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| import os | |
| import gradio as gr | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from typing import Tuple, List | |
| from constants import MODEL_PATH, DATABASE_DIR, DATABASE_PATH | |
| from detector import SignatureDetector, download_model | |
| def create_gradio_interface(): | |
| # Download model if it doesn't exist | |
| if not os.path.exists(MODEL_PATH): | |
| download_model() | |
| # Initialize the detector | |
| detector = SignatureDetector(MODEL_PATH) | |
| css = """ | |
| .custom-button { | |
| background-color: #b0ffb8 !important; | |
| color: black !important; | |
| } | |
| .custom-button:hover { | |
| background-color: #b0ffb8b3 !important; | |
| } | |
| .container { | |
| max-width: 1200px !important; | |
| margin: auto !important; | |
| } | |
| .main-container { | |
| gap: 20px !important; | |
| } | |
| .metrics-container { | |
| padding: 1.5rem !important; | |
| border-radius: 0.75rem !important; | |
| background-color: #1f2937 !important; | |
| margin: 1rem 0 !important; | |
| box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1) !important; | |
| } | |
| .metrics-title { | |
| font-size: 1.25rem !important; | |
| font-weight: 600 !important; | |
| color: #1f2937 !important; | |
| margin-bottom: 1rem !important; | |
| } | |
| .metrics-row { | |
| display: flex !important; | |
| gap: 1rem !important; | |
| margin-top: 0.5rem !important; | |
| } | |
| """ | |
| def process_image(image: Image.Image, conf_thres: float, iou_thres: float) -> Tuple[Image.Image, str, plt.Figure, plt.Figure, str, str]: | |
| if image is None: | |
| return None, None, None, None, None, None | |
| output_image, metrics = detector.detect(image, conf_thres, iou_thres) | |
| # Create plots data | |
| hist_data = pd.DataFrame({"Time (ms)": metrics["times"]}) | |
| indices = range( | |
| metrics["start_index"], metrics["start_index"] + len(metrics["times"]) | |
| ) | |
| line_data = pd.DataFrame( | |
| { | |
| "Inference": indices, | |
| "Time (ms)": metrics["times"], | |
| "Mean": [metrics["avg_time"]] * len(metrics["times"]), | |
| } | |
| ) | |
| hist_fig, line_fig = detector.create_plots(hist_data, line_data) | |
| return ( | |
| output_image, | |
| gr.update( | |
| value=f"{metrics['total_inferences']}", | |
| container=True, | |
| ), | |
| hist_fig, | |
| line_fig, | |
| f"{metrics['avg_time']:.2f}", | |
| f"{metrics['times'][-1]:.2f}", | |
| ) | |
| def process_folder(files_paths: List[str], conf_thres: float, iou_thres: float): | |
| if not files_paths: | |
| return None, None, None, None, None, None | |
| valid_extensions = [".jpg", ".jpeg", ".png"] | |
| image_files = [ | |
| f for f in files_paths if os.path.splitext(f.lower())[1] in valid_extensions | |
| ] | |
| if not image_files: | |
| return None, None, None, None, None, None | |
| for img_file in image_files: | |
| image = Image.open(img_file) | |
| yield process_image(image, conf_thres, iou_thres) | |
| with gr.Blocks( | |
| theme=gr.themes.Soft( | |
| primary_hue="indigo", secondary_hue="gray", neutral_hue="gray" | |
| ), | |
| css=css, | |
| ) as iface: | |
| gr.HTML( | |
| """ | |
| <h1>Tech4Humans - Signature Detector</h1> | |
| <div style="display: flex; align-items: center; gap: 10px;"> | |
| <a href="https://huggingface.co/tech4humans/yolov8s-signature-detector"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-md-dark.svg" alt="Model on HF"> | |
| </a> | |
| <a href="https://huggingface.co/datasets/tech4humans/signature-detection"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg" alt="Dataset on HF"> | |
| </a> | |
| <a href="https://github.com/tech4ai/t4ai-signature-detect-server"> | |
| <img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"> | |
| </a> | |
| </div> | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| This system uses the [**YOLOv8s**](https://huggingface.co/tech4humans/yolov8s-signature-detector) model, specially fine-tuned for detecting handwritten signatures in document images. | |
| With this detector, it is possible to identify signatures in digital documents with high accuracy in real time, making it ideal for applications involving validation, organization, and document processing. | |
| --- | |
| """ | |
| ) | |
| with gr.Row(equal_height=True, elem_classes="main-container"): | |
| # Left column for controls and information | |
| with gr.Column(scale=1): | |
| with gr.Tab("Single Image"): | |
