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| # app.py | |
| import gradio as gr | |
| #import spaces | |
| #import torch | |
| from PIL import Image | |
| from transformers import pipeline | |
| import matplotlib.pyplot as plt | |
| import io | |
| import os | |
| list_models = ["facebook/detr-resnet-50", "facebook/detr-resnet-101", "hustvl/yolos-tiny", "hustvl/yolos-small"] | |
| list_models_simple = [os.path.basename(model) for model in list_models] | |
| COLORS = [ | |
| [0.000, 0.447, 0.741], | |
| [0.850, 0.325, 0.098], | |
| [0.929, 0.694, 0.125], | |
| [0.494, 0.184, 0.556], | |
| [0.466, 0.674, 0.188], | |
| [0.301, 0.745, 0.933], | |
| ] | |
| def load_pipeline(model): | |
| model_pipeline = pipeline(model=model) | |
| return model_pipeline | |
| def get_output_figure(pil_img, results, threshold): | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(pil_img) | |
| ax = plt.gca() | |
| colors = COLORS * 100 | |
| for result in results: | |
| score = result["score"] | |
| label = result["label"] | |
| box = list(result["box"].values()) | |
| if score > threshold: | |
| c = COLORS[hash(label) % len(COLORS)] | |
| ax.add_patch( | |
| plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color=c, linewidth=3) | |
| ) | |
| text = f"{label}: {score:0.2f}" | |
| ax.text(box[0], box[1], text, fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) | |
| plt.axis("off") | |
| return plt.gcf() | |
| #@spaces.GPU | |
| def detect(image, model_id, threshold=0.9): | |
| print("model:", list_models[model_id]) | |
| model_pipeline = load_pipeline(list_models[model_id]) | |
| results = model_pipeline(image) | |
| print(results) | |
| output_figure = get_output_figure(image, results, threshold=threshold) | |
| buf = io.BytesIO() | |
| output_figure.savefig(buf, bbox_inches="tight") | |
| buf.seek(0) | |
| output_pil_img = Image.open(buf) | |
| return output_pil_img | |
| def demo(): | |
| with gr.Blocks(theme="base") as demo: | |
| gr.Markdown("# Object detection on COCO dataset") | |
| gr.Markdown( | |
| """ | |
| This application uses transformer-based models to detect objects on images. | |
| This version was trained using the COCO dataset. | |
| You can load an image and see the predictions for the objects detected. | |
| """ | |
| ) | |
| with gr.Row(): | |
| model_id = gr.Radio(list_models, \ | |
| label="Detection models", value=list_models[3], type="index", info="Choose your detection model") | |
| with gr.Row(): | |
| threshold = gr.Slider(0, 1.0, value=0.9, label='Detection threshold', info="Choose your detection threshold") | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input image", type="pil") | |
| output_image = gr.Image(label="Output image", type="pil") | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit") | |
| clear_button = gr.ClearButton() | |
| gr.Examples(['samples/savanna.jpg'], inputs=input_image) | |
| submit_btn.click(fn=detect, inputs=[input_image, model_id, threshold], outputs=[output_image]) | |
| clear_button.click(lambda: [None, None], \ | |
| inputs=None, \ | |
| outputs=[input_image, output_image], \ | |
| queue=False) | |
| demo.queue().launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() |