--- a
+++ b/features/FeatureExtract.py
@@ -0,0 +1,140 @@
+'''
+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)