Create cv.py

cv.py

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+import cv2
+import numpy as np
+
+# Read the image
+image = cv2.imread('your_image_path.jpg')
+
+# Convert the image to grayscale
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# Apply GaussianBlur to reduce noise and improve edge detection
+blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+# Perform edge detection using Canny
+edges = cv2.Canny(blurred, 50, 150)
+
+# Find contours in the edge-detected image
+contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+# Loop over the contours
+for contour in contours:
+    # Calculate the area of the contour
+    area = cv2.contourArea(contour)
+    
+    # If the area is small (indicating a small patch), draw a bounding box around it
+    if area < 500:  # Adjust this threshold according to your needs
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+# Save or display the image with detected patches
+cv2.imwrite('detected_patches.jpg', image)
+# Or to display:
+# cv2.imshow('Detected Patches', image)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()