#使用本地上传文件
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(name=fn, length=len(uploaded[fn])))
#删除文件以及文件夹
import os
import shutil
path='../source_file_clxiao/'
#os.remove(path) #删除文件
#os.removedirs(path) #删除空文件夹
#shutil.rmtree(path) #递归删除文件夹
#CV2图像显示
from google.colab.patches import cv2_imshow
!curl -o logo.png https://colab.research.google.com/img/colab_favicon_256px.png
import cv2
img = cv2.imread('logo.png', cv2.IMREAD_UNCHANGED)
cv2_imshow(img)
#文件上传加文件读取
from google.colab import files
import cv2
uploaded = files.upload()
ii=0
for fn in uploaded.keys():
input=cv2.imread(fn)
ii=ii+1
#图片读取加图像扩增
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
rotation_range=1,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.6,
zoom_range=0.6,
horizontal_flip=True,
fill_mode='nearest')
img = load_img('test_1.tif') # this is a PIL image
x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150)
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)
# the .flow() command below generates batches of randomly transformed images
# and saves the results to the `preview/` directory
i = 0
import matplotlib.pyplot as plt
from PIL import Image
list=datagen.flow(x, batch_size=4,save_to_dir='test_1/', save_prefix='test_1_', save_format='tif')
#print(list.size)
for batch in list:
i += 1
if i > 5:
break # otherwise the generator would loop indefinitely
print(batch.size)
#plt.imshow(batch)
#cv2.WaitKey(20)