# 資料增強
## 隨機裁切

%matplotlib inline
import tensorflow as tf
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt 
raw_image = Image.open('view.jpg')
raw_image = np.asarray(raw_image) #array和asarray都可以將結構資料轉化為ndarray，但是主要區別就是當資料來源是ndarray時，array仍然會copy出一個副本，佔用新的記憶體，但asarray不會。
print('origin image')
print(raw_image.shape)
plt.imshow(raw_image)
plt.show()

rand_crop_img = tf.random_crop(raw_image, [300,200,3])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    img_ = sess.run(rand_crop_img)
    print('random crop')
    print(img_.shape)
    plt.imshow(img_)
    plt.show()

## 翻面

%matplotlib inline
import tensorflow as tf
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt 
raw_image = Image.open('view.jpg')
raw_image = np.asarray(raw_image)
print('origin image')
print(raw_image.shape)
plt.imshow(raw_image)
plt.show()

flip_img = tf.image.flip_left_right(raw_image) # 左右翻轉 
# flip_img = tf.image.flip_up_down(raw_image) #上下翻轉
# flip_img = tf.image.transpose_image(raw_image) #對角線翻轉

# flip_img = tf.image.random_flip_up_down(raw_image) #隨機上下翻轉
# flip_img = tf.image.random_flip_left_right(raw_image) #隨機左右翻轉 ; 50% will flip image

with tf.Session() as sess:

    img_ = sess.run(flip_img)
    print('flip')
    print(img_.shape)
    plt.imshow(img_)
    plt.show()

## 調整亮度

%matplotlib inline
import tensorflow as tf
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt 
raw_image = Image.open('view.jpg')
raw_image = np.asarray(raw_image)
print('origin image')
print(raw_image.shape)
plt.imshow(raw_image)
plt.show()

bright_img = tf.image.adjust_brightness(raw_image, delta= 0.5) #delta值應在0~1之間 ;因為以float type添加到圖片中 ; 數值越大越亮

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    img_ = sess.run(bright_img)
    print('adjust brightness')
    print(img_.shape)
    plt.imshow(img_)
    plt.show()