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()