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Copy pathVisualization04_tSNEforYourImages.py
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Visualization04_tSNEforYourImages.py
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# -*- coding: utf-8 -*-
"""
#对自己的图片文件做tSNE分类可视化
Created on Mon Dec 30 10:24:13 2019
@author: liujie
@code source: https://zhuanlan.zhihu.com/p/67491123
"""
import glob
from skimage import io
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
data_dir = "./data4/"
#--------------------------------------函数定义-----------------------------
#读取彩色图片,仅支持2级文件夹
def get_data_RGB(path):
cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
imgs=[]
labels=[]
print("reading:", cate)
#读取子文件里的图片文件
#！将循环次数idx赋值给图片的标记label
for idx,folder in enumerate(cate):
for im in glob.glob(folder+'/*.jpg'):
#print('reading the images:%s'%(im))
#print('reading the image label:%s'%(idx))
img=cv2.imread(im)
#将彩色图片灰度化
img=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
#将所有的图片resize = 32×32
img=cv2.resize(img,(32,32))
#将img numpy格式转为1维度
img=img.reshape(1024)
imgs.append(img)
labels.append(idx)
return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
#读取灰色图片,仅支持2级文件夹
def get_data_gray(path):
cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
imgs=[]
labels=[]
print("reading:", cate)
#读取子文件里的图片文件
#！将循环次数idx赋值给图片的标记label
for idx,folder in enumerate(cate):
for im in glob.glob(folder+'/*.png'):
img=cv2.imread(im)
#虽然MNIST是灰度的，但是这里不只为什么还是要执行将彩色图片灰度化
img=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
#将所有的图片resize = 32×32
img=cv2.resize(img,(28,28))
#将img numpy格式转为1维度
img=img.reshape(784)
imgs.append(img)
labels.append(idx)
return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
#下面的两个函数，
#一个定义了2维数据，一个定义了3维数据的可视化
def plot_embedding_2D(data, label, title):
x_min, x_max = np.min(data, 0), np.max(data, 0)
data = (data - x_min) / (x_max - x_min)
fig = plt.figure()
for i in range(data.shape[0]):
plt.text(data[i, 0], data[i, 1], str(label[i]),
color=plt.cm.Set1(label[i]),
fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.title(title)
return fig
def plot_embedding_3D(data,label,title):
x_min, x_max = np.min(data,axis=0), np.max(data,axis=0)
data = (data- x_min) / (x_max - x_min)
ax = plt.figure().add_subplot(111,projection='3d')
fig = plt.figure()
for i in range(data.shape[0]):
ax.text(data[i, 0], data[i, 1], data[i,2],str(label[i]), color=plt.cm.Set1(label[i]),fontdict={'weight': 'bold', 'size': 9})
return fig
#主函数
def main():
data, label = get_data_gray(data_dir) #根据自己的路径合理更改
print('Begining......') #时间会较长，所有处理完毕后给出finished提示
tsne_2D = TSNE(n_components=2, init='pca', random_state=0, verbose=1, perplexity=5, n_iter=1000, learning_rate=100) #调用TSNE
result_2D = tsne_2D.fit_transform(data)
#tsne_3D = TSNE(n_components=3, init='pca', random_state=0, perplexity=5)
# result_3D = tsne_3D.fit_transform(data)
print('Finished......')
#调用上面的两个函数进行可视化
fig1 = plot_embedding_2D(result_2D, label,'t-SNE')
plt.show(fig1)
#fig2 = plot_embedding_3D(result_3D, label,'t-SNE')
#plt.show(fig2)
#--------------------------------------执行-----------------------------
if __name__ == '__main__':
main()