tf.image.random_crop

tf.image.random_crop是TensorFlow中的一个函数，用于随机裁剪图像。它可以在给定的图像中随机选择一个区域，并将其裁剪为指定的大小。这个函数可以用于数据增强，以增加模型的鲁棒性和泛化能力。

相关问题

def load_image_train(img_path,mask_path): img = read_png(img_path) mask = read_png_label(mask_path) # 获取路径 img,mask = crop_img(img,mask) # 调用随机裁剪函数对图片进行裁剪 if tf.random.uniform(())>0.5: # 从均匀分布中返回随机值 如果大于0.5就执行下面的随机翻转 img = tf

.image.flip_left_right(img) # 随机水平翻转图片 mask = tf.image.flip_left_right(mask) # 随机水平翻转标签图 img,mask = normalize(img,mask) # 调用归一化函数对图片进行归一化 return img,mask # 返回处理后的图片和标签图

def load_image_test(img_path): img = read_png(img_path) img, _ = crop_img(img,None) # 调用随机裁剪函数对图片进行裁剪 img, _ = normalize(img,None) # 调用归一化函数对图片进行归一化 return img

def read_png(path): image = tf.io.read_file(path) # 读取路径的图片 image = tf.image.decode_png(image) # 对图片进行解码 image = tf.cast(image, tf.float32) # 转换数据类型为float32 return image

def read_png_label(path): image = tf.io.read_file(path) # 读取路径的图片 image = tf.image.decode_png(image) # 对图片进行解码 image = tf.image.rgb_to_grayscale(image) # 将图片转换为灰度图 image = tf.cast(image, tf.int32) # 转换数据类型为int32 return image

def crop_img(img,mask): img = tf.image.random_crop(img, [256, 256, 3]) # 随机裁剪图片为256x256 if mask is not None: # 如果标签图不为空 mask = tf.image.random_crop(mask, [256, 256, 1]) # 随机裁剪标签图为256x256 return img,mask

def normalize(img,mask): img = img / 255.0 # 将图片像素值归一化到0-1之间 if mask is not None: # 如果标签图不为空 mask = mask // 255 # 将标签图像素值归一化到0-1之间 return img,mask

这段代码是一个图像处理的函数，主要用于加载训练和测试图像数据。函数中的read_png()函数用于读取图片文件并解码，crop_img()函数用于进行随机裁剪，normalize()函数用于归一化处理。load_image_train()函数用于加载训练图像和标签数据，并进行随机翻转操作。load_image_test()函数用于加载测试图像数据。请问你对这段代码有什么疑问吗？

def crop(example: dict[str, tf.Tensor], TypeError: 'type' object is not subscriptable

):
"""
Crops the image tensor in the input dictionary to a random size and aspect ratio.

Args:
- example: A dictionary containing the image tensor to be cropped.

Returns:
- A dictionary containing the cropped image tensor.
"""
image = example['image']
shape = tf.shape(image)
height, width = shape[0], shape[1]
aspect_ratio = tf.cast(width, tf.float32) / tf.cast(height, tf.float32)

# Define the range of possible aspect ratios and choose one randomly
min_aspect_ratio = tf.constant(0.8, dtype=tf.float32)
max_aspect_ratio = tf.constant(1.2, dtype=tf.float32)
aspect_ratio_range = tf.linspace(min_aspect_ratio, max_aspect_ratio, 100)
random_aspect_ratio = tf.random.shuffle(aspect_ratio_range)[0]

# Define the range of possible heights and choose one randomly
max_height = tf.cast(tf.math.ceil(tf.cast(width, tf.float32) / random_aspect_ratio), tf.int32)
height_range = tf.linspace(tf.cast(tf.math.floor(tf.cast(height, tf.float32) * 0.8), tf.int32), max_height, 100)
random_height = tf.random.shuffle(height_range)[0]

# Define the range of possible widths based on the chosen height and aspect ratio
random_width = tf.cast(tf.math.ceil(tf.cast(random_height, tf.float32) * random_aspect_ratio), tf.int32)

# Crop the image tensor to the randomly chosen size
cropped_image = tf.image.random_crop(image, [random_height, random_width, 3])

# Update the dictionary with the cropped image tensor
example['image'] = cropped_image
return example