I am new in image processing and I am trying to improve myself by doing some projects and I have a problem about my project. I have an image dataset containing lakes with their corresponding binary mask images. I want to calculate the perimeter (boundary length) and area of the lake in each image using OpenCV. So far, I have tried Canny Edge Detection and cv2.findContours() to detect the lake boundaries to find lake's real area and real perimeter, but I am struggling to get only the lake's contour without including unwanted edges (such as image borders or noise). Here is my current code: import cv2 import matplotlib.pyplot as plt import numpy as np # Load the image image = cv2.imread("water_body_3.jpg") # Apply Canny edge detection edged = cv2.Canny(image, 50, 100) # Find contours contours, _ = cv2.findContours(edged, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) largest_contour = max(contours, key=cv2.contourArea) # Draw the detected contours cv2.drawContours(image, [largest_contour], -1, (0, 255, 0), 2) plt.imshow(image, cmap="gray") plt.title("Canny Edge Detection") plt.axis("off") plt.show() Problems I'm Facing: The detected contours sometimes include the image border or noise instead of just the lake. I want to calculate the perimeter and area of the lake, but I am not sure if I am selecting the correct contour. Expected Output: Extract only the lake contour from the image. Compute the perimeter and area . Question: How can I ensure that I am only selecting the lake's contour and not unwanted edges? What is the best way to calculate the lake's area and perimeter correctly? Should I preprocess the image differently (e.g., thresholding, morphological operations)? Images: code output image: https://i.sstatic.net/mLCvT5sD.png mask image that I use in code (water_body_3.jpg): https://i.sstatic.net/psXSsJfg.jpg original image:https://i.sstatic.net/4v6f4wLj.jpg Continue reading...
