# import face_recognition
import cv2
from skimage.feature import hog
from skimage import data, exposure
import matplotlib.pyplot as plt
import numpy as np
import dlib
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# 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.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(2)
face_detector = dlib.get_frontal_face_detector()

process_this_frame = True

while True:
    if process_this_frame:
        # Grab a single frame of video
        ret, frame = video_capture.read()

        small_frame = cv2.resize(frame, (0, 0), fx=0.7, fy=0.7)

        fd, hog_image = hog(small_frame, orientations=8, pixels_per_cell=(16, 16),
                        cells_per_block=(1, 1), visualize=True, multichannel=True)
        hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 15))

        # 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)
        rgb_small_frame = small_frame[:, :, ::-1]
        dets, scores, idxs = face_detector.run(rgb_small_frame, 1, -1)
        print(dets, scores, idxs)

        # Display the results
        for i, rectangle in enumerate(dets):
            probability = scores[i]
            print(rectangle)


            # Scale back up face locations since the frame we detected in was scaled to 1/4 size
            top = rectangle.top() #* 4
            right = rectangle.right() #* 4
            bottom = rectangle.bottom() #* 4
            left = rectangle.left() #* 4

            brightness = (probability + 1)/3 

            # Draw a box around the face
            cv2.rectangle(hog_image_rescaled, (left, top), (right, bottom), brightness, 2)

            # # Draw a label with a name below the face
            # cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

        # Display the resulting image
        cv2.imshow('Video', hog_image_rescaled)
    
    process_this_frame = not process_this_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()