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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."""
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
dh, dw, _ = design_np.shape
# Resize design to fit within 70% of the clothing area
scale_factor = min(w / dw, h / dh) * 0.7
new_w, new_h = int(dw * scale_factor), int(dh * scale_factor)
design_np = cv2.resize(design_np, (new_w, new_h))
# Create blank canvas with transparent background
design_canvas = np.zeros_like(cloth_np, dtype=np.uint8)
x_offset = (w - new_w) // 2
y_offset = (h - new_h) // 2
design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np
# Estimate depth map
depth_map = estimate_depth(cloth_img)
depth_map = cv2.resize(depth_map, (w, h))
# 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)
displacement_x = cv2.normalize(displacement_x, None, -3, 3, cv2.NORM_MINMAX)
displacement_y = cv2.normalize(displacement_y, None, -3, 3, cv2.NORM_MINMAX)
map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
map_x = np.clip(np.float32(map_x + displacement_x), 0, w - 1)
map_y = np.clip(np.float32(map_y + displacement_y), 0, h - 1)
warped_design = cv2.remap(design_canvas, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
# Blend images without excessive transparency
mask = np.any(warped_design > 0, axis=-1).astype(np.uint8) * 255
blended = cloth_np.copy()
np.copyto(blended, warped_design, where=(mask[..., None] > 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)
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