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jeremyarancio
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fd7a708
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Parent(s):
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Init
Browse files- README.md +3 -3
- app.py +55 -0
- images/244-186-801-1681-1.jpg +0 -0
- images/560-085-462-5494-1.jpg +0 -0
- images/761-650-065-7595-1.jpg +0 -0
- images/761-670-010-2017-1.jpg +0 -0
- images/800-434-901-2658-1.jpg +0 -0
- requirements.txt +4 -0
- weights/best.pt +3 -0
README.md
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---
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title: Crop Detection
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emoji:
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colorFrom:
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colorTo: red
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: agpl-3.0
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short_description:
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Crop Detection
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emoji: πβπ©
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colorFrom: purple
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colorTo: red
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: agpl-3.0
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short_description: 'Detect product on pictures and crop it. '
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from pathlib import Path
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import gradio as gr
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from ultralytics import YOLO
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from PIL import Image
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# Load YOLOv8n model
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MODEL = YOLO('weights/best.pt')
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IMAGES_PATH = Path("images/")
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INF_PARAMETERS = {
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"imgsz": 640, # image size
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"conf": 0.8, # confidence threshold
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"max_det": 1 # maximum number of detections
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}
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EXAMPLES = [path for path in IMAGES_PATH.iterdir()]
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# Function to detect objects and crop the image
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def detect_and_crop(image: Image.Image) -> Image.Image:
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# Perform object detection
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results = MODEL.predict(image,**INF_PARAMETERS)
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result = results[0]
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for box in result.boxes.xyxy.cpu().numpy():
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cropped_image = image.crop(box=box)
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return cropped_image
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# Gradio UI
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title = "Crop-Detection"
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description = """## πβπ© Automatically crop product pictures! πβπ©
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When contributors use the mobile app, they are asked to take pictures of the product, then to crop it.
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To assist users during the process, we create a crop-detection model desin to detect the product edges.
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We fine-tuned Yolov8n on images extracted from the Open Food Facts database.
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Check the [model repo page](https://huggingface.co/openfoodfacts/crop-detection) for more information.
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"""
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# Gradio Interface
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demo = gr.Interface(
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fn=detect_and_crop,
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inputs=gr.Image(type="pil", width=300),
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outputs=gr.Image(type="pil", width=300),
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title=title,
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description=description,
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allow_flagging="never",
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examples=EXAMPLES
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)
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# Launch the Gradio app
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if __name__ == "__main__":
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demo.launch()
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images/244-186-801-1681-1.jpg
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images/560-085-462-5494-1.jpg
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images/761-650-065-7595-1.jpg
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images/761-670-010-2017-1.jpg
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images/800-434-901-2658-1.jpg
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requirements.txt
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ultralytics=8.2.94
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gradio
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spaces
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numpy
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weights/best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:e1b6eeff46da1f6c57d6835b695d074b1cb4d1194cee8f395007a48f99fd077c
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size 6267491
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