Update app.py

app.py

@@ -4,8 +4,9 @@ import cv2
 import numpy as np
 from torchvision import transforms
 from PIL import Image
+from scipy.interpolate import Rbf
 
-# Load MiDaS depth estimation model from torch.hub
+# Load MiDaS depth estimation model
 midas_model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
 midas_model.eval()
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -13,74 +14,59 @@ midas_model.to(device)
 midas_transform = torch.hub.load("intel-isl/MiDaS", "transforms").default_transform
 
 def estimate_depth(image):
-    """Estimate depth map using MiDaS v3."""
-    image = image.convert("RGB")  # Ensure it's in RGB format
-
-    # Convert PIL image to a NumPy array and normalize it
-    img_np = np.array(image, dtype=np.float32) / 255.0  # Normalize to [0, 1]
-
-    # Convert NumPy array to a Torch tensor
-    img_tensor = torch.tensor(img_np).permute(2, 0, 1).unsqueeze(0).to(device)
-
-    # Ensure tensor shape is [1, 3, H, W]
-    if img_tensor.dim() == 5:  # If an extra batch dimension is present
-        img_tensor = img_tensor.squeeze(1)
-
+    image = image.convert("RGB")
+    img_tensor = midas_transform(image).to(device)
     with torch.no_grad():
         depth = midas_model(img_tensor).squeeze().cpu().numpy()
-
     depth = cv2.resize(depth, (image.size[0], image.size[1]))
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)
 
-def apply_tps_warping(design, depth):
-    """Apply Thin Plate Spline (TPS) warping based on depth."""
+def compute_optical_flow(depth):
+    depth_blurred = cv2.GaussianBlur(depth, (5, 5), 0)
+    flow = cv2.calcOpticalFlowFarneback(depth_blurred, depth, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+    displacement_x = cv2.normalize(flow[..., 0], None, -5, 5, cv2.NORM_MINMAX)
+    displacement_y = cv2.normalize(flow[..., 1], None, -5, 5, cv2.NORM_MINMAX)
+    return displacement_x, displacement_y
+
+def apply_tps_interpolation(design, depth):
     h, w = depth.shape
     grid_x, grid_y = np.meshgrid(np.arange(w), np.arange(h))
-    displacement_x = cv2.Sobel(depth, cv2.CV_32F, 1, 0, ksize=5)
-    displacement_y = cv2.Sobel(depth, cv2.CV_32F, 0, 1, ksize=5)
-    displacement_x = cv2.normalize(displacement_x, None, -5, 5, cv2.NORM_MINMAX)
-    displacement_y = cv2.normalize(displacement_y, None, -5, 5, cv2.NORM_MINMAX)
-    
-    map_x = np.clip(grid_x + displacement_x, 0, w - 1).astype(np.float32)
-    map_y = np.clip(grid_y + displacement_y, 0, h - 1).astype(np.float32)
-    
-    warped_design = cv2.remap(design, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
-    return warped_design
+    edges = cv2.Canny(depth.astype(np.uint8), 50, 150)
+    points = np.column_stack(np.where(edges > 0))
+    tps_x = Rbf(points[:, 1], points[:, 0], grid_x[points[:, 0], points[:, 1]], function="thin_plate")
+    tps_y = Rbf(points[:, 1], points[:, 0], grid_y[points[:, 0], points[:, 1]], function="thin_plate")
+    map_x = tps_x(grid_x, grid_y).astype(np.float32)
+    map_y = tps_y(grid_x, grid_y).astype(np.float32)
+    return cv2.remap(design, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
+
+def compute_adaptive_alpha(depth):
+    grad_x = cv2.Sobel(depth, cv2.CV_32F, 1, 0, ksize=3)
+    grad_y = cv2.Sobel(depth, cv2.CV_32F, 0, 1, ksize=3)
+    grad_magnitude = np.sqrt(grad_x**2 + grad_y**2)
+    alpha = cv2.normalize(grad_magnitude, None, 0, 1, cv2.NORM_MINMAX)
+    return alpha
 
 def blend_design(cloth_img, design_img):
-    """Blend design onto clothing naturally with fold adaptation using TPS warping."""
     cloth_img = cloth_img.convert("RGB")
     design_img = design_img.convert("RGBA")
     cloth_np = np.array(cloth_img)
     design_np = np.array(design_img)
-    
-    # Resize design
     h, w, _ = cloth_np.shape
     dh, dw, _ = design_np.shape
     scale_factor = min(w / dw, h / dh) * 0.4  
     new_w, new_h = int(dw * scale_factor), int(dh * scale_factor)
     design_np = cv2.resize(design_np, (new_w, new_h), interpolation=cv2.INTER_AREA)
-    
-    # Extract alpha channel
     alpha_channel = design_np[:, :, 3] / 255.0
     design_np = design_np[:, :, :3]
-    
-    # Create placement area
     x_offset = (w - new_w) // 2
     y_offset = int(h * 0.35)
     design_canvas = np.zeros_like(cloth_np)
     design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np
-    
-    # Estimate depth and apply TPS warping
     depth_map = estimate_depth(cloth_img)
-    warped_design = apply_tps_warping(design_canvas, depth_map)
-    
-    # Ensure alpha is applied correctly
-    mask = np.zeros_like(cloth_np, dtype=np.float32)
-    mask[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = np.expand_dims(alpha_channel, axis=-1)
-    cloth_np = (cloth_np * (1 - mask) + warped_design * mask).astype(np.uint8)
-    
+    warped_design = apply_tps_interpolation(design_canvas, depth_map)
+    adaptive_alpha = compute_adaptive_alpha(depth_map)
+    cloth_np = (cloth_np * (1 - adaptive_alpha) + warped_design * adaptive_alpha).astype(np.uint8)
     return Image.fromarray(cloth_np)
 
 def main(cloth, design):