import numpy as np import tensorflow as tf import matplotlib.pyplot as plt image_raw_data = tf.gfile.FastGFile("F:\\TensorFlowGoogle\\201806-github\\datasets\\cat.jpg",‘rb‘).read() with tf.Session() as sess: img_data = tf.image.decode_jpeg(image_raw_data) # 输出解码之后的三维矩阵。 print(img_data.eval()) img_data.set_shape([1797, 2673, 3]) print(img_data.get_shape())
with tf.Session() as sess: plt.imshow(img_data.eval()) plt.show()
with tf.Session() as sess: # 如果直接以0-255范围的整数数据输入resize_images,那么输出将是0-255之间的实数, # 不利于后续处理。本书建议在调整图片大小前,先将图片转为0-1范围的实数。 image_float = tf.image.convert_image_dtype(img_data, tf.float32) resized = tf.image.resize_images(image_float, [300, 300], method=0) plt.imshow(resized.eval()) plt.show()
with tf.Session() as sess: croped = tf.image.resize_image_with_crop_or_pad(img_data, 1000, 1000) padded = tf.image.resize_image_with_crop_or_pad(img_data, 3000, 3000) plt.imshow(croped.eval()) plt.show() plt.imshow(padded.eval()) plt.show()
with tf.Session() as sess: central_cropped = tf.image.central_crop(img_data, 0.5) plt.imshow(central_cropped.eval()) plt.show()
with tf.Session() as sess: # 上下翻转 #flipped1 = tf.image.flip_up_down(img_data) # 左右翻转 #flipped2 = tf.image.flip_left_right(img_data) #对角线翻转 transposed = tf.image.transpose_image(img_data) plt.imshow(transposed.eval()) plt.show() # 以一定概率上下翻转图片。 #flipped = tf.image.random_flip_up_down(img_data) # 以一定概率左右翻转图片。 #flipped = tf.image.random_flip_left_right(img_data)
原文地址:https://www.cnblogs.com/tszr/p/10885336.html
时间: 2024-10-10 15:38:35