OpenCV Image Comparison in Python: Detect Differences Like a Pro.

### Import OpenCV for image processing and computer vision operations. import cv2  ### Import NumPy for numerical array operations and mask handling. import numpy as np   ### Read the collage that contains two side-by-side images into a NumPy array. img = cv2.imread("Open-CV/Find-10-Differences/two-images.jpg")  ### Convert the image to grayscale to simplify thresholding and edge detection. gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)   ### Apply a binary inverse threshold to separate the two image panels from the background. _ , thresh = cv2.threshold(gray , 230, 255 , cv2.THRESH_BINARY_INV)   ### Run Canny edge detection to get crisp borders around the two panels. canny = cv2.Canny(thresh , 254 , 255)   ### Find external contours to capture the two largest rectangular regions that contain the images. contours , hierarchy = cv2.findContours(canny.copy() , cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  ### Optionally draw contours for debugging to verify panel detection accuracy. #cv2.drawContours(img, contours, -1 , (0,255,0) ,2 )   ### Sort contours by area so we can retrieve the two biggest panels reliably. sortedCon = sorted(contours, key = cv2.contourArea)   ### Compute a bounding rectangle around the largest panel. x,y,w,h = cv2.boundingRect(sortedCon[0])  ### Draw a rectangle on the collage to visualize the first panel boundaries. cv2.rectangle(img, (x,y), (x+w , y+h), (255,0,0), 3)  ### Crop the first region of interest (ROI) that represents the first image. roi = img.copy()[y:y+h , x:x+w]   ### Compute a bounding rectangle around the second panel. x1,y1,w1,h1 = cv2.boundingRect(sortedCon[1])  ### Draw a rectangle to visualize the second panel boundaries. cv2.rectangle(img, (x1,y1), (x1+w1 , y1+h1), (255,0,0), 3)  ### Crop the second region of interest (ROI) that represents the second image. roi2 = img.copy()[y1:y1+h1 , x1:x1+w1]   ### Print the shapes so we can confirm both ROIs and plan alignment if needed. print(roi.shape)  ### Print the second ROI shape for comparison against the first ROI. print(roi2.shape)   ### Retrieve the height of the second ROI to trim one row if necessary for shape alignment. roi2H = roi2.shape[0]  ### Print the current height for transparency during debugging. print(roi2H)  ### Align heights by removing a bottom row if the sizes are off by one pixel. roi2 = roi2[0:roi2H-1, :]  ### Confirm the new shape after alignment. print(roi2.shape)   ### Retrieve the width of the first ROI to trim one column if necessary for shape alignment. roi1W = roi.shape[1]  ### Print the current width for transparency during debugging. print(roi1W)  ### Align widths by removing the last column if the sizes are off by one pixel. roi = roi[: , 0:roi1W-1]  ### Confirm the new shape after alignment. print(roi.shape)    ### Compute pixel-wise absolute difference and invert to emphasize similar background while keeping differences bright. diff = 255 - cv2.absdiff(roi,roi2)   ### Define the lower HSV threshold for masking potential difference regions. lower = np.array([0,0,0])  ### Define the upper HSV threshold to include the full range of values. upper = np.array([180,255,255])   ### Convert the difference image from BGR to HSV for robust value-based masking. imgHSV = cv2.cvtColor(diff, cv2.COLOR_BGR2HSV)  ### Create a binary mask selecting all HSV values in the defined range. mask = cv2.inRange(imgHSV, lower, upper)   ### Keep only the masked regions from the difference image for cleaner downstream processing. result = cv2.bitwise_and(diff, diff , mask=mask)   ### Apply Gaussian blur to reduce salt-and-pepper noise before edge detection. blur = cv2.GaussianBlur(result, (11,11), 0)  ### Run Canny a second time on the denoised image to outline difference boundaries sharply. canny = cv2.Canny(blur, 50 , 150)  ### Dilate the edges to close small gaps and make contours easier to detect. edges = cv2.dilate(canny, None)   ### Extract contours corresponding to distinct difference regions between the two images. contours , hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)   ### Iterate over each contour to compute bounding boxes around detected differences. for cnt in contours:      ### Get the top-left coordinate and size of the bounding rectangle for the current difference region.     x, y, width, height = cv2.boundingRect(cnt)      ### Draw the bounding box on the result canvas to visualize detected differences.     cv2.rectangle(result, (x,y), (x+width, y+height), (0,0,255), 2)      ### Draw the same bounding box on the first ROI for side-by-side visualization.     cv2.rectangle(roi, (x,y), (x+width, y+height), (0,0,255), 2)      ### Draw the same bounding box on the second ROI for side-by-side visualization.     cv2.rectangle(roi2, (x,y), (x+width, y+height), (0,0,255), 2)    ### Display the composite result with highlighted differences. cv2.imshow("result", result)    ### Display the first cropped image with bounding boxes. cv2.imshow("roi",roi)  ### Display the second cropped image with bounding boxes. cv2.imshow("roi2",roi2)    ### Display the Canny output used to detect edges of the difference regions. cv2.imshow("canny", canny)  ### Display the dilated edges that feed the contour extraction. cv2.imshow("edges", edges)  ### Wait for a key press to keep windows open until the user is ready to close them. cv2.waitKey(0)   ### Close all OpenCV windows to release resources gracefully. cv2.destroyAllWindows()