app.py

import cv2
import gradio as gr
from ultralytics import YOLO

def inference(path:str, threshold:float=0.6):
    print("trying inference with path", path)
    if path is None:
        return None,0
    model = YOLO('yolov8m.pt')

    model.classes = [0]  # only considering class 'person' and not the 79 other classes...
    image = cv2.imread(path)
    outputs = model.predict(source=path, return_outputs=True)
    for output in outputs:  # mono item batch
        detections = output['det']
        counter=0
        for detection in detections:
            if detection[4]<threshold:
                break
            cv2.rectangle(
                image,
                (int(detection[0]), int(detection[1])),
                (int(detection[2]), int(detection[3])),
                color=(0, 0, 255),
                thickness=2,
            )
            counter+=1
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), counter

gr.Interface(
    fn = inference,
    inputs = [ gr.components.Image(type="filepath", label="Input"), gr.Slider(minimum=0.5, maximum=0.9, step=0.05, value=0.7, label="Confidence threshold") ],
    outputs = [ gr.components.Image(type="numpy", label="Output"), gr.Label(label="nb of persons detected for given confidence threshold") ],
    title="Person detection with YOLO v8",
    description="Person detection, you can tweak the corresponding confidence threshold. Good results even when face not visible.",
    examples=[ ['data/businessmen-612.jpg'], ['data/businessmen-back.jpg']],
    allow_flagging="never"
).launch(debug=True, enable_queue=True)