Update cv.py

cv.py

@@ -1,56 +1,50 @@
 import cv2
 import numpy as np
-from sklearn.neighbors import NearestNeighbors
-import os
 
-def detect_back_patches(image_path, threshold_distance, save_path):
-    # Read the image
-    image = cv2.imread(image_path)
-    
-    # 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]
-    
-    # Save the black patches as images
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    
-    for i, roi in enumerate(ROIs):
-        patch_image = roi.reshape(gray_image.shape[0] // 32, gray_image.shape[1] // 32)
-        cv2.imwrite(os.path.join(save_path, f"black_patch_{i}.jpg"), patch_image)
-    
-    return ROIs
+def detect_background(gray, threshold=0.70):
+    # Convert image to grayscale
+    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # Thresholding to binarize the image
+    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    
+    # Find contours
+    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    # Initialize list to store identified background regions
+    background_regions = []
+    
+    # Iterate through contours
+    for contour in contours:
+        # Calculate area of contour
+        area = cv2.contourArea(contour)
+        
+        # Calculate bounding rectangle
+        x, y, w, h = cv2.boundingRect(contour)
+        
+        # Calculate aspect ratio of bounding rectangle
+        aspect_ratio = w / h if h != 0 else 0
+        
+        # Calculate ratio of area of contour to area of bounding rectangle
+        ratio = area / (w * h) if (w * h) != 0 else 0
+        
+        # If aspect ratio is close to 1 (nearly square) and ratio is greater than threshold, it's likely background
+        if aspect_ratio > 0.9 and ratio > threshold:
+            background_regions.append((x, y, w, h))
+    
+    return background_regions
 
-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)
+def save_background_regions(image, regions, output_prefix="background_region"):
+    for i, region in enumerate(regions):
+        x, y, w, h = region
+        region_image = image[y:y+h, x:x+w]
+        cv2.imwrite(f"black_patches/{output_prefix}_{i}.png", region_image)
+
+# Load document image
+gray = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
 
-# Example usage
-image_path = 'document_image.jpg'
-threshold_distance = 100  # Adjust this threshold based on your requirements
-save_path = 'black_patches'
-back_patches_ROIs = detect_back_patches(image_path, threshold_distance, save_path)
+# Detect continuous black or white background regions
+background_regions = detect_background(gray)
 
+# Save identified background regions as images
+save_background_regions(gray, background_regions)