = kp1[idx1]
+    (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])
+    (best_distance, best_idx2) = (float('inf'), -1)
+
+    if kpa_response < RESPONSE_THRESHOLD:
+        return
+
+    for idx2 in range(len(kp2)):
+        kpb = kp2[idx2]
+        (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])
+        if kpb_response < RESPONSE_THRESHOLD \
+            or (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2 >= RADIUS_SQUARED \
+            or abs(kpa_size - kpb_size) > SCALE_THRESHOLD:
+            continue
+
+        # 3: Descriptor L2 Norm
+        d_des = np.linalg.norm(dpb - dpa)
+        if d_des < best_distance:
+            best_distance = d_des
+            best_idx2 = idx2
+
+    if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
+        return
+
+    return np.array([idx1, best_idx2, best_distance], dtype=np.int32)
+
+def match_v2(kp1, des1, kp2, des2):
+    matches = (match_v2_helper(kp1,des1,kp2,des2,idx1) for idx1 in range(len(kp1)))
+
+def match(kp1, des1, kp2, des2):
+    matches = []
+    for idx1 in range(len(kp1)):
+        kpa = kp1[idx1]
+        (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])
+        (best_distance, best_idx2) = (float('inf'), -1)
+
+        if kpa_response < RESPONSE_THRESHOLD:
+            return
+
+        for idx2 in range(len(kp2)):
+            kpb = kp2[idx2]
+            (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])
+            if kpb_response < RESPONSE_THRESHOLD \
+                    or (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2 >= RADIUS_SQUARED \
+                    or abs(kpa_size - kpb_size) > SCALE_THRESHOLD:
+                continue
+
+            # 3: Descriptor L2 Norm
+            d_des = np.linalg.norm(dpb - dpa)
+            if d_des < best_distance:
+                best_distance = d_des
+                best_idx2 = idx2
+
+        if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
+            return
+
+        matches.append(np.array([idx1, best_idx2, best_distance], dtype=np.int32))
+
+    return np.asarray(matches)
+
+def perform_match(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    matches = []
+    print('s_idx', s_idx)
+    for pw_idx in range(5):
+        matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))
+
+def perform_match_v2(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    matches = []
+    print('s_idx', s_idx)
+    matches = [match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5)]
+
+def test():
+    s_idx = 0
+    return [match_v3(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(pw_kp.shape[0])]
+
+def perform_match_v3(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    print('s_idx', s_idx)
+    # matches = (match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5))
+    # matches = (match_v2(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5))
+    matches = (match_v3(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(pw_kp.shape[0]))
+    # np.savez_compressed(str(s_idx) + '-M', m=np.asarray(matches2))
+
+if __name__ == '__main__':
+    print('Begin pool work')
+    pool = Pool()
+    # s_idx = range(2)
+    s_idx = range(s_kp.shape[0])
+    time_start = timer()
+    # pool.map(perform_match, s_idx)
+    # pool.map(perform_match_v2, s_idx)
+    pool.map(perform_match_v3, s_idx)
+    time_end = timer()
+    pool.close()
+    pool.join()
+    duration = time_end - time_start
+    print("Program took %.3fs" % duration)
\ No newline at end of file
diff --git a/feature_matching_v3/exp_pwarp.py b/feature_matching_v3/exp_pwarp.py
new file mode 100644
index 0000000..0c34e84
--- /dev/null
+++ b/feature_matching_v3/exp_pwarp.py
@@ -0,0 +1,79 @@
+import pylab as plt
+import numpy as np
+from PIL import Image
+import time
+
+im = np.array(Image.open('face.jpg'), dtype=np.uint8)
+(rows, cols, colors) = im.shape
+
+#%% Control point loading
+data = np.load('face.npz')
+src_pts = data['src']
+dst_pts = data['dst']
+
+src_pts[0] = [0,0]
+src_pts[1] = [im.shape[1], 0]
+src_pts[2] = [0,im.shape[0]]
+src_pts[3] = [im.shape[1], im.shape[0]]
+
+dst_pts[0] = [0,0]
+dst_pts[1] = [im.shape[1], 0]
+dst_pts[2] = [0,im.shape[0]]
