app.py

import gradio as gr
import cv2
import requests
import os

from ultralytics import YOLO

file_urls = [
    'https://media.discordapp.net/attachments/901110195810336798/1157962848840986674/image0.png?ex=651a8471&is=651932f1&hm=22ab125d48df34021a6713f3f9303ae73e99a3460fad7c2d88ff9318dd56afa7&=&width=1177&height=662',
    'https://media.discordapp.net/attachments/901110195810336798/1157962849465925702/image1.png?ex=651a8471&is=651932f1&hm=f904ceebe946b74e596a0aeb8ef69a8062f95fd854c7095432da1ec0573abad4&=&width=757&height=662',
    'https://cdn.discordapp.com/attachments/901110195810336798/1157962847985336370/video.mp4?ex=651a8470&is=651932f0&hm=6d5f5e6676e20339c1e69785f0778b76361c0e1df125afbc4c4e0a96e9ac91f4&'
]


def download_file(url, save_name):
    url = url
    if not os.path.exists(save_name):
        file = requests.get(url)
        open(save_name, 'wb').write(file.content)


for i, url in enumerate(file_urls):
    if 'mp4' in file_urls[i]:
        download_file(
            file_urls[i],
            f"video.mp4"
        )
    else:
        download_file(
            file_urls[i],
            f"image_{i}.jpg"
        )

model = YOLO('YOLOv8-SafetyProtocol-best.pt')
path = [['image_0.jpg'], ['image_1.jpg']]
video_path = [['video.mp4']]


def show_preds_image(image_path):
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path)
    results = outputs[0].cpu().numpy()
    for i, det in enumerate(results.boxes.xyxy):
        cv2.rectangle(
            image,
            (int(det[0]), int(det[1])),
            (int(det[2]), int(det[3])),
            color=(0, 0, 255),
            thickness=2,
            lineType=cv2.LINE_AA
        )
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="👷🦺 Safety Gear Detector",
    examples=path,
    cache_examples=False,
)


def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    while (cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frame_copy = frame.copy()
            outputs = model.predict(source=frame)
            results = outputs[0].cpu().numpy()
            for i, det in enumerate(results.boxes.xyxy):
                cv2.rectangle(
                    frame_copy,
                    (int(det[0]), int(det[1])),
                    (int(det[2]), int(det[3])),
                    color=(0, 0, 255),
                    thickness=2,
                    lineType=cv2.LINE_AA
                )
            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)


inputs_video = [
    gr.components.Video(type="filepath", label="Input Video"),

]
outputs_video = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="👷🦺 Safety Gear Detector",
    examples=video_path,
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image inference', 'Video inference']
).queue().launch()