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- # -*- encoding:utf-8 -*-
- import numpy as np
- from PIL import Image
- import random
- def read_data(path):
- with open(path) as f :
- lines=f.readlines()
- lines=random.sample(lines,int(len(lines)/1000))
- lines=[eval(line.strip()) for line in lines]
- X,Y=zip(*lines)
- X=np.array(X)
- X=X.reshape(-1,28*28)
- Y=np.array(Y)
- return X,Y
- def plot(x,width,height,path):
- img=[[0 for _ in range(0,width) ] for _ in range(0,height)]
- for i in range(0,height):
- for j in range(0,width):
- img[i][j]=x[i*height+j]
- img=np.array(img).astype('uint8')
- new_im = Image.fromarray(img)
- new_im.save(path)
- train_x,train_y = read_data("train_data")
- train_x_10,train_y_10 = read_data("train_data_10")
- train_x = np.concatenate((train_x,train_x_10))
- train_y = np.concatenate((train_y,train_y_10))
- count=0
- for i in range(0,len(train_x)):
- print(count)
- path="img/{}-{}.png".format(train_y[i],count)
- print(path)
- count+=1
- plot(train_x[i],28,28,path)
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