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utils.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
def train_model(loss, global_step, data_num, args):
lr_factor = 0.1
lr_epoch = args.lr_epoch.strip().split(',')
lr_epoch = list(map(int, lr_epoch))
boundaries = [epoch*data_num//args.batch_size for epoch in lr_epoch]
lr_values = [args.learning_rate*(lr_factor**x) for x in range(0, len(lr_epoch)+1)]
lr_op = tf.train.piecewise_constant(global_step, boundaries, lr_values)
optimizer = tf.train.AdamOptimizer(lr_op)
train_op = slim.learning.create_train_op(loss, optimizer, global_step=global_step)
return train_op, lr_op
def GaussianMaps(sigma):
d = int(3 * sigma + 0.5)
cx, cy = (d, d)
Pixels = np.zeros((2 * d, 2 * d, 2), dtype=np.int32)
value = np.zeros((2 * d, 2 * d, 1), dtype=np.float32)
for x in range(2 * d):
for y in range(2 * d):
D = (x - cx) ** 2 + (y - cy) ** 2
Pixels[y,x] = (x - cx, y - cy)
if D < (3 * sigma) ** 2:
value[y,x] = np.exp(-D / 2.0 / sigma / sigma)
else:
value[y,x] = 0.0
return Pixels, value, (2*d,2*d)
def LandmarkImage(Landmarks, image_size, sigma=None):
if sigma is None:
sigma = tf.to_float(tf.reduce_max(image_size[1:3]))/4
d = tf.to_int32(3 * sigma + 0.5)
xx = tf.tile(tf.expand_dims(tf.range(-d, d, 1), 0), (2 * d, 1))
yy = tf.tile(tf.expand_dims(tf.range(-d, d, 1), 1), (1, 2 * d))
Pixels = tf.concat([tf.expand_dims(yy,-1), tf.expand_dims(xx,-1)], axis=-1)
D = tf.reduce_sum(tf.square(tf.to_float(Pixels)), axis=-1)
zeros = tf.zeros((2 * d, 2 * d), dtype=tf.float32)
Gaussian = tf.exp(-D/(2*sigma*sigma))
values = tf.where(tf.greater(D, (3 * sigma) ** 2), zeros, Gaussian)
shape = tf.to_float(tf.expand_dims(image_size[1:3],axis=0))
def Do(L):
def DoIn(Point):
intPoint = tf.to_int32(Point)
locations = Pixels + intPoint
img = tf.scatter_nd(locations, values, shape=(image_size[1], image_size[2]))
return img
L = tf.reverse(tf.reshape(L, [-1, 2]), [-1])*shape
L = tf.map_fn(DoIn, L)
L = tf.reshape(tf.reduce_max(L, axis=0), (image_size[1], image_size[2]))
return L
Landmarks = tf.clip_by_value(Landmarks, 0, 1)
return tf.map_fn(Do, Landmarks)
def LandmarkImage_98(Landmarks, image_size, sigma=None):
if sigma is None:
sigma = tf.to_float(tf.reduce_max(image_size[1:3]))/4
d = tf.to_int32(3 * sigma + 0.5)
xx = tf.tile(tf.expand_dims(tf.range(-d, d, 1), 0), (2 * d, 1))
yy = tf.tile(tf.expand_dims(tf.range(-d, d, 1), 1), (1, 2 * d))
Pixels = tf.concat([tf.expand_dims(yy,-1), tf.expand_dims(xx,-1)], axis=-1)
D = tf.reduce_sum(tf.square(tf.to_float(Pixels)), axis=-1)
zeros = tf.zeros((2 * d, 2 * d), dtype=tf.float32)
Gaussian = tf.exp(-D/(2*sigma*sigma))
values = tf.where(tf.greater(D, (3 * sigma) ** 2), zeros, Gaussian)
shape = tf.to_float(tf.expand_dims(image_size[1:3],axis=0))
#print('debug 0 ::','shape value :',shape) #(1,2)
def Do(L):
def DoIn(Point):
intPoint = tf.to_int32(Point)
locations = Pixels + intPoint
#print('debug 2','intpoint shape:{} Pixels shape{} locations shape{}'.format(intPoint.shape,Pixels.shape,locations.shape))
#(2,) (,,2) (,,2)
img = tf.scatter_nd(locations, values, shape=(image_size[1], image_size[2]))
#print('debug 3','img shape:',img.shape)
#(56,56)
return img
L = tf.reverse(tf.reshape(L, [-1, 2]), [-1])*shape
#print('debug 5',L.shape)
#(98,2)
L = tf.map_fn(DoIn, L)
# L = tf.reshape(tf.reduce_max(L, axis=0), (image_size[1], image_size[2]))
L = tf.transpose(L,[1,2,0])
#print('debug 4:','L shape:',L.shape)
#(56,56)
return L
Landmarks = tf.clip_by_value(Landmarks, 0, 1)
#print('debug 1 ::','Landmarks shape',Landmarks.shape) #(n,196)
return tf.map_fn(Do, Landmarks)
if __name__ == "__main__":
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import cv2
landmarks_placeholder = tf.placeholder(tf.float32, shape=(None, 18), name='landmarks')
landmarks = np.asarray([[0, 0, 0, 5, 0, 10,\
5, 0, 5, 5, 5, 10,\
10,0, 10,5, 10,10]], dtype=np.float32)/16
img = LandmarkImage(landmarks_placeholder, (1,16,16,1))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(img,feed_dict={landmarks_placeholder:landmarks})
# print(out)
print(out.shape)
# for img in out:
# plt.xticks([])
# plt.yticks([])
# plt.imshow(img,cmap=plt.cm.hot)
# plt.show()
# plt.clf()
image=out[0]*255
for i in range(image.shape[2]):
print(image[:,:,i].shape)
# cv2.imwrite('./result{}.png'.format(i),image[:,:,i])