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main_flownetS_pyramid_highTV_noBBloss.py
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main_flownetS_pyramid_highTV_noBBloss.py
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from config import sp_config, config, log_config
from utils import *
from model import *
from spatial_transformer import ProjectiveSymmetryTransformer, ProjectiveTransformer, AffineSymmetryTransformer,SimilarityTransformer
from tensorlayer.layers import *
import NLDF
from cell import ConvLSTMCell, ConvGRUCell
import os
from scipy import ndimage
import scipy
import tensorflow as tf
import tensorlayer as tl
import numpy as np
from random import shuffle
import matplotlib
import datetime
import time
import shutil
from numpy.linalg import inv
import cv2
import abc
import os
import uuid
import random
from PIL import Image
slim = tf.contrib.slim
batch_size = 10 # config.batch_size
lr_init = config.TRAIN.lr_init
beta1 = config.TRAIN.beta1
n_epoch = config.TRAIN.n_epoch
lr_decay = config.TRAIN.lr_decay
decay_every = config.TRAIN.decay_every
ni = int(np.ceil(np.sqrt(batch_size)))
def get_pixel_value(img, x, y):
"""
Utility function to get pixel value for coordinate
vectors x and y from a 4D tensor image.
Input
-----
- img: tensor of shape (B, H, W, C)
- x: flattened tensor of shape (B*H*W, )
- y: flattened tensor of shape (B*H*W, )
Returns
-------
- output: tensor of shape (B, H, W, C)
"""
shape = tf.shape(x)
batch_size = shape[0]
height = shape[1]
width = shape[2]
batch_idx = tf.range(0, batch_size)
batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
b = tf.tile(batch_idx, (1, height, width))
indices = tf.stack([b, y, x], 3)
return tf.gather_nd(img, indices)
def tf_warp(img, flow, H, W):
flow = tf.transpose(flow, [0, 3, 1, 2])
x,y = tf.meshgrid(tf.range(W), tf.range(H))
x = tf.expand_dims(x,0)
x = tf.expand_dims(x,0)
y = tf.expand_dims(y,0)
y = tf.expand_dims(y,0)
x = tf.cast(x, tf.float32)
y = tf.cast(y, tf.float32)
grid = tf.concat([x,y],axis = 1)
flows = grid+flow
max_y = tf.cast(H - 1, tf.int32)
max_x = tf.cast(W - 1, tf.int32)
zero = tf.zeros([], dtype=tf.int32)
x = flows[:,0,:,:]
y = flows[:,1,:,:]
x0 = x
y0 = y
x0 = tf.cast(x0, tf.int32)
x1 = x0 + 1
y0 = tf.cast(y0, tf.int32)
y1 = y0 + 1
# clip to range [0, H/W] to not violate img boundaries
x0 = tf.clip_by_value(x0, zero, max_x)
x1 = tf.clip_by_value(x1, zero, max_x)
y0 = tf.clip_by_value(y0, zero, max_y)
y1 = tf.clip_by_value(y1, zero, max_y)
# get pixel value at corner coords
Ia = get_pixel_value(img, x0, y0)
Ib = get_pixel_value(img, x0, y1)
Ic = get_pixel_value(img, x1, y0)
Id = get_pixel_value(img, x1, y1)
# recast as float for delta calculation
x0 = tf.cast(x0, tf.float32)
x1 = tf.cast(x1, tf.float32)
y0 = tf.cast(y0, tf.float32)
y1 = tf.cast(y1, tf.float32)
# calculate deltas
wa = (x1-x) * (y1-y)
wb = (x1-x) * (y-y0)
wc = (x-x0) * (y1-y)
wd = (x-x0) * (y-y0)
# add dimension for addition
wa = tf.expand_dims(wa, axis=3)
wb = tf.expand_dims(wb, axis=3)
wc = tf.expand_dims(wc, axis=3)
wd = tf.expand_dims(wd, axis=3)
# compute output
out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])
return out
def grayscale(input):
output = np.zeros( [128,128,config.prev_output_num])
for i in range(config.prev_output_num):
output[:,:,i] = color.rgb2gray(cv2.resize(np.squeeze(input[i,:,:,:]),dsize=(128,128),interpolation=cv2.INTER_CUBIC) )
return output
def train():
## CREATE DIRECTORIES
mode_dir = config.root_dir + '{}'.format(tl.global_flag['mode'])
ckpt_dir = mode_dir + '/checkpoint'
init_dir = mode_dir + '/init'
log_dir_scalar = mode_dir + '/log/scalar'
log_dir_image = mode_dir + '/log/image'
sample_dir = mode_dir + '/samples/0_train'
config_dir = mode_dir + '/config'
img_dir = mode_dir + '/img'
if tl.global_flag['delete_log']:
shutil.rmtree(ckpt_dir, ignore_errors = True)
shutil.rmtree(log_dir_scalar, ignore_errors = True)
shutil.rmtree(log_dir_image, ignore_errors = True)
shutil.rmtree(sample_dir, ignore_errors = True)
shutil.rmtree(config_dir, ignore_errors = True)
shutil.rmtree(img_dir, ignore_errors = True)
tl.files.exists_or_mkdir(ckpt_dir)
tl.files.exists_or_mkdir(log_dir_scalar)
tl.files.exists_or_mkdir(log_dir_image)
tl.files.exists_or_mkdir(sample_dir)
tl.files.exists_or_mkdir(config_dir)
tl.files.exists_or_mkdir(img_dir)
log_config(config_dir, config)
## DEFINE SESSION
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False))
## DEFINE MODEL
# input
with tf.variable_scope('input'):
#patches_in = tf.placeholder('float32', [batch_size, sample_num, h, w, 3], name = 'input_frames')
unstab_image = tf.placeholder('float32', [batch_size, 384, 512, 3], name = 'input_frames')
stab_image = tf.placeholder('float32', [batch_size, 384, 512, 3*8], name = 'input_flows')
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid( tf.concat([stab_image,unstab_image],3) ,batch_size,is_train=True )
outputs_test = flownetS_pyramid(tf.concat([stab_image,unstab_image],3),batch_size,is_train=False,reuse=True )
def masked_MSE(pred, gt, mask):
pred_mask = pred * mask
gt_mask = gt * mask
MSE = tf.reduce_sum(tf.squared_difference(pred_mask, gt_mask), axis = [1, 2, 3])
safe_mask = tf.cast(tf.where(tf.equal(mask, tf.zeros_like(mask)), mask + tf.constant(1e-8), mask), tf.float32)
MSE = MSE / tf.reduce_sum(safe_mask, axis = [1, 2, 3])
# MSE = tf.div_no_nan(MSE, tf.reduce_sum(mask, axis = [1, 2, 3]))
return tf.reduce_mean(MSE)
def lossterm(predict_flow,stab_image,unstab_image):
size = [predict_flow.shape[1], predict_flow.shape[2]]
downsampled_stab = tf.image.resize_images(stab_image, size )#downsample(gt_flow, size)
downsampled_unstab = tf.image.resize_images(unstab_image, size )#downsample(gt_flow, size)
warpedimg = tf_warp(downsampled_unstab, predict_flow, predict_flow.shape[1], predict_flow.shape[2])
#loss6 = tl.cost.mean_squared_error(downsampled_stab6, warpedimg6, is_mean = True, name = 'loss6') #* 0.32
mask = tf_warp(tf.ones_like(downsampled_unstab), predict_flow, predict_flow.shape[1], predict_flow.shape[2])
return masked_MSE(warpedimg, downsampled_stab, mask),warpedimg
## DEFINE LOSS
with tf.variable_scope('loss'):
loss6,warpedimg6 = lossterm(outputs['predict_flow6'],stab_image[:,:,:,0:3],unstab_image)
loss5,warpedimg5 = lossterm(outputs['predict_flow5'],stab_image[:,:,:,0:3],unstab_image)
loss4,warpedimg4 = lossterm(outputs['predict_flow4'],stab_image[:,:,:,0:3],unstab_image)
loss3,warpedimg3 = lossterm(outputs['predict_flow3'],stab_image[:,:,:,0:3],unstab_image)
loss2,warpedimg2 = lossterm(outputs['predict_flow2'],stab_image[:,:,:,0:3],unstab_image)
loss6_1,_ = lossterm(outputs['predict_flow6'],stab_image[:,:,:,3:6],unstab_image)
loss5_1,_ = lossterm(outputs['predict_flow5'],stab_image[:,:,:,3:6],unstab_image)
loss4_1,_ = lossterm(outputs['predict_flow4'],stab_image[:,:,:,3:6],unstab_image)
