app.py

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

import os

# colors for 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]
]

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 fig2img(fig):
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    img = Image.open(buf)
    return img


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())

def detect_objects(model_name,url,image_upload,threshold):
    
    #Extract model and feature extractor
    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
    
    if 'detr' in model_name:
        
        model = DetrForObjectDetection.from_pretrained(model_name)
        
    elif 'yolos' in model_name:
    
        model = YolosForObjectDetection.from_pretrained(model_name)
        
    if validators.url(url):
        image = Image.open(requests.get(url, stream=True).raw)
    elif image_upload:
        image = image_upload
    
    #Make prediction
    processed_outputs = make_prediction(image, feature_extractor, model)
    
    #Visualize prediction
    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
    
    return viz_img   

examples=[['facebook/detr-resnet-50','https://media-cldnry.s-nbcnews.com/image/upload/t_fit-1500w,f_auto,q_auto:best/newscms/2020_14/3290756/200331-wall-street-ew-343p.jpg',,0.7],
['facebook/detr-resnet-50',,'IMG_5205.JPG',0.7]] 

title = 'Object Detection App with DETR and YOLOS'

description = """
Links to HuggingFace Models:

- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)

"""

models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small']

options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)

app = gr.blocks()

with app:
    gr.Markdown(title)
    gr.Markdown(description)
    options
    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
    
    with gr.Tabs():
        with gr.Tabitem('Image URL'):
            with gr.Row():
                url_input = gr.Textbox(lines=1,label='Enter valid image URL here..')
                img_output_from_url = gr.Image(shape=(450,450))
            
            url_but = gr.Button('Detect')
     
        with gr.Tabitem('Image Upload):
            with gr.Row():
                img_input = gr.Image(type='pil')
                img_output_from_upload= gr.Image(shape=(450,450))
                
            img_but = gr.Button('Detect')
        
    
    url_but.click(detect_objects,inputs=[options,url_input,None,slider_input],outputs=img_output_from_url,queue=True)
    img_but.click(detect_objects,inputs=[options,None,img_input,slider_input],outputsimg_output_from_upload,queue=True)
    
    
app.launch(enable_queue=True)