Update cv.py

cv.py

@@ -1,34 +1,46 @@
 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()
+def find_black_roi(image_path, threshold_area=1000):
+    # Read the image
+    image = cv2.imread(image_path)
+    if image is None:
+        print("Error: Image not found.")
+        return
+
+    # Convert image to grayscale
+    grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Apply binary thresholding to separate black patches
+    _, thresholded = cv2.threshold(grayscale, 30, 255, cv2.THRESH_BINARY)
+
+    # Find contours
+    contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Filter contours based on area
+    roi_list = []
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area > threshold_area:
+            # Get bounding box coordinates
+            x, y, w, h = cv2.boundingRect(contour)
+            roi = image[y:y+h, x:x+w]  # Extract the ROI
+            roi_list.append(roi)
+
+    return roi_list
+
+# Example usage
+image_path = "example_image.jpg"
+rois = find_black_roi(image_path)
+
+
+
+import matplotlib.pyplot as plt
+import cv2
+
+def display_image(image):
+    plt.imshow(image)
+    plt.axis('off')  # Hide axis
+    plt.show()
+
+display_image(gray)