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import torch |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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REPO_ID = "thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch" |
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FILENAME = "best.pt" |
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yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) |
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model = torch.hub.load('Owaiskhan9654/yolov7-1:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True) |
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def object_detection( |
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image: gr.inputs.Image = None, |
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model_path: gr.inputs.Dropdown = None, |
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image_size: gr.inputs.Slider = 640, |
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conf_threshold: gr.inputs.Slider = 0.25, |
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iou_threshold: gr.inputs.Slider = 0.45,): |
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results = model(image) |
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results.render() |
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count_dict = results.pandas().xyxy[0]['name'].value_counts().to_dict() |
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if len(count_dict)>0: |
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return Image.fromarray(results.imgs[0]),str(count_dict) |
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else: |
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return Image.fromarray(results.imgs[0]),'No object Found. Add more Custom classes in the training set' |
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title = "Yolov7 Custom" |
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inputs = [ |
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gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Upload Image", optional=False), |
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gr.inputs.Dropdown(["best.pt",], |
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default="best.pt", label="Model"), |
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gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), |
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), |
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"), |
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] |
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outputs = gr.outputs.Image(type="pil", label="Output Image") |
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outputs_cls = gr.Label(label= "Categories Detected Proportion Statistics" ) |
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Custom_description="<center>Custom Training Performed on Kaggle <a href='https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook' style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> on around 140 general items in Stores" |
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Footer = ( |
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"<br><br><br><br><center>Model Trained by: Owais Ahmad Data Scientist at <b> Thoucentric </b> <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Visit Profile</a> <br></center>" |
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"<center> Model Trained Kaggle Kernel <a href=\"https://www.kaggle.com/code/owaiskhan9654/shelf-object-detection-yolov7-pytorch/notebook\">Link</a> <br></center>" |
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"<center> HuggingFace🤗 Model Deployed Repository <a href=\"https://huggingface.co/thoucentric/Shelf_Objects_Detection_Yolov7_Pytorch\">Link</a> <br></center>" |
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) |
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examples1=[["Images/Image1.jpg"],["Images/Image2.jpg"],["Images/Image3.jpg"],["Images/Image4.jpg"],["Images/Image5.jpg"],["Images/Image6.jpg"]] |
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Top_Title="<br><br><br><center>Yolov7 🚀 Custom Trained by <a href='https://www.linkedin.com/in/owaiskhan9654/' style='text-decoration: underline' target='_blank'>Owais Ahmad </center></a> on around 140 general items in Stores" |
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css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}" |
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css = ".image-preview {height: auto !important;}" |
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gr.Interface( |
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fn=object_detection, |
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inputs=inputs, |
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outputs=[outputs,outputs_cls], |
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title=Top_Title, |
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description=Custom_description, |
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article=Footer, |
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examples=examples1).launch(debug=True) |
