From 238f967a0a9b3087e3e0175f192a86fb61d06c6b Mon Sep 17 00:00:00 2001
From: Lynx <NixLynx@tutanota.com>
Date: Sun, 11 Aug 2024 16:54:51 +0200
Subject: [PATCH] Initial Commit

---
 .gitignore  |   1 +
 0.png       | Bin 0 -> 1093 bytes
 1.png       | Bin 0 -> 999 bytes
 2.png       | Bin 0 -> 1078 bytes
 3.png       | Bin 0 -> 1063 bytes
 4.png       | Bin 0 -> 975 bytes
 5.png       | Bin 0 -> 1103 bytes
 6.png       | Bin 0 -> 1012 bytes
 7.png       | Bin 0 -> 1055 bytes
 8.png       | Bin 0 -> 951 bytes
 9.png       | Bin 0 -> 1073 bytes
 License.txt |  19 ++++++
 Readme.md   |  33 +++++++++++
 match.py    | 168 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 14 files changed, 221 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 0.png
 create mode 100644 1.png
 create mode 100644 2.png
 create mode 100644 3.png
 create mode 100644 4.png
 create mode 100644 5.png
 create mode 100644 6.png
 create mode 100644 7.png
 create mode 100644 8.png
 create mode 100644 9.png
 create mode 100644 License.txt
 create mode 100644 Readme.md
 create mode 100644 match.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3bd52bb
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+**/localvenv
\ No newline at end of file
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diff --git a/License.txt b/License.txt
new file mode 100644
index 0000000..e8c5121
--- /dev/null
+++ b/License.txt
@@ -0,0 +1,19 @@
+modified silly license
+  -------------
+
+    copyright (c) :3
+
+    permission is granted to the entity reading this text ("that one")
+    to use this project for silly and mischievous purposes,
+    subject to the following conditions:
+
+    - say "be gay, do crime" at least once during use of this project
+
+    - if that one represents a for-profit endeavour, all previously granted
+      permissions are immediately and permanently retracted
+
+    - there's no warranty of any kind
+      that one should expect the project to crash and burn in the worst possible ways
+      no further warnings will be issued 
+
+    - no attribution required
\ No newline at end of file
diff --git a/Readme.md b/Readme.md
new file mode 100644
index 0000000..081d1de
--- /dev/null
+++ b/Readme.md
@@ -0,0 +1,33 @@
+# Purpose
+
+- It was fun to mess around with :3
+- Making this public so cqql can also mess around with it
+
+# Dependencies
+- opencv-python
+    - all of the image stuff
+- matplotlib
+    - color lookup table
+- (numpy)
+    - used internally by opencv, but part of stdlib
+
+# Running
+- python -m venv localvenv
+- ./localvenv/bin/pip install opencv-python
+- ./localvenv/bin/pip install matplotlib
+- ./localvenv/bin/python match.py
+
+
+# Modifications
+- If you have more/less images, change MAX global
+- If you want to test rotational stuff, uncomment the line in main
+- It is assumed that the shape is a darker shape on a ligher background
+    - The image gets converted into grayscale and assumes 
+    - the shape has an intensity below 200
+    - the background has an intensity above 200
+- Depending on how dissimilar the shapes are, NormUpperBound should be increased
+
+# Attributions
+
+- The test images were generated using
+    - https://moonlit.technology/cqql/frost_patterns
diff --git a/match.py b/match.py
new file mode 100644
index 0000000..e234bf8
--- /dev/null
+++ b/match.py
@@ -0,0 +1,168 @@
+# import required libraries
+import cv2
+import numpy as np
+import matplotlib
+import math
+
+MAX = 10 # should currently be 10
+NormUpperBound = 2.0 #Highest Expected Number
+GrayscaleThreshhold = 200
+RotationAngle = 45
+
+
+### Read Images from Disk, Assumed Naming: 0.png 1.png ... n.png depending on MAX
+
+#Turns Images into Contours based on Greyscale Threshhold
+def GetContoursFromFiles():
+    cnt = []
+    for i in range (0, MAX, 1):
+        # Read image as grayscale images
+        img = cv2.imread(str(i)+'.png',0)
+        # Apply thresholding on the images to convert to binary images
+        ret, thresh1 = cv2.threshold(img, GrayscaleThreshhold, 255,cv2.THRESH_BINARY_INV)
+        # find the contours in the binary image
+        contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        print("Number of Shapes detected in Image "+str(i)+":",len(contours))
+        cnt.append(contours[0])
+    return cnt
+
+
+#Does N to N matching, resulting in a 1d array that should be interpreted as a 2d array
+# Could easily be modified for 1 to N matching
+def MatchAll(cnt):
+    mat = []
+    for i in range(0, MAX, 1):
+        for j in range(0, MAX, 1):
+            mat.append(cv2.matchShapes(cnt[i],cnt[j],1,0.0))
+    return mat
+
+
+def PrintMatchingMatrix(mat):
+    print("Similarity of Images: \n")
+    for i in range(0, MAX, 1):
+        for j in range(0, MAX, 1):
+            print(f"{mat[i*MAX+j]:.2f}", end='')
+            print("\t", end='')
+        print("\n")
+
+def PrintMatchingMatrixNormalized(mat):
+    print("Similarity of Images Normalized: \n")
+    norm = matplotlib.colors.Normalize(vmin=0.0, vmax=NormUpperBound)
+    for i in range(0, MAX, 1):
+        for j in range(0, MAX, 1):
+            print(f"{norm(mat[i*MAX+j]):.2f}", end='')
+            print("\t", end='')
+        print("\n")
+
+
+
+# Does all of the heavy lifting when it comes to displaying it in a nice graphical way
+# Compact but also quickly hacked together
+# Builds the Visual matrix using images, 1 image = 1 coordinate unit
+def CreateMatchingColorMatrix(mat):
+
+    im_res = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
+    height, width, channels = im_res.shape
+    # (Coordinate 0/0) Color (white)
+    im_temp = np.full((height, width, 3), 255, np.uint8)
+
+    norm = matplotlib.colors.Normalize(vmin=0.0, vmax=NormUpperBound)
+
+    # Build Topmost row (just iterate through all images and concat them sequentially)
+    for i in range(0, MAX, 1):
+        img = cv2.imread(str(i)+'.png',cv2.IMREAD_COLOR)
+        im_temp = cv2.hconcat([im_temp, img])
+    # This top row is now our first row
+    im_res = im_temp
+    # Build The matrix row by row
+    for i in range(0, MAX, 1):
+        im_temp = cv2.imread(str(i)+'.png',cv2.IMREAD_COLOR)
+        img = np.full((height, width, 3), 255, np.uint8)
+        img[:] = (0, 0, 255)
+        # Individual row here, current sequential image gets chosen above, so here, we can do square coloring
+        for j in range(0, MAX, 1):
+            cmap = matplotlib.cm.get_cmap('brg_r')
+            cmap.set_over((0.0,0.0,0.0))
+            # Gets current weight, normalises it, looks it up in color map, converts it to full scale, colors
+            img[:] = NtoF(cmap(norm(mat[i*MAX+j]))[:-1])
+            # build up row
+            im_temp = cv2.hconcat([im_temp, img])
+        #build up matrix
+        im_res = cv2.vconcat([im_res, im_temp])
+    DebugDrawImage(im_res)
+
+
+# Helper to convert Normalized color to Full-scale (255) color
+def NtoF(rgb):
+    return tuple([255*x for x in rgb])
+
+
+
+
+def DebugDrawImage(img):
+    cv2.imshow("Image", img)
+    cv2.waitKey(0)
+
+### Rotates Image 0.png 4 times to test how rotations affect outcome (they don't)
+
+def RotationTest():
+    global MAX
+    MAX = math.ceil(360/RotationAngle)
+
+    cnt = []
+    img = cv2.imread(str(0)+'.png',0)
+    for i in range (0, MAX, 1):
+        ret, thresh1 = cv2.threshold(img, GrayscaleThreshhold, 255,cv2.THRESH_BINARY_INV)
+        contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        cnt.append(contours[0])
+        print("Number of Shapes detected in Rotation "+str(i)+":",len(contours))
+        img = rotate_image(img, RotationAngle)
+    
+    mat = MatchAll(cnt)
+    PrintMatchingMatrix(mat)
+    CreateMatchingColorMatrixRotation(mat)
+
+# Quick hack of above function to work with rotation instead of multiple images
+def CreateMatchingColorMatrixRotation(mat):
+    im_rot = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
+    im_res = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
+    height, width, channels = im_res.shape
+    im_temp = np.full((height, width, 3), 255, np.uint8)
+    norm = matplotlib.colors.Normalize(vmin=0.0, vmax=NormUpperBound)
+    for i in range(0, MAX, 1):
+        img = im_rot
+        im_temp = cv2.hconcat([im_temp, img])
+        im_rot = rotate_image(img, RotationAngle)
+    im_res = im_temp
+    #reset
+    im_rot = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
+    for i in range(0, MAX, 1):
+        im_temp = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
+        im_temp = im_rot
+        img = np.full((height, width, 3), 255, np.uint8)
+        img[:] = (0, 0, 255)
+        for j in range(0, MAX, 1):
+            cmap = matplotlib.cm.get_cmap('brg_r')
+            cmap.set_over((0.0,0.0,0.0))
+            img[:] = NtoF(cmap(norm(mat[i*MAX+j]))[:-1])
+            im_temp = cv2.hconcat([im_temp, img])
+        im_res = cv2.vconcat([im_res, im_temp])
+        im_rot = rotate_image(im_rot, RotationAngle)
+    DebugDrawImage(im_res)
+
+# Stolen from Stackoverflow and modified: https://stackoverflow.com/a/9042907
+# NOTE: this function assumes the background is a lighter shade than the form to detect
+# NOTE: INTER_NEAREST or other interpolation strategies might work better, depending on rotation and some other factors
+def rotate_image(image, angle):
+  image_center = tuple(np.array(image.shape[1::-1]) / 2)
+  rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
+  result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(255,255,255))
+  return result
+
+
+if __name__ == "__main__":
+    mat = MatchAll(GetContoursFromFiles())
+    PrintMatchingMatrix(mat)
+    PrintMatchingMatrixNormalized(mat)
+    CreateMatchingColorMatrix(mat)
+    #RotationTest()
\ No newline at end of file