app.py

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/anime.jpg','samples/cartoon.jpg','samples/CATTY.jpg','samples/food.png','samples/ghibli.jpeg', 'samples/news.png', 'samples/people.png','samples/road.png','samples/Paris.jpg','samples/Blobfish.jpg', 'samples/Pokemon.jpg', 'samples/Egg Tart.jpg', 'samples/Rose.jpg', 'samples/CEO.jpg',
 'samples/juice.jpeg','samples/mountain.jpeg'], 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 ###