Tensorflow 图像处理函数

import matplotlib.pyplot as plt
import tensorflow as tf

image_raw_data=tf.gfile.FastGFile("/home/cynthia/Pictures/IMG_20170701_150105.jpeg",'r').read()

with tf.Session() as sess:
img_data=tf.image.decode_jpeg(image_raw_data)
print img_data.eval()
plt.imshow(img_data.eval())
plt.show()
img_data=tf.image.convert_image_dtype(img_data,dtype=tf.uint8)
encoded_image=tf.image.encode_jpeg(img_data)
with tf.gfile.GFile("/home/cynthia/Pictures/1.jpeg","wb") as f:
f.write(encoded_image.eval())

resized=tf.image.resize_images(img_data,[300,300],method=0)
print img_data.get_shape()

tf.image.resize_images函数中method参数取值与相应的图像大小调整算法

[code]
0   双线行插值法
    ResizeMethod.BILINEAR: Bilinear interpolation.[/code]

1   最近邻居法      ResizeMethod.NEAREST_NEIGHBOR: Nearest neighbor interpolation.
2   双三次插值法    ResizeMethod.BICUBIC: Bicubic interpolation.
3   面积插值法      ResizeMethod.AREA: Area interpolation

In [1]:    
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
1. 读取图片
In [2]:    
image_raw_data = tf.gfile.FastGFile("../../datasets/cat.jpg",'r').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()
[[[163 161 140]
[163 161 138]
[163 161 140]
...,
[106 139  48]
[101 137  47]
[104 140  52]]

[[164 162 141]
[164 162 139]
[163 161 138]
...,
[107 138  45]
[103 138  46]
[108 138  50]]

[[167 162 142]
[167 162 140]
[164 162 139]
...,
[105 136  42]
[103 137  43]
[108 139  46]]

...,
[[207 200 181]
[207 200 181]
[206 199 180]
...,
[109  84  54]
[109  83  56]
[107  82  52]]

[[207 200 181]
[206 199 180]
[206 199 180]
...,
[108  83  52]
[106  81  51]
[106  81  50]]

[[207 200 181]
[206 199 180]
[206 199 180]
...,
[109  85  51]
[107  82  51]
[106  81  50]]]
(1797, 2673, 3)
2. 打印图片
In [3]:    
with tf.Session() as sess:
plt.imshow(img_data.eval())
plt.show()
3. 重新调整图片大小
In [4]:    
with tf.Session() as sess:
resized = tf.image.resize_images(img_data, [300, 300], method=0)

# TensorFlow的函数处理图片后存储的数据是float32格式的，需要转换成uint8才能正确打印图片。
print "Digital type: ", resized.dtype
cat = np.asarray(resized.eval(), dtype='uint8')
# tf.image.convert_image_dtype(rgb_image, tf.float32)
plt.imshow(cat)
plt.show()
Digital type:  <dtype: 'float32'>
4. 裁剪和填充图片
In [5]:    
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()
5. 截取中间50%的图片
In [6]:    
with tf.Session() as sess:
central_cropped = tf.image.central_crop(img_data, 0.5)
plt.imshow(central_cropped.eval())
plt.show()
6. 翻转图片
In [7]:    
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)
7. 图片色彩调整
In [8]:    
with tf.Session() as sess:
# 将图片的亮度-0.5。
#adjusted = tf.image.adjust_brightness(img_data, -0.5)

# 将图片的亮度-0.5
#adjusted = tf.image.adjust_brightness(img_data, 0.5)

# 在[-max_delta, max_delta)的范围随机调整图片的亮度。
adjusted = tf.image.random_brightness(img_data, max_delta=0.5)

# 将图片的对比度-5
#adjusted = tf.image.adjust_contrast(img_data, -5)

# 将图片的对比度+5
#adjusted = tf.image.adjust_contrast(img_data, 5)

# 在[lower, upper]的范围随机调整图的对比度。
#adjusted = tf.image.random_contrast(img_data, lower, upper)

plt.imshow(adjusted.eval())
plt.show()
8. 添加色相和饱和度
In [9]:    
with tf.Session() as sess:
adjusted = tf.image.adjust_hue(img_data, 0.1)
#adjusted = tf.image.adjust_hue(img_data, 0.3)
#adjusted = tf.image.adjust_hue(img_data, 0.6)
#adjusted = tf.image.adjust_hue(img_data, 0.9)

# 在[-max_delta, max_delta]的范围随机调整图片的色相。max_delta的取值在[0, 0.5]之间。
#adjusted = tf.image.random_hue(image, max_delta)

# 将图片的饱和度-5。
#adjusted = tf.image.adjust_saturation(img_data, -5)
# 将图片的饱和度+5。
#adjusted = tf.image.adjust_saturation(img_data, 5)
# 在[lower, upper]的范围随机调整图的饱和度。
#adjusted = tf.image.random_saturation(img_data, lower, upper)

# 将代表一张图片的三维矩阵中的数字均值变为0，方差变为1。
#adjusted = tf.image.per_image_whitening(img_data)

plt.imshow(adjusted.eval())
plt.show()
9. 添加标注框并裁减。
In [10]:    
with tf.Session() as sess:

boxes = tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]])

begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
tf.shape(img_data), bounding_boxes=boxes)

batched = tf.expand_dims(tf.image.convert_image_dtype(img_data, tf.float32), 0)
image_with_box = tf.image.draw_bounding_boxes(batched, bbox_for_draw)

distorted_image = tf.slice(img_data, begin, size)
plt.imshow(distorted_image.eval())
plt.show()


完整代码：代码源地址：https://github.com/caicloud/tensorflow-tutorial/blob/master/Deep_Learning_with_TensorFlow/0.12.0/Chapter07/2.2. 图像预处理完整样例.ipynb

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 1.随机调整图片的色彩，定义两种顺序。In[2]:

def distort_color(image, color_ordering=0):
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
else:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)

return tf.clip_by_value(image, 0.0, 1.0)

# 2.对图片进行预处理，将图片转化成神经网络的输入层数据。In[3]:

def preprocess_for_train(image, height, width, bbox):
# 查看是否存在标注框。
if bbox is None:
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)

# 随机的截取图片中一个块。
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image), bounding_boxes=bbox)
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image), bounding_boxes=bbox)
distorted_image = tf.slice(image, bbox_begin, bbox_size)

# 将随机截取的图片调整为神经网络输入层的大小。
distorted_image = tf.image.resize_images(distorted_image, [height, width], method=np.random.randint(4))
distorted_image = tf.image.random_flip_left_right(distorted_image)
distorted_image = distort_color(distorted_image, np.random.randint(2))
return distorted_image

# 3.读取图片。In[4]:

image_raw_data = tf.gfile.FastGFile("/home/cynthia/Pictures/cat.jpg", "r").read()
with tf.Session() as sess:
img_data = tf.image.decode_jpeg(image_raw_data)
boxes = tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]])
for i in range(9):
result = preprocess_for_train(img_data, 299, 299, boxes)
plt.imshow(result.eval())
plt.show()