Update app.py

app.py

@@ -34,28 +34,40 @@ def blend_design(cloth_img, design_img):
     # Resize design to fit within clothing
     h, w, _ = cloth_np.shape
     dh, dw, _ = design_np.shape
-    scale_factor = min(w / dw, h / dh) * 0.6  # Scale to 60% of clothing area
+    scale_factor = min(w / dw, h / dh) * 0.4  # Scale to 40% of clothing area
     new_w, new_h = int(dw * scale_factor), int(dh * scale_factor)
     design_np = cv2.resize(design_np, (new_w, new_h))
     
-    # Convert design to grayscale and darken
+    # Convert design to grayscale and darken for print effect
     design_gray = cv2.cvtColor(design_np, cv2.COLOR_RGB2GRAY)
     design_np = cv2.cvtColor(design_gray, cv2.COLOR_GRAY2RGB)
-    design_np = cv2.convertScaleAbs(design_np, alpha=1.2, beta=-30)  # Increase contrast
+    design_np = cv2.convertScaleAbs(design_np, alpha=1.5, beta=-40)  # Increase contrast
     
     # Create a blank canvas and paste the resized design at the center
     design_canvas = np.zeros_like(cloth_np)
     x_offset = (w - new_w) // 2
-    y_offset = (h - new_h) // 2
+    y_offset = int(h * 0.35)  # Move slightly upward for a natural position
     design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np
     
     # Estimate depth for fold detection
     depth_map = estimate_depth(cloth_img)
     depth_map = cv2.resize(depth_map, (w, h))
     
+    # Generate displacement map based on depth
+    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)
+    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)
+    
     # Use Poisson blending for seamless integration
-    mask = (design_canvas > 0).astype(np.uint8) * 255
-    blended = cv2.seamlessClone(design_canvas, cloth_np, mask, (w//2, h//2), cv2.NORMAL_CLONE)
+    mask = (warped_design > 0).astype(np.uint8) * 255
+    blended = cv2.seamlessClone(warped_design, cloth_np, mask, (w//2, int(h * 0.35 + new_h//2)), cv2.NORMAL_CLONE)
     
     return Image.fromarray(blended)