+dst_pts[3] = [im.shape[1], im.shape[0]]
+
+
+plt.figure(1)
+plt.imshow(im)
+for i in range(len(src_pts)):
+    (x1,y1) = src_pts[i]
+    (x2,y2) = dst_pts[i]
+    plt.plot([x1,x2], [y1,y2], color='purple')
+    plt.plot(x1, y1, marker='x', markersize=3, color='blue')
+    plt.plot(x2, y2, marker='o', markersize=3, color='red')
+
+start = time.time()
+
+#%% Generate pixels coordinates in the destination image
+dest_im = np.zeros(im.shape, dtype=np.uint8)
+max_row = im.shape[0] - 1
+max_col = im.shape[1] - 1
+dest_rows = dest_im.shape[0]
+dest_cols = dest_im.shape[1]
+
+# Painting outline of source image black, so out of bounds pixels can be painted black
+im[0] = 0
+im[max_row] = 0
+im[:, 0] = 0
+im[:, max_col] = 0
+
+# Generate pixel coordinates in the destination image
+ind = np.arange(dest_rows * dest_cols)
+row_vect = ind // dest_cols
+col_vect = ind % dest_cols
+coords = np.vstack((row_vect, col_vect))
+
+# Computing pixel weights, pixels close to p[1] will have higher weights
+dist = np.sqrt(np.square(p[1][1] - row_vect) + np.square(p[1][0] - col_vect))
+weight = np.exp(-dist / 100)  # Constant needs to be tweaked depending on image size
+
+# Computing pixel weights, pixels close to p[1] will have higher weights
+source_coords = np.zeros(coords.shape, dtype=np.int)
+disp_r = (weight * (p[0][1] - p[1][1])).astype(int)
+disp_c = (weight * (p[0][0] - p[1][0])).astype(int)
+source_coords[0] = coords[0] + disp_r
+source_coords[1] = coords[1] + disp_c
+
+# Fixing out-of-bounds coordinates
+source_coords[source_coords < 0] = 0
+source_coords[0, source_coords[0] > max_row] = max_row
+source_coords[1, source_coords[1] > max_col] = max_col
+
+dest_im = source_im[source_coords[0], source_coords[1], :].reshape(dest_rows, dest_cols, 3)
+
+plt.figure(2)
+plt.imshow(dest_im)
+plt.show()
+
+elapsed_time = time.time() - start
+print('Elapsed time: {0:.2f} '.format(elapsed_time))
+
diff --git a/feature_matching_v3/exp_sfn19.py b/feature_matching_v3/exp_sfn19.py
new file mode 100644
index 0000000..15ff0ac
--- /dev/null
+++ b/feature_matching_v3/exp_sfn19.py
@@ -0,0 +1,143 @@
+import numpy as np
+import os
+import pylab as plt
+import cv2
+from skimage.transform import warp, PiecewiseAffineTransform
+from PIL import Image
+from timeit import default_timer as timer
+
+def load(filename, split=True):
+    WIDTH = 400
+    HEIGHT = 400
+    im = Image.open(filename)
+    im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)
+    P = np.array(im, dtype=np.uint8)
+    if split:
+        return P[:, 0:P.shape[1] // 2, :]
+    else:
+        return P
+
+def to_gray(P):
+    return P.mean(axis=2)
+
+
+#%% Load images
+print("Loading images")
+dir1 = r'C:\Users\xeroj\Dropbox\Training data sets - Khan-Fuentes\Paxinos and Watson, 2014 (7th Edition) Image set'
+dir2 = r'C:\Users\xeroj\Desktop\Local_Schoolwork\_Research\CytoData - Processed'
+dir3 = r'C:\Users\xeroj\Downloads\cne24381-sup-0011-suppinfo11\SI Folder 4 BM4 Nissls 300 dpi'
+
+P1 = load(os.path.join(dir1, 'RBSC7-068.jpg'))
+P2 = load(os.path.join(dir1, 'RBSC7-070.jpg'))
+
+SW1 = load(os.path.join(dir3, 'Level 31 photo.tif'), False)
+SW2 = load(os.path.join(dir3, 'Level 32 photo.tif'), False)
+SW3 = load(os.path.join(dir3, 'Level 33 photo.tif'), False)
+
+PM1 = load(os.path.join(dir2, '18-016 LHA s4t2.tif'))
+PM2 = load(os.path.join(dir2, '18-016 LHA s4t3.tif'))
+PM3 = load(os.path.join(dir2, '18-016 LHA s4t4.tif'))
+
+#%% Plate selection
+S1 = SW1
+S2 = SW3
+
+
+#%% Stack
+plt.figure()
+plt.gray()
+plt.suptitle("Intermediate")
+plt.imshow(SW2)
+
+S1_pts = [[0, 0], [S1.shape[0], 0], [0, S1.shape[1]], [S1.shape[0], S1.shape[1]]]
+S2_pts = [[0, 0], [S2.shape[0], 0], [0, S2.shape[1]], [S2.shape[0], S2.shape[1]]]
+
+HS1 = ((S1 * 0.8) + (S2 * 0.2)).astype(np.uint8)
+HS2 = ((S1 * 0.2) + (S2 * 0.8)).astype(np.uint8)
+
+# plt.figure()
+# plt.suptitle("Originals")
+# plt.imshow(np.hstack((S1, S2)))
+
+plt.figure()
+plt.imshow(np.hstack((HS1, HS2)))
+while True:
+    plt.figure(1)
+    plt.suptitle('Select point on left plate or press enter to generate warp with current points')
+    p1 = plt.ginput(n=1, timeout=0)
+
+    if len(p1) == 0:
+        plt.suptitle("Generating warp with current points...")
