KevinPatel04

KevinPatel04

# find the mean of the pixels
x.mean()

KevinPatel04

•
├── images
│   ├── test-1.jpg
│   └── test-2.jpg
└── face-makeup-on-image.py

KevinPatel04

# detect face from image using cnn_face_detector
all_face_locations = cnn_face_detector(face_image, 1)

KevinPatel04

# importing required libraries
import cv2
import  face_recognition
import os
import numpy as np

# initialize the empty list of name label
known_face_names = []

# initialize the empty list for storing the encoding of each face

KevinPatel04

•
├── training-images
│   ├── Barack Obama.jpg
│   ├── Narendra Modi.jpg
│   ├── Boris Johnson.jpg
│   ├── Donald Trump.jpg
│   └── Justin Trudeau.jpg
├── models
│ ├── mmod_human_face_detector.dat

KevinPatel04

# TOLERANCE defines the tolerance for face comparisons
# The lower the number - the stricter the comparison
# To avoid false matches, use lower value
# To avoid false negatives (i.e. faces of the same person doesn't match), use higher value
# 0.5-0.6 works well
TOLERANCE = 0.50

# This function returns the name of the person whose image matches with the given face (or 'Unknown Face')
# face_encoding is the face encoding of the face we are looking for
def find_match(face_encoding):

KevinPatel04

# capture the video from default camera
# video_stream = cv2.VideoCapture(0)

# read video from video file
video_file_path = 'test/face-demographics-walking.mp4'
video_stream = cv2.VideoCapture(video_file_path)

# initialize the number of frame needed to be skipped
skip = 0

KevinPatel04

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow

KevinPatel04

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow