# THIS IS A TESTING FILE AND IS NOT PART OF THE PROJECT
# The code below can be called from a diffrerent file with import face_recon
# and webcam_detection(numpy_array_image, user_name)
# If you want to test this file on your own make sure you install face_recognition lib (tutorial found on Trello board)
# and also opencv-python and numpy

import face_recognition
import cv2
import numpy as np


def webcam_detection(known_image, display_name):
    # Get a reference to webcam #0 (the default one)
    video_capture = cv2.VideoCapture(0)

    # Load a sample picture and learn how to recognize it.
    known_face_encoding = face_recognition.face_encodings(known_image)[0]

    # Create arrays of known face encodings and their names
    known_face_encodings = [
        known_face_encoding
    ]
    known_face_names = [
        display_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 user"

                # # 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]
                    return True
                face_names.append(name)

        process_this_frame = not process_this_frame

        # Display the resulting image
        # cv2.imshow('SindiAI-Detector', frame)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    video_capture.release()
    cv2.destroyAllWindows()

# THIS IS A TESTING FILE AND IS NOT PART OF THE PROJECT
# The code below can be called from a diffrerent file with import face_recon
# and webcam_detection(numpy_array_image, user_name)
# If you want to test this file on your own make sure you install face_recognition lib (tutorial found on Trello board)
# and also opencv-python and numpy

import face_recognition
import cv2
import numpy as np


def webcam_detection(known_image, display_name):
    # Get a reference to webcam #0 (the default one)
    video_capture = cv2.VideoCapture(0)

    # Load a sample picture and learn how to recognize it.
    known_face_encoding = face_recognition.face_encodings(known_image)[0]

    # Create arrays of known face encodings and their names
    known_face_encodings = [
        known_face_encoding
    ]
    known_face_names = [
        display_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 user"

                # # 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]
                    return True
                face_names.append(name)

        process_this_frame = not process_this_frame

        # Display the resulting image
        # cv2.imshow('SindiAI-Detector', frame)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    video_capture.release()
    cv2.destroyAllWindows()

# THIS IS A TESTING FILE AND IS NOT PART OF THE PROJECT # The code below can be called from a diffrerent file with import face_recon # and webcam_detection(numpy_array_image, user_name) # If you want to test this file on your own make sure you install face_recognition lib (tutorial found on Trello board) # and also opencv-python and numpy # Get a reference to webcam #0 (the default one) # Load a sample picture and learn how to recognize it. # Create arrays of known face encodings and their names # Initialize some variables # Grab a single frame of video # Resize frame of video to 1/4 size for faster face recognition processing # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) # Only process every other frame of video to save time # Find all the faces and face encodings in the current frame of video # See if the face is a match for the known face(s) # # 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 # Display the resulting image # cv2.imshow('SindiAI-Detector', frame) # Hit 'q' on the keyboard to quit!