app.py

import io
import gradio as gr
import matplotlib.pyplot as plt
import requests, validators
import torch
import pathlib
from PIL import Image
from transformers import AutoFeatureExtractor, DetrForObjectDetection

import os
# Defining functions for the code

def make_prediction(img, feature_extractor, model):
    inputs = feature_extractor(img, return_tensors="pt")
    outputs = model(**inputs)
    img_size = torch.tensor([tuple(reversed(img.size))])
    processed_outputs = feature_extractor.post_process(outputs, img_size)
    return processed_outputs[0]

def detect_objects(url_input):
    
    #Extract model and feature extractor
    feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
    
    # if image comes from URL
    if validators.url(url_input):
        image = Image.open(requests.get(url_input, stream=True).raw)
    
    #Make prediction
    processed_outputs = make_prediction(image, feature_extractor, model)
    
    #Visualize prediction
    viz_img = visualize_prediction(image, processed_outputs, 0.7, model.config.id2label)
    
    return viz_img   

# visualization
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]
]

# Draw the bounding boxes on image.
def fig2img(fig):
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    img = Image.open(buf)
    return img

# Draw the bounding boxes.
def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
    keep = output_dict["scores"] > threshold
    boxes = output_dict["boxes"][keep].tolist()
    scores = output_dict["scores"][keep].tolist()
    labels = output_dict["labels"][keep].tolist()
    if id2label is not None:
        labels = [id2label[x] for x in labels]

    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100
    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
    plt.axis("off")
    return fig2img(plt.gcf())


# Gradio interface
title = """<h1 id="title">Object Detection App with DETR</h1>"""

css = '''
h1#title {
  text-align: center;
}
'''
demo = gr.Blocks(css=css)

with demo:
    gr.Markdown(title)
    
    with gr.Tabs():
        with gr.TabItem('Image URL'):
            with gr.Row():
                url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
                img_output_from_url = gr.Image(shape=(650,650))

            
            url_but = gr.Button('Detect')
        
    
    url_but.click(detect_objects,inputs=[url_input],outputs=img_output_from_url,queue=True)
  
demo.launch(enable_queue=True)