Tensorflow:裁剪图像的最大中央正方形区域 [英] Tensorflow: Crop largest central square region of image

问题描述

我的网络拍摄的图像尺寸为100 x 100像素.因此，我必须调整数据集大小不同的图像的大小.我希望能够从给定图像中提取最大的中央正方形区域，然后将其调整为100 x 100.

My network takes images of size 100 x 100 pixels. Therefore I have to resize the images of my dataset which are of different size. I want to be able to extract the largest central square region from a given image and then resize it to 100 x 100.

更准确地说，假设图像的宽度为200像素，高度为50像素.然后，我想提取最大的中央正方形区域，在此示例中为50 x 50，然后将图像调整为100 x 100像素.

To be more precisely, let's say an image has a width of 200 pixels and a height of 50 pixels. Then I want to extract the largest central square region which is in this example 50 x 50 followed by resizing the image to 100 x 100 pixels.

使用Tensorflow的正确方法是什么?现在，我正在使用tf.image.resize_images()扭曲图像，我想摆脱它.

What is the right way to do that using Tensorflow? Right now I am using tf.image.resize_images() which distorts the image and I want to get rid of that.

推荐答案

类似 crop_to_bounding_box 正在执行您需要的操作:

Sounds like crop_to_bounding_box is doing what you need:

import tensorflow as tf

def crop_center(image):
    h, w = image.shape[-3], image.shape[-2]
    if h > w:
        cropped_image = tf.image.crop_to_bounding_box(image, (h - w) // 2, 0, w, w)
    else:
        cropped_image = tf.image.crop_to_bounding_box(image, 0, (w - h) // 2, h, h)
    return tf.image.resize_images(cropped_image, (100, 100))

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