loss3_1,_ = lossterm(outputs['predict_flow3'],stab_image[:,:,:,3:6],unstab_image)
loss2_1,_ = lossterm(outputs['predict_flow2'],stab_image[:,:,:,3:6],unstab_image)
loss_1 = (loss6_1+loss5_1+loss4_1+loss3_1+loss2_1)*np.exp(-1/3)
loss6_2,_ = lossterm(outputs['predict_flow6'],stab_image[:,:,:,6:9],unstab_image)
loss5_2,_ = lossterm(outputs['predict_flow5'],stab_image[:,:,:,6:9],unstab_image)
loss4_2,_ = lossterm(outputs['predict_flow4'],stab_image[:,:,:,6:9],unstab_image)
loss3_2,_ = lossterm(outputs['predict_flow3'],stab_image[:,:,:,6:9],unstab_image)
loss2_2,_ = lossterm(outputs['predict_flow2'],stab_image[:,:,:,6:9],unstab_image)
loss_2 = (loss6_2+loss5_2+loss4_2+loss3_2+loss2_2)*np.exp(-2/3)
loss6_3,_ = lossterm(outputs['predict_flow6'],stab_image[:,:,:,9:12],unstab_image)
loss5_3,_ = lossterm(outputs['predict_flow5'],stab_image[:,:,:,9:12],unstab_image)
loss4_3,_ = lossterm(outputs['predict_flow4'],stab_image[:,:,:,9:12],unstab_image)
loss3_3,_ = lossterm(outputs['predict_flow3'],stab_image[:,:,:,9:12],unstab_image)
loss2_3,_ = lossterm(outputs['predict_flow2'],stab_image[:,:,:,9:12],unstab_image)
loss_3 = (loss6_3+loss5_3+loss4_3+loss3_3+loss2_3)*np.exp(-3/3)
loss6_4,_ = lossterm(outputs['predict_flow6'],stab_image[:,:,:,12:15],unstab_image)
loss5_4,_ = lossterm(outputs['predict_flow5'],stab_image[:,:,:,12:15],unstab_image)
loss4_4,_ = lossterm(outputs['predict_flow4'],stab_image[:,:,:,12:15],unstab_image)
loss3_4,_ = lossterm(outputs['predict_flow3'],stab_image[:,:,:,12:15],unstab_image)
loss2_4,_ = lossterm(outputs['predict_flow2'],stab_image[:,:,:,12:15],unstab_image)
loss_4 = (loss6_4+loss5_4+loss4_4+loss3_4+loss2_4)*np.exp(-6/3)
loss6_5,_ = lossterm(outputs['predict_flow6'],stab_image[:,:,:,15:18],unstab_image)
loss5_5,_ = lossterm(outputs['predict_flow5'],stab_image[:,:,:,15:18],unstab_image)
loss4_5,_ = lossterm(outputs['predict_flow4'],stab_image[:,:,:,15:18],unstab_image)
loss3_5,_ = lossterm(outputs['predict_flow3'],stab_image[:,:,:,15:18],unstab_image)
loss2_5,_ = lossterm(outputs['predict_flow2'],stab_image[:,:,:,15:18],unstab_image)
loss_5 = (loss6_5+loss5_5+loss4_5+loss3_5+loss2_5)*np.exp(-14/3)
var6 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow6']))/1e6
var5 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow5']))/1e6
var4 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow4']))/1e6
var3 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow3']))/2e6
var2 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow2']))/2e6
loss_main = tf.identity( loss6+loss5+loss4+loss3+loss2+var6+var5+var3+var2+loss_1+loss_2+loss_3+loss_4+loss_5, name = 'total')
with tf.variable_scope('loss_test'):
loss6,warpedimg6 = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,0:3],unstab_image)
loss5,warpedimg5 = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,0:3],unstab_image)
loss4,warpedimg4 = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,0:3],unstab_image)
loss3,warpedimg3 = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,0:3],unstab_image)
loss2,warpedimg2 = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,0:3],unstab_image)
loss6_1,_ = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,3:6],unstab_image)
loss5_1,_ = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,3:6],unstab_image)
loss4_1,_ = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,3:6],unstab_image)
