app.py

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import gradio as gr
import io

# Load the processor and model
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")

def detect_and_display_image(image):
    # Ensure image is in PIL format
    if isinstance(image, bytes):
        image = Image.open(io.BytesIO(image))
    elif isinstance(image, str):
        image = Image.open(image)

    # Process the image
    inputs = processor(images=image, return_tensors="pt")

    # Perform object detection
    outputs = model(**inputs)

    # Convert outputs to COCO API format
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    # Create a figure and axis for visualization
    fig, ax = plt.subplots(1, figsize=(12, 9))
    ax.imshow(image)

    # Add bounding boxes and labels to the image
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        # Create a Rectangle patch
        rect = patches.Rectangle(
            (box[0], box[1]),
            box[2] - box[0],
            box[3] - box[1],
            linewidth=2,
            edgecolor='red',
            facecolor='none'
        )
        # Add the patch to the Axes
        ax.add_patch(rect)
        # Add label and confidence score
        plt.text(
            box[0], box[1],
            f'{model.config.id2label[label.item()]}: {round(score.item(), 3)}',
            color='red',
            fontsize=12,
            bbox=dict(facecolor='yellow', alpha=0.5)
        )

    plt.axis('off')  # Hide the axes

    # Save the figure to a BytesIO object and return it
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    return Image.open(buf)

# Create a Gradio interface
iface = gr.Interface(
    fn=detect_and_display_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="Object Detection with DETR",
    description="Upload an image to detect objects using the DETR model.",
    live=True
)

# Launch the Gradio app
iface.launch()