| | import supervision as sv |
| | import gradio as gr |
| | from ultralytics import YOLO |
| | import sahi |
| | import numpy as np |
| |
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| | |
| | sahi.utils.file.download_from_url( |
| | "https://www.erbanotizie.com/wp-content/uploads/2014/01/Casello.jpg", |
| | "ocr1.jpg", |
| | ) |
| | sahi.utils.file.download_from_url( |
| | "https://media-cdn.tripadvisor.com/media/photo-s/15/1d/03/18/receipt.jpg", |
| | "ocr2.jpg", |
| | ) |
| | sahi.utils.file.download_from_url( |
| | "https://upload.forumfree.net/i/ff11450850/b5ef33b7-01da-4055-9ece-089b2a35a193.jpg", |
| | "ocr3.jpg", |
| | ) |
| |
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| |
|
| | annotatorbbox = sv.BoxAnnotator() |
| | annotatormask=sv.MaskAnnotator() |
| | model = YOLO("best_Receipt.pt") |
| |
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| |
|
| | def yolov8_inference( |
| | image: gr.inputs.Image = None, |
| | conf_threshold: gr.inputs.Slider = 0.5, |
| | iou_threshold: gr.inputs.Slider = 0.45, |
| | ): |
| |
|
| | image=image[:, :, ::-1].astype(np.uint8) |
| | model = YOLO("https://huggingface.co/spaces/devisionx/first-demo/blob/main/best_Receipt.pt") |
| | results = model(image,imgsz=320,conf=conf_threshold,iou=iou_threshold)[0] |
| | image=image[:, :, ::-1].astype(np.uint8) |
| | detections = sv.Detections.from_yolov8(results) |
| | annotated_image = annotatormask.annotate(scene=image, detections=detections) |
| | annotated_image = annotatorbbox.annotate(scene=annotated_image , detections=detections) |
| |
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| | |
| |
|
| | return annotated_image |
| | ''' |
| | image_input = gr.inputs.Image() # Adjust the shape according to your requirements |
| | |
| | inputs = [ |
| | gr.inputs.Image(label="Input Image"), |
| | gr.Slider( |
| | minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold" |
| | ), |
| | gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"), |
| | ] |
| | |
| | outputs = gr.Image(type="filepath", label="Output Image") |
| | title = "OCR Demo" |
| | ''' |
| | examples = [ |
| | ["ocr1.jpg", 0.6, 0.45], |
| | ["ocr2.jpg", 0.25, 0.45], |
| | ["ocr3.jpg", 0.25, 0.45], |
| | ] |
| | outputs_images = [ |
| | ["1.jpg"], |
| | ["2.jpg"] |
| | ,["3.jpg"] |
| | ] |
| |
|
| | readme_html = """ |
| | <html> |
| | <head> |
| | <style> |
| | .description { |
| | margin: 20px; |
| | padding: 10px; |
| | border: 1px solid #ccc; |
| | } |
| | </style> |
| | </head> |
| | <body> |
| | <div class="description"> |
| | <p><strong>More details:</strong></p> |
| | <p>We present a demo for performing object segmentation using a model trained on OCR-Receipt dataset. The model was trained on 54 training images and validated on 15 images.</p> |
| | <p><strong>Usage:</strong></p> |
| | <p>You can upload receipt images, and the demo will provide you with your segmented image.</p> |
| | <p><strong>Dataset:</strong></p> |
| | <p>This dataset comprises a total of 77 images, which are divided into three distinct sets for various purposes:</p> |
| | <ul> |
| | <li><strong>Training Set:</strong> It includes 54 images and is intended for training the model.</li> |
| | <li><strong>Validation Set:</strong> There are 15 images in the validation set, which is used for optimizing model parameters during development.</li> |
| | <li><strong>Test Set:</strong> This set consists of 8 images and serves as a separate evaluation dataset to assess the performance of trained models.</li> |
| | </ul> |
| | <p><strong>License:</strong> This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p> |
| | <p>To access and download this dataset, please follow this link: <a href=" https://universe.roboflow.com/study-0w9zw/ocr-receipt" target="_blank">Dataset Download</a></p> |
| | |
| | |
| | </body> |
| | </html> |
| | """ |
| | with gr.Blocks() as demo: |
| | gr.Markdown( |
| | """ |
| | <div style="text-align: center;"> |
| | <h1>OCR Demo</h1> |
| | Powered by <a href="https://Tuba.ai">Tuba</a> |
| | </div> |
| | """ |
| | ) |
| |
|
| |
|
| | |
| | with gr.Row(): |
| | image_input = gr.inputs.Image() |
| | |
| | |
| | outputs = gr.Image(type="filepath", label="Output Image") |
| | |
| | |
| | with gr.Row(): |
| | conf_slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold" ) |
| | with gr.Row(): |
| | IOU_Slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold") |
| | |
| | |
| | |
| |
|
| | button = gr.Button("Run") |
| | |
| | |
| | |
| | button.click(fn=yolov8_inference, inputs=[image_input, conf_slider,IOU_Slider], outputs=outputs, api_name="yolov8_inference") |
| | |
| | gr.Examples( |
| | fn=yolov8_inference, |
| | examples=examples, |
| | inputs=[image_input, conf_slider,IOU_Slider], |
| | outputs=[outputs] |
| | ) |
| | |
| | |
| | gr.Markdown(readme_html) |
| | |
| | demo.launch(share=False) |
