The kernel appears to have died. It will restart automatically. (Dead Kernel)

[AhmedEwis]

Hello,

Thanks for this great effort.

I am trying the below code as it is to perform the following:

Recognize faces in a video file and write out a new video file (Requires OpenCV to be installed)

But I am always getting this error:

The kernel appears to have died. It will restart automatically.

import face_recognition import cv2

This is a demo of running face recognition on a video file and saving the results to a new video file.

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.

Open the input movie file

input_movie = cv2.VideoCapture("blur2_video_moderate_1_Trim2.mp4") length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))

Create an output movie file (make sure resolution/frame rate matches input video!)

fourcc = cv2.VideoWriter_fourcc(*'XVID') output_movie = cv2.VideoWriter('output_trim.avi', fourcc, 29.97, (640, 360))

Load some sample pictures and learn how to recognize them.

lmm_image = face_recognition.load_image_file("Scarlet.png") lmm_face_encoding = face_recognition.face_encodings(lmm_image)[0]

al_image = face_recognition.load_image_file("Daniel.png") al_face_encoding = face_recognition.face_encodings(al_image)[0]

known_faces = [ lmm_face_encoding, al_face_encoding ]

Initialize some variables

face_locations = [] face_encodings = [] face_names = [] frame_number = 0

while True: # Grab a single frame of video ret, frame = input_movie.read() frame_number += 1

# Quit when the input video file ends
if not ret:
    break

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]

# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

face_names = []
for face_encoding in face_encodings:
    # See if the face is a match for the known face(s)
    match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)

    # If you had more than 2 faces, you could make this logic a lot prettier
    # but I kept it simple for the demo
    name = None
    if match[0]:
        name = "Scarlet"
    elif match[1]:
        name = "Daniel"

    face_names.append(name)

# Label the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
    if not name:
        continue

    # Draw a box around the face
    cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

    # Draw a label with a name below the face
    cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 0, 255), cv2.FILLED)
    font = cv2.FONT_HERSHEY_DUPLEX
    cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)

# Write the resulting image to the output video file
print("Writing frame {} / {}".format(frame_number, length))
output_movie.write(frame)

All done!

input_movie.release() cv2.destroyAllWindows()