!pip3 install opencv-python

Collecting opencv-python
  Downloading opencv_python-4.2.0.32-cp38-cp38-manylinux1_x86_64.whl (28.2 MB)
     |████████████████████████████████| 28.2 MB 2.1 MB/s eta 0:00:01
Requirement already satisfied: numpy>=1.17.3 in /home/siva/.asdf/installs/python/3.8.0/lib/python3.8/site-packages (from opencv-python) (1.18.1)
Installing collected packages: opencv-python
Successfully installed opencv-python-4.2.0.32

!pip3 install face_recognition

%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

Populating the interactive namespace from numpy and matplotlib

video_capture = cv2.VideoCapture("my_videos/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)