Loading video for face detection - Face Recognition

This demo shows how to detect faces from video and displays result in realtime

You need OpenCV installed to run this example. To install it, run pip install opencv-python

python

%pylab inline 
import face_recognition
import cv2
import matplotlib.patches as patches
from IPython.display import clear_output
from matplotlib.pyplot import imshow
import matplotlib.pylab as plt

python

# Loading video for face detection
video_capture = cv2.VideoCapture("../hamilton_clip.mp4")

frame_count = 0

while video_capture.isOpened():    
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Bail out when the video file ends
    if not ret:
        video_capture.release()
        break
        
    # We will search face in every 15 frames to speed up process.
    frame_count += 1
    if frame_count % 15 == 0:    
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        
        # Display video frame
        title("Input Stream")
        plt.imshow(frame)        

        # Find all the faces and face encodings in the current frame of video
        rgb_frame = frame[:, :, ::-1]
        face_locations = face_recognition.face_locations(rgb_frame)
        
        # If faces were found, we will mark it on frame with blue dots
        for face_location in face_locations:        
            plt.plot(face_location[1], face_location[0], 'bo')
            plt.plot(face_location[1], face_location[2], 'bo')
            plt.plot(face_location[3], face_location[2], 'bo')
            plt.plot(face_location[3], face_location[0], 'bo')

        # Show frame...
        plt.show() 
        # ... and hold it until a new frame appears
        clear_output(wait=True)