Live visualisation of various facial recognition algorithms.
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# import face_recognition
import cv2
from skimage.feature import hog
from skimage import data, exposure
import matplotlib.pyplot as plt
import numpy as np
import dlib
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
# 2. Only detect faces in every other frame of video.
# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(2)
face_detector = dlib.get_frontal_face_detector()
process_this_frame = True
while True:
if process_this_frame:
# Grab a single frame of video
ret, frame = video_capture.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.7, fy=0.7)
fd, hog_image = hog(small_frame, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualize=True, multichannel=True)
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 15))
# Resize frame of video to 1/4 size for faster face recognition processing
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
dets, scores, idxs = face_detector.run(rgb_small_frame, 1, -1)
print(dets, scores, idxs)
# Display the results
for i, rectangle in enumerate(dets):
probability = scores[i]
print(rectangle)
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top = rectangle.top() #* 4
right = rectangle.right() #* 4
bottom = rectangle.bottom() #* 4
left = rectangle.left() #* 4
brightness = (probability + 1)/3
# Draw a box around the face
cv2.rectangle(hog_image_rescaled, (left, top), (right, bottom), brightness, 2)
# # Draw a label with a name below the face
# cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
# Display the resulting image
cv2.imshow('Video', hog_image_rescaled)
process_this_frame = not process_this_frame
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()