| import gradio as gr |
| import torch |
| import cv2 |
| import numpy as np |
| from torchvision import transforms |
| from PIL import Image |
| from scipy.interpolate import Rbf |
|
|
| |
| midas_model = torch.hub.load("intel-isl/MiDaS", "DPT_Hybrid") |
| midas_model.eval() |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| midas_model.to(device) |
| midas_transform = torch.hub.load("intel-isl/MiDaS", "transforms").default_transform |
|
|
| def estimate_depth(image): |
| image = image.convert("RGB") |
| image_np = np.array(image) / 255.0 |
| image_tensor = torch.tensor(image_np, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(device) |
| |
| with torch.no_grad(): |
| depth = midas_model(image_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 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)) |
| 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): |
| cloth_img = cloth_img.convert("RGB") |
| design_img = design_img.convert("RGBA") |
| cloth_np = np.array(cloth_img) |
| design_np = np.array(design_img) |
| 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) |
| alpha_channel = design_np[:, :, 3] / 255.0 |
| design_np = design_np[:, :, :3] |
| 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 |
| depth_map = estimate_depth(cloth_img) |
| 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): |
| global midas_model |
| if midas_model is None: |
| midas_model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small").to(device).eval() |
| return blend_design(cloth, design) |
|
|
| iface = gr.Interface( |
| fn=main, |
| inputs=[gr.Image(type="pil"), gr.Image(type="pil")], |
| outputs=gr.Image(type="pil"), |
| title="AI Cloth Design Warping", |
| description="Upload a clothing image and a design to blend it naturally, ensuring it stays centered and follows fabric folds." |
| ) |
|
|
| if __name__ == "__main__": |
| iface.launch(share=False) |
