tensorflow image

1.tensorflow read image

import tensorflow as tf
import matplotlib.pyplot as plt

image_raw_data_jpg = tf.gfile.FastGFile('./img1.jpg', 'rb').read() #'r'的形式报错
image_raw_data_png = tf.gfile.FastGFile('./img1.png', 'rb').read()
with tf.Session() as sess:
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg) #图像解码
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.float32)

    img_data_png = tf.image.decode_png(image_raw_data_png)
    img_data_png = tf.image.convert_image_dtype(img_data_png, dtype=tf.float32)
    print(sess.run(img_data_png))
    plt.figure(1)
    plt.imshow(img_data_jpg.eval())
    plt.figure(2)
    plt.imshow(sess.run(img_data_png))
    plt.show()

 

2.tensorflow encode image

import tensorflow as tf
import matplotlib.pyplot as plt

image_raw_data_jpg = tf.gfile.FastGFile('img1.jpg', 'rb').read()

with tf.Session() as sess:
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg) #解码
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.uint8)
    encode_image_jpg = tf.image.encode_jpeg(img_data_jpg) #jpg编码
    encode_image_png = tf.image.encode_png(img_data_jpg)  #png编码

    with tf.gfile.GFile('encode_jpg.jpg', 'wb') as f:
        f.write(encode_image_jpg.eval())
    with tf.gfile.FastGFile('encode_png.png', 'wb') as f:#无阻赛以较快的方式获取文本操作句柄
        f.write(sess.run(encode_image_png))

3.tensorflow resize image

import tensorflow as tf
import matplotlib.pyplot as plt

image_raw_data_jpg = tf.gfile.FastGFile('img1.jpg', 'rb').read()
with tf.Session() as sess:
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg)
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg,dtype=tf.float32)
    resize0 = tf.image.resize_images(img_data_jpg, (200, 200), method=0)
    resize1 = tf.image.resize_images(img_data_jpg, (300, 300), method=1)
    resize2 = tf.image.resize_images(img_data_jpg, (400, 400), method=2)
    resize3 = tf.image.resize_images(img_data_jpg, (500, 500), method=3)
    print(img_data_jpg.get_shape())
    print(resize0.get_shape())
    print(resize1.get_shape())
    print(resize2.get_shape())
    print(resize3.get_shape())
    plt.figure(0)
    plt.imshow(img_data_jpg.eval())
    plt.figure(1)
    plt.imshow(resize0.eval())
    plt.figure(2)
    plt.imshow(resize1.eval())
    plt.figure(3)
    plt.imshow(resize2.eval())
    plt.figure(4)
    plt.imshow(resize3.eval())
    plt.show()
"""
class ResizeMethod(object):
  BILINEAR = 0
  NEAREST_NEIGHBOR = 1
  BICUBIC = 2
  AREA = 3
0：双线性差值。1：最近邻居法。2：双三次插值法。3：面积插值法。

def resize_images(images,
                  size,
                  method=ResizeMethod.BILINEAR,
                  align_corners=False):
"""

C:\Python36\python.exe C:/tensorflow/imageresize.py
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(200, 200, ?)
(300, 300, ?)
(400, 400, ?)
(500, 500, ?)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

4.tensorflow image crop or pad

import matplotlib.pyplot as plt
import tensorflow as tf
image_raw_data_jpg = tf.gfile.FastGFile('img1.jpg', 'rb').read()
with tf.Session() as sess:
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg)
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, dtype=tf.float32)
    crop = tf.image.resize_image_with_crop_or_pad(img_data_jpg, 200, 200)
    pad = tf.image.resize_image_with_crop_or_pad(img_data_jpg, 5000, 5000)
    crop1 = tf.image.central_crop(img_data_jpg, 1)  #原图
    pad1 = tf.image.central_crop(img_data_jpg, 0.5) #原图的一半
    plt.figure('crop')
    plt.imshow(crop.eval())
    plt.figure('pad')
    plt.imshow(pad.eval())
    plt.figure('crop1')
    plt.imshow(crop1.eval())
    plt.figure('pad1')
    plt.imshow(pad1.eval())
    plt.show()

5.色彩调整

import tensorflow as tf
import matplotlib.pyplot as plt

image_raw_data_jpg = tf.gfile.FastGFile('img1.jpg', 'rb').read()

with tf.Session() as sess:
    img_data_jpg = tf.image.decode_jpeg(image_raw_data_jpg)
    img_data_jpg = tf.image.convert_image_dtype(img_data_jpg, tf.float32)
    img1 = tf.image.adjust_brightness(img_data_jpg, 0.5)#调整图像的亮度
    img2 = tf.image.adjust_contrast(img_data_jpg, 5)#调整图像的对比度
    img3 = tf.image.adjust_hue(img_data_jpg, 0.8)#调整色相
    img4 = tf.image.adjust_saturation(img_data_jpg, 5)#调整饱和度
    plt.figure('org')
    plt.imshow(img_data_jpg.eval())
    plt.figure('adjust_brightness')
    plt.imshow(img1.eval())
    plt.figure('adjust_contrast')
    plt.imshow(img2.eval())
    plt.figure('adjust_hue')
    plt.imshow(img3.eval())
    plt.figure('adjust_saturation')
    plt.imshow(img4.eval())
    plt.show()