+
+        tform = PiecewiseAffineTransform()
+        e1 = tform.estimate(np.array(S2_pts), np.array(S1_pts))
+        im_warp1 = warp(S1, tform)
+        # im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))
+        im_warp1 = ((im_warp1 * 255).astype(np.uint8))
+
+        tform = PiecewiseAffineTransform()
+        e2 = tform.estimate(np.array(S1_pts), np.array(S2_pts))
+        im_warp2 = warp(S2, tform)
+        # im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))
+        im_warp2 = ((im_warp2 * 255).astype(np.uint8))
+
+        # plt.figure()
+        # plt.suptitle("Warps")
+        # plt.imshow(np.hstack((im_warp1, im_warp2)), cmap='gray')
+
+        # plt.figure()
+        # plt.suptitle("Original Plate (Left), Warped Plate (Right)")
+        # plt.imshow(np.hstack((to_gray(S1), im_warp1)), cmap='gray')
+        #
+        # plt.figure()
+        # plt.suptitle("Original Plate (Left), Warped Plate (Right)")
+        # plt.imshow(np.hstack((to_gray(S2), im_warp2)), cmap='gray')
+
+        plt.figure()
+        plt.suptitle("Cross-dissolve")
+        im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)
+        plt.imshow(im_gen, cmap='gray')
+
+        continue
+
+    c = np.random.uniform(0, 1, 3)
+    (x1, y1) = (p1[0][0], p1[0][1])
+    l1 = plt.plot(x1, y1, marker='x', markersize=10, markeredgewidth=5, color=c)
+    plt.suptitle('Select point on right plate')
+
+    p2 = plt.ginput(n=1, timeout=0)
+    if len(p2) == 0:
+        l1.pop(0).remove()
+        plt.suptitle("Breaking out of infinite loop")
+        break
+
+    # Translate
+    (x2, y2) = (p2[0][0], p2[0][1])
+    xviz = x2
+    yviz = y2
+
+    # If you click on the right side
+    if x2 > S1.shape[0]:
+        x2 = x2 - S1.shape[0]
+        print("Right side click")
+    else:
+        print("Left side click")
+
+    # x2 = (x2 + x1) / 2
+    # y2 = (y2 + y1) / 2
+
+    x2 = (x2 + x1) / 2
+    y2 = (y2 + y1) / 2
+
+    plt.plot(xviz, yviz, marker='x', markersize=10, markeredgewidth=5, color=c)
+
+    S1_pts.append([x1, y1])
+    S2_pts.append([x2, y2])
+    print("Total points so far: ", len(S1_pts))
+    # plt.figure(2)
+    # plt.imshow(S1)
+    # plt.plot(x1, y1, marker='x', markersize=5, color='red')
+    #
+    # plt.figure(3)
+    # plt.imshow(S2)
+    # plt.plot(x2, y2, marker='x', markersize=5, color='red')
\ No newline at end of file
diff --git a/feature_matching_v3/exp_similarity_matrix.py b/feature_matching_v3/exp_similarity_matrix.py
new file mode 100644
index 0000000..49205da
--- /dev/null
+++ b/feature_matching_v3/exp_similarity_matrix.py
@@ -0,0 +1,80 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+from util_visualization import imshow_matches
+from util_sm import load_sm, norm_sm
+from util_sift import precompute_sift, load_sift
+import pylab as plt
+import numpy as np
+precompute_sift('S_BB_V4', 'PW_BB_V4')
+s_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')
+pw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')
+
+# sm_v1_match, sm_v1_metric = load_sm('sm_v1', s_kp, pw_kp)
+sm_matches, sm_metric = load_sm('sm_v4', s_kp, pw_kp)
+
+# imshow_matches(norm_sm(sm_v1_match), 'Experiment 1: Matches Count')
+# imshow_matches(norm_sm(sm_v1_metric), 'Experiment 1: Metric')
+#
+# imshow_matches(norm_sm(sm_v2_match), 'Experiment 2: Matches Count')
+# imshow_matches(norm_sm(sm_v2_metric), 'Experiment 2: Metric')
+
+#%%
+pw_ticks_idxs = [0]
+pw_ticks_vals = [pw_label[0]]
+for x in range(len(pw_label)):
+    try:
+        diff = pw_label[x+1] - pw_label[x]
+        if diff > 1:
+            pw_ticks_idxs.append(x)
+            pw_ticks_vals.append(pw_label[x])
+            # print("IDX: ", x, "DIFF:", diff)
+    except:
+        continue
+
+pw_ticks_idxs.append(len(pw_label)-1)
+pw_ticks_vals.append(pw_label[-1])
+
+#%%
+plt.figure()
+ax = plt.gca()
+ax.set_title('Similarity Matrix (Matches)')
+plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+plt.imshow(sm_matches)
+plt.xticks(pw_ticks_idxs, pw_ticks_vals)
+plt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label)+1))
+
+for tick in ax.xaxis.get_major_ticks():
+    tick.label.set_fontsize(8)
+
+for tick in ax.yaxis.get_major_ticks():
+    tick.label.set_fontsize(8)
+    # tick.label.set_rotation('vertical')
+
+plt.xlabel('PW Level')
+plt.ylabel('S Level')
+# heatmap = plt.pcolor(sm_matches)
+colorbar = plt.colorbar()
+colorbar.set_label('# of Matches')
+
+#%%
+plt.figure()
+ax = plt.gca()
+ax.set_title('Similarity Matrix (Metric)')
+plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+plt.imshow(sm_metric)
