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get_data_mini_after.py
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get_data_mini_after.py
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import tensorflow as tf
import scipy.misc
import random
from config import *
import utils
logger = utils.get_logger()
def get_rand_para(seed):
h = int(height / random_crop_rate)
w = int(width / random_crop_rate)
hh = tf.random_uniform([], minval=0, maxval=h - height, dtype=tf.int32, seed=seed)
ww = tf.random_uniform([], minval=0, maxval=w - width, dtype=tf.int32, seed=seed)
return {"h": hh, "w": ww, "flip": (hh + ww) % 2}
def warp_img(image, seed, para):
h = int(height / random_crop_rate)
w = int(width / random_crop_rate)
image = tf.image.resize_images(image, (h, w), method=tf.image.ResizeMethod.BILINEAR)
image = tf.slice(image, [para['h'], para['w'], 0], [height, width, 1])
image = tf.cond(tf.equal(para['flip'], 0), lambda: image, lambda: tf.image.flip_left_right(image))
image = tf.image.random_contrast(image, lower=0.5, upper=1.5, seed = seed)
image = tf.image.random_brightness(image, max_delta=32./255., seed = seed)
''' #random noise
noise = np.random.normal(0,0.05,image.shape)
image = image + noise
'''
return tf.clip_by_value(image, -0.5, 0.5)
def warp_flow(flow, para):
flow_x = tf.slice(flow, [0, 0, 0], [-1, -1, 1])
flow_y = tf.slice(flow, [0, 0, 1], [-1, -1, 1])
h = int(height / random_crop_rate)
w = int(width / random_crop_rate)
flow_x = tf.image.resize_images(flow_x, (h, w), method=tf.image.ResizeMethod.BILINEAR)
flow_y = tf.image.resize_images(flow_y, (h, w), method=tf.image.ResizeMethod.BILINEAR)
flow_x = tf.slice(flow_x, [para['h'], para['w'], 0], [height, width, 1])
flow_y = tf.slice(flow_y, [para['h'], para['w'], 0], [height, width, 1])
flow_x = (flow_x + (1 - tf.cast(para['w'], tf.float32) / w * 2)) / (height / float(h)) - 1
flow_y = (flow_y + (1 - tf.cast(para['h'], tf.float32) / h * 2)) / (width / float(w)) - 1
fliped_y = tf.image.flip_left_right(flow_y)
fliped_x = tf.image.flip_left_right(flow_x) * (-1) - 1.0 / width
flow_x = tf.cond(tf.equal(para['flip'], 0), lambda: flow_x, lambda: fliped_x)
flow_y = tf.cond(tf.equal(para['flip'], 0), lambda: flow_y, lambda: fliped_y)
return tf.concat([flow_x, flow_y], axis=2)
def warp_point(points, mask, para):
h = int(height / random_crop_rate)
w = int(width / random_crop_rate)
# points = points / [width, height, width, height] * 2 - 1
points_x = tf.stack([points[:,0], points[:,2]], axis=1)
points_y = tf.stack([points[:,1], points[:,3]], axis=1)
points_x = (points_x + (1 - tf.cast(para['w'], tf.float32) / w * 2)) / (height / float(h)) - 1
points_y = (points_y + (1 - tf.cast(para['h'], tf.float32) / h * 2)) / (width / float(w)) - 1
fliped_x = points_x * (-1) - 1.0 / width
points_x = tf.cond(tf.equal(para['flip'], 0), lambda: points_x, lambda: fliped_x)
points = tf.stack([points_x[:,0], points_y[:,0], points_x[:,1], points_y[:,1]], axis=1)
mask = tf.logical_and(tf.reduce_all(tf.logical_and(points >= -1, points <= 1), axis=1), mask)
logger.info('points.shape, mask.shape={},{}'.format(points.shape, mask.shape))
return points, mask
def get_rand_H(is_first, last_H):
H = tf.random_uniform([1], minval=rand_H_min[0, 0], maxval=rand_H_max[0, 0], dtype=tf.float32)
for i in range(3):
for j in range(3):
if (i == 0 and j == 0):
continue
H = tf.concat([H, tf.random_uniform([1], minval=rand_H_min[i, j], maxval=rand_H_max[i, j], dtype=tf.float32)], axis=0)
