Duplicate from MobvxtR/MbModel

Co-authored-by: El harmali <MobvxtR@users.noreply.huggingface.co>

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README.md

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+---
+license: openrail
+language:
+- en
+base_model:
+- Ultralytics/YOLOv8
+pipeline_tag: image-segmentation
+tags:
+- clothes
+- instance
+- segmentation
+- computer
+- vision
+---
+---
+license: openrail
+language:
+- en
+
+# Fashion Item Segmenter 🧥
+
+This app segments clothing items from images using YOLOv8 and RemBG.
+
+## How to Use
+1. Upload an image of clothing.
+2. Click "Process Image".
+3. View segmented items in the gallery.
+
+## Example Images
+- `sample1.jpg`
+- `sample2.jpg`
+
+## Requirements
+- Python 3.8+
+- See `requirements.txt` for dependencies.
+
+## Deployment
+Deployed on Hugging Face Spaces: [model](https://huggingface.co/MobvxtR/mbModelo)

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from utils.preprocessing import ImageProcessor
+
+# Initialize processor
+processor = ImageProcessor("models/best.pt")
+
+def process_image(input_image):
+    if input_image is None:
+        raise gr.Error("Please upload an image first!")
+    
+    # Convert Gradio Image to bytes
+    _, img_bytes = cv2.imencode(".png", input_image)
+    
+    # Process image
+    results = processor.process_image(img_bytes.tobytes())
+    
+    # Format outputs
+    return {
+        class_name: (mask * 255).astype(np.uint8)
+        for class_name, mask in results.items()
+    }
+
+# Gradio interface
+with gr.Blocks(title="Fashion Segmenter") as demo:
+    gr.Markdown("# 🧥 Fashion Item Segmenter")
+    
+    with gr.Row():
+        input_image = gr.Image(label="Upload Clothing Image", type="numpy")
+        output_gallery = gr.Gallery(label="Segmented Items", columns=2)
+    
+    with gr.Row():
+        run_btn = gr.Button("Process Image", variant="primary")
+        examples = gr.Examples(
+            examples=["sample1.jpg", "sample2.jpg"],
+            inputs=[input_image],
+            label="Example Images"
+        )
+
+    run_btn.click(
+        fn=process_image,
+        inputs=[input_image],
+        outputs=[output_gallery],
+        show_progress=True
+    )
+
+if __name__ == "__main__":
+    demo.launch()

models/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4fb30fc987fc472b7c8b383a8a4fba7d4aa9b2bd1b2e97e0705005134edcf6b6
+size 276355837

requirements.txt

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+ultralytics==8.0.0
+rembg==2.0.38
+gradio>=3.36.0
+opencv-python-headless==4.7.0.72
+numpy==1.23.5
+huggingface_hub>=0.14.0

utils/model_loader.py

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+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+def load_model():
+    # Download model from Hugging Face Hub (if hosted)
+    model_path = hf_hub_download(
+        repo_id="MobvxtR/mbModel/models"
+        filename="best.pt",
+        revision="main"
+    )
+    return YOLO(model_path)

utils/preprocessing.py

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+import cv2
+import numpy as np
+from rembg import remove
+from ultralytics import YOLO
+
+class ImageProcessor:
+    def __init__(self, model_path):
+        self.model = YOLO(model_path)
+        self.class_names = {0: "upper_clothes", 1: "lower_clothes"}
+
+    def remove_background(self, image_bytes):
+        return remove(image_bytes)
+
+    def process_image(self, image_bytes):
+        # Background removal
+        bg_removed = self.remove_background(image_bytes)
+        
+        # Convert to OpenCV format
+        nparr = np.frombuffer(bg_removed, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        
+        # Segmentation
+        results = self.model.predict(img)
+        return self._process_masks(results, img)
+
+    def _process_masks(self, results, img):
+        segmented = {}
+        if results[0].masks is not None:
+            for mask, class_id in zip(results[0].masks.data, results[0].boxes.cls):
+                class_id = int(class_id.item())
+                mask_np = mask.cpu().numpy()
+                mask_resized = cv2.resize(mask_np, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST)
+                _, binary_mask = cv2.threshold(mask_resized, 0.5, 255, cv2.THRESH_BINARY)
+                binary_mask = binary_mask.astype(np.uint8)
+                segmented[self.class_names[class_id]] = binary_mask
+        return segmented