app.py

import gradio as gr
import spaces
import torch

from PIL import Image
from transformers import pipeline
import matplotlib.pyplot as plt
import io

model_pipeline = pipeline("object-detection", model="edm-research/detr-resnet-50-dc5-fashionpedia-finetuned")


COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
          [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]


def get_output_figure(pil_img, results, threshold):
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100

    for result in results:
        score = result['score']
        label = result['label']
        box = list(result['box'].values())
        if score > threshold:
            c = COLORS[hash(label) % len(COLORS)]
            ax.add_patch(plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color=c, linewidth=3))
            text = f'{label}: {score:0.2f}'
            ax.text(box[0], box[1], text, fontsize=15,
                    bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')

    return plt.gcf()

@spaces.GPU
def detect(image):
    results = model_pipeline(image)
    print(results)

    output_figure = get_output_figure(image, results, threshold=0.7)

    buf = io.BytesIO()
    output_figure.savefig(buf, bbox_inches='tight')
    buf.seek(0)
    output_pil_img = Image.open(buf)

    return output_pil_img

with gr.Blocks() as demo:
    gr.Markdown("# Object detection with DETR fine tuned on detection-datasets/fashionpedia")
    gr.Markdown(
        """
        This application uses a fine tuned DETR (DEtection TRansformers) to detect objects on images.
        This version was trained using detection-datasets/fashionpedia dataset.
        You can load an image and see the predictions for the objects detected.
        """
    )

    gr.Interface(
        fn=detect,
        inputs=gr.Image(label="Input image", type="pil"),
        outputs=[
            gr.Image(label="Output prediction", type="pil")
        ]
    )

demo.launch(show_error=True)