import random
from _ast import mod

import tensorflow as tf
import numpy as np
from multiprocessing import Process
import cv2
import os
import copy

import moviepy.editor as mpe

import cv2
from time import sleep

minimum_probability = 0.6
sizes = [10,20,50,100]
IMG_SIZE = 96

faces = tf.keras.models.load_model("model/arctur8.h5")
plates = tf.keras.models.load_model("model/letters2.h5")

video = mpe.VideoFileClip('video.mp4')
frames = int(video.fps * video.duration)
print(video.fps)

def prepare(image):
  IMG_SIZE = 136
  new_array = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
  return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)

def image_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
  dim = None
  (h, w) = image.shape[:2]
  if width is None and height is None:
    return image
  if width is None:
    r = height / float(h)
    dim = (int(w * r), height)
  else:
    r = width / float(w)
    dim = (width, int(h * r))
  resized = cv2.resize(image, dim, interpolation=inter)
  return resized

def get_face_probability(img):
  # img2 = cv2.resize(img,(136,136))
  prediction = faces.predict([prepare(img)])
  return 1 - prediction[0]

def get_plate_probability(img):
  # img2 = cv2.resize(img,(136,136))
  prediction = plates.predict([prepare(img)])
  return 1 - prediction[0]

def check_face(y, x, h, w, imgG, img, size):
  cropped = imgG[x: x + size, y: y + size]
  plate_probability = get_plate_probability(cropped)
  face_probability = get_face_probability(cropped)
  if face_probability > minimum_probability or plate_probability > minimum_probability:
    cv2.rectangle(img, (y, x), (y + size, x + size), (size * 1.6, 255, 255 - size * 1.2), cv2.FILLED)
  return img

def censor(img):
  h, w, a = img.shape
  # print(h,w,a)
  img = image_resize(img, height=480)
  imgG = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  h, w = imgG.shape
  # print(w,h)
  # num = gcd(w,h)
  # counter = 0
  print(h, w)
  for size in range((h + w) // 12, 25, -12):
    for x in range(0, h - size, size // 6):
      for y in range(0, w - size, size // 6):
        # counter += 1
        # print(x, y, size)
        img = check_face(y, x, h, w, imgG, img, size)
        #tmp_img = copy.deepcopy(img)
        #cv2.rectangle(tmp_img, (y, x), (y + size, x + size), (0, 0, 255))
        #cv2.imshow('image', tmp_img)
        #cv2.waitKey(1)
        # print(str(x)+"/"+str(h),str(y)+"/"+str(w),size)
  # imgc = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
  #cv2.imwrite(imagepath, img)
  #cv2.destroyAllWindows()
  return img

clip = mpe.VideoFileClip("video.mp4")
new_frames = [frame for frame in clip.iter_frames()]
new_clip = mpe.ImageSequenceClip(new_frames,fps=clip.fps)
new_clip.write_videofile("test.mp4")