import face_recognition
import cv2
import numpy as np
import os
# coding=utf-8

video_capture = cv2.VideoCapture(0)
#video_capture = cv2.VideoCapture('./video/demo_clip.mp4')

path_know = './people_i_know'

known_face_encodings = []
known_face_names = []

for path in os.listdir(path_know):
 img = face_recognition.load_image_file(path_know + '/' + path)
 encoding = face_recognition.face_encodings(img)[0]
 known_face_encodings.append(encoding)
 name = path.split('.')[0]
 known_face_names.append(name)

# 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]

 # Or instead, use the known face with the smallest distance to the new face
 face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
 best_match_index = np.argmin(face_distances)
 if matches[best_match_index]:
 name = known_face_names[best_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.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()