from statistics import mode

import cv2
from keras.models import load_model
import numpy as np

from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input
import time
import shutil, os
import matplotlib.pyplot as plt
import face_recognition

# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# starting lists for calculating modes
emotion_window = []

# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)

try:
    shutil.rmtree('OUTPUT')
finally:
    os.mkdir('OUTPUT')

fo = open("emoLog.txt", "w")
fifthFrame = 1

# FACE REQ

suleymanov_image = face_recognition.load_image_file("suleymanov.jpg")
suleymanov_face_encoding = face_recognition.face_encodings(suleymanov_image)[0]

# Load a second sample picture and learn how to recognize it.
rim_image = face_recognition.load_image_file("remorenko.jpg")
rim_face_encoding = face_recognition.face_encodings(rim_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    suleymanov_face_encoding,
    rim_face_encoding
]
known_face_names = [
    "Ruslan",
    "RIM"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

####

while True:
    bgr_image = video_capture.read()[1]
    frame = bgr_image
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
    faces = detect_faces(face_detection, gray_image)

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

    for face_coordinates in faces:

        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]
        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_prediction = emotion_classifier.predict(gray_face)
        emotion_probability = np.max(emotion_prediction)
        emotion_label_arg = np.argmax(emotion_prediction)
        emotion_text = emotion_labels[emotion_label_arg]
        emotion_window.append(emotion_text)

        if len(emotion_window) > frame_window:
            emotion_window.pop(0)
        try:
            emotion_mode = mode(emotion_window)
        except:
            continue

        if emotion_text == 'angry':
            color = emotion_probability * np.asarray((255, 0, 0))
        elif emotion_text == 'sad':
            color = emotion_probability * np.asarray((0, 0, 255))
        elif emotion_text == 'happy':
            color = emotion_probability * np.asarray((255, 255, 0))
        elif emotion_text == 'surprise':
            color = emotion_probability * np.asarray((0, 255, 255))
        else:
            color = emotion_probability * np.asarray((0, 255, 0))

        color = color.astype(int)
        color = color.tolist()

        emotion_mode += ' ' + str(int(emotion_probability * 100))

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_mode,
                  color, 0, -45, 1, 1)

        emotion_mode = emotion_mode.replace(' ', '\t')
        emotion_mode = emotion_mode.replace('\n', '')

    bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

    ### FACE

    # 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(bgr_image, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(bgr_image, (left, bottom + 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(bgr_image, name, (left + 6, bottom + 25), font, 1.0, (255, 255, 255), 1)

    ###

    cv2.imshow('window_frame', bgr_image)

    if (fifthFrame % 10 == 0):
        ts = int(time.time())
        photoFileName = str(ts) + '_' + str(fifthFrame)
        fo.write(photoFileName + '\t' + emotion_mode + '\n')
        cv2.imwrite('OUTPUT/' + photoFileName + '.jpg', bgr_image)

    fifthFrame+=1

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

fo.close()

stats = {'angry':0, 'disgust':0, 'fear':0, 'happy':0, 'sad':0, 'surprise':0, 'neutral':0}

fo = open("emoLog.txt")
line = fo.readline()
while line:
    temp = line.split('\t')
    stats[temp[1]]+=1
    line = fo.readline()
fo.close()

print (stats)

total = 0

for cnt in stats.values():
    total += cnt
print (total)

labels = stats.keys()
sizes = stats.values()
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'red', 'brown', 'green']
explode = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2)  # explode 1st slice

# Plot
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        autopct='%1.1f%%', shadow=True, startangle=140)

plt.axis('equal')
plt.show()