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import torch
import cv2
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8m-hard-hat-detection')
# 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
from IPython.display import display, Javascript
from google.colab.output import eval_js
from base64 import b64decode
def take_photo(filename='photo.jpg', quality=0.8):
js = Javascript('''
async function takePhoto(quality) {
const div = document.createElement('div');
const capture = document.createElement('button');
capture.textContent = 'Capture';
div.appendChild(capture);
const video = document.createElement('video');
video.style.display = 'block';
const stream = await navigator.mediaDevices.getUserMedia({video: true});
document.body.appendChild(div);
div.appendChild(video);
video.srcObject = stream;
await video.play();
// Resize the output to fit the video element.
google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);
// Wait for Capture to be clicked.
await new Promise((resolve) => capture.onclick = resolve);
const canvas = document.createElement('canvas');
canvas.width = video.videoWidth;
canvas.height = video.videoHeight;
canvas.getContext('2d').drawImage(video, 0, 0);
stream.getVideoTracks()[0].stop();
div.remove();
return canvas.toDataURL('image/jpeg', quality);
}
''')
display(js)
data = eval_js('takePhoto({})'.format(quality))
binary = b64decode(data.split(',')[1])
with open(filename, 'wb') as f:
f.write(binary)
return filename
from ultralyticsplus import YOLO, render_result
def safety_helmet():
# perform inference
#results = model.predict(image_path)
results = model.predict(filename)
# self.assertEqual(array.shape, (1, 224, 224, 3))
# observe results
print(results[0].boxes)
render = render_result(model=model, image=filename, result=results[0])
render.show()
import gradio as gr
demo = gr.Interface(fn=safety_helmet, inputs=take_photo(), outputs=safety_helmet(), description="Safety Helmet Detection")
demo.launch(share=True) |