__author__ = 'ding'
'''
完整的图像预处理案例
'''
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
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)
elif color_ordering == 1:
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)
def preprocess_for_train(image, height, width, bbox):
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)
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image), bounding_boxes=bbox, min_object_covered=0.1
)
distort_image = tf.slice(image, bbox_begin, bbox_size)
distort_image = tf.image.resize_images(
distort_image, (height, width), method=np.random.randint(4)
)
distort_image = tf.image.random_flip_left_right(distort_image)
distort_image = distort_color(distort_image, np.random.randint(2))
return distort_image
image_raw_data = tf.gfile.GFile('./path/to/picture1.jpeg', 'rb').read()
with tf.Session() as sess:
image_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(6):
result = preprocess_for_train(image_data, 299, 299, boxes)
plt.figure(i)
plt.imshow(result.eval())
plt.show()
