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import os
import time
import cv2
import dlib
import mediapipe as mp
import numpy as np
import pymongo
import pyrebase
import streamlit as st
from bson.objectid import ObjectId
from imutils import face_utils
from mediapipe.python.solutions.drawing_utils import \
_normalized_to_pixel_coordinates as denormalize_coordinates
from scipy.spatial import distance as dist
config = {"apiKey": "AIzaSyCNPBcskQFs2tn5UfdFbP8LzbnEMIarsWc",
"authDomain": "aps-csia.firebaseapp.com",
"databaseURL": "https://aps-csia-default-rtdb.asia-southeast1.firebasedatabase.app/",
"projectId": "aps-csia",
"storageBucket": "aps-csia.appspot.com",
"messagingSenderId": "1069559357849",
"appId": "1:1069559357849:web:39e9d0139d42a206973308",
"measurementId": "G-FVTG7XGLN7"}
firebase = pyrebase.initialize_app(config)
db = firebase.database()
print("-> Loading the predictor and detector...")
detector = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml") #Faster but less accurate
predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
def get_mediapipe_app(
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5,
min_tracking_confidence=0.5,
):
"""Initialize and return Mediapipe FaceMesh Solution Graph object"""
face_mesh = mp.solutions.face_mesh.FaceMesh(
max_num_faces=max_num_faces,
refine_landmarks=refine_landmarks,
min_detection_confidence=min_detection_confidence,
min_tracking_confidence=min_tracking_confidence,
)
return face_mesh
def distance(point_1, point_2):
"""Calculate l2-norm between two points"""
dist = sum([(i - j) ** 2 for i, j in zip(point_1, point_2)]) ** 0.5
return dist
def get_ear(landmarks, refer_idxs, frame_width, frame_height):
"""
Calculate Eye Aspect Ratio for one eye.
Args:
landmarks: (list) Detected landmarks list
refer_idxs: (list) Index positions of the chosen landmarks
in order P1, P2, P3, P4, P5, P6
frame_width: (int) Width of captured frame
frame_height: (int) Height of captured frame
Returns:
ear: (float) Eye aspect ratio
"""
try:
# Compute the euclidean distance between the horizontal
coords_points = []
for i in refer_idxs:
lm = landmarks[i]
coord = denormalize_coordinates(lm.x, lm.y, frame_width, frame_height)
coords_points.append(coord)
# Eye landmark (x, y)-coordinates
P1_P4 = dist.euclidean(coords_points[0], coords_points[3])
P2_P6 = dist.euclidean(coords_points[1], coords_points[5])
P3_P5 = dist.euclidean(coords_points[2], coords_points[4])
# Compute the eye aspect ratio
ear = (P2_P6 + P3_P5) / (2.0 * P1_P4)
except:
ear = 0.0
coords_points = None
return ear, coords_points
def calculate_avg_ear(landmarks, left_eye_idxs, right_eye_idxs, image_w, image_h):
# Calculate Eye aspect ratio
left_ear, left_lm_coordinates = get_ear(landmarks, left_eye_idxs, image_w, image_h)
right_ear, right_lm_coordinates = get_ear(landmarks, right_eye_idxs, image_w, image_h)
Avg_EAR = (left_ear + right_ear) / 2.0
return Avg_EAR, (left_lm_coordinates, right_lm_coordinates)
def plot_eye_landmarks(frame, left_lm_coordinates, right_lm_coordinates, color):
for lm_coordinates in [left_lm_coordinates, right_lm_coordinates]:
if lm_coordinates:
for coord in lm_coordinates:
cv2.circle(frame, coord, 2, color, -1)
return frame
def lip_distance(shape):
top_lip = shape[50:53]
top_lip = np.concatenate((top_lip, shape[61:64]))
low_lip = shape[56:59]
low_lip = np.concatenate((low_lip, shape[65:68]))
top_mean = np.mean(top_lip, axis=0)
low_mean = np.mean(low_lip, axis=0)
distance = abs(top_mean[1] - low_mean[1])
return distance
def plot_text(image, text, origin, color, font=cv2.FONT_HERSHEY_SIMPLEX, fntScale=0.8, thickness=2):
image = cv2.putText(image, text, origin, font, fntScale, color, thickness)
return image
class VideoFrameHandler:
def __init__(self):
"""
Initialize the necessary constants, mediapipe app
and tracker variables
"""
# Left and right eye chosen landmarks.
self.count_drowsy = 0
self.count_yawn = 0
self.eye_idxs = {
"left": [362, 385, 387, 263, 373, 380],
"right": [33, 160, 158, 133, 153, 144],
}
# Used for coloring landmark points.
# Its value depends on the current EAR value.
self.RED = (0, 0, 255) # BGR
self.GREEN = (0, 255, 0) # BGR
# Initializing Mediapipe FaceMesh solution pipeline
self.facemesh_model = get_mediapipe_app()
# For tracking counters and sharing states in and out of callbacks.
self.state_tracker = {
"start_time": time.perf_counter(),
"DROWSY_TIME": 0.0, # Holds the amount of time passed with EAR < EAR_THRESH
"COLOR": self.GREEN,
"play_alarm": False,
}
self.EAR_txt_pos = (10, 30)
def process(self, frame: np.array, thresholds: dict):
"""
This function is used to implement our Drowsy detection algorithm
Args:
frame: (np.array) Input frame matrix.
thresholds: (dict) Contains the two threshold values
WAIT_TIME and EAR_THRESH.
