我希望根据边界框获得边界框并剪裁图片,因此我使用了tf.image.sample_distorted_bounding_box
。但是我失败了,我做错了什么?
我的结果看起来像
根据边界框的边界框和剪裁图片不匹配。
我的代码:
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()
答案 0 :(得分:0)
这是因为每次调用.eval()
都会触发新的图形运行,从而产生一个新的随机边界框。
要获得一致的输出,您需要同时运行运算符,例如
res = sess.run([image_with_box[0], distorted_image])