+plt.xticks(pw_ticks_idxs, pw_ticks_vals)
+plt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label)+1))
+
+for tick in ax.xaxis.get_major_ticks():
+    tick.label.set_fontsize(8)
+
+for tick in ax.yaxis.get_major_ticks():
+    tick.label.set_fontsize(8)
+    # tick.label.set_rotation('vertical')
+
+plt.xlabel('PW Level')
+plt.ylabel('S Level')
+# heatmap = plt.pcolor(sm_matches)
+colorbar = plt.colorbar()
+colorbar.set_label('Metric Value')
\ No newline at end of file
diff --git a/feature_matching_v3/exp_svg.py b/feature_matching_v3/exp_svg.py
new file mode 100644
index 0000000..30586e2
--- /dev/null
+++ b/feature_matching_v3/exp_svg.py
@@ -0,0 +1,16 @@
+import numpy as np
+import pylab as plt
+from PIL import Image
+import random
+from svgpathtools import svg2paths2, wsvg
+paths, attributes, svg_attributes = svg2paths2('Level33.svg')
+
+for i in range(len(attributes)):
+    attributes[i]['fill'] = "#%06x" % random.randint(0, 0xFFFFFF)
+
+#%%
+wsvg(paths[0:5], attributes=attributes[0:5], svg_attributes=svg_attributes, filename='output.svg')
+
+#%%
+for i in range(len(paths)):
+    wsvg(paths[i], attributes=[attributes[i]], svg_attributes=svg_attributes, filename='output'+str(i)+'.svg')
\ No newline at end of file
diff --git a/feature_matching_v3/experimental/18-016 LHA s1t6.tif b/feature_matching_v3/experimental/18-016 LHA s1t6.tif
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index 0000000..04cefcc
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diff --git a/feature_matching_v3/experimental/18-016 LHA s2t3.tif b/feature_matching_v3/experimental/18-016 LHA s2t3.tif
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index 0000000..5e541ef
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diff --git a/feature_matching_v3/experimental/18-016 LHA s3t3.tif b/feature_matching_v3/experimental/18-016 LHA s3t3.tif
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diff --git a/feature_matching_v3/experimental/18-016 LHA s4t2.tif b/feature_matching_v3/experimental/18-016 LHA s4t2.tif
new file mode 100644
index 0000000..7d97fa6
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diff --git a/feature_matching_v3/face.jpg b/feature_matching_v3/face.jpg
new file mode 100644
index 0000000..b2ea370
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diff --git a/feature_matching_v3/face.npz b/feature_matching_v3/face.npz
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diff --git a/feature_matching_v3/norm-M.npz b/feature_matching_v3/norm-M.npz
new file mode 100644
index 0000000..cb7fead
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diff --git a/feature_matching_v3/norm.npz b/feature_matching_v3/norm.npz
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diff --git a/feature_matching_v3/output.svg b/feature_matching_v3/output.svg
new file mode 100644
index 0000000..ed454eb
--- /dev/null
+++ b/feature_matching_v3/output.svg
@@ -0,0 +1,9 @@
+<?xml version="1.0" ?>
+<svg baseProfile="full" height="100%" style="display: block;margin-left: auto;margin-right: auto;" version="1.1" viewBox="0 0 1870 1210" width="100%" x="0" xmlns="http://www.w3.org/2000/svg" xmlns:ev="http://www.w3.org/2001/xml-events" xmlns:xlink="http://www.w3.org/1999/xlink" y="0">
+	<defs/>
+	<path d="M 0.0,0.0 L 0.0,1210.0 L 1870.0,1210.0 L 1870.0,0.0 L 0.0,0.0" fill="#5ba06c" stroke="none"/>
+	<path class="g0_1" d="M 935.8,931.9 C 940.5,927.1999999999999 945.9,920.4 950.6999999999999,914.3 C 955.4,908.1999999999999 958.0999999999999,904.8 958.8,902.0999999999999 C 959.5,899.3999999999999 956.8,895.3 953.4,893.9999999999999 C 952.1999999999999,893.4999999999999 940.3,890.5999999999999 935.8,890.5999999999999 L 935.8,787.3 C 942.5999999999999,782.5999999999999 945.3,772.0999999999999 945.9,760.5 C 946.6,749.0 944.6,740.2 942.5,735.5 C 940.5,730.7 937.8,723.3 935.8,723.3 L 935.8,551.2 C 941.1999999999999,549.9000000000001 946.3,547.2 950.0,544.5 C 953.7,541.8 955.1,534.3 955.4,528.9 C 955.6999999999999,523.5 956.4,518.1 958.1,513.3 C 959.8000000000001,508.59999999999997 966.9,499.4 972.0,491.99999999999994 C 977.1,484.49999999999994 982.5,478.79999999999995 984.5,474.99999999999994 C 986.6,471.29999999999995 986.9,469.59999999999997 986.9,468.29999999999995 C 986.9,466.9 985.9,464.49999999999994 985.9,464.49999999999994 C 985.9,464.49999999999994 977.4,466.59999999999997 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diff --git a/feature_matching_v3/similarity_matrix.npz b/feature_matching_v3/similarity_matrix.npz