Returns:
The processed frame and a boolean flag to
indicate if the alarm should be played or not.
"""
# To improve performance,
# mark the frame as not writeable to pass by reference.
frame = cv2.flip(frame,1)
frame.flags.writeable = False
frame_h, frame_w, _ = frame.shape
DROWSY_TIME_txt_pos = (10, int(frame_h // 2 * 1.7))
ALM_txt_pos = (10, int(frame_h // 2 * 1.85))
#frame = cv2.flip(frame, 1)
results = self.facemesh_model.process(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in rects:
rect = dlib.rectangle(int(x), int(y), int(x + w),int(y + h))
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
distance = lip_distance(shape)
lip = shape[48:60]
cv2.drawContours(frame, [lip], -1, (0, 255, 0), 1)
if (distance > thresholds["LIP_THRESH"]):
plot_text(frame, "Yawn Alert", (460, 440), (0, 0, 255))
time.sleep(1)
self.count_yawn += 1
if os.environ.get("logged_in") == "True":
user_id = os.environ.get("user_id")
email = os.environ.get("email")
client = pymongo.MongoClient("mongodb+srv://admin:Admin123@aps.agcjjww.mongodb.net/?retryWrites=true&w=majority")
db = client["aps-db"]
users = db["count"]
user = users.find_one({"_id": ObjectId(user_id)})
if user:
users.update_one({"_id": ObjectId(user_id)}, {"$set": {"count_yawn": self.count_yawn, "email": email}})
else:
users.insert_one({"_id": ObjectId(user_id), "count_yawn": self.count_yawn, "email": email})
else:
st.error("Not logged in")
frame = plot_text(frame, f"Yawn count: {self.count_yawn}",(420,410), color=(0, 255, 0), thickness=2)
if results.multi_face_landmarks:
landmarks = results.multi_face_landmarks[0].landmark
EAR, coordinates = calculate_avg_ear(landmarks, self.eye_idxs["left"], self.eye_idxs["right"], frame_w, frame_h)
frame = plot_eye_landmarks(frame, coordinates[0], coordinates[1], self.state_tracker["COLOR"])
if EAR < thresholds["EAR_THRESH"]:
# Increase DROWSY_TIME to track the time period with EAR less than the threshold
# and reset the start_time for the next iteration.
end_time = time.perf_counter()
self.state_tracker["DROWSY_TIME"] += end_time - self.state_tracker["start_time"]
self.state_tracker["start_time"] = end_time
self.state_tracker["COLOR"] = self.RED
if self.state_tracker["DROWSY_TIME"] >= thresholds["WAIT_TIME"]:
self.state_tracker["play_alarm"] = True
plot_text(frame, "WAKE UP! WAKE UP", ALM_txt_pos, self.state_tracker["COLOR"])
time.sleep(1)
self.count_drowsy += 1
if os.environ.get("logged_in") == "True":
user_id = os.environ.get("user_id")
email = os.environ.get("email")
client = pymongo.MongoClient("mongodb+srv://admin:Admin123@aps.agcjjww.mongodb.net/?retryWrites=true&w=majority")
db = client["aps-db"]
users = db["count"]
user = users.find_one({"_id": ObjectId(user_id)})
if user:
users.update_one({"_id": ObjectId(user_id)}, {"$set": {"count_drowsy": self.count_drowsy, "email": email}})
else:
users.insert_one({"_id": ObjectId(user_id), "count_drowsy": self.count_drowsy, "email": email})
else:
self.state_tracker["start_time"] = time.perf_counter()
self.state_tracker["DROWSY_TIME"] = 0.0
self.state_tracker["COLOR"] = self.GREEN
self.state_tracker["play_alarm"] = False
EAR_txt = f"EAR: {round(EAR, 2)}"
DROWSY_TIME_txt = f"DROWSY: {round(self.state_tracker['DROWSY_TIME'], 3)} Secs"
plot_text(frame, EAR_txt, self.EAR_txt_pos, self.state_tracker["COLOR"])
plot_text(frame, DROWSY_TIME_txt, DROWSY_TIME_txt_pos, self.state_tracker["COLOR"])
frame = plot_text(frame, f"Drowsy count: {self.count_drowsy}",(400,30), color=(0, 255, 0), thickness=2)
else:
self.state_tracker["start_time"] = time.perf_counter()
self.state_tracker["DROWSY_TIME"] = 0.0
self.state_tracker["COLOR"] = self.GREEN
self.state_tracker["play_alarm"] = False
return frame, self.state_tracker["play_alarm"] |