| input_image = gr.Image(label="Upload your document", type="pil") | |
| with gr.Row(): | |
| clear_single_btn = gr.ClearButton([input_image], value="Clear") | |
| detect_single_btn = gr.Button( | |
| "Detect", elem_classes="custom-button" | |
| ) | |
| with gr.Tab("Image Folder"): | |
| input_folder = gr.File( | |
| label="Upload a folder with images", | |
| file_count="directory", | |
| type="filepath", | |
| ) | |
| with gr.Row(): | |
| clear_folder_btn = gr.ClearButton([input_folder], value="Clear") | |
| detect_folder_btn = gr.Button( | |
| "Detect", elem_classes="custom-button" | |
| ) | |
| with gr.Group(): | |
| confidence_threshold = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.25, | |
| step=0.05, | |
| label="Confidence Threshold", | |
| info="Adjust the minimum confidence score required for detection.", | |
| ) | |
| iou_threshold = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.5, | |
| step=0.05, | |
| label="IoU Threshold", | |
| info="Adjust the Intersection over Union threshold for Non-Maximum Suppression (NMS).", | |
| ) | |
| with gr.Column(scale=1): | |
| output_image = gr.Image(label="Detection Results") | |
| with gr.Accordion("Examples", open=True): | |
| gr.Examples( | |
| label="Image Examples", | |
| examples=[ | |
| ["assets/images/example_{i}.jpg".format(i=i)] | |
| for i in range( | |
| 0, len(os.listdir(os.path.join("assets", "images"))) | |
| ) | |
| ], | |
| inputs=input_image, | |
| outputs=output_image, | |
| fn=detector.detect_example, | |
| cache_examples=True, | |
| cache_mode="lazy", | |
| ) | |
| with gr.Row(elem_classes="metrics-container"): | |
| with gr.Column(scale=1): | |
| total_inferences = gr.Textbox( | |
| label="Total Inferences", show_copy_button=True, container=True | |
| ) | |
| hist_plot = gr.Plot(label="Time Distribution", container=True) | |
| with gr.Column(scale=1): | |
| line_plot = gr.Plot(label="Time History", container=True) | |
| with gr.Row(elem_classes="metrics-row"): | |
| avg_inference_time = gr.Textbox( | |
| label="Average Inference Time (ms)", | |
| show_copy_button=True, | |
| container=True, | |
| ) | |
| last_inference_time = gr.Textbox( | |
| label="Last Inference Time (ms)", | |
| show_copy_button=True, | |
| container=True, | |
| ) | |
| with gr.Row(elem_classes="container"): | |
| gr.Markdown( | |
| """ | |
| --- | |
| ## About the Project | |
| This project uses the YOLOv8s model fine-tuned for detecting handwritten signatures in document images. It was trained with data from the [Tobacco800](https://paperswithcode.com/dataset/tobacco-800) and [signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up) datasets, undergoing preprocessing and data augmentation processes. | |
| ### Key Metrics: | |
| - **Precision:** 94.74% | |
| - **Recall:** 89.72% | |
| - **mAP@50:** 94.50% | |
| - **mAP@50-95:** 67.35% | |
| - **Inference Time (CPU):** 171.56 ms | |
| Complete details on the training process, hyperparameter tuning, model evaluation, dataset creation, and inference server can be found in the links below. | |
| --- | |
| **Developed by [Tech4Humans](https://www.tech4h.com.br/)** | **Model:** [YOLOv8s](https://huggingface.co/tech4humans/yolov8s-signature-detector) | **Dataset:** [Tobacco800 + signatures-xc8up](https://huggingface.co/datasets/tech4humans/signature-detection) | |
| """ | |
| ) | |
| clear_single_btn.add([output_image]) | |
| clear_folder_btn.add([output_image]) | |
| detect_single_btn.click( | |
| fn=process_image, | |
| inputs=[input_image, confidence_threshold, iou_threshold], | |
| outputs=[ | |
| output_image, | |
| total_inferences, | |
| hist_plot, | |
| line_plot, | |
| avg_inference_time, | |
| last_inference_time, | |
| ], | |
| ) | |
| detect_folder_btn.click( | |
| fn=process_folder, | |
| inputs=[input_folder, confidence_threshold, iou_threshold], | |
| outputs=[ | |
| output_image, | |
| total_inferences, | |
| hist_plot, | |
| line_plot, | |
| avg_inference_time, | |
| last_inference_time, | |
| ], | |
| ) | |
| # Carregar métricas iniciais ao carregar a página | |
| iface.load( | |
| fn=detector.load_initial_metrics, | |
| inputs=None, | |
| outputs=[ | |
| output_image, | |
| total_inferences, | |
| hist_plot, | |
| line_plot, | |
| avg_inference_time, | |
| last_inference_time, | |
| ], | |
| ) | |
| return iface | |
| if __name__ == "__main__": | |
| if not os.path.exists(DATABASE_PATH): | |
| os.makedirs(DATABASE_DIR, exist_ok=True) | |
| iface = create_gradio_interface() | |
| iface.launch() | |