new file mode 100644
index 0000000..16c418d
Binary files /dev/null and b/feature_matching_v3/similarity_matrix.npz differ
diff --git a/feature_matching_v3/slider.py b/feature_matching_v3/slider.py
new file mode 100644
index 0000000..70e6cb8
--- /dev/null
+++ b/feature_matching_v3/slider.py
@@ -0,0 +1,48 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Slider, Button, RadioButtons
+
+fig, ax = plt.subplots()
+plt.subplots_adjust(left=0.25, bottom=0.25)
+t = np.arange(0.0, 1.0, 0.001)
+a0 = 5
+f0 = 3
+s = a0*np.sin(2*np.pi*f0*t)
+l, = plt.plot(t, s, lw=2, color='red')
+plt.axis([0, 1, -10, 10])
+
+axcolor = 'lightgoldenrodyellow'
+axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
+axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
+
+sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
+samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
+
+
+def update(val):
+    amp = samp.val
+    freq = sfreq.val
+    l.set_ydata(amp*np.sin(2*np.pi*freq*t))
+    fig.canvas.draw_idle()
+sfreq.on_changed(update)
+samp.on_changed(update)
+
+resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
+button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
+
+
+def reset(event):
+    sfreq.reset()
+    samp.reset()
+button.on_clicked(reset)
+
+rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
+radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
+
+
+def colorfunc(label):
+    l.set_color(label)
+    fig.canvas.draw_idle()
+radio.on_clicked(colorfunc)
+
+plt.show()
diff --git a/feature_matching_v3/sm_matches.csv b/feature_matching_v3/sm_matches.csv
new file mode 100644
index 0000000..a589f5f
--- /dev/null
+++ b/feature_matching_v3/sm_matches.csv
@@ -0,0 +1,74 @@
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diff --git a/feature_matching_v3/sm_metric.csv b/feature_matching_v3/sm_metric.csv
new file mode 100644
index 0000000..7da0014
--- /dev/null
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diff --git a/feature_matching_v3/util_cv.py b/feature_matching_v3/util_cv.py
new file mode 100644
index 0000000..1eb5f2d
--- /dev/null
+++ b/feature_matching_v3/util_cv.py
@@ -0,0 +1,12 @@
+import cv2
+def match_to_cv(matches):
+    cv = []
+    for i in range(matches.shape[0]):
+        m = matches[i]
+        temp = cv2.DMatch()
+        temp.queryIdx = int(m[0])
+        temp.imgIdx = int(m[0])
+        temp.trainIdx = int(m[1])
+        temp.distance = int(m[2])
+        cv.append(temp)
+    return cv
\ No newline at end of file
diff --git a/feature_matching_v3/util_matching.py b/feature_matching_v3/util_matching.py
new file mode 100644
index 0000000..34c7841
--- /dev/null
+++ b/feature_matching_v3/util_matching.py
@@ -0,0 +1,151 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import cv2
+import cProfile
+RADIUS = 25 # 25
+RADIUS_SQUARED = RADIUS ** 2
+SCALE_THRESHOLD = 3 # 3
+DISTANCE_THRESHOLD = 200 # 200
+RESPONSE_THRESHOLD = 0.01 # 0.01
+
+def perform_match(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    matches = []
+    print('s_idx', s_idx)
+    for pw_idx in range(pw_kp.shape[0]):
+        matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))
+
+    np.savez_compressed(str(s_idx) + '-M', m=matches)
+
+def match(kp1, des1, kp2, des2):
+    matches = []
+    for idx1 in range(len(kp1)):
+        kpa = kp1[idx1]
+        kpa_response = kpa[4]
+        if kpa_response < RESPONSE_THRESHOLD:
+            continue
+
+        kpa_x = kpa[0]
+        kpa_y = kpa[1]
+        kpa_size = kpa[2]
+        #kpa_angle = kpa[3]
+        dpa = des1[idx1]
+
+        best_distance = float('inf')
+        best_idx2 = -1
+
+        for idx2 in range(len(kp2)):
+            kpb = kp2[idx2]
+            # 0: Response Strength
+            kpb_response = kpb[4]
+            if kpb_response < RESPONSE_THRESHOLD:
+                continue
+
+            # 1: Distance/Radius Check
+            kpb_x = kpb[0]
+            kpb_y = kpb[1]
+            d_pt_squared = (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2
+            if d_pt_squared >= RADIUS_SQUARED:
+                continue
+
+            # 2: Scale Difference
+            kpb_size = kpb[2]
+            scale_diff = abs(kpa_size - kpb_size)
+            if scale_diff > SCALE_THRESHOLD:
+                continue
+
+            # 3: Descriptor L2 Norm
+            dpb = des2[idx2]
+            d_des = np.linalg.norm(dpb - dpa)
+            if d_des < best_distance:
+                best_distance = d_des
+                best_idx2 = idx2
+
+            # 4: ?? Angle ??
+            #kpb_angle = kpb[3]
+
+        if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
+            continue
+
+        match = np.array([idx1, best_idx2, best_distance], dtype=np.int32)
+        matches.append(match)
+
+    return np.asarray(matches)
+
+def match_details(kp1, des1, kp2, des2):
+    matches = []
+    matches_cv = []
+    stat_s_diff = set()
+    stat_d_dist = set()
+    stat_resp_kp1 = set()
+    stat_resp_kp2 = set()
+    stat_rad = set()
+    for idx1 in range(len(kp1)):
+        kpa = kp1[idx1]
+        kpa_response = kpa[4]
+        stat_resp_kp1.add(kpa_response)
+        if kpa_response < RESPONSE_THRESHOLD:
+            continue
+
+        kpa_x = kpa[0]
+        kpa_y = kpa[1]
+        kpa_size = kpa[2]
+        #kpa_angle = kpa[3]
+        dpa = des1[idx1]
+
+        best_distance = float('inf')
+        best_idx2 = -1
+
+        for idx2 in range(len(kp2)):
+            kpb = kp2[idx2]
+            # 0: Response Strength
+            kpb_response = kpb[4]
+            stat_resp_kp2.add(kpb_response)
+            if kpb_response < RESPONSE_THRESHOLD:
+                continue
+
+            # 1: Distance/Radius Check
+            kpb_x = kpb[0]
+            kpb_y = kpb[1]
+            d_pt_squared = (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2
+            stat_rad.add(np.linalg.norm(np.array([kpb_x,kpb_y]) - np.array([kpa_x,kpa_y])))
+            if d_pt_squared >= RADIUS_SQUARED:
+                continue
+
+            # 2: Scale Difference
+            kpb_size = kpb[2]
+            scale_diff = abs(kpa_size - kpb_size)
+            stat_s_diff.add(scale_diff)
+            if scale_diff > SCALE_THRESHOLD:
+                continue
+
+            # 3: Descriptor L2 Norm
+            dpb = des2[idx2]
+            d_des = np.linalg.norm(dpb - dpa)
+            stat_d_dist.add(d_des)
+            if d_des < best_distance:
+                best_distance = d_des
+                best_idx2 = idx2
+
+            # 4: ?? Angle ??
+            # kpb_angle = kpb[3]
+
+        if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:
+            continue
+
+        match = np.array([idx1, best_idx2, best_distance], dtype=np.int32)
+        matches.append(match)
+
+        match = cv2.DMatch()
+        match.queryIdx = idx1
+        match.imgIdx = idx1
+        match.trainIdx = best_idx2
+        match.distance = best_distance
+        matches_cv.append(match)
+
+    return np.asarray(matches), np.asarray(matches_cv), \
+           np.asarray(stat_s_diff), np.asarray(stat_d_dist), \
+           np.asarray(stat_resp_kp1), np.asarray(stat_resp_kp2), \
+           np.asarray(stat_rad)
\ No newline at end of file
diff --git a/feature_matching_v3/util_ransac.py b/feature_matching_v3/util_ransac.py
new file mode 100644
index 0000000..333d81e
--- /dev/null
+++ b/feature_matching_v3/util_ransac.py
@@ -0,0 +1,46 @@
+import numpy as np
+import cv2
+
+DISTANCE_RATIO = 0.95
+RANSAC_REPROJ_TRESHHOLD = 10 # The higher the threshold, the lower the inliers
+RANSAC_MAX_ITERS = 2000
+RANSAC_CONFIDENCE = 0.99
+BF = cv2.BFMatcher(normType=cv2.NORM_L2)
+
+def perform_ransac(s_idx):
+    global s_kp, s_des, pw_kp, pw_des
+    matches = []
+    print('s_idx', s_idx)
+    for pw_idx in range(pw_kp.shape[0]):
+        mat = BF.knnMatch(s_des[s_idx], pw_des[pw_idx], k=2)
+        mat = [m[0] for m in mat if len(m) == 2 and m[0].distance < m[1].distance * DISTANCE_RATIO]
+        src_pts = np.float32([(s_kp[s_idx][m.queryIdx][0],s_kp[s_idx][m.queryIdx][1]) for m in mat])
+        dst_pts = np.float32([(pw_kp[pw_idx][m.trainIdx][0],pw_kp[pw_idx][m.trainIdx][1]) for m in mat])
+        H, mask = cv2.findHomography(src_pts, dst_pts, method=cv2.RANSAC, ransacReprojThreshold=RANSAC_REPROJ_TRESHHOLD,maxIters=RANSAC_MAX_ITERS, confidence=RANSAC_CONFIDENCE)
+        matches.append(np.array([mask.sum()]))
+
+    np.savez_compressed(str(s_idx) + '-R', m=matches)
+
+def imshow_ransac():
+    import os
+    import pylab as plt
+    im_inliers = np.zeros((73, 89))
+    for sidx in range(73):
+        path = os.path.join('sm_ransac', str(sidx) + '-R.npz')
+        m = np.load(path)['m']
+        inliers = []
+        for pidx in range(89):
+            pw_matches = m[pidx]
+            inliers.append(pw_matches[0])
+        inlier_norm = np.array(inliers) / np.max(inliers)
+        im_inliers[sidx] = (inlier_norm * 255).reshape(1, 89)
+
+    fig = plt.figure(0)
+    ax = fig.add_subplot(111)
+    ax.set_xlabel('PW Level')
+    ax.set_ylabel('S Level')
+    ax.set_xticks(np.arange(0,89,5))
