Question

这是代码的核心：

import numpy as np

def float_im(img):
    return np.divide(img, 255.)

# adapted from: https://stackoverflow.com/a/52463034/9768291
def random_crop(images):
    print("Started randomly cropping HR images")
    crop_h, crop_w = img_width, img_height
    y = []
    i = -1  # To begin with 0

    for img in images:
        if (img.shape[0] >= crop_h) and (img.shape[1] >= crop_w):
            print(i + 1, "images processed")
            #img = rgb2ycbcr(img)  # TODO: switch from RGB channels to CbCrY
            for _ in range(crops_p_img):
                # Cropping a random part of the image
                rand_h = np.random.randint(0, img.shape[0]-crop_h)
                rand_w = np.random.randint(0, img.shape[1]-crop_w)
                tmp_img = img[rand_h:rand_h+crop_h, rand_w:rand_w+crop_w]

                # Saving the images
                i += 1
                initial_image = save_np_img(tmp_img, y_data_path, str(i) + ".png")
                y.append(float_im(initial_image))  # From [0,255] to [0.,1.]

                # Augmenting the image  TODO: look into integrating "imgaug" library
                if augment_img:
                    # Vertical axis flip
                    i += 1
                    tmp_img = np.fliplr(initial_image)
                    y.append(float_im(save_np_img(tmp_img, y_data_path, str(i) + ".png")))

                    # Horizontal axis flip of tmp_img
                    i += 1
                    y.append(float_im(save_np_img(np.flipud(tmp_img), y_data_path, str(i) + ".png")))

                    # Horizontal axis flip of initial_image
                    i += 1
                    y.append(float_im(save_np_img(np.flipud(initial_image), y_data_path, str(i) + ".png")))
            else:
                continue
    return np.array(y)

def save_np_img(np_img, path, name):
    """
    To save the image.
    :param np_img: numpy_array type image
    :param path: string type of the existing path where to save the image
    :param name: string type that includes the format (ex:"bob.png")
    :return: numpy array
    """

    if type(np_img[0][0][0].item()) != int:
        np_img = np.multiply(np_img, 255).astype(int)

    im = Image.fromarray(np_img)
    im.save(path + name)

    return np_img

这是我的任务管理器中的示例。查看左下方的Committed字段：

它一直不断增加直到达到最大值（23.9GB），然后发生以下错误：

...
2184 images processed
2212 images processed
2240 images processed
2268 images processed
2296 images processed
Traceback (most recent call last):
  File "main.py", line 11, in <module>
    prepare_data.load_imgs(hr_img_path + "*.png")  # TODO: customize path (command line)
  File "C:\Users\payne\Documents\GitHub\PixelEnhancer\SR-ResCNN-Keras-\prepare_data.py", line 73, in load_imgs
    y_train = random_crop(images)
  File "C:\Users\payne\Documents\GitHub\PixelEnhancer\SR-ResCNN-Keras-\prepare_data.py", line 62, in random_crop
    y.append(float_im(save_np_img(np.flipud(initial_image), y_data_path, str(i) + ".png")))
  File "C:\Users\payne\Documents\GitHub\PixelEnhancer\SR-ResCNN-Keras-\utils.py", line 27, in float_im
    return np.divide(img, 255.)
MemoryError

我应该如何修改我的代码，以使该错误不再发生？ python 3.5如何检测到我已达到内存的最大容量？我正在使用它来预处理图像以训练神经网络。

检测内存使用情况

0 个答案: