zoom of the image

As an AI language model, I don't have the ability to show images, but I can provide information on how to zoom an image. To zoom an image, you can use any photo editing software or image viewer program that allows you to zoom in and out. You can also use the zoom feature on your computer or mobile device, which is usually found in the accessibility or display settings. By zooming in, you can see more details in the image, and by zooming out, you can see more of the image in a smaller space.

相关问题

class GraspDatasetBase(torch.utils.data.Dataset): """ An abstract dataset for training GG-CNNs in a common format. """ def __init__(self, output_size=300, include_depth=True, include_rgb=False, random_rotate=False, random_zoom=False, input_only=False): """ :param output_size: Image output size in pixels (square) :param include_depth: Whether depth image is included :param include_rgb: Whether RGB image is included :param random_rotate: Whether random rotations are applied :param random_zoom: Whether random zooms are applied :param input_only: Whether to return only the network input (no labels) """ self.output_size = output_size self.random_rotate = random_rotate self.random_zoom = random_zoom self.input_only = input_only self.include_depth = include_depth self.include_rgb = include_rgb self.grasp_files = [] if include_depth is False and include_rgb is False: raise ValueError('At least one of Depth or RGB must be specified.')

这段代码是一个抽象类 GraspDatasetBase，用于在一个通用的格式中训练 GG-CNNs。该类的构造函数包含了多个参数，例如输出图像的大小、是否包括深度图像、是否包括 RGB 图像、是否进行随机旋转、是否进行随机缩放以及是否仅返回网络输入等。在该类中，还定义了一个变量 grasp_files 用于存储夹爪数据文件。如果 include_depth 和 include_rgb 均为 False，会抛出 ValueError 异常。

x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.1, random_state = 999) # Reshape image in 3 dimensions (height = 100, width = 125 , canal = 3) x_train = x_train.reshape(x_train.shape[0], *(100, 125, 3)) x_test = x_test.reshape(x_test.shape[0], *(100, 125, 3)) x_validate = x_validate.reshape(x_validate.shape[0], *(100, 125, 3)) # With data augmentation to prevent overfitting datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180) zoom_range = 0.1, # Randomly zoom image width_shift_range=0.12, # randomly shift images horizontally (fraction of total width) height_shift_range=0.12, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=True) # randomly flip images datagen.fit(x_train)代码段一开始没有问题，但是多次执行后出现报错MemoryError: Unable to allocate 1.52 GiB for an array with shape (5423, 100, 125, 3) and data type float64

这个错误提示表明在程序执行过程中发生了内存不足的错误，无法为一个形状为(5423, 100, 125, 3)、数据类型为float64的数组分配1.52 GiB的内存空间。这通常是因为程序在多次执行后，内存占用逐渐增加，导致内存不足而出现的。有以下几种方法可以尝试解决这个问题：

1. 增加计算机的物理内存：可以增加计算机的物理内存，以提供更多的内存空间给程序使用。

2. 减少数据集大小：可以减少数据集的大小，以减少程序所需的内存空间。

3. 减少图像尺寸：可以尝试减小图像的尺寸，以减少图像所需的内存空间。

4. 减少数据增强的程度：可以减少数据增强的程度，以降低程序所需的内存空间。

5. 释放不再使用的内存：可以在程序执行过程中，释放不再使用的内存，以提高内存使用效率。可以使用Python内置的`gc`模块手动触发垃圾回收机制，回收不再使用的内存。