I am trying to solve problem where i have two digital handwritten signature i have to find similarity percentage between them Below is the code import cv2 from skimage.metrics import structural_similarity as ssim import numpy as np # TODO add contour detection for enhanced accuracy def removeWhiteSpace(img): # img = cv2.imread('ws.png') # Read in the image and convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = 255*(gray < 128).astype(np.uint8) # To invert the text to white coords = cv2.findNonZero(gray) # Find all non-zero points (text) x, y, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box rect = img[y:y+h, x:x+w] return rect def match(path1, path2): # read the images img1 = cv2.imread(path1) img2 = cv2.imread(path2) img1 = removeWhiteSpace(img1) img2 = removeWhiteSpace(img2) # turn images to grayscale img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) # resize images for comparison img1 = cv2.resize(img1, (300, 300)) img2 = cv2.resize(img2, (300, 300)) # display both images cv2.imshow("One", img1) cv2.imshow("Two", img2) cv2.waitKey(0) cv2.destroyAllWindows() similarity_value = "{:.2f}".format(ssim(img1, img2,gaussian_weights = True,sigma= 1.2,use_sample_covariance = False)*100) # print("answer is ", float(similarity_value), # "type=", type(similarity_value)) return float(similarity_value) path1 = 'sample_images\img3.jpeg' path2 = 'sample_images\img4.jpeg' similarity_value = match(path1,path2) print(similarity_value) Example#1 Below are the two images Image1: Image2: when execute the above code i am getting 77.93 similarity which is good percentage Example#2 Below are the two images Image1: Image2: when execute the above code i am getting 66.36 similarity match Ask How can i improve the similarity match percentage in example#2 as both images look similar? Continue reading...
