import numpy as np
import cv2

import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))  # /*

from viddeid.ioutils import path, directory
from viddeid import visualization
import viddeid.filters.space as sfilters
from viddeid import filters

#CONTROLS#
#=======================#
#Show the initial video on start up
show_initial_video = False
#Process all the videos in the test_videos folder
process_all = True
#Process a specific video with index (ignored if process_all is active)
process_index = 0
#=======================#

#Get test videos directory
dir_path = path.find_in_ancestor(__file__, "data/test_videos")
#Save all videos form the video directory
videos = [cv2.VideoCapture(p) for p in directory.get_files(dir_path) ]
#Initialize a  viewer for the processing
viewer = visualization.Viewer()
#Load the haar cascades
face_cascade = cv2.CascadeClassifier(
    path.find_in_ancestor(
        __file__,
        "data/cascade_classifiers/haarcascade_frontalface_default.xml"))

def filter(image):
    ft_im = np.fft.fft2(image)

    ker = sfilters.gaussian_mixture([
        ((2, 3), np.array([[2, 0], [0, 2]]), 1),
        ((5, -4), np.array([[2, 1], [1, 2]]), 1),
        ((-2, -4), np.array([[2, -1], [-1, 2]]), 1),
    ])
    ker = filters.center_to_origin(ker, image.shape)

    ft_ker = np.fft.fft2(ker)

    ft_conv = ft_im * ft_ker
    im_conv = np.fft.ifft2(ft_conv)
    filtered_image = np.clip(im_conv.real, 0, 255).astype(np.uint8) #Not sure about this part
    return filtered_images


if process_all != True:
    videos = [videos[process_index]]
for vidcap in videos:
    #Make an array for the frames and keep count of the current frame
    count = 0
    images = []
    success,image = vidcap.read()
    while success:
        #Convert to grayscale
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        images.append(gray)
        count += 1
        print('Reading frame: ' + count.__str__())
        success, image = vidcap.read()
    print ('Succesfully read video')
    #Show the initial video
    if show_initial_video ==True:
        print('Showing initial video...')
        viewer.display_video(images, 'Initial video')
    #Process the images
    print('Processing images...')
    filtered_images =[]
    reconstructed_images = []
    #===============CASCADES=============#
    for im in images:
        faces = face_cascade.detectMultiScale(im, 1.3, 1)
    for (x, y, w, h) in faces:
        #Filter the pixels that contain the face
        face_pixels = im[y:y+h , x:x+w]
        im[y:y+h , x:x+w] = filter(face_pixels) #This function must be created
        filtered_images.append(im)
        #To reconstruct the face you can apply a reconstruct function to those pixels
        im[y:y+h , x:x+w]  = reconstruct(im[y:y+h , x:x+w])
        reconstructed_images.append(im)
    #====================================#
    print('Processing done.')
    print('Showing processing video...')
    print('Press q or ESC to exit the video')
    viewer.display_video(filtered_images, 'Filtered images')
    viewer.display_video(reconstructed_images, 'Reconstructed images')