图像处理——tensorflow 数据增强

1-1  读取图像函数

tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

1-2  解码函数

tf.image.decode_jpeg(img)

   tf.image.decode_jepg 和 tf.image.decode_png函数分别用于解码 jpg 格式和 png 格式的图像。之后，可以显示图像。

   如果没有解码，读取的图像是一个字符串，无法显示。

 

1-3  

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

# 建立会话
with tf.Session() as sess:
    # 解码图像
    img = tf.image.decode_jpeg(img)
    # print(img.eval())

    # 显示图像
    plt.imshow(img.eval())
    plt.show()

2-1   编码图像与解码图像

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    # 解码图像
    img = tf.image.decode_jpeg(img) 
    # 编码图像
    img_encode = tf.image.encode_jpeg(img)
    with tf.gfile.GFile("D:\\2.jpg", 'wb') as file:
        file.write(img_encode.eval())

3-1  调整图片大小

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    # 调整图片大小
    img = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img = tf.image.resize_images(img, [400, 600], method=0)
    plt.imshow(img.eval())
    plt.show()

4-1  裁剪

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_cut = tf.image.resize_image_with_crop_or_pad(img, 200, 200)
    img_pad = tf.image.resize_image_with_crop_or_pad(img, 1500, 1200)
    plt.imshow(img_cut.eval())
    plt.show()

4-2  自动填充

5-1  按照比例调整图像大小

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)

    img = tf.image.central_crop(img, 0.5)
    plt.imshow(img.eval())
    plt.show()

6-1  翻转

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)

    # 上下
    img_0 = tf.image.flip_up_down(img)
    # 左右
    img_1 = tf.image.flip_left_right(img)
    # 对角线
    img_2 = tf.image.transpose_image(img)
    plt.imshow(img_0.eval())
    plt.show()

7-1  随机翻转图像

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
# print(img)

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.random_flip_up_down(img)
    img_1 = tf.image.random_flip_left_right(img)
    plt.imshow(img_0.eval())
    plt.show()

8-1  图像亮度调整

调整后，为保证像素值在0-1或者0-255的范围内，需要截断操作。

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img_reduce_brightness = tf.image.adjust_brightness(img_0, -0.5)
    img_add_brightness = tf.image.adjust_brightness(img_0, 0.5)
    img_reduce_brightness = tf.clip_by_value(img_reduce_brightness, 0.0, 1.0)
    plt.imshow(img_reduce_brightness .eval())
    plt.show()

img_add_brightness = tf.clip_by_value(img_add_brightness, 0.0, 255.0)

9-1  图像对比度按倍数减少或者增加

import tensorflow as tf
import matplotlib.pyplot as plt

# 读取图片
img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

# 建立会话
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img_reduce = tf.image.adjust_contrast(img_0, 0.5)
    img_add = tf.image.adjust_contrast(img_0, 5)
    plt.imshow(img_reduce.eval())
    plt.show()

增加对比度

10-1  调整图像饱和度

import tensorflow as tf
import matplotlib.pyplot as plt

img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()
with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img_reduce = tf.image.adjust_saturation(img_0, -0.5)
    img_add = tf.image.adjust_saturation(img_0, 0.5)
    img_reduce = tf.clip_by_value(img_reduce, 0.0, 1.0)
    plt.imshow(img_reduce.eval())
    plt.show()

11-1  图像标准化

将图像的像素调整为均值为0，方差为1。

import tensorflow as tf
import matplotlib.pyplot as plt

img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img_norm = tf.image.per_image_standardization(img_0)
    plt.imshow(img_norm.eval())
    plt.show()

12-1  调整图像色相

import tensorflow as tf
import matplotlib.pyplot as plt

img = tf.gfile.FastGFile("D:\\1.jpg", 'rb').read()

with tf.Session() as sess:
    img = tf.image.decode_jpeg(img)
    img_0 = tf.image.convert_image_dtype(img, dtype=tf.float32)
    img_reduce_hue = tf.image.adjust_hue(img_0, 0.1)
    img_add_hue = tf.image.adjust_hue(img_0, -0.1)
    img_reduce_hue = tf.clip_by_value(img_reduce_hue, 0.0, 1.0)
    plt.imshow(img_reduce_hue.eval())
    plt.show()