I want get a bounding box and clipped picture according to the bounding box , so I used tf.image.sample_distorted_bounding_box
. But I failed, What I did wrong? My result looks like
The bounding box and the clipped picture according to the bounding box does not match.
My code:
with tf.Session() as sess:
boxes = tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]])
image_float = tf.image.convert_image_dtype(img_data, tf.float32) # uint8 -> float
# resize image
image_small = tf.image.resize_images(image_float, [180, 267], method=0)
# Generate a single distorted bounding box.
begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
tf.shape(image_small),
bounding_boxes=boxes,
min_object_covered=0.1)
# Draw the bounding box in an image summary.
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image_small, 0),
bbox_for_draw)
tf.summary.image('images_with_box', image_with_box)
# Employ the bounding box to distort the image.
distorted_image = tf.slice(image_small, begin, size)
plt.figure(figsize = (30, 20))
plt.subplot(3, 1, 1)
plt.title("image with a random box")
plt.imshow(image_with_box[0].eval())
plt.subplot(3, 1, 2)
plt.title("destorted image")
plt.imshow(distorted_image.eval())
plt.show()
That is because each call to .eval()
triggers a new run of the graph and therefore produces a new random bounding box.
To have consistent outputs you need to run operators simultaneously, eg
res = sess.run([image_with_box[0], distorted_image])
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