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| import torch | |
| from transformers import pipeline | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as patches | |
| from random import choice | |
| import io | |
| detector50 = pipeline(model="facebook/detr-resnet-50") | |
| detector101 = pipeline(model="facebook/detr-resnet-101") | |
| import gradio as gr | |
| COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff", | |
| "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf", | |
| "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"] | |
| fdic = { | |
| "family" : "Impact", | |
| "style" : "italic", | |
| "size" : 15, | |
| "color" : "yellow", | |
| "weight" : "bold" | |
| } | |
| def get_figure(in_pil_img, in_results): | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(in_pil_img) | |
| #pyplot.gcf() | |
| 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(model, in_pil_img): | |
| results = None | |
| if model == "detr-resnet-101": | |
| results = detector101(in_pil_img) | |
| else: | |
| 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 - ClassCat", | |
| css=".gradio-container {background:lightyellow;}" | |
| ) as demo: | |
| #sample_index = gr.State([]) | |
| gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>""") | |
| gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""") | |
| model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name") | |
| gr.HTML("""<br/>""") | |
| gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""") | |
| gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""") | |
| 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(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image) | |
| gr.HTML("""<br/>""") | |
| gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""") | |
| send_btn = gr.Button("Infer") | |
| send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image]) | |
| gr.HTML("""<br/>""") | |
| gr.HTML("""<h4 style="color:navy;">Reference</h4>""") | |
| gr.HTML("""<ul>""") | |
| gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""") | |
| gr.HTML("""</ul>""") | |
| #demo.queue() | |
| demo.launch(debug=True) | |
| ### EOF ### | |