20065320rp
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Parent(s):
a4d5a9a
Create app.py
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app.py
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import torch
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import cv2
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from ultralyticsplus import YOLO, render_result
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# load model
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model = YOLO('keremberke/yolov8m-hard-hat-detection')
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# set model parameters
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model.overrides['conf'] = 0.25 # NMS confidence threshold
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model.overrides['iou'] = 0.45 # NMS IoU threshold
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model.overrides['agnostic_nms'] = False # NMS class-agnostic
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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from IPython.display import display, Javascript
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from google.colab.output import eval_js
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from base64 import b64decode
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def take_photo(filename='photo.jpg', quality=0.8):
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js = Javascript('''
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async function takePhoto(quality) {
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const div = document.createElement('div');
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const capture = document.createElement('button');
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capture.textContent = 'Capture';
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div.appendChild(capture);
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const video = document.createElement('video');
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video.style.display = 'block';
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const stream = await navigator.mediaDevices.getUserMedia({video: true});
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document.body.appendChild(div);
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div.appendChild(video);
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video.srcObject = stream;
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await video.play();
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// Resize the output to fit the video element.
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google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);
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// Wait for Capture to be clicked.
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await new Promise((resolve) => capture.onclick = resolve);
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const canvas = document.createElement('canvas');
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canvas.width = video.videoWidth;
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canvas.height = video.videoHeight;
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canvas.getContext('2d').drawImage(video, 0, 0);
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stream.getVideoTracks()[0].stop();
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div.remove();
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return canvas.toDataURL('image/jpeg', quality);
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}
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''')
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display(js)
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data = eval_js('takePhoto({})'.format(quality))
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binary = b64decode(data.split(',')[1])
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with open(filename, 'wb') as f:
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f.write(binary)
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return filename
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from ultralyticsplus import YOLO, render_result
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def safety_helmet():
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# perform inference
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#results = model.predict(image_path)
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results = model.predict(filename)
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# self.assertEqual(array.shape, (1, 224, 224, 3))
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# observe results
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print(results[0].boxes)
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render = render_result(model=model, image=filename, result=results[0])
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render.show()
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import gradio as gr
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demo = gr.Interface(fn=safety_helmet, inputs=take_photo(), outputs=safety_helmet(), description="Safety Helmet Detection")
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demo.launch(share=True)
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