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import face_recognition | |
import cv2 | |
import os | |
my_portal='https://yourserver:yourport/zm' | |
my_user='admin' | |
my_pass='yourpass' | |
my_monitor=11 | |
# src=0 # this is for your first local camera | |
# use this to connect to ZM monitors | |
src='{}/cgi-bin/nph-zms?mode=jpeg&maxfps=5&buffer=1000&monitor={}&user={}&pass={}'.format(my_portal,my_monitor,my_user,my_pass) | |
print ('capturing {}'.format(src)) | |
video_capture = cv2.VideoCapture(src) | |
known_face_encodings = [] | |
known_face_names = [] | |
directory = 'known/' | |
ext = [".jpg", ".jpeg", ".png", ".gif"] | |
for filename in os.listdir(directory): | |
if filename.endswith(tuple(ext)): | |
print ("Processing {}".format(filename)) | |
known_image = face_recognition.load_image_file("known/{}".format(filename)) | |
known_face_encodings.append(face_recognition.face_encodings(known_image)[0]) | |
known_face_names.append(os.path.splitext(filename)[0]) | |
# Initialize some variables | |
face_locations = [] | |
face_encodings = [] | |
face_names = [] | |
process_this_frame = True | |
while True: | |
# Grab a single frame of video | |
ret, frame = video_capture.read() | |
# Resize frame of video to 1/4 size for faster face recognition processing | |
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) | |
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) | |
rgb_small_frame = small_frame[:, :, ::-1] | |
# Only process every other frame of video to save time | |
if process_this_frame: | |
# Find all the faces and face encodings in the current frame of video | |
face_locations = face_recognition.face_locations(rgb_small_frame) | |
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations) | |
face_names = [] | |
for face_encoding in face_encodings: | |
# See if the face is a match for the known face(s) | |
matches = face_recognition.compare_faces(known_face_encodings, face_encoding) | |
name = "Unknown" | |
# If a match was found in known_face_encodings, just use the first one. | |
if True in matches: | |
first_match_index = matches.index(True) | |
name = known_face_names[first_match_index] | |
face_names.append(name) | |
process_this_frame = not process_this_frame | |
# Display the results | |
for (top, right, bottom, left), name in zip(face_locations, face_names): | |
# Scale back up face locations since the frame we detected in was scaled to 1/4 size | |
top *= 4 | |
right *= 4 | |
bottom *= 4 | |
left *= 4 | |
# 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 - 35), (right, bottom), (0, 0, 255), cv2.FILLED) | |
font = cv2.FONT_HERSHEY_DUPLEX | |
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) | |
# Display the resulting image | |
cv2.namedWindow('Video',cv2.WINDOW_NORMAL) | |
cv2.resizeWindow('Video', 800,600) | |
cv2.imshow('Video', 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() |
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