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Update 22-08-2020
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‎Drowsiness/Notebook.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import torch.hub\n",
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"import os\n",
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"import model\n",
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"from PIL import Image\n",
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"from torchvision import transforms\n",
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"from grad_cam import BackPropagation\n",
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"import time \n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.image as mpimg\n",
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"from IPython import display\n",
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"import threading\n",
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"import vlc\n",
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"\n",
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"#import smbus\n",
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"#import requests\n",
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"#from twilio.rest import Client\n",
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"#import urllib.request\n",
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"#import json\n",
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"\n",
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"# Alarm sound file\n",
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"file = 'alarm.mp3'\n",
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"# Sound player start\n",
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"p = vlc.MediaPlayer(\"alarm.mp3\")\n",
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"\n",
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"\"\"\"\n",
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"class MMA7455():\n",
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" bus = smbus.SMBus(1)\n",
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" def __init__(self):\n",
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" self.bus.write_byte_data(0x1D, 0x16, 0x55) # Setup the Mode\n",
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" self.bus.write_byte_data(0x1D, 0x10, 0) # Calibrate\n",
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" self.bus.write_byte_data(0x1D, 0x11, 0) # Calibrate\n",
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" self.bus.write_byte_data(0x1D, 0x12, 0) # Calibrate\n",
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" self.bus.write_byte_data(0x1D, 0x13, 0) # Calibrate\n",
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" self.bus.write_byte_data(0x1D, 0x14, 0) # Calibrate\n",
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" self.bus.write_byte_data(0x1D, 0x15, 0) # Calibrate\n",
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" def getValueX(self):\n",
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" return self.bus.read_byte_data(0x1D, 0x06)\n",
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" def getValueY(self):\n",
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" return self.bus.read_byte_data(0x1D, 0x07)\n",
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" def getValueZ(self):\n",
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" return self.bus.read_byte_data(0x1D, 0x08)\n",
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"\n",
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"\n",
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"# Crash Sensibility\n",
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"sens=30\n",
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"\n",
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"# Sending SMS if Crash Detected\n",
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"\n",
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"\n",
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"def send():\n",
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" # Your Account SID from twilio.com/console\n",
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" account_sid = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n",
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" # Your Auth Token from twilio.com/console\n",
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" auth_token = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n",
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" client = Client(account_sid, auth_token)\n",
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" phone = \"+XXXXXXXXXXXX\"\n",
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" print('crash')\n",
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" send_url = 'http://ip-api.com/json'\n",
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" r = requests.get(send_url)\n",
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" j = json.loads(r.text)\n",
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" text=\"The Driver Crash Here: \"\n",
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" text+=\"http://maps.google.com/maps?q=loc:{},{}\".format(j['lat'],j['lon'])\n",
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" print(text)\n",
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" message = client.messages.create(to=phone, from_=\"++XXXXXXXXXXXX\",body=text)\n",
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" print(message.sid)\n",
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" time.sleep(10)\n",
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" stop()\n",
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"\n",
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"\n",
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"# Accelerometer Declaration\n",
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"mma = MMA7455()\n",
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"\n",
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"# Obtaining the X, Y and Z values.\n",
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"\n",
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"xmem=mma.getValueX()\n",
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"ymem=mma.getValueY()\n",
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"zmem=mma.getValueZ()\n",
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"x = mma.getValueX()\n",
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"y = mma.getValueY()\n",
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"z = mma.getValueZ()\n",
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"\n",
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"\n",
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"# Creating the base accelerometer values.\n",
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"\n",
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"if(xmem > 127):\n",
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" xmem=xmem-255\n",
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"if(ymem > 127):\n",
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" ymem=ymem-255\n",
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"if(zmem > 127):\n",
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" zmem=zmem-255\n",
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"if(x > 127):\n",
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" x=x-255\n",
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"if(y > 127):\n",
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" y=y-255\n",
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"if(z > 127):\n",
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" z=z-255\n",
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"\n",
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"\"\"\"\n",
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"\n",
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"timebasedrow= time.time()\n",
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"timebasedis= time.time()\n",
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"timerundrow= time.time()\n",
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"timerundis= time.time()\n",
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"\n",
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"face_cascade = cv2.CascadeClassifier('haar_models/haarcascade_frontalface_default.xml')\n",
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"eye_cascade = cv2.CascadeClassifier('haar_models/haarcascade_eye.xml') \n",
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"MyModel=\"BlinkModel.t7\"\n",
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"\n",
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"shape = (24,24)\n",
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"classes = [\n",
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" 'Close',\n",
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" 'Open',\n",
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"]\n",
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"\n",
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"eyess=[]\n",
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"cface=0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def preprocess(image_path):\n",
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" global cface\n",
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" transform_test = transforms.Compose([\n",
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" transforms.ToTensor()\n",
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" ])\n",
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" image = cv2.imread(image_path['path']) \n",
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" faces = face_cascade.detectMultiScale(\n",
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" image,\n",
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" scaleFactor=1.1,\n",
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" minNeighbors=5,\n",
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" minSize=(1, 1),\n",
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" flags=cv2.CASCADE_SCALE_IMAGE\n",
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" )\n",
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" if len(faces) == 0:\n",
