Update cv.py

cv.py

@@ -1,46 +1,45 @@
 import cv2
 import numpy as np
+from sklearn.neighbors import NearestNeighbors
 
-def find_black_roi(image_path, threshold_area=1000):
+def detect_back_patches(image_path, threshold_distance):
     # 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
+    
+    # Convert image to grayscale if necessary
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # Apply nearest neighbor algorithm
+    # Assuming patches are represented as feature vectors
+    patches = extract_patches(gray_image)  # Extract patches from the image
+    nn = NearestNeighbors(n_neighbors=2, algorithm='auto')
+    nn.fit(patches)
+    
+    # Find nearest neighbors
+    distances, indices = nn.kneighbors(patches)
+    
+    # Filter patches based on threshold distance
+    filtered_patches_indices = np.where(distances[:,1] > threshold_distance)[0]
+    
+    # Get the ROIs corresponding to the filtered patches
+    ROIs = [patches[idx] for idx in filtered_patches_indices]
+    
+    return ROIs
+
+def extract_patches(image, patch_size=(32, 32)):
+    # Extract patches from the image
+    patches = []
+    height, width = image.shape[:2]
+    patch_height, patch_width = patch_size
+    
+    for y in range(0, height - patch_height + 1, patch_height):
+        for x in range(0, width - patch_width + 1, patch_width):
+            patch = image[y:y+patch_height, x:x+patch_width]
+            patches.append(patch.flatten())  # Flatten patch to create feature vector
+    
+    return np.array(patches)
 
 # 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)
+image_path = 'document_image.jpg'
+threshold_distance = 100  # Adjust this threshold based on your requirements
+back_patches_ROIs = detect_back_patches(image_path, threshold_distance)