app.py

import io
from random import choice
from PIL import Image
import gradio as gr
from transformers import pipeline
import matplotlib.pyplot as plt

# Initialize the models
detector50 = pipeline(model="facebook/detr-resnet-50")
detector101 = pipeline(model="facebook/detr-resnet-101")

# Define colors and font dictionary for bounding boxes and labels
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):
    # Create a figure to display the image and annotations
    plt.figure(figsize=(16, 10))
    plt.imshow(in_pil_img)
    ax = plt.gca()

    # Add bounding boxes and labels to the image
    for prediction in in_results:
        selected_color = choice(COLORS)
        x, y = prediction['box']['xmin'], prediction['box']['ymin']
        w, h = prediction['box']['xmax'] - x, prediction['box']['ymax'] - y
        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")
    plt.tight_layout()

    # Convert the figure to a PIL Image and return
    buf = io.BytesIO()
    plt.savefig(buf, format='png', bbox_inches='tight')
    buf.seek(0)
    return Image.open(buf)

def infer(model, in_pil_img):
    # Perform inference using the specified model and input image
    results = detector101(in_pil_img) if model == "detr-resnet-101" else detector50(in_pil_img)
    return get_figure(in_pil_img, results)

# Define Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## DETR Object Detection")
    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
    input_image = gr.Image(label="Input image", type="pil")
    output_image = gr.Image(label="Output image")
    send_btn = gr.Button("Infer")
    send_btn.click(fn=infer, inputs=[model, input_image], outputs=output_image)

demo.launch()