本文整理汇总了Python中PoseTools.randomly_rotate方法的典型用法代码示例。如果您正苦于以下问题:Python PoseTools.randomly_rotate方法的具体用法?Python PoseTools.randomly_rotate怎么用?Python PoseTools.randomly_rotate使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类PoseTools
的用法示例。
在下文中一共展示了PoseTools.randomly_rotate方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: readImages
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import randomly_rotate [as 别名]
def readImages(conf,dbType,distort,sess,data):
train_data,val_data = data
cur_data = val_data if (dbType=='val') else train_data
xs = []; locs = []
count = 0
while count < conf.batch_size:
[curxs,curlocs] = sess.run(cur_data)
# kk = curlocs[conf.eval2_selpt2,:]-curlocs[conf.eval2_selpt1,:]
# dd = np.sqrt(kk[0]**2 + kk[1]**2 )
# if dd>150:
# continue
if np.ndim(curxs)<3:
xs.append(curxs[np.newaxis,:,:])
else:
xs.append(curxs)
locs.append(curlocs)
count = count+1
xs = np.array(xs)
locs = np.array(locs)
locs = multiResData.sanitize_locs(locs)
if distort:
if conf.horzFlip:
xs,locs = PoseTools.randomly_flip_lr(xs, locs)
if conf.vertFlip:
xs,locs = PoseTools.randomly_flip_ud(xs, locs)
xs,locs = PoseTools.randomly_rotate(xs, locs, conf)
xs = PoseTools.randomly_adjust(xs, conf)
return xs,locs
示例2: read_images
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import randomly_rotate [as 别名]
def read_images(self, db_type, distort, sess, shuffle=None):
conf = self.conf
cur_data = self.val_data if (db_type == self.DBType.Val)\
else self.train_data
xs = []
locs = []
info = []
if shuffle is None:
shuffle = distort
# Tfrecords doesn't allow shuffling. Skipping a random
# number of records
# as a way to simulate shuffling. very hacky.
for _ in range(conf.batch_size):
if shuffle:
for _ in range(np.random.randint(100)):
sess.run(cur_data)
[cur_xs, cur_locs, cur_info] = sess.run(cur_data)
xs.append(cur_xs)
locs.append(cur_locs)
info.append(cur_info)
xs = np.array(xs)
tw = (2*conf.time_window_size + 1)
b_sz = conf.batch_size * tw
xs = xs.reshape( (b_sz, ) + xs.shape[2:])
locs = np.array(locs)
locs = multiResData.sanitize_locs(locs)
xs = PoseTools.adjust_contrast(xs, conf)
# ideally normalize_mean should be here, but misc.imresize in scale_images
# messes up dtypes. It converts float64 back to uint8.
# so for now it'll be in update_fd.
# xs = PoseTools.normalize_mean(xs, conf)
if distort:
if conf.horzFlip:
xs, locs = PoseTools.randomly_flip_lr(xs, locs, tw)
if conf.vertFlip:
xs, locs = PoseTools.randomly_flip_ud(xs, locs, tw)
xs, locs = PoseTools.randomly_rotate(xs, locs, conf, tw)
xs, locs = PoseTools.randomly_translate(xs, locs, conf, tw)
xs = PoseTools.randomly_adjust(xs, conf, tw)
# else:
# rows, cols = xs.shape[2:]
# for ndx in range(xs.shape[0]):
# orig_im = copy.deepcopy(xs[ndx, ...])
# ii = copy.deepcopy(orig_im).transpose([1, 2, 0])
# mat = np.float32([[1, 0, 0], [0, 1, 0]])
# ii = cv2.warpAffine(ii, mat, (cols, rows))
# if ii.ndim == 2:
# ii = ii[..., np.newaxis]
# ii = ii.transpose([2, 0, 1])
# xs[ndx, ...] = ii
self.xs = xs
self.locs = locs
self.info = info
示例3: read_images
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import randomly_rotate [as 别名]
def read_images(conf, db_type, distort, sess, data):
train_data, val_data = data
cur_data = val_data if (db_type == 'val') else train_data
xs = []
locs = []
exp_data = []
count = 0
while count < conf.batch_size:
[cur_xs, cur_locs, cur_exp_data] = sess.run(cur_data)
# kk = cur_locs[conf.shape_selpt2, :] - cur_locs[conf.shape_selpt1, :]
# dd = np.sqrt(kk[0] ** 2 + kk[1] ** 2)
# if dd>150:
# continue
if np.ndim(cur_xs) < 3:
xs.append(cur_xs[np.newaxis, :, :])
else:
cur_xs = np.transpose(cur_xs,[2,0,1])
xs.append(cur_xs)
locs.append(cur_locs)
exp_data.append(cur_exp_data)
count += 1
xs = np.array(xs)
locs = np.array(locs)
if distort:
if conf.horzFlip:
xs, locs = PoseTools.randomly_flip_lr(xs, locs)
if conf.vertFlip:
xs, locs = PoseTools.randomly_flip_ud(xs, locs)
xs, locs = PoseTools.randomly_rotate(xs, locs, conf)
# xs = PoseTools.randomlyAdjust(xs, conf)
return xs, locs, exp_data
示例4: readImages
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import randomly_rotate [as 别名]
def readImages(conf,dbType,distort,sess,data):
train_data,val_data = data
cur_data = val_data if (dbType=='val') else train_data
xs = []; locs = []
for ndx in range(conf.batch_size):
[curxs,curlocs] = sess.run(cur_data)
if np.ndim(curxs)<3:
xs.append(curxs[np.newaxis,:,:])
else:
xs.append(curxs)
locs.append(curlocs)
xs = np.array(xs)
locs = np.array(locs)
locs = multiResData.sanitize_locs(locs)
if distort:
if conf.horzFlip:
xs,locs = PoseTools.randomly_flip_lr(xs, locs)
if conf.vertFlip:
xs,locs = PoseTools.randomly_flip_ud(xs, locs)
xs,locs = PoseTools.randomly_rotate(xs, locs, conf)
xs = PoseTools.randomly_adjust(xs, conf)
return xs,locs
示例5: range
# 需要导入模块: import PoseTools [as 别名]
# 或者: from PoseTools import randomly_rotate [as 别名]
for n in range(50):
X_train, in_locs = build_toy_dataset(50)
X_all.append(X_train)
l_all.append(in_locs)
#
count = 0
from stephenHeadConfig import conf
conf.trange = 10
conf.rrange = 90
for i in range(n_epoch):
count = (count+1) % len(X_all)
X_test, test_locs = build_toy_dataset(50)
X_train = copy.deepcopy(X_all[count][:,np.newaxis,...])
in_locs = copy.deepcopy(l_all[count][:,np.newaxis,...])
X_train, in_locs = PoseTools.randomly_translate(X_train, in_locs, conf)
X_train, in_locs = PoseTools.randomly_rotate(X_train, in_locs, conf)
X_train = X_train[:,0,...]
in_locs = in_locs[:,0,...]
if myOpt:
sess.run(optimizer,feed_dict={X_ph:X_train,
y_ph:in_locs,
step:i})
train_loss[i] = sess.run(inference.loss,
feed_dict={X_ph: X_train,
y_ph: in_locs})
test_loss[i] = sess.run(inference.loss,
feed_dict={X_ph: X_test,
y_ph: test_locs})
if i%50==0:
pred_weights, pred_means, pred_std = \