This Content is from Stack Overflow. Question asked by Abdullah
Hi All I am new to image processing and I was developing a code to detect cracks within an image. The purpose of the code is to build a machine learning algorithm to help detecting and quantifying cracks within an image. The images attached below shows the image before and after processing. I have been trying to mask the area between the edges using morphology for visualizing and quantifying purposes based on total area of masked image but it is not doing the job as some of the major cracks are left unmasked after applying the morphology transformation. Hope if someone can help me with this. The code used is also shown here.
# importing necessary libraries import numpy as np import cv2 from matplotlib import pyplot as plt # read a cracked sample image img = cv2.imread('Input-Set/Original.tif') # Convert into gray scale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Image processing ( smoothing ) # Averaging blur = cv2.blur(gray,(3,3)) # Apply logarithmic transform img_log = (np.log(blur+1)/(np.log(1+np.max(blur))))*255 # Specify the data type img_log = np.array(img_log,dtype=np.uint8) # Image smoothing: bilateral filter bilateral = cv2.bilateralFilter(img_log, 5, 75, 75) # Canny Edge Detection edges = cv2.Canny(bilateral,20,20) # Morphological Closing Operator kernel = np.ones((10,10),np.uint8) closing = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel) # Create feature detecting method orb = cv2.ORB_create(nfeatures=1500) # Make featured Image keypoints, descriptors = orb.detectAndCompute(closing, None) featuredImg = cv2.drawKeypoints(closing, keypoints, None) # Create an output image cv2.imwrite('Output-Set/Edges.tif', edges) cv2.imwrite('Output-Set/Morphology.tif', closing)
This question is not yet answered, be the first one who answer using the comment. Later the confirmed answer will be published as the solution.
This Question and Answer are collected from stackoverflow and tested by JTuto community, is licensed under the terms of CC BY-SA 2.5. - CC BY-SA 3.0. - CC BY-SA 4.0.