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" ...\n",
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" else:\n",
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" cface=1\n",
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" (x, y, w, h) = faces[0]\n",
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" face = image[y:y + h, x:x + w]\n",
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" cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,0),2)\n",
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" roi_color = image[y:y+h, x:x+w]\n",
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" \"\"\"\n",
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" Depending on the quality of your camera, this number can vary \n",
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" between 10 and 40, since this is the \"sensitivity\" to detect the eyes.\n",
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" \"\"\"\n",
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" sensi=20\n",
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" eyes = eye_cascade.detectMultiScale(face,1.3, sensi) \n",
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" i=0\n",
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" for (ex,ey,ew,eh) in eyes:\n",
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" (x, y, w, h) = eyes[i]\n",
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" cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\n",
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" eye = face[y:y + h, x:x + w]\n",
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" eye = cv2.resize(eye, shape)\n",
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" eyess.append([transform_test(Image.fromarray(eye).convert('L')), eye, cv2.resize(face, (48,48))])\n",
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" i=i+1\n",
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" cv2.imwrite('temp-images/display.jpg',image) \n",
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" \n",
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"\n",
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"def eye_status(image, name, net):\n",
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" img = torch.stack([image[name]])\n",
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" bp = BackPropagation(model=net)\n",
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" probs, ids = bp.forward(img)\n",
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" actual_status = ids[:, 0]\n",
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" prob = probs.data[:, 0]\n",
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" if actual_status == 0:\n",
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" prob = probs.data[:,1]\n",
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"\n",
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" #print(name,classes[actual_status.data], probs.data[:,0] * 100)\n",
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" return classes[actual_status.data]\n",
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"\n",
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"def func(imag,modl):\n",
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" drow(images=[{'path': imag, 'eye': (0,0,0,0)}],model_name=modl)\n",
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"\n",
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"def drow(images, model_name):\n",
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" global eyess\n",
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" global cface\n",
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" global timebasedrow\n",
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" global timebasedis\n",
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" global timerundrow\n",
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" global timerundis\n",
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" net = model.Model(num_classes=len(classes))\n",
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" checkpoint = torch.load(os.path.join('model', model_name), map_location=torch.device('cpu'))\n",
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" net.load_state_dict(checkpoint['net'])\n",
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" net.eval()\n",
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" \n",
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" flag =1\n",
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" status=\"\"\n",
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" for i, image in enumerate(images):\n",
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" if(flag):\n",
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" preprocess(image)\n",
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" flag=0\n",
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" if cface==0:\n",
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" image = cv2.imread(\"temp-images/display.jpg\")\n",
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" image = cv2.putText(image, 'No face Detected', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)\n",
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" cv2.imwrite('temp-images/display.jpg',image)\n",
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" timebasedrow= time.time()\n",
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" timebasedis= time.time()\n",
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" timerundrow= time.time()\n",
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" timerundis= time.time()\n",
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" elif(len(eyess)!=0):\n",
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" eye, eye_raw , face = eyess[i]\n",
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" image['eye'] = eye\n",
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" image['raw'] = eye_raw\n",
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" image['face'] = face\n",
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" timebasedrow= time.time()\n",
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" timerundrow= time.time()\n",
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" for index, image in enumerate(images):\n",
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" status = eye_status(image, 'eye', net)\n",
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" if(status ==\"Close\"):\n",
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" timerundis= time.time()\n",
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" if((timerundis-timebasedis)>1.5):\n",
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" image = cv2.imread(\"temp-images/display.jpg\")\n",
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" image = cv2.putText(image, 'Distracted', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)\n",
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" cv2.imwrite('temp-images/display.jpg',image)\n",
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" if(not(p.is_playing())):\n",
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" p.play()\n",
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" else:\n",
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" p.stop() \n",
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" else:\n",
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" timerundrow= time.time()\n",
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" if((timerundrow-timebasedrow)>3):\n",
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" if(not(p.is_playing())):\n",
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" p.play()\n",
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" image = cv2.imread(\"temp-images/display.jpg\")\n",
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" image = cv2.putText(image, 'Drowsy', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)\n",
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" cv2.imwrite('temp-images/display.jpg',image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def main():\n",
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" while 1:\n",
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" global eyess\n",
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" global cface \n",
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" eyess=[]\n",
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" cface=0\n",
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" ret, img = cap.read() \n",
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" cv2.imwrite('img.jpg',img) \n",
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" func('img.jpg',MyModel)\n",
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" \n",
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"def disp():\n",
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" while 1:\n",
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" try:\n",
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" img=mpimg.imread('temp-images/display.jpg')\n",
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" plt.imshow(img)\n",
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" display.clear_output(wait=True)\n",
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" display.display(plt.gcf())\n",
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" except:\n",
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" ..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cap = cv2.VideoCapture(0)\n",
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"timebasedrow= time.time()\n",
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"timebasedis= time.time()\n",
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"timerundrow= time.time()\n",
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"timerundis= time.time()\n",
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" \n",
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"d = threading.Thread(target=disp, name='disp')\n",
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"m = threading.Thread(target=main, name='main')\n",
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"\n",
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"d.start()\n",
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"m.start()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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