问题
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.
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.
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.
回答1:
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))
回答2:
I think this does what you want:
import tensorflow as tf
def crop_center_and_resize(img, size):
s = tf.shape(img)
w, h = s[0], s[1]
c = tf.minimum(w, h)
w_start = (w - c) // 2
h_start = (h - c) // 2
center = img[w_start:w_start + c, h_start:h_start + c]
return tf.image.resize_images(img, [size, size])
print(crop_center_and_resize(tf.zeros((80, 50, 3)), 100))
# Tensor("resize_images/Squeeze:0", shape=(100, 100, 3), dtype=float32)
There is also tf.image.crop_and_resize, which can do both things in one go, but you have to use normalized image coordinates with that:
import tensorflow as tf
def crop_center_and_resize(img, size):
s = tf.shape(img)
w, h = s[0], s[1]
c = tf.minimum(w, h)
wn, hn = h / c, w / c
result = tf.image.crop_and_resize(tf.expand_dims(img, 0),
[[(1 - wn) / 2, (1 - hn) / 2, wn, hn]],
[0], [size, size])
return tf.squeeze(result, 0)
回答3:
import tensorflow as tf
def central_square_crop(image):
h, w = image.get_shape()[0].value, image.get_shape()[1].value
side = tf.minimum(h, w)
begin_h = tf.maximum(0, h - side) // 2
begin_w = tf.maximum(0, w - side) // 2
return tf.slice(image, [begin_h, begin_w, 0], [side, side, -1])
def main():
image_t = tf.reshape(tf.range(5 * 7), [5, 7])
image_t = tf.transpose(tf.stack([image_t, image_t, image_t]), [1, 2, 0])
cropped_image_t = central_square_crop(image_t)
with tf.Session() as sess:
image, cropped_image = sess.run([image_t, cropped_image_t])
print(image[:, :, 0])
print(cropped_image[:, :, 0])
if __name__ == '__main__':
main()
Output before crop:
[[ 0 1 2 3 4 5 6]
[ 7 8 9 10 11 12 13]
[14 15 16 17 18 19 20]
[21 22 23 24 25 26 27]
[28 29 30 31 32 33 34]]
After crop:
[[ 1 2 3 4 5]
[ 8 9 10 11 12]
[15 16 17 18 19]
[22 23 24 25 26]
[29 30 31 32 33]]
Then, apply resizing as usual.
来源：https://stackoverflow.com/questions/54865717/tensorflow-crop-largest-central-square-region-of-image