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#Run detector on images captured by webcam for your custom YOLOv4 trained model | |
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 IPython.display import Image | |
try: | |
filename = take_photo() | |
print('Saved to {}'.format(filename)) | |
# Show the image which was just taken. | |
display(Image(filename)) | |
except Exception as err: | |
# Errors will be thrown if the user does not have a webcam or if they do not | |
# grant the page permission to access it. | |
print(str(err)) | |
!./darknet detector test data/obj.data cfg/yolov4-custom.cfg /mydrive/yolov4/training/yolov4-custom_best.weights photo.jpg -thresh 0.3 | |
imShow('predictions.jpg') |
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