if (is_first):
return tf.reshape(H, [3, 3])
else:
return tf.reshape(H, [3, 3]) * rand_H_change_rate + last_H * (1 - rand_H_change_rate)
def mesh_grid(height, width):
with tf.variable_scope('_meshgrid'):
x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])),
tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0]))
y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1),
tf.ones(shape=tf.stack([1, width])))
x_t_flat = tf.reshape(x_t, (1, -1))
y_t_flat = tf.reshape(y_t, (1, -1))
ones = tf.ones_like(x_t_flat)
grid = tf.concat([x_t_flat, y_t_flat, ones], 0)
return grid
def get_rand_mask(is_first, last_H):
H = get_rand_H(is_first, last_H)
grid = mesh_grid(height, width)
T_g = tf.matmul(H, grid)
x_s = tf.slice(T_g, [0, 0], [1, -1])
y_s = tf.slice(T_g, [1, 0], [1, -1])
z_s = tf.slice(T_g, [2, 0], [1, -1])
x_s_flat = tf.reshape(x_s / z_s, [-1])
y_s_flat = tf.reshape(y_s / z_s, [-1])
t_1 = tf.ones(shape = tf.shape(x_s_flat))
t_0 = tf.zeros(shape = tf.shape(x_s_flat))
cond = tf.logical_or(tf.logical_or(tf.greater(t_1 * -1, x_s_flat), tf.greater(x_s_flat, t_1)),
tf.logical_or(tf.greater(t_1 * -1, y_s_flat), tf.greater(y_s_flat, t_1)))
black_pix = tf.reshape(tf.where(cond, t_1, t_0), [height, width])
return black_pix, H
def get_rand_black_mask():
max_dh = int(height * max_crop_rate / 2)
max_dh = max(0, max_dh)
max_dw = int(width * max_crop_rate / 2)
max_dw = max(0, max_dw)
h = tf.random_uniform([1], minval=0, maxval=max_dh + 1, dtype=tf.int32)
w = tf.random_uniform([1], minval=0, maxval=max_dw + 1, dtype=tf.int32)
zero = tf.zeros(shape=[1, 1])
one = tf.ones(shape=[1,1])
mask = tf.tile(zero, tf.concat([h, tf.ones([1], dtype=tf.int32) * width], axis=0))
temp = tf.concat([tf.tile(zero, tf.concat([height - h * 2, w], axis=0)), tf.tile(one, tf.concat([height - h * 2, width - w * 2], axis=0)), tf.tile(zero, tf.concat([height - h * 2, w], axis=0))], axis=1)
mask = tf.concat([mask, temp, mask], axis = 0)
mask = tf.reshape(mask, [height, width, 1])
return mask
def add_mask(pics):
is_first = True
last_H = tf.zeros([3, 3])
for i in range(before_ch):
temp = tf.reshape(tf.slice(pics, [0, 0, i], [-1, -1, 1]), [height, width])
mask, last_H = get_rand_mask(is_first, last_H)
is_first = False
temp = temp * (1 - mask) + mask * -1
temp = tf.expand_dims(temp, 2)
mask = tf.expand_dims(mask, 2)
if (i == 0):
ans = temp
ans_mask = mask
else:
ans = tf.concat([ans, temp], axis = 2)
ans_mask = tf.concat([ans_mask, mask], axis=2)
if input_mask:
return tf.concat([ans_mask, ans], axis=2)
else:
return ans
def get_img(path, pos):
image = tf.image.decode_jpeg( tf.read_file(tf.string_join([path, tf.as_string(pos), '.jpg'])))
image = tf.image.rgb_to_grayscale(image)
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.image.resize_images(image, (height, width), 0)
image = image - 0.5
image = tf.reshape(image, [1, height, width, 1])
return image
def read_and_decode(filepath, num_epochs, shuffle=True):
file_obj = open(filepath + 'list.txt')
file_txt = file_obj.read()
file_list = []
for f in file_txt.split(' '):
file_list.append(filepath + f.strip())
filename_queue = tf.train.string_input_producer(file_list, num_epochs=None, shuffle=shuffle)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'stable_path': tf.FixedLenFeature([], tf.string),
'unstable_path': tf.FixedLenFeature([], tf.string),
'pos': tf.FixedLenFeature([], tf.int64),
'flow': tf.VarLenFeature(tf.float32),
'feature_matches1': tf.VarLenFeature(tf.float32),
