| import torch | |
| from transformers import pipeline | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from random import choice | |
| import io | |
| detector50 = pipeline(model="TuningAI/DETR-BASE_Marine") | |
| import gradio as gr | |
| COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff", | |
| "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf", | |
| "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"] | |
| fdic = { | |
| "family" : "Impact", | |
| "style" : "italic", | |
| "size" : 10, | |
| "color" : "red", | |
| "weight" : "bold" | |
| } | |
| 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 = choice(COLORS) | |
| 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"{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") | |
| send_btn = gr.Button("Infer") | |
| send_btn.click(fn=infer, inputs=input_image, outputs=[output_image]) | |
| demo.launch(debug=True) |