| import torch | |
| from transformers import pipeline | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import io | |
| detector50 = pipeline(model="TuningAI/DETR-BASE_Marine") | |
| import gradio as gr | |
| fdic = { | |
| "style" : "italic", | |
| "size" : 13, | |
| "color" : "red", | |
| "weight" : "bold" | |
| } | |
| labels_ = { "LABEL_0":"None" , "LABEL_1": "Boat" ,"LABEL_2": "Car" ,"LABEL_3" : "Dock" , "LABEL_4" : "Jetski" ,"LABEL_5" : "Lift"} | |
| def get_figure(in_pil_img, in_results): | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(in_pil_img) | |
| ax = plt.gca() | |
| for prediction in in_results: | |
| selected_color ="#008000" | |
| x, y = prediction['box']['xmin'], prediction['box']['ymin'], | |
| w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin'] | |
| ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3)) | |
| ax.text(x, y, f"{labels_[prediction['label']]}: {round(prediction['score']*100, 1)}%", fontdict=fdic) | |
| plt.axis("off") | |
| return plt.gcf() | |
| def infer(in_pil_img): | |
| results = detector50(in_pil_img) | |
| figure = get_figure(in_pil_img, results) | |
| buf = io.BytesIO() | |
| figure.savefig(buf, bbox_inches='tight') | |
| buf.seek(0) | |
| output_pil_img = Image.open(buf) | |
| return output_pil_img | |
| with gr.Blocks(title="DETR Object Detection") as demo: | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input image", type="pil") | |
| output_image = gr.Image(label="Output image with predicted instances", type="pil") | |
| gr.Examples(["1.jpg" , "5.jpg"], inputs=input_image) | |
| send_btn = gr.Button("start") | |
| send_btn.click(fn=infer, inputs=input_image, outputs=[output_image]) | |
| demo.launch(debug=True) |