'''

Created by Wang Qiuli

2020/5/23

'''

import cv2

import numpy as np 

import matplotlib.pyplot as plt

import features.slic

import features.rgb_gr

from PIL import Image

from skimage import io, color

from skimage import data, filters

def truncate_hu(image_array, max = 400, min = -900):

    image = image_array.copy()

    image[image > max] = max

    image[image < min] = min

    return image

def hist(img):

    '''

    return histgram values

    1 * 128

    '''

    img = truncate_hu(img)

    hist = cv2.calcHist([img],[0],None,[128],[-900,400])

    # print(hist.shape)

    # plt.subplot(121)

    # plt.imshow(img,'gray')

    # plt.xticks([])

    # plt.yticks([])

    # plt.title("Original")

    # plt.subplot(122)

    # plt.hist(img.ravel(),128,[-900,400])

    # plt.show()    

    return hist

def gray2rgb(rgb,imggray):

   R = rgb[:,:,0]

   G = rgb[:,:,1]

   B = ((imggray) - 0.299 * R - 0.587 * G) / 0.114

   grayRgb = np.zeros((rgb.shape))

   grayRgb[:, :, 2] = B

   grayRgb[:, :, 0] = R

   grayRgb[:, :, 1] = G

   return grayRgb

def super_pixel(img):

    '''

    return super_pixel images

    img w * h

    '''

    img = truncate_hu(img)

    # io.imsave('ori.png', img)

    img = np.expand_dims(img, 2)

    # # print(img.shape)

    rgb = np.concatenate((img, img, img), 2)

    # io.imsave('ori2.png', rgb)

    obj = slic.SLICProcessor(rgb, 4096, 5)

    res = obj.iterate_10times()

    return res

def standard_deviation(img):

    hist_value = hist(img)

    std = np.std(hist_value)

    # print(std)

    return std

def edge_detection(img):

    '''

    edge detection

    '''

    img = truncate_hu(img)

    # io.imsave('ori.png', img)

    # img = np.expand_dims(img, 2)

    # # # print(img.shape)

    # rgb = np.concatenate((img, img, img), 2)

    # gray= cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)

    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)

    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)

    z = cv2.Sobel(img, cv2.CV_16S, 1, 1)

    return x

    # io.imsave('canny1.png', x)

    # io.imsave('canny2.png', y)

    # io.imsave('canny3.png', z)

def gabor(img):

    filt_real, filt_imag = filters.gabor(img,frequency=0.6)   

    # io.imsave('filt_imag.png', filt_imag)

    return filt_imag

def threshold_void(img):

    void = truncate_hu(img, -600, -900)

    # io.imsave('void.png', void)

    return void

def normalization(image_array):

    image_array = image_array + 900

    max = 1300

    min = 0

    image_array = (image_array-min)/(max-min)  # float cannot apply the compute,or array error will occur

    # avg = image_array.mean()

    # image_array = image_array-avg

    image_array = image_array * 255

    return image_array   # a bug here, a array must be returned,directly appling function did't work

def toGrey(img):

    '''

    get grey-level images

    0-256

    '''

    img = truncate_hu(img)

    img_nor = normalization(img)

    # io.imsave('img_nor.png', img_nor)

    return img_nor

def OTSU(img):

    gray = toGrey(img)

    print(gray)

    gray.dtype="int16"

    print('int16')

    print(gray)

    retval, dst = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)

    io.imsave('OTSU.png', dst)