+    ax.set_yticks(np.arange(0,72,5))
+    ax.set_title('Inliers')
+    plt.set_cmap(plt.get_cmap('hot'))
+    plt.imshow(im_inliers)
\ No newline at end of file
diff --git a/feature_matching_v3/util_sift.py b/feature_matching_v3/util_sift.py
new file mode 100644
index 0000000..54235dc
--- /dev/null
+++ b/feature_matching_v3/util_sift.py
@@ -0,0 +1,49 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import cv2
+import os
+SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.05, edgeThreshold=100, sigma=2)
+
+def kp_to_array(kp):
+    array = np.zeros((len(kp), 7), dtype=np.float32)
+    for idx in range(array.shape[0]):
+        k = kp[idx]
+        array[idx] = np.array([k.pt[0], k.pt[1], k.size,k.angle,k.response,k.octave,k.class_id])
+    return array
+
+def array_to_kp(array):
+    kp = []
+    for idx in range(array.shape[0]):
+        k = array[idx]
+        kp.append(cv2.KeyPoint(k[0],k[1],k[2],k[3],k[4],k[5],k[6]))
+    return kp
+
+def __precompute_atlas(name):
+    if not os.path.isfile(name + '_SIFT.npz'):
+        print('Precomputing SIFT for ', name)
+        atlas_data = np.load(name + ".npz")
+        atlas_im = atlas_data['images']
+        atlas_labels = atlas_data['labels']
+        atlas_kp = []
+        atlas_des = []
+
+        for i in range(0, atlas_im.shape[0]):
+            kp, des = SIFT.detectAndCompute(atlas_im[i], None)
+            kp = kp_to_array(kp)
+            atlas_kp.append(kp)
+            atlas_des.append(des)
+
+        atlas_kp = np.asarray(atlas_kp)
+        atlas_des = np.asarray(atlas_des)
+
+        np.savez_compressed(name + '_SIFT', images=atlas_im, labels=atlas_labels, kp=atlas_kp, des=atlas_des)
+
+def precompute_sift(S_NAME, PW_NAME):
+    __precompute_atlas(S_NAME)
+    __precompute_atlas(PW_NAME)
+
+def load_sift(path):
+    data = np.load(path)
+    return data['images'], data['labels'], data['kp'], data['des']
\ No newline at end of file
diff --git a/feature_matching_v3/util_sm.py b/feature_matching_v3/util_sm.py
new file mode 100644
index 0000000..b157549
--- /dev/null
+++ b/feature_matching_v3/util_sm.py
@@ -0,0 +1,42 @@
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: January 31, 2018
+import numpy as np
+import os
+
+def load_sm(folder, s_kp, pw_kp):
+    sm_match = np.zeros((s_kp.shape[0],pw_kp.shape[0]))
+    sm_metric = np.zeros((s_kp.shape[0],pw_kp.shape[0]))
+    for sidx in range(sm_match.shape[0]):
+        path = os.path.join(folder, str(sidx) + '-M.npz')
+        m = np.load(path)['m']
+        count = []
+        metric = []
+        for pidx in range(sm_match.shape[1]):
+            pw_matches = m[pidx]
+            count.append(len(pw_matches))
+            metric.append(len(pw_matches) / (len(s_kp[sidx]) + len(pw_kp[pidx]) - len(pw_matches)))
+        sm_match[sidx] = np.asarray(count)
+        sm_metric[sidx] = np.asarray(metric)
+    return sm_match, sm_metric
+
+def norm2_sm(sm, max_value=255):
+    im_result = np.zeros_like(sm)
+    for idx in range(sm.shape[0]):
+        norm = sm[idx] / np.max(sm[idx])
+        im_result[idx] = (norm*max_value).reshape(1, sm.shape[1])
+    return im_result
+
+def norm_prob_sm(sm):
+    norm = np.zeros_like(sm)
+    for idx in range(sm.shape[0]):
+        norm[idx] = sm[idx] / np.sum(sm[idx])
+    return norm
+
+def norm_sm(sm, max_value=255, min_value=0):
+    im_result = np.zeros_like(sm)
+    for idx in range(sm.shape[0]):
+        x = sm[idx]
+        norm = ((x - np.min(x))*(max_value-min_value)) / (np.max(x)-np.min(x)) + min_value
+        im_result[idx] = (norm).reshape(1, sm.shape[1])
+    return im_result
diff --git a/feature_matching_v3/util_visualization.py b/feature_matching_v3/util_visualization.py
new file mode 100644
index 0000000..dce860a
--- /dev/null
+++ b/feature_matching_v3/util_visualization.py
@@ -0,0 +1,84 @@
+#%% Visualization utilities for experiments
+# Author: Jose G Perez
+# Version 1.0
+# Last Modified: March 20, 2020
+from skimage import color
+import pylab as plt
+import numpy as np
+
+
+def imshow(im, title=''):
+    figure = plt.figure()
+    plt.axis('off')
+    plt.tick_params(axis='both',
+                    left='off', top='off', right='off', bottom='off',
+                    labelleft='off', labeltop='off', labelright='off', labelbottom='off')
+
+    plt.title(title)
+    plt.imshow(im)
+    return figure
+
+
+def imshow_matches__(im, title):
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.set_xlabel('PW Level')
+    ax.set_ylabel('S Level')
+    ax.set_title(title)
+    plt.set_cmap(plt.get_cmap('hot'))
