Create app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+from torchvision import transforms
+from PIL import Image
+from transformers import DPTForDepthEstimation, DPTFeatureExtractor
+
+# Load depth estimation model
+model_name = "Intel/dpt-large"
+feature_extractor = DPTFeatureExtractor.from_pretrained(model_name)
+depth_model = DPTForDepthEstimation.from_pretrained(model_name)
+depth_model.eval()
+
+def estimate_depth(image):
+    """Estimate depth map from image."""
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = depth_model(**inputs)
+        depth = outputs.predicted_depth.squeeze().numpy()
+    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
+    return depth.astype(np.uint8)
+
+def warp_design(cloth_img, design_img):
+    """Warp the design onto the clothing while preserving folds."""
+    # Convert images to numpy arrays
+    cloth_np = np.array(cloth_img)
+    design_np = np.array(design_img)
+    
+    # Estimate depth for fold detection
+    depth_map = estimate_depth(cloth_img)
+    
+    # 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)
+    
+    # Normalize displacement values
+    displacement_x = cv2.normalize(displacement_x, None, -10, 10, cv2.NORM_MINMAX)
+    displacement_y = cv2.normalize(displacement_y, None, -10, 10, cv2.NORM_MINMAX)
+    
+    # Warp design using displacement map
+    h, w, _ = cloth_np.shape
+    map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
+    map_x = np.float32(map_x + displacement_x)
+    map_y = np.float32(map_y + displacement_y)
+    warped_design = cv2.remap(design_np, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+    
+    # Blend images
+    blended = cv2.addWeighted(cloth_np, 0.6, warped_design, 0.4, 0)
+    return Image.fromarray(blended)
+
+def main(cloth, design):
+    return warp_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, considering fabric folds."
+)
+
+if __name__ == "__main__":
+    iface.launch()