'feature_matches2': tf.VarLenFeature(tf.float32),
})
pos = tf.cast(features['pos'], tf.int64)
unstable_ = tf.concat([get_img(features['unstable_path'], pos - 1), get_img(features['unstable_path'], pos)], axis=0)
unstable1 = []
unstable2 = []
stable1 = []
stable2 = []
for i in indices:
if (i >= 0):
stable1.append(get_img(features['stable_path'], pos - 1 - i))
stable2.append(get_img(features['stable_path'], pos - i))
if (i <= 0):
unstable1.append(get_img(features['unstable_path'], pos - 1 - i))
unstable2.append(get_img(features['unstable_path'], pos - i))
stable1_ = tf.concat(stable1, axis=3)
stable2_ = tf.concat(stable2, axis=3)
stable_ = tf.concat([stable1_, stable2_], axis=0)
unstable1_ = tf.concat(unstable1, axis=3)
unstable2_ = tf.concat(unstable2, axis=3)
unstable_ = tf.concat([unstable1_, unstable2_], axis=0)
#stable_input = tf.reshape(tf.sparse_tensor_to_dense(features['stable']), [2, height, width, -1])
#stable_ = tf.concat((stable_input[..., 0, None], stable_input[:, :, :, -before_ch:]), axis=3)
#unstable_ = tf.reshape(tf.sparse_tensor_to_dense(features['unstable']), [2, height, width, -1])#[:, :, :after_ch + 2]
#with tf.control_dependencies([
# tf.assert_equal(tf.shape(stable_[0]), tf.constant((height, width, before_ch + 1))),
# tf.assert_equal(tf.shape(unstable_[0]), tf.constant((height, width, 1))),
# ]):
# stable_ = tf.identity(stable_)
logger.info('stable_[0].shape={}'.format(stable_[0].shape))
logger.info('unstable_[0].shape={}'.format(unstable_[0].shape))
stable_ = tf.concat([stable_[0], stable_[1]], axis=2)
unstable_ = tf.concat([unstable_[0], unstable_[1]], axis=2)
flow_ = tf.reshape(tf.sparse_tensor_to_dense(features['flow']), [height, width, -1])[:, :, :2]
feature_matches1_ = tf.reshape(tf.sparse_tensor_to_dense(features['feature_matches1']), [-1, 4])
feature_matches2_ = tf.reshape(tf.sparse_tensor_to_dense(features['feature_matches2']), [-1, 4])
num_matches1_ = tf.shape(feature_matches1_)[0]
num_matches2_ = tf.shape(feature_matches2_)[0]
logger.info('feature_matches1_.shape={}, feature_matches2_.shape=q{}'.format(feature_matches1_.shape, feature_matches2_.shape))
with tf.control_dependencies([tf.assert_less(num_matches1_, tf.constant(max_matches)),
tf.assert_less(num_matches2_, tf.constant(max_matches)),
]):
feature_matches1_ = tf.identity(feature_matches1_)
feature_matches1_ = tf.pad(feature_matches1_, ((0, max_matches - num_matches1_), (0, 0)))
feature_matches2_ = tf.pad(feature_matches2_, ((0, max_matches - num_matches2_), (0, 0)))
feature_matches1_.set_shape([max_matches, 4])
feature_matches2_.set_shape([max_matches, 4])
mask1_ = tf.sequence_mask([num_matches1_], max_matches)[0]
mask2_ = tf.sequence_mask([num_matches2_], max_matches)[0]
seed = random.randint(0, 2**31 - 1)
para = get_rand_para(seed)
for i in range((before_ch + 1) * 2):
temp = tf.slice(stable_, [0, 0, i], [-1, -1, 1])
if (i == 0):
stable = warp_img(temp, seed, para)
else:
stable = tf.concat([stable, warp_img(temp, seed, para)], 2)
for i in range((after_ch + 1) * 2):
temp = tf.slice(unstable_, [0, 0, i], [-1, -1, 1])
if (i == 0):
unstable = warp_img(temp, seed, para)
else:
unstable = tf.concat([unstable, warp_img(temp, seed, para)], 2)
x1 = tf.concat([add_mask(tf.slice(stable, [0, 0, 1], [-1, -1, before_ch])),
tf.slice(unstable, [0, 0, 0], [-1, -1, after_ch + 1])], 2)
y1 = tf.slice(stable, [0, 0, 0], [-1, -1, 1])
x2 = tf.concat([add_mask(tf.slice(stable, [0, 0, before_ch + 2], [-1, -1, before_ch])),
