def gauss_smooth(mask, FILTER_SIZE):
SIGMA = 0.3 * (
(FILTER_SIZE - 1) * 0.5 - 1) + 0.8 #0.3*(FILTER_SIZE-1) + 0.8
smoother = Smoother({'data': mask}, FILTER_SIZE, SIGMA)
new_mask = smoother.get_output()
return new_mask
def gauss_smooth(self, mask, FILTER_SIZE):
'''
A tensorflow gauss smooth function.
Args:
mask: A 'Tensor' that to be smoothed
FILTER_SIZE: Spatial size of gaussian filter
Output:
A gauss smoothed 'Tensor' of same size as 'mask'
'''
SIGMA = 0.3 * (
(FILTER_SIZE - 1) * 0.5 - 1) + 0.8 #0.3*(FILTER_SIZE-1) + 0.8
smoother = Smoother({'data': mask}, FILTER_SIZE, SIGMA)
new_mask = smoother.get_output()
return new_mask
def smooth():
input_image = tf.placeholder(tf.float32, shape=[1, None, None, 3])
smoother = Smoother({'data': input_image}, FILTER_SIZE, SIGMA)
smoothed_image = smoother.get_output()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
image = Image.open(FLAGS.image_path)
image = np.array(image, dtype=np.float32)
image = image.reshape((1, image.shape[0], image.shape[1], 1))
smoothed = sess.run(smoothed_image,
feed_dict={image_input: image})
smoothed = smoothed / np.max(smoothed)
out_image = np.squeeze(smoothed)
out_image = Image.fromarray(np.squeeze(np.uint8(out_image * 255)))
def smooth():
Image_Placeholder = tf.placeholder( tf.float32, shape = [1, None, None, 3])
smoother = Smoother({'data':Image_Placeholder}, FILTER_SIZE, SIGMA)
smoothed_image = smoother.get_output()
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
image = Image.open(FLAGS.image_path)
image = np.array(image, dtype = np.float32)
image = image.reshape((1, image.shape[0], image.shape[1], 3))
smoothed = sess.run(smoothed_image,
feed_dict = {Image_Placeholder: image})
smoothed = smoothed / np.max(smoothed)
out_image = np.squeeze(smoothed)
out_image = Image.fromarray(np.squeeze(np.uint8(out_image * 255)))
out_image.show()