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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
import torchvision.transforms.functional as F
# 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."""
image = image.convert("RGB").resize((384, 384)) # Resize for model input
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = depth_model(**inputs)
depth = outputs.predicted_depth.squeeze().cpu().numpy()
depth = cv2.resize(depth, (image.width, image.height)) # Resize back
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."""
cloth_img = cloth_img.convert("RGB")
design_img = design_img.convert("RGB")
cloth_np = np.array(cloth_img)
design_np = np.array(design_img)
h, w, _ = cloth_np.shape
# Estimate depth map
depth_map = estimate_depth(cloth_img)
depth_map = cv2.resize(depth_map, (w, h))
# Compute optical flow for warping
flow = cv2.calcOpticalFlowFarneback(depth_map, depth_map, None, 0.5, 3, 15, 3, 5, 1.2, 0)
flow_map = np.column_stack((flow[..., 0] + np.arange(w), flow[..., 1] + np.arange(h)[:, None]))
warped_design = cv2.remap(design_np, flow_map, None, cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
# Blending
blended = cv2.addWeighted(cloth_np, 0.7, warped_design, 0.3, 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, ensuring it stays centered and follows fabric folds."
)
if __name__ == "__main__":
iface.launch(share=True) |