Computer vision:Drowsiness Detection using YOLOv5

 

#!pip install torch==1.12.1+cpu torchvision==0.13.1+cpu torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cpu

#!git clone https://github.com/ultralytics/yolov5

#!cd yolov5 & pip install -r requirements.txt

import os 

os.chdir(r"C:\Users\amb\Downloads\drownsenesyolov5")

import torch

from matplotlib import pyplot as plt

import numpy as np

import cv2

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

img = 'https://resources.stuff.co.nz/content/dam/images/1/k/q/r/d/c/image.related.StuffLandscapeSixteenByNine.1420x800.1kseqt.png/1501707394942.jpg'

results = model(img)

results.print()

results.render()

%matplotlib inline 

plt.imshow(np.squeeze(results.render()))

plt.show()

results.show()

results.xyxy

np.squeeze(results.render()).shape

cap = cv2.VideoCapture(0)

while cap.isOpened():

    ret, frame = cap.read()

    

    # Make detections 

    results = model(frame)

    

    cv2.imshow('YOLO', np.squeeze(results.render()))

    

    if cv2.waitKey(10)== ord('q'):

        break

cap.release()

cv2.destroyAllWindows()

import uuid   # Unique identifier

import os

import time

IMAGES_PATH = os.path.join('data', 'images') #/data/images

labels = ['awake', 'drowsy']

number_imgs = 20

cap = cv2.VideoCapture(0)

# Loop through labels

for label in labels:

    print('Collecting images for {}'.format(label))

    time.sleep(5)

    

    # Loop through image range

    for img_num in range(number_imgs):

        print('Collecting images for {}, image number {}'.format(label, img_num))

        

        # Webcam feed

        ret, frame = cap.read()

        

        # Naming out image path

        imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')

        

        # Writes out image to file 

        cv2.imwrite(imgname, frame)

        

        # Render to the screen

        cv2.imshow('Image Collection', frame)

        

        # 2 second delay between captures

        time.sleep(3)

        

        if cv2.waitKey(10) == ord('q'):

            break

cap.release()

cv2.destroyAllWindows()

for label in labels:

    print('Collecting images for {}'.format(label))

    for img_num in range(number_imgs):

        print('Collecting images for {}, image number {}'.format(label, img_num))

        imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')

        print(imgname) 

!cd yolov5 && python train.py --img 320 --batch 16 --epochs 50 --data dataset.yaml --weights yolov5s.pt --workers 2

model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp20/weights/last.pt', force_reload=True)

img = os.path.join('data', 'images', 'awake-572bea22-6e6f-11ed-a895-8cdcd4d59021_jpg.rf.fda3b6c3f6b56d4d0726c67894d25117.jpg')

resul = model(img)

resul.print()

resul.show()

cap = cv2.VideoCapture(0)

while cap.isOpened():

    ret, frame = cap.read()

    

    # Make detections 

    results = model(frame)

    

    cv2.imshow('YOLO', np.squeeze(results.render()))

    

    if cv2.waitKey(10) & 0xFF == ord('q'):

        break

cap.release()

cv2.destroyAllWindows()

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