import face_recognition
import cv2
import numpy as np
import joblib
import time
import os

clf = joblib.load('saved_model.pkl')
classes = clf.classes_

threshold = 0.65

test_image = face_recognition.load_image_file(os.path.join('application_data', 'input_image', 'input_image2.jpg'))

while True:

    rgb_frame = test_image[:, :, ::-1]

    # Find all the faces and face enqcodings in the frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    no = len(face_locations)
    print("Number of faces detected: ", no)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop through each face in this frame of video
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        start_time = time.perf_counter_ns()
        proba_list = clf.predict_proba([face_encoding])
        end_time = time.perf_counter_ns()
        process_time = end_time - start_time
        i = np.argmax(proba_list)
        proba = list(*proba_list)[i]
        name = classes[i]
        print(name, "{:.2f}".format(proba), proba_list, process_time)

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

        # Draw a label with a name below the face
        cv2.rectangle(test_image, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        if(proba > threshold):
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(test_image, "{} {:.2f}".format(name, proba), (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)


    # Display the resulting image
    cv2.imshow('Video', test_image)

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

# Release handle to the webcam
cv2.destroyAllWindows()