+    plt.imshow(im)
+
+
+def imshow_matches(im, title):
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    # ax.set_xlabel('PW Level')
+    # ax.set_ylabel('S Level')
+    # ax.set_xticks(s_label)
+    # ax.set_yticks(pw_label)
+    ax.set_title(title)
+    plt.set_cmap(plt.get_cmap('hot'))
+    plt.imshow(im)
+
+
+def overlay(im_bg, im_fg):
+    img_color = np.stack((im_bg,)*3,axis=2)
+    img_hsv = color.rgb2hsv(img_color)
+    color_mask_hsv = color.rgb2hsv(im_fg)
+    img_hsv[..., 0] = color_mask_hsv[..., 0]
+    img_hsv[..., 1] = color_mask_hsv[..., 1]
+    im_overlay = color.hsv2rgb(img_hsv)
+
+    return im_overlay
+
+
+def imshow_detailed(np_arr, title, axis_xlabel, axis_ylabel, xlabel_arr, ylabel_arr, value_to_str_func=lambda i, j, val: val):
+    fig = plt.figure()
+    ax = fig.gca()
+
+    plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
+    for tick in ax.xaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    for tick in ax.yaxis.get_major_ticks():
+        tick.label.set_fontsize(8)
+
+    ax.set_title(title)
+    ax.set_xlabel(axis_xlabel)
+    ax.set_ylabel(axis_ylabel)
+
+    # Show all ticks
+    ax.set_xticks(np.arange(len(xlabel_arr)))
+    ax.set_yticks(np.arange(len(ylabel_arr)))
+
+    # Proper labels
+    ax.set_xticklabels(xlabel_arr)
+    ax.set_yticklabels(ylabel_arr)
+
+    # Show values
+    if value_to_str_func is not None:
+        for i in range(np_arr.shape[0]):
+            for j in range(np_arr.shape[1]):
+                text = ax.text(j, i, value_to_str_func(i, j, np_arr[i, j]), ha="center", va="center", color="w")
+
+    ax.imshow(np_arr)
\ No newline at end of file
diff --git a/matching_networks/LICENSE b/matching_networks/LICENSE
deleted file mode 100644
index 5295f51..0000000
--- a/matching_networks/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2017 AntreasAntoniou
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/matching_networks/README.md b/matching_networks/README.md
deleted file mode 100644
index daa6ac4..0000000
--- a/matching_networks/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# MatchingNetworks Tensorflow Implementation
-This repo provides code that replicated the results of the Matching Networks for One Shot Learning paper on the Omniglot dataset.
-
-# Training a model
-To train a model simply modify the experiment parameters in the [train_one_shot_learning_matching_network.py](https://github.com/AntreasAntoniou/MatchingNetworks/blob/master/train_one_shot_learning_matching_network.py) to match your requirements, for a one shot, 20-way experiment leave the parameters to default, for a 5-way one shot learning modify 
-```
-classes_per_set = 20
-samples_per_class = 1
-```
-to 
-```
-classes_per_set = 5
-samples_per_class = 1
-```
-And then run `python train_one_shot_learning_matching_network.py`
-
-# Features
-The code supports automatic checkpointing as well as statistics saving. It uses 1200 classes for training, 300 classes for testing and 122 classes for validation. We check for a better validation score at each epoch and produce test statistics when the validation score has improved. Furthermore the number of classes and samples per class can be modified and the code will be able to handle any combinations that do not exceed the available memory. As an additional feature we have added support for full context embeddings in our implementation.
-
-Our implementation uses the omniglot dataset, but one can easily add a new data provider and then build a new experiment by passing the data provider to the ExperimentBuilder class and the system should work with it, as long as it provides the batches in the same way as our data provider, more details can be found in data.py
-
-# Acknowledgements
-Special thanks to https://github.com/zergylord for his Matching Networks implementation of which parts were used for this implementation. More details at https://github.com/zergylord/oneshot
-
-Additional thanks to my colleagues https://github.com/gngdb, https://github.com/ZackHodari and https://github.com/artur-bekasov for reviewing my code and providing pointers.
diff --git a/matching_networks/saved_models/README b/matching_networks/saved_models/README
deleted file mode 100644
index 2651cc4..0000000
--- a/matching_networks/saved_models/README
+++ /dev/null
@@ -1 +0,0 @@
-This is where we save our model checkpoints.
\ No newline at end of file
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diff --git a/siamese_network/atlas_sw.npz b/siamese_network/atlas_sw.npz
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