Update app.py

app.py

@@ -14,13 +14,12 @@ depth_model.eval()
 
 def estimate_depth(image):
     """Estimate depth map from image."""
-    image = image.convert("RGB")
-    image = image.resize((384, 384))  # Resize for model input
+    image = image.convert("RGB").resize((384, 384))  # Resize for model input
     inputs = feature_extractor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = depth_model(**inputs)
         depth = outputs.predicted_depth.squeeze().cpu().numpy()
-    depth = cv2.resize(depth, (image.width, image.height))  # Resize back to original
+    depth = cv2.resize(depth, (image.width, image.height))  # Resize back
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)
 
@@ -30,21 +29,21 @@ def warp_design(cloth_img, design_img):
     design_img = design_img.convert("RGB")
     cloth_np = np.array(cloth_img)
     design_np = np.array(design_img)
-    
-    # Ensure design fits within the center of the clothing
     h, w, _ = cloth_np.shape
     dh, dw, _ = design_np.shape
-    scale_factor = min(w / dw, h / dh) * 0.8  # Scale to 80% of the available space
+    
+    # Resize design to fit within 70% of the clothing area
+    scale_factor = min(w / dw, h / dh) * 0.7
     new_w, new_h = int(dw * scale_factor), int(dh * scale_factor)
     design_np = cv2.resize(design_np, (new_w, new_h))
     
-    # Create a blank canvas and paste the resized design at the center
-    design_canvas = np.zeros_like(cloth_np)
+    # Create blank canvas with transparent background
+    design_canvas = np.zeros_like(cloth_np, dtype=np.uint8)
     x_offset = (w - new_w) // 2
     y_offset = (h - new_h) // 2
     design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np
     
-    # Estimate depth for fold detection
+    # Estimate depth map
     depth_map = estimate_depth(cloth_img)
     depth_map = cv2.resize(depth_map, (w, h))
     
@@ -52,18 +51,19 @@ def warp_design(cloth_img, design_img):
     displacement_x = cv2.Sobel(depth_map, cv2.CV_32F, 1, 0, ksize=5)
     displacement_y = cv2.Sobel(depth_map, cv2.CV_32F, 0, 1, ksize=5)
     
-    # Normalize displacement values
-    displacement_x = cv2.normalize(displacement_x, None, -5, 5, cv2.NORM_MINMAX)
-    displacement_y = cv2.normalize(displacement_y, None, -5, 5, cv2.NORM_MINMAX)
+    displacement_x = cv2.normalize(displacement_x, None, -3, 3, cv2.NORM_MINMAX)
+    displacement_y = cv2.normalize(displacement_y, None, -3, 3, cv2.NORM_MINMAX)
     
-    # Warp design using displacement map
     map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
     map_x = np.clip(np.float32(map_x + displacement_x), 0, w - 1)
     map_y = np.clip(np.float32(map_y + displacement_y), 0, h - 1)
     warped_design = cv2.remap(design_canvas, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
     
     # Blend images without excessive transparency
-    blended = cv2.addWeighted(cloth_np, 0.7, warped_design, 0.7, 0)
+    mask = np.any(warped_design > 0, axis=-1).astype(np.uint8) * 255
+    blended = cloth_np.copy()
+    np.copyto(blended, warped_design, where=(mask[..., None] > 0))
+    
     return Image.fromarray(blended)
 
 def main(cloth, design):
@@ -78,4 +78,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch(share=True)
+    iface.launch(share=True)