def preprocess(x, y):
# x: 图片的路径，y：图片的数字编码
x = tf.io.read_file(x)
x = tf.image.decode_jpeg(x, channels=3) # RGBA
# 图片缩放
# x = tf.image.resize(x, [244, 244])
# 图片旋转
# x = tf.image.rot90(x,2)
# 随机水平翻转
x = tf.image.random_flip_left_right(x)
# 随机竖直翻转
# x = tf.image.random_flip_up_down(x)
# 图片先缩放到稍大尺寸
x = tf.image.resize(x, [244, 244])
# 再随机裁剪到合适尺寸
x = tf.image.random_crop(x, [224, 224, 3])
# x: [0,255]=> -1~1
x = tf.cast(x, dtype=tf.float32) / 255.
x = normalize(x)
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=5)
return x, y
def preprocess(x, y):
# x: 图片的路径，y：图片的数字编码
x = tf.io.read_file(x)
x = tf.image.decode_jpeg(x, channels=3) # RGBA
x = tf.image.resize(x, [256, 256])
x = tf.image.random_flip_left_right(x)
# x = tf.image.random_flip_up_down(x)
x = tf.image.random_brightness(x, max_delta=0.5) # 在某范围随机调整图片亮度
x = tf.image.random_contrast(x, 0.1, 0.6) # 在某范围随机调整图片对比度
x = tf.image.random_crop(x, [224, 224, 3])
# x: [0,255]=> 0~1
x = tf.cast(x, dtype=tf.float32) / 255.
x = normalize(x)
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=5)
return x, y