loss3_1,_ = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,3:6],unstab_image)
loss2_1,_ = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,3:6],unstab_image)
loss_1 = (loss6_1+loss5_1+loss4_1+loss3_1+loss2_1)*np.exp(-1/3)
loss6_2,_ = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,6:9],unstab_image)
loss5_2,_ = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,6:9],unstab_image)
loss4_2,_ = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,6:9],unstab_image)
loss3_2,_ = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,6:9],unstab_image)
loss2_2,_ = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,6:9],unstab_image)
loss_2 = (loss6_2+loss5_2+loss4_2+loss3_2+loss2_2)*np.exp(-2/3)
loss6_3,_ = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,9:12],unstab_image)
loss5_3,_ = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,9:12],unstab_image)
loss4_3,_ = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,9:12],unstab_image)
loss3_3,_ = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,9:12],unstab_image)
loss2_3,_ = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,9:12],unstab_image)
loss_3 = (loss6_3+loss5_3+loss4_3+loss3_3+loss2_3)*np.exp(-3/3)
loss6_4,_ = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,12:15],unstab_image)
loss5_4,_ = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,12:15],unstab_image)
loss4_4,_ = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,12:15],unstab_image)
loss3_4,_ = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,12:15],unstab_image)
loss2_4,_ = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,12:15],unstab_image)
loss_4 = (loss6_4+loss5_4+loss4_4+loss3_4+loss2_4)*np.exp(-6/3)
loss6_5,_ = lossterm(outputs_test['predict_flow6'],stab_image[:,:,:,15:18],unstab_image)
loss5_5,_ = lossterm(outputs_test['predict_flow5'],stab_image[:,:,:,15:18],unstab_image)
loss4_5,_ = lossterm(outputs_test['predict_flow4'],stab_image[:,:,:,15:18],unstab_image)
loss3_5,_ = lossterm(outputs_test['predict_flow3'],stab_image[:,:,:,15:18],unstab_image)
loss2_5,_ = lossterm(outputs_test['predict_flow2'],stab_image[:,:,:,15:18],unstab_image)
loss_5 = (loss6_5+loss5_5+loss4_5+loss3_5+loss2_5)*np.exp(-14/3)
var6 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow6']))/1e6
var5 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow5']))/1e6
var4 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow4']))/1e6
var3 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow3']))/2e6
var2 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow2']))/2e6
loss_main_test = tf.identity( loss6+loss5+loss4+loss3+loss2+var6+var5+var3+var2+loss_1+loss_2+loss_3+loss_4+loss_5 , name = 'totaltest')
## DEFINE OPTIMIZER
# variables to save / train
main_vars = tl.layers.get_variables_with_name('main_net', True, False)
save_vars = tl.layers.get_variables_with_name('main_net', False, False)
# define optimizer
with tf.variable_scope('Optimizer'):
learning_rate = tf.Variable(lr_init, trainable = False)
optim_main = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(loss_main, var_list = main_vars)
## DEFINE SUMMARY
# writer
writer_scalar = tf.summary.FileWriter(log_dir_scalar, sess.graph, flush_secs=30, filename_suffix = '.loss_log')
writer_image = tf.summary.FileWriter(log_dir_image, sess.graph, max_queue=100, flush_secs=30, filename_suffix = '.image_log')
# for train
loss_sum_main_list = []
with tf.variable_scope('loss'):
loss_sum_main_list.append(tf.summary.scalar('total', loss_main))
loss_sum_main_list.append(tf.summary.scalar('loss6', loss6))
loss_sum_main_list.append(tf.summary.scalar('loss5', loss5))
loss_sum_main_list.append(tf.summary.scalar('loss4', loss4))
loss_sum_main_list.append(tf.summary.scalar('loss3', loss3))
loss_sum_main_list.append(tf.summary.scalar('loss2', loss2))
loss_sum_main_list.append(tf.summary.scalar('var6', var6))
loss_sum_main_list.append(tf.summary.scalar('var5', var5))
loss_sum_main_list.append(tf.summary.scalar('var4', var4))
loss_sum_main_list.append(tf.summary.scalar('var3', var3))
loss_sum_main_list.append(tf.summary.scalar('var2', var2))
loss_sum_main = tf.summary.merge(loss_sum_main_list)
loss_sum_test_list = []
with tf.variable_scope('loss_test'):
loss_sum_test_list.append(tf.summary.scalar('totaltest', loss_main_test))
loss_sum_test_main = tf.summary.merge(loss_sum_test_list)
image_sum_list = []
image_sum_list.append(tf.summary.image('unstab_image', fix_image_tf(unstab_image, 1)))
image_sum_list.append(tf.summary.image('stab_image', fix_image_tf(stab_image[:,:,:,0:3], 1)))
image_sum_list.append(tf.summary.image('warpedimg2', fix_image_tf(warpedimg2, 1)))
image_sum_list.append(tf.summary.image('warpedimg3', fix_image_tf(warpedimg3, 1)))
image_sum_list.append(tf.summary.image('warpedimg4', fix_image_tf(warpedimg4, 1)))
image_sum_list.append(tf.summary.image('warpedimg5', fix_image_tf(warpedimg5, 1)))
image_sum_list.append(tf.summary.image('warpedimg6', fix_image_tf(warpedimg6, 1)))
image_sum = tf.summary.merge(image_sum_list)
## INITIALIZE SESSION
tl.layers.initialize_global_variables(sess)
#flownetSaver = tf.train.Saver(tl.layers.get_variables_with_name('FlowNetSD'))
#flownetSaver.restore(sess, './flownet/checkpoints/FlowNetSD/flownet-SD.ckpt-0')
tl.files.load_and_assign_npz_dict(name = '/Jarvis/logs/LJH/deep_video_stabilization/VS_flownetS_pyramid/fixed_ckpt/VS_flownetS_pyramid_31k.npz', sess = sess)
## START TRAINING
print '*****************************************'
print ' TRAINING START'
print '*****************************************'
global_step = 0
for epoch in range(0, n_epoch + 1):
total_loss, n_iter = 0, 0
stab_video_list = np.array(sorted(tl.files.load_file_list(path = config.TRAIN.stab_path, regx = '.*', printable = False)))
unstab_video_list = np.array(sorted(tl.files.load_file_list(path = config.TRAIN.unstab_path, regx = '.*', printable = False)))
test_stab_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.stab_path, regx = '.*', printable = False)))
test_unstab_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.unstab_path, regx = '.*', printable = False)))
# update learning rate
if epoch != 0 and (epoch % decay_every == 0):
new_lr_decay = lr_decay ** (epoch // decay_every)
sess.run(tf.assign(learning_rate, lr_init * new_lr_decay))
elif epoch == 0:
sess.run(tf.assign(learning_rate, lr_init))
epoch_time = time.time()
for frame_idx in range(0, 10000000):
print('==============================================================')
step_time = time.time()
curUnstabImg = np.zeros([batch_size, 384, 512, 3])
curStabImg = np.zeros([batch_size, 384, 512, 3*8])
for i in range(batch_size):
randCapidx = random.randrange(0,len(stab_video_list))
cap_stab = cv2.VideoCapture(config.TRAIN.stab_path + stab_video_list[randCapidx])
cap_unstab = cv2.VideoCapture(config.TRAIN.unstab_path + unstab_video_list[randCapidx])
total_frames = int(cap_stab.get(7))
randFrameidx = random.randrange(1,total_frames)
cap_unstab.set(1,randFrameidx)
ret_unstab, frame_unstab = cap_unstab.read()
curUnstabImg[i] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
#cap_stab.set(1,randFrameidx)
#ret_stab, frame_stab = cap_stab.read()
stabidxs = [1,2,3,4,7,15,23,31]
for j in range(len(stabidxs)):
if randFrameidx - stabidxs[j] < 0:
cap_stab.set(1,0)
else:
cap_stab.set(1,randFrameidx - stabidxs[j])
ret_stab, frame_stab = cap_stab.read()
curStabImg[i,:,:,j*3:(j+1)*3] = cv2.cvtColor(cv2.resize(frame_stab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
## RUN NETWORK
feed_dict = {unstab_image:curUnstabImg, stab_image:curStabImg}
_ = sess.run(optim_main, feed_dict)
# print status
err_main, lr = \
sess.run([loss_main, learning_rate], feed_dict)
print('[%s] %4d/%4d time: %4.2fs, err[main: %1.2e], lr: %1.2e' % \
(tl.global_flag['mode'], frame_idx, 10000000, time.time() - step_time, err_main, lr))
## SAVE LOGS
# save loss & image log
if global_step % config.TRAIN.write_log_every == 0:
summary_loss = sess.run(loss_sum_main, feed_dict)
summary_image = sess.run(image_sum, feed_dict)
writer_scalar.add_summary(summary_loss, global_step)
writer_image.add_summary(summary_image, global_step)
print('save loss log')
# save checkpoint
if global_step != 0 and global_step % config.TRAIN.write_ckpt_every == 0:
remove_file_end_with(ckpt_dir, '*.npz')
tl.files.save_npz_dict(save_vars, name = ckpt_dir + '/{}.npz'.format(tl.global_flag['mode']), sess = sess)
# save test loss
if global_step % config.TRAIN.write_ckpt_every == 0:
testcurUnstabImg = np.zeros([batch_size, 384, 512, 3])
testcurStabImg = np.zeros([batch_size, 384, 512, 3*8])
for k in range(10):
for i in range(batch_size):
randCapidx = random.randrange(0,len(test_stab_video_list))
cap_stab = cv2.VideoCapture(config.TEST.stab_path + test_stab_video_list[randCapidx])
cap_unstab = cv2.VideoCapture(config.TEST.unstab_path + test_unstab_video_list[randCapidx])
total_frames = int(cap_stab.get(7))
randFrameidx = random.randrange(1,total_frames)
cap_unstab.set(1,randFrameidx)
ret_unstab, frame_unstab = cap_unstab.read()
#print(ret_unstab)
testcurUnstabImg[i] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
#cap_stab.set(1,randFrameidx)
#ret_stab, frame_stab = cap_stab.read()
stabidxs = [1,2,3,4,7,15,23,31]
for j in range(len(stabidxs)):
if randFrameidx - stabidxs[j] < 0:
cap_stab.set(1,0)
else:
cap_stab.set(1,randFrameidx - stabidxs[j])
ret_stab, frame_stab = cap_stab.read()
testcurStabImg[i,:,:,j*3:(j+1)*3] = cv2.cvtColor(cv2.resize(frame_stab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
feed_dict = {unstab_image:testcurUnstabImg, stab_image:testcurStabImg}
testsummary_loss = sess.run(loss_sum_test_main, feed_dict)
writer_scalar.add_summary(testsummary_loss, global_step+k-1)
global_step += 1
#print('[*] Epoch: [%2d/%2d] time: %4.4fs, total_err: %1.2e' % (epoch, n_epoch, time.time() - epoch_time, total_loss/n_iter/n_frames))
# reset image log
if epoch % config.TRAIN.refresh_image_log_every == 0:
writer_image.close()
remove_file_end_with(log_dir_image, '*.image_log')
writer_image.reopen()
def evaluate():
print 'Evaluation Start'
date = datetime.datetime.now().strftime('%Y_%m_%d/%H-%M')
# directories
mode_dir = config.root_dir + '{}'.format(tl.global_flag['mode'])
ckpt_dir = mode_dir + '/fixed_ckpt'
save_dir = mode_dir + '/result_video/'
tl.files.exists_or_mkdir(ckpt_dir)
tl.files.exists_or_mkdir(save_dir)
sp_config.batch_size = 1
batch_size = 1
test_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.unstab_path_testdataset, regx = '.*', printable = False)))
for k in np.arange(len(test_video_list)):
print(test_video_list[k])
# input
for k in np.arange(len(test_video_list)):
test_video_name = test_video_list[k]
#if test_video_name == '18.avi' or test_video_name == '18AF.avi' or test_video_name == '6.avi':
if test_video_name != '18.avi' and test_video_name != '18AF.avi' and test_video_name != '6.avi' and test_video_name != '0.avi': # and test_video_name != '3.avi':
#if test_video_name != '3.avi' and test_video_name != '6.avi' and test_video_name != '652.avi' and test_video_name != '653.avi' and test_video_name != '654.avi':
#if test_video_name != '100.avi' and test_video_name != '101.avi' and test_video_name != '102.avi' and test_video_name != '103.avi': # and test_video_name != '3.avi':
continue
print(test_video_name)
#raise
tf.reset_default_graph()
cap = cv2.VideoCapture(config.TEST.unstab_path_testdataset + test_video_name)
fps = cap.get(5)
total_frames = cap.get(7)-2
out_h = 384 #int(cap.get(4))
out_w = 512 #int(cap.get(3))
#fourcc = cv2.cv.CV_FOURCC('M','J','P','G')
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
base = os.path.basename(test_video_name)
base_name = os.path.splitext(base)[0]
out = cv2.VideoWriter(save_dir + base_name + '_out.avi', fourcc, fps, (2 * out_w, out_h))
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False))
with tf.variable_scope('input'):
inputs = tf.placeholder('float32', [batch_size, 384, 512, 3*9], name = 'input_frames')
# define model
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid(inputs,batch_size,is_train=False )
unstabimg = tf.image.resize_images(inputs[:,:,:,24:27],[382,510])
outputs_warpedimg = tf_warp(unstabimg, outputs['predict_flow2'], 382, 510)
# init session
tl.layers.initialize_global_variables(sess)
# load checkpoint
tl.files.load_and_assign_npz_dict(name = ckpt_dir + '/{}.npz'.format(tl.global_flag['mode']), sess = sess)
# read frame
def refine_frame(frame):
#return cv2.resize(frame / 255., (512, 384))
return cv2.resize(frame, (512, 384))
def read_frame(cap):
ref, frame = cap.read()
if ref != False:
frame = cv2.resize(frame,(out_w,out_h))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return ref, frame
totaloutputFrame = np.zeros([int(total_frames),out_h,out_w,3])
#cap.set(1,0)
#ret_unstab, frame_unstab = cap.read()
for i in range(int(total_frames)):
print(i)
curinput = np.zeros([1,out_h,out_w,27])
#cap.set(1,i)
ret_unstab, frame_unstab = cap.read()
if i == 0:
totaloutputFrame[0] = cv2.resize(frame_unstab, (512, 384))
curinput[0,:,:,24:27] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
stabidxs = [1,2,3,4,7,15,23,31]
#stabidxs = [31,23,15,7,4,3,2,1]
for j in range(len(stabidxs)):
if i - stabidxs[j] < 0:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[0]), cv2.COLOR_RGB2BGR))/255.0
else:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[i - stabidxs[j]]), cv2.COLOR_RGB2BGR))/255.0
#feed_dict = {inputs: curinput}
#curflow = sess.run(outputs , feed_dict)
#curflow = curflow['predict_flow2']
#curloc = np.dstack(np.meshgrid(np.arange(out_w), np.arange(out_h)))
#curloc = curloc + cv2.resize(np.squeeze(curflow),(512,384))
#curