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import gradio as gr |
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import torch |
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import cv2 |
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import numpy as np |
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from torchvision import transforms |
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from PIL import Image |
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from scipy.interpolate import Rbf |
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midas_model = torch.hub.load("intel-isl/MiDaS", "DPT_Hybrid") |
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midas_model.eval() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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midas_model.to(device) |
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midas_transform = torch.hub.load("intel-isl/MiDaS", "transforms").default_transform |
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def estimate_depth(image): |
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image = image.convert("RGB") |
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image_np = np.array(image) / 255.0 |
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image_tensor = torch.tensor(image_np, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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depth = midas_model(image_tensor).squeeze().cpu().numpy() |
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depth = cv2.resize(depth, (image.size[0], image.size[1])) |
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255 |
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return depth.astype(np.uint8) |
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def compute_optical_flow(depth): |
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depth_blurred = cv2.GaussianBlur(depth, (5, 5), 0) |
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flow = cv2.calcOpticalFlowFarneback(depth_blurred, depth, None, 0.5, 3, 15, 3, 5, 1.2, 0) |
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displacement_x = cv2.normalize(flow[..., 0], None, -5, 5, cv2.NORM_MINMAX) |
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displacement_y = cv2.normalize(flow[..., 1], None, -5, 5, cv2.NORM_MINMAX) |
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return displacement_x, displacement_y |
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def apply_tps_interpolation(design, depth): |
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h, w = depth.shape |
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grid_x, grid_y = np.meshgrid(np.arange(w), np.arange(h)) |
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edges = cv2.Canny(depth.astype(np.uint8), 50, 150) |
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points = np.column_stack(np.where(edges > 0)) |
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tps_x = Rbf(points[:, 1], points[:, 0], grid_x[points[:, 0], points[:, 1]], function="thin_plate") |
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tps_y = Rbf(points[:, 1], points[:, 0], grid_y[points[:, 0], points[:, 1]], function="thin_plate") |
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map_x = tps_x(grid_x, grid_y).astype(np.float32) |
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map_y = tps_y(grid_x, grid_y).astype(np.float32) |
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return cv2.remap(design, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT) |
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def compute_adaptive_alpha(depth): |
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grad_x = cv2.Sobel(depth, cv2.CV_32F, 1, 0, ksize=3) |
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grad_y = cv2.Sobel(depth, cv2.CV_32F, 0, 1, ksize=3) |
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grad_magnitude = np.sqrt(grad_x**2 + grad_y**2) |
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alpha = cv2.normalize(grad_magnitude, None, 0, 1, cv2.NORM_MINMAX) |
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return alpha |
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def blend_design(cloth_img, design_img): |
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cloth_img = cloth_img.convert("RGB") |
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design_img = design_img.convert("RGBA") |
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cloth_np = np.array(cloth_img) |
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design_np = np.array(design_img) |
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h, w, _ = cloth_np.shape |
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dh, dw, _ = design_np.shape |
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scale_factor = min(w / dw, h / dh) * 0.4 |
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new_w, new_h = int(dw * scale_factor), int(dh * scale_factor) |
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design_np = cv2.resize(design_np, (new_w, new_h), interpolation=cv2.INTER_AREA) |
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alpha_channel = design_np[:, :, 3] / 255.0 |
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design_np = design_np[:, :, :3] |
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x_offset = (w - new_w) // 2 |
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y_offset = int(h * 0.35) |
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design_canvas = np.zeros_like(cloth_np) |
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design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np |
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depth_map = estimate_depth(cloth_img) |
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warped_design = apply_tps_interpolation(design_canvas, depth_map) |
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adaptive_alpha = compute_adaptive_alpha(depth_map) |
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cloth_np = (cloth_np * (1 - adaptive_alpha) + warped_design * adaptive_alpha).astype(np.uint8) |
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return Image.fromarray(cloth_np) |
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def main(cloth, design): |
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global midas_model |
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if midas_model is None: |
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midas_model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small").to(device).eval() |
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return blend_design(cloth, design) |
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iface = gr.Interface( |
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fn=main, |
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inputs=[gr.Image(type="pil"), gr.Image(type="pil")], |
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outputs=gr.Image(type="pil"), |
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title="AI Cloth Design Warping", |
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description="Upload a clothing image and a design to blend it naturally, ensuring it stays centered and follows fabric folds." |
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) |
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if __name__ == "__main__": |
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iface.launch(share=False) |
