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import torch |
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import cv2 |
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import numpy as np |
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import gradio as gr |
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from PIL import Image |
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from torchvision import transforms |
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from skimage.restoration import denoise_tv_chambolle |
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from transformers import SamModel, SamProcessor |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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model = SamModel.from_pretrained("facebook/sam-vit-huge").to(DEVICE) |
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processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") |
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def segment_dress(image): |
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"""Segments the dress from an input image using SAM.""" |
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input_points = [[[image.size[0] // 2, image.size[1] // 2]]] |
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inputs = processor(image, input_points=input_points, return_tensors="pt").to(DEVICE) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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masks = processor.image_processor.post_process_masks( |
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outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu() |
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) |
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return masks[0][0].numpy() if masks else None |
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def warp_design(design, mask, warp_scale): |
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"""Warp the design using TPS and scale control.""" |
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h, w = mask.shape[:2] |
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design_resized = cv2.resize(design, (w, h)) |
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scaled_mask = (mask * 255 * (warp_scale / 100)).astype(np.uint8) |
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if len(scaled_mask.shape) == 3: |
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scaled_mask = cv2.cvtColor(scaled_mask, cv2.COLOR_BGR2GRAY) |
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return cv2.bitwise_and(design_resized, design_resized, mask=scaled_mask) |
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def blend_images(base, overlay, mask): |
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"""Blends the design onto the dress using seamless cloning.""" |
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center = tuple(np.array(base.shape[:2]) // 2) |
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return cv2.seamlessClone(overlay, base, mask, center, cv2.NORMAL_CLONE) |
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def apply_design(image_path, design_path, warp_scale): |
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"""Pipeline to segment, warp, and blend design onto dress.""" |
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image = Image.open(image_path).convert("RGB") |
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design = cv2.imread(design_path) |
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mask = segment_dress(image) |
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if mask is None: |
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return "Segmentation Failed!" |
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warped_design = warp_design(design, mask, warp_scale) |
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blended = blend_images(np.array(image), warped_design, mask) |
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return Image.fromarray(blended) |
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def main(image, design, warp_scale): |
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return apply_design(image, design, warp_scale) |
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demo = gr.Interface( |
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fn=main, |
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inputs=[ |
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gr.Image(type="filepath", label="Upload Dress Image"), |
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gr.Image(type="filepath", label="Upload Design Image"), |
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gr.Slider(0, 100, value=50, label="Warp Scale (%)") |
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], |
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outputs=gr.Image(label="Warped Design on Dress"), |
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title="AI-Powered Dress Designer", |
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description="Upload a dress image and a design pattern. The AI will warp and blend the design onto the dress while preserving natural folds!" |
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) |
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demo.launch() |
