--- a
+++ b/features/read_csv.py
@@ -0,0 +1,148 @@
+import csv 
+import platform 
+import numpy as np 
+import matplotlib.pyplot as plt 
+import os
+import otsu 
+from FeatureExtract import *
+from skimage.feature import hog, haar_like_feature,local_binary_pattern,multiblock_lbp,daisy
+import pysift
+import time 
+
+def readCSV(filename):
+    lines = []
+    with open(filename, "r") as f:
+        csvreader = csv.reader(f)
+        for line in csvreader:
+            lines.append(line)
+    return lines 
+
+ori_path = 'C:/Users/RL/Desktop/可解释性的特征学习分类/nodules/ori_hu/'
+
+def get_feature_name(temp, index, value):
+    name = []
+    for t in temp:
+        if float(t[index]) == value:
+            if len(t[1]) == 1:
+                name.append('LIDC-IDRI-000' + t[1] + '_' + t[3] + '_' + t[2] + '.npy')
+            elif len(t[1]) == 2:
+                name.append('LIDC-IDRI-00' + t[1] + '_' + t[3] + '_' + t[2] + '.npy')
+            elif len(t[1]) == 3:
+                name.append('LIDC-IDRI-0' + t[1] + '_' + t[3] + '_' + t[2] + '.npy')
+            else:
+                name.append('LIDC-IDRI-' + t[1] + '_' + t[3] + '_' + t[2] + '.npy')
+    return name 
+
+def read_name():
+    labelCSV = readCSV('C:/Users/RL/Desktop/可解释性的特征学习分类/特征图片/malignancy.csv')
+    Max = []
+    Min = []
+    temp = [labelCSV[i] for i in range(len(labelCSV))]
+    final = np.array([[float(temp[i][j + 21]) for j in range(9)]  for i in range(len(temp)) if '0' not in temp[i]])
+    Max = final.max(axis=0)
+    Min = final.min(axis=0)
+    # prob_map = [[final[i][j] / (Max[j] + Min[j] + 1) if j != 8 else final[i][j] >= 3.5 for j in range(final.shape[1])] for i in range(final.shape[0])]
+    # 29 28 27毛刺 26分叶 25 24 23 22内在的 21
+    # print(prob_map[0])  
+    name_maoci= get_feature_name(temp, 27, Max[6])
+    name_fenye = get_feature_name(temp, 26, Max[5])
+    name_solid = get_feature_name(temp, 28, Max[7])
+    name_non_solid = get_feature_name(temp, 28, 3)
+    name_moboli = get_feature_name(temp, 28, 1)
+    # with open('C:/Users/RL/Desktop/可解释性的特征学习分类/特征图片/name.txt',"w") as f:
+    #     f.write("*" * 10 + "maoci:"+ '\n')
+    #     for name in name_maoci:
+    #         f.write(name + '\n')
+    #     f.write("*" * 10 + "fenye:"+ '\n')
+    #     for name in name_fenye:
+    #         f.write(name+ '\n')
+    #     f.write("*" * 10 + "shixing:"+ '\n')
+    #     for name in name_solid:
+    #         f.write(name+ '\n')
+    #     f.write("*" * 10 + "yashixing:"+ '\n')
+    #     for name in name_non_solid:
+    #         f.write(name+ '\n')
+    #     f.write("*" * 10 + "moboli:"+ '\n')
+    #     for name in name_moboli:
+    #         f.write(name+ '\n')
+    return name_maoci, name_fenye, name_solid, name_non_solid, name_moboli
+
+def read_lidc(filename):
+    image = np.load(ori_path + filename)
+    return image 
+ 
+def image_feature_extract(image):
+    features = [image]
+    # otsu_image = otsu.helper(image.copy())
+    features.append(otsu.helper(image.copy()))
+    features.append(gabor(image.copy()))
+
+
+    # 添加新的方式
+    # lbp = local_binary_pattern(image.copy(), 3, 3, method='var')
+    # features.append(otsu._otsu(lbp))
+    # features.append(edge_detection(image.copy()))
+    _, hog_image = hog(gabor(image.copy()), orientations=9, pixels_per_cell=(8, 8),cells_per_block=(2, 2), visualize=True, multichannel=False)
+    features.append(hog_image) 
+
+
+    features.append(np.transpose(super_pixel(image.copy()),(2,0,1))[0])
+    features.append(local_binary_pattern(image.copy(), 4, 4, method='var')) # 会产生无穷大或者是无穷小
+    fd, hog_image = hog(image.copy(), orientations=9, pixels_per_cell=(4, 4),cells_per_block=(2, 2), visualize=True, multichannel=False)
+
+    _, descs_img = daisy(image.copy(), step=180, radius=58, rings = 2,  histograms=10,orientations=8,visualize=True)
+    features.append(hog_image)
+    features.append(np.transpose(descs_img.copy(),(2,0,1))[0])
+    features.append(descs_img)
+    kps, _ = pysift.computeKeypointsAndDescriptors(image.copy())
+    x = []
+    y = []
+    for kp in kps:
+        x.append(kp.pt[0])
+        y.append(kp.pt[1])
+    return features, x, y
+
+# 接下里就是 统计每个属性的图像特征输出
+if __name__ == "__main__":
+    a1, a2, a3, a4, a5 = read_name()
+    ori_image = []
+    # 不清楚normalize的影响
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0007_2_3000631.npy')))])# 毛刺
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0060_1_3000575.npy')))])# 分叶
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0003_4_3000611.npy')))]) # 磨玻璃
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0003_2_3000611.npy')))]) # 实性
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0008_1_3000549.npy')))]) # 亚实性 
+    ori_image.append([normalization(truncate_hu(read_lidc('LIDC-IDRI-0132_1_5418.npy')))]) # 空洞
+    X = []
+    Y = []
+    for i in range(6):
+        features, x, y = image_feature_extract(ori_image[i][0])
+        for feature in features:
+            ori_image[i].append(feature)
+        # print(len(ori_image[i]))
+        X.append(x)
+        Y.append(y)
+    name = ["origial","sift-key-point","otsu","gabor","new","super-pixel","lbp","hog","daisy-three-dim","daisy-gray"]
+    plt.figure()
+    # 对实性结节 对亚实性结节 对分叶 对毛玻璃 对空洞 对毛刺
+    numRows = 6
+    numCols = 10
+    font2 = {'family' : 'Times New Roman',
+            'weight' : 'normal',
+            'size'   : 7,
+            }
+    for i in range(numRows):
+        for j in range(numCols):
+            ax = plt.subplot(numRows,numCols,1 + i * numCols + j)
+            if i == 0:
+                ax.set_title(name[j],font2)
+            if j != numCols - 1:
+                plt.imshow(ori_image[i][j], cmap="gray")
+            else:
+                plt.imshow(ori_image[i][j])
+            if j == 1:
+                plt.scatter(X[i], Y[i], color='red', s=3, alpha=0.5)
+            plt.xticks([])
+            plt.yticks([])
+    # plt.savefig("C:/Users/RL/Desktop/可解释性的特征学习分类/特征图片/pictures/test" + str(time.time()) + ".png",dpi=1500)
+    plt.show()