tf.slice(unstable, [0, 0, after_ch + 1], [-1, -1, after_ch + 1])], 2)
y2 = tf.slice(stable, [0, 0, before_ch + 1], [-1, -1, 1])
flow = warp_flow(flow_, para)
feature_matches1, mask1 = warp_point(feature_matches1_, mask1_, para)
feature_matches2, mask2 = warp_point(feature_matches2_, mask2_, para)
return x1, y1, x2, y2, flow, feature_matches1, mask1, feature_matches2, mask2
def run():
x, y = read_and_decode("data/train.tfrecords", 3, False)
x_batch, y_batch = tf.train.shuffle_batch([x, y],
batch_size=30, capacity=2000,
min_after_dequeue=1000)
init = tf.initialize_all_variables()
coord = tf.train.Coordinator()
with tf.Session() as sess:
sess.run(init)
sess.run(tf.initialize_local_variables())
threads = tf.train.start_queue_runners(sess=sess, coord = coord)
x_b, y_b = sess.run([x_batch, y_batch])
logger.info(x_b.shape)
logger.info(x_b)
mage_summary = tf.summary.image('y', y_b, 5)
for i in range(tot_ch):
temp = tf.slice(x_b, [0, 0, 0, i], [-1, -1, -1, 1])
mage_summary = tf.summary.image('x' + str(i), temp, 5)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('./log/', sess.graph)
summary_all = sess.run(merged)
summary_writer.add_summary(summary_all, 0)
summary_writer.close()
def convert_to_coordinate(pts, width=width, height=height):
return tuple( ((pts + 1) / 2 * [width, height]).astype(np.int32) )
def test():
batch_size = 1
data_x1, data_y1, data_x2, data_y2, data_flow, feature_matches1, mask1, feature_matches2, mask2 = \
read_and_decode("data9_/test/", 20, False)
#read_and_decode("/Users/lazycal/workspace/lab/3.1/qudou/data3/test/", 20)
x1_batch, y1_batch, x2_batch, y2_batch, flow_batch, \
feature_matches1_batch, mask1_batch, feature_matches2_batch, mask2_batch \
= tf.train.batch(
[data_x1, data_y1, data_x2, data_y2, data_flow, feature_matches1, mask1, feature_matches2, mask2],
batch_size=batch_size)
sv = tf.train.Supervisor(logdir='./tmp/log', save_summaries_secs=0, saver=None)
with sv.managed_session(config=tf.ConfigProto(gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3))) as sess:
import cv2
import numpy as np
import feature_fetcher
batch_x1s, batch_y1s, batch_x2s, batch_y2s, batch_flows, batch_feature_matches1, batch_mask1, batch_feature_matches2, batch_mask2 = sess.run(
[x1_batch, y1_batch, x2_batch, y2_batch, flow_batch, feature_matches1_batch, mask1_batch, feature_matches2_batch, mask2_batch])
unstable = np.tile((batch_x1s[0, :, :, before_ch] + 1)[...,None] / 2 * 255, [1,1,3])
stable = np.tile((batch_y1s[0, :, :, 0] + 1)[...,None] / 2 * 255, [1,1,3])
img = np.concatenate([stable, unstable], axis=1)
gt_matches = feature_fetcher.fetch('6.mp4.avi', 7)
logger.info('false: ',batch_mask1[0,431:], batch_mask2[0,459:])
logger.info(gt_matches, batch_feature_matches1)
for (match, mask) in zip(batch_feature_matches1[0], batch_mask1[0]):
if not mask: continue
if np.random.uniform(0, 1) > 0.1: continue
cv2.line(img, convert_to_coordinate(match[:2]), convert_to_coordinate(match[2:] + [2, 0]), tuple(np.random.rand(3) * 255))
cv2.imwrite('./test.jpg', img)
img1 = np.concatenate([cv2.imread('./frames/stable/6/image-0008.jpg'), cv2.imread('./frames/unstable/6/image-0008.jpg')],
axis=1)
logger.info('---------------------------------')
cvt = lambda x:convert_to_coordinate(x, img1.shape[1] / 2, img1.shape[0])
for match in gt_matches:
if np.random.uniform(0, 1) > 0.1: continue
cv2.line(img1, cvt(match[:2]), cvt(match[2:] + [2, 0]), tuple(np.random.rand(3) * 255))
cv2.imwrite('./test1.jpg', img1)
if __name__ == '__main__':
test()