shape_matching/match.py

# 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()