Update apply_mask.py

apply_mask.py

@@ -1,72 +1,72 @@
-import cv2
-import os
-import glob
-import numpy as np
-
-def apply_mask_and_crop(input_folder, mask_path, output_folder):
-    """
-    Apply binary mask to all images, crop to masked region, and save to output folder
-    
-    Args:
-        input_folder (str): Path to folder containing input images
-        mask_path (str): Path to binary mask image
-        output_folder (str): Path to save cropped masked images
-    """
-    # Load and prepare mask
-    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
-    if mask is None:
-        raise ValueError(f"Could not load mask from {mask_path}")
-    
-    _, binary_mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
-    
-    # Create output directory if it doesn't exist
-    os.makedirs(output_folder, exist_ok=True)
-    
-    # Get list of image files
-    image_files = glob.glob(os.path.join(input_folder, "*.jpg")) + \
-                 glob.glob(os.path.join(input_folder, "*.png")) + \
-                 glob.glob(os.path.join(input_folder, "*.bmp"))
-    
-    if not image_files:
-        print(f"No images found in {input_folder}")
-        return
-    
-    print(f"Found {len(image_files)} images to process")
-    
-    for img_path in image_files:
-        # Load image
-        img = cv2.imread(img_path)
-        if img is None:
-            print(f"Warning: Could not read image {img_path}")
-            continue
-        
-        # Resize mask if dimensions don't match
-        if img.shape[:2] != binary_mask.shape[:2]:
-            resized_mask = cv2.resize(binary_mask, (img.shape[1], img.shape[0]))
-        else:
-            resized_mask = binary_mask
-        
-        # Apply mask
-        masked_img = cv2.bitwise_and(img, img, mask=resized_mask)
-        
-        # Find contours to get bounding box of mask
-        contours, _ = cv2.findContours(resized_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        if not contours:
-            print(f"No mask area found in {img_path} - skipping")
-            continue
-        
-        # Get bounding rectangle of largest contour
-        x, y, w, h = cv2.boundingRect(max(contours, key=cv2.contourArea))
-        
-        # Crop to masked region
-        cropped_img = masked_img[y:y+h, x:x+w]
-        
-        # Create output path (preserve original filename)
-        filename = os.path.basename(img_path)
-        output_path = os.path.join(output_folder, filename)
-        
-        # Save cropped image
-        cv2.imwrite(output_path, cropped_img)
-        # print(f"Processed and saved: {output_path}")
-    
-    print(f"\nProcessing complete! Saved {len(image_files)} cropped images to {output_folder}")
+import cv2
+import os
+import glob
+import numpy as np
+
+def apply_mask_and_crop(input_folder, mask_path, output_folder):
+    """
+    Apply binary mask to all images, crop to masked region, and save to output folder
+    
+    Args:
+        input_folder (str): Path to folder containing input images
+        mask_path (str): Path to binary mask image
+        output_folder (str): Path to save cropped masked images
+    """
+    # Load and prepare mask
+    # mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+    if mask is None:
+        raise ValueError(f"Could not load mask from {mask_path}")
+    
+    _, binary_mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
+    
+    # Create output directory if it doesn't exist
+    os.makedirs(output_folder, exist_ok=True)
+    
+    # Get list of image files
+    image_files = glob.glob(os.path.join(input_folder, "*.jpg")) + \
+                 glob.glob(os.path.join(input_folder, "*.png")) + \
+                 glob.glob(os.path.join(input_folder, "*.bmp"))
+    
+    if not image_files:
+        print(f"No images found in {input_folder}")
+        return
+    
+    print(f"Found {len(image_files)} images to process")
+    
+    for img_path in image_files:
+        # Load image
+        img = cv2.imread(img_path)
+        if img is None:
+            print(f"Warning: Could not read image {img_path}")
+            continue
+        
+        # Resize mask if dimensions don't match
+        if img.shape[:2] != binary_mask.shape[:2]:
+            resized_mask = cv2.resize(binary_mask, (img.shape[1], img.shape[0]))
+        else:
+            resized_mask = binary_mask
+        
+        # Apply mask
+        masked_img = cv2.bitwise_and(img, img, mask=resized_mask)
+        
+        # Find contours to get bounding box of mask
+        contours, _ = cv2.findContours(resized_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            print(f"No mask area found in {img_path} - skipping")
+            continue
+        
+        # Get bounding rectangle of largest contour
+        x, y, w, h = cv2.boundingRect(max(contours, key=cv2.contourArea))
+        
+        # Crop to masked region
+        cropped_img = masked_img[y:y+h, x:x+w]
+        
+        # Create output path (preserve original filename)
+        filename = os.path.basename(img_path)
+        output_path = os.path.join(output_folder, filename)
+        
+        # Save cropped image
+        cv2.imwrite(output_path, cropped_img)
+        # print(f"Processed and saved: {output_path}")
+    
+    print(f"\nProcessing complete! Saved {len(image_files)} cropped images to {output_folder}")