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import torch |
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from transformers import pipeline |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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from random import choice |
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import io |
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detector50 = pipeline(model="facebook/detr-resnet-50") |
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detector101 = pipeline(model="facebook/detr-resnet-101") |
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import gradio as gr |
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COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff", |
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"#7f7fff", "#7fbfff", "#7fffff", "#7fffbf", |
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"#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"] |
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fdic = { |
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"family" : "Impact", |
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"style" : "italic", |
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"size" : 15, |
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"color" : "yellow", |
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"weight" : "bold" |
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} |
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def get_figure(in_pil_img, in_results): |
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plt.figure(figsize=(16, 10)) |
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plt.imshow(in_pil_img) |
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ax = plt.gca() |
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for prediction in in_results: |
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selected_color = choice(COLORS) |
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x, y = prediction['box']['xmin'], prediction['box']['ymin'], |
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w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin'] |
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ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3)) |
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ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic) |
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plt.axis("off") |
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return plt.gcf() |
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def infer(model, in_pil_img): |
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results = None |
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if model == "detr-resnet-101": |
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results = detector101(in_pil_img) |
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else: |
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results = detector50(in_pil_img) |
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figure = get_figure(in_pil_img, results) |
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buf = io.BytesIO() |
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figure.savefig(buf, bbox_inches='tight') |
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buf.seek(0) |
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output_pil_img = Image.open(buf) |
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return output_pil_img |
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with gr.Blocks(title="DETR Object Detection - ClassCat", |
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css=".gradio-container {background:lightyellow;}" |
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) as demo: |
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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>""") |
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gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""") |
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model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Select a model") |
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gr.HTML("""<br/>""") |
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gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""") |
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gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""") |
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with gr.Row(): |
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input_image = gr.Image(label="Input image", type="pil") |
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output_image = gr.Image(label="Output image with predicted instances", type="pil") |
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gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image) |
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gr.HTML("""<br/>""") |
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gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 15-20 seconds (on cpu)</h4>""") |
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send_btn = gr.Button("Infer") |
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send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image]) |
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gr.HTML("""<br/>""") |
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gr.HTML("""<h4 style="color:navy;">Reference</h4>""") |
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gr.HTML("""<ul>""") |
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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>""") |
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gr.HTML("""</ul>""") |
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demo.launch(debug=True) |
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