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, YolosForObjectDetection
from ultralyticsplus import YOLO, render_result

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

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]

    # print("Labels " + str(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_input,image_input,threshold):
    

    if 'yolov8' in model_name:
        # Working on getting this to work, another approach
        # https://docs.ultralytics.com/modes/predict/#key-features-of-predict-mode 

        model = YOLO(model_name)
        # set model parameters
        model.overrides['conf'] = 0.25  # NMS confidence threshold
        model.overrides['iou'] = 0.45  # NMS IoU threshold
        model.overrides['agnostic_nms'] = False  # NMS class-agnostic
        model.overrides['max_det'] = 1000  # maximum number of detections per image

        results = model.predict(image_input)

        render = render_result(model=model, image=image_input, result=results[0])

        final_str = ""
        final_str_abv = ""
        final_str_else = ""

        for r in results:
            if r.boxes.conf >= threshold:
                final_str_abv =+ str(r.boxes) + "\n"
            else:
                final_str_else =+ str(r.boxes) + "\n"

        final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else

        return render, final_str
    else:
        
        #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)
    
        tb_label = ""
        if validators.url(url_input):
            image = Image.open(requests.get(url_input, stream=True).raw)
            tb_label = "Confidence Values URL"
            
        elif image_input:
            image = image_input
            tb_label = "Confidence Values Upload"
        
        #Make prediction
        processed_output_list = make_prediction(image, feature_extractor, model)
        print("After make_prediction" + str(processed_output_list))
        processed_outputs = processed_output_list[0]
        
        #Visualize prediction
        viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
        
        # return [viz_img, processed_outputs]
        # print(type(viz_img))
    
        final_str_abv = ""
        final_str_else = ""
        for score, label, box in sorted(zip(processed_outputs["scores"], processed_outputs["labels"], processed_outputs["boxes"]), key = lambda x: x[0].item(), reverse=True):
            box = [round(i, 2) for i in box.tolist()]
            if score.item() >= threshold:
                final_str_abv += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n"
            else:
                final_str_else += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n"
    
        # https://docs.python.org/3/library/string.html#format-examples
        final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else
            
        return viz_img, final_str
        
def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])

def set_example_url(example: list) -> dict:
    return gr.Textbox.update(value=example[0])


title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""

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)
- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny)
- [facebook/detr-resnet-101-dc5](https://huggingface.co/facebook/detr-resnet-101-dc5)
- [hustvl/yolos-small-300](https://huggingface.co/hustvl/yolos-small-300)
- [mshamrai/yolov8x-visdrone](https://huggingface.co/mshamrai/yolov8x-visdrone)

"""

models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny','facebook/detr-resnet-101-dc5', 'hustvl/yolos-small-300', 'mshamrai/yolov8x-visdrone']
urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]

# twitter_link = """
# [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
# """

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

with demo:
    gr.Markdown(title)
    gr.Markdown(description)
    # gr.Markdown(twitter_link)
    options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
    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=2,label='Enter valid image URL here..')
                img_output_from_url = gr.Image(shape=(650,650))
                
            with gr.Row():
                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
            
            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=(650,650))
                
            with gr.Row(): 
                example_images = gr.Dataset(components=[img_input],
                                            samples=[[path.as_posix()]
                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
                
            img_but = gr.Button('Detect')

    # output_text1 = gr.outputs.Textbox(label="Confidence Values")
    output_text1 = gr.components.Textbox(label="Confidence Values")
    # https://huggingface.co/spaces/vishnun/CLIPnCROP/blob/main/app.py -- Got .outputs. from this
    
    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True)
    # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
    # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
    
    # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
    # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)

    
    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
    example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
    

    # gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")

    
# demo.launch(enable_queue=True)
demo.launch() #removed (share=True)