fix: improve image quality with LANCZOS4 interpolation, better mask validation, and high-quality PNG output

api/main.py

@@ -204,7 +204,7 @@ def inpaint(req: InpaintRequest, _: None = Depends(bearer_auth)) -> Dict[str, st
         result = process_inpaint(np.array(img_rgba), mask_rgba, invert_mask=req.invert_mask)
     result_name = f"output_{uuid.uuid4().hex}.png"
     result_path = os.path.join(OUTPUT_DIR, result_name)
-    Image.fromarray(result).save(result_path)
+    Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
 
     logs.append({"result": result_name, "timestamp": datetime.utcnow().isoformat()})
     return {"result": result_name}
@@ -228,7 +228,7 @@ def inpaint_url(req: InpaintRequest, request: Request, _: None = Depends(bearer_
         result = process_inpaint(np.array(img_rgba), mask_rgba, invert_mask=req.invert_mask)
     result_name = f"output_{uuid.uuid4().hex}.png"
     result_path = os.path.join(OUTPUT_DIR, result_name)
-    Image.fromarray(result).save(result_path)
+    Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
 
     url = str(request.url_for("download_file", filename=result_name))
     logs.append({"result": result_name, "url": url, "timestamp": datetime.utcnow().isoformat()})
@@ -254,7 +254,7 @@ def inpaint_multipart(
         result = np.array(img.convert("RGB"))
         result_name = f"output_{uuid.uuid4().hex}.png"
         result_path = os.path.join(OUTPUT_DIR, result_name)
-        Image.fromarray(result).save(result_path)
+        Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
 
         url: Optional[str] = None
         try:
@@ -326,7 +326,7 @@ def inpaint_multipart(
             result = np.array(img.convert("RGB")) if img else np.array(m.convert("RGB"))
             result_name = f"output_{uuid.uuid4().hex}.png"
             result_path = os.path.join(OUTPUT_DIR, result_name)
-            Image.fromarray(result).save(result_path)
+            Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
             return {"result": result_name, "error": "pink/magenta paint detection failed - very few pixels detected"}
         
         # Create black/white mask: white = remove (pink areas), black = keep (everything else)
@@ -349,7 +349,7 @@ def inpaint_multipart(
     result = process_inpaint(np.array(img), mask_rgba, invert_mask=actual_invert)
     result_name = f"output_{uuid.uuid4().hex}.png"
     result_path = os.path.join(OUTPUT_DIR, result_name)
-    Image.fromarray(result).save(result_path)
+    Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
 
     url: Optional[str] = None
     try:
@@ -417,7 +417,7 @@ def remove_pink_segments(
         result = np.array(img.convert("RGB"))
         result_name = f"output_{uuid.uuid4().hex}.png"
         result_path = os.path.join(OUTPUT_DIR, result_name)
-        Image.fromarray(result).save(result_path)
+        Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
         return {
             "result": result_name,
             "error": "No pink/magenta segments detected. Please paint areas to remove with magenta/pink color (RGB 255,0,255)."
@@ -439,7 +439,7 @@ def remove_pink_segments(
     result = process_inpaint(np.array(img), mask_rgba, invert_mask=False)
     result_name = f"output_{uuid.uuid4().hex}.png"
     result_path = os.path.join(OUTPUT_DIR, result_name)
-    Image.fromarray(result).save(result_path)
+    Image.fromarray(result).save(result_path, "PNG", optimize=False, compress_level=1)
     
     url: Optional[str] = None
     try:

src/core.py

@@ -449,11 +449,16 @@ def process_inpaint(image, mask, invert_mask=True):
     """
     image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
     original_shape = image.shape
-    interpolation = cv2.INTER_CUBIC
+    # Use INTER_LANCZOS4 for better quality (higher quality interpolation)
+    interpolation = cv2.INTER_LANCZOS4
 
-    size_limit = max(image.shape)
+    # Increase size limit to preserve quality (up to 2048px max dimension)
+    # Reference model uses max(image.shape) but we can optimize for quality
+    max_dimension = max(image.shape)
+    size_limit = min(max_dimension, 2048)  # Cap at 2048 for quality/speed balance
 
     print(f"Origin image shape: {original_shape}")
+    print(f"Size limit: {size_limit} (max dimension was {max_dimension})")
     image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
     print(f"Resized image shape: {image.shape}")
     image = norm_img(image)
@@ -489,13 +494,25 @@ def process_inpaint(image, mask, invert_mask=True):
         if not invert_mask:
             mask = 255 - mask  # double invert back to original
     
-    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
+    # Resize mask to match image dimensions (use INTER_NEAREST for binary mask)
+    mask = resize_max_size(mask, size_limit=size_limit, interpolation=cv2.INTER_NEAREST)
     
-    # Debug: log mask statistics
+    # Debug: log mask statistics BEFORE normalization
     mask_nonzero = int((mask > 128).sum())
     mask_total = mask.shape[0] * mask.shape[1]
     print(f"Mask shape: {mask.shape}, pixels to remove (>128): {mask_nonzero}/{mask_total} ({100*mask_nonzero/mask_total:.1f}%)")
     
+    if mask_nonzero < 10:
+        print("ERROR: Mask is empty or almost empty! Cannot proceed with inpainting.")
+        print("DEBUG INFO:")
+        print(f"  - Alpha channel mean: {alpha_mean}")
+        print(f"  - RGB channels min/max: {rgb_channels.min()}/{rgb_channels.max()}")
+        print(f"  - Alpha channel min/max: {alpha_channel.min()}/{alpha_channel.max()}")
+        # Return original image if mask is invalid
+        return cv2.cvtColor(cv2.resize(cv2.cvtColor(np.array(image*255, dtype=np.uint8), cv2.COLOR_RGB2BGR), 
+                                       (original_shape[1], original_shape[0]), 
+                                       interpolation=cv2.INTER_LANCZOS4), cv2.COLOR_BGR2RGB)
+    
     # Normalize: values > 0 become 1.0, 0 stays 0 (LaMa expects this)
     mask = norm_img(mask)
     
@@ -504,8 +521,17 @@ def process_inpaint(image, mask, invert_mask=True):
     print(f"After normalization: {mask_final_pixels} pixels marked for removal (value > 0.5)")
     
     if mask_final_pixels < 10:
-        print("WARNING: Very few pixels marked for removal! Check mask format.")
+        print("ERROR: After normalization, mask is still empty! Returning original image.")
+        return cv2.cvtColor(cv2.resize(cv2.cvtColor(np.array(image*255, dtype=np.uint8), cv2.COLOR_RGB2BGR), 
+                                       (original_shape[1], original_shape[0]), 
+                                       interpolation=cv2.INTER_LANCZOS4), cv2.COLOR_BGR2RGB)
 
     res_np_img = run(image, mask)
 
+    # Resize back to original dimensions if needed (for quality preservation)
+    if res_np_img.shape[:2] != original_shape[:2]:
+        res_np_img = cv2.resize(res_np_img, (original_shape[1], original_shape[0]), 
+                                interpolation=cv2.INTER_LANCZOS4)
+        print(f"Resized output back to original: {res_np_img.shape}")
+    
     return cv2.cvtColor(res_np_img, cv2.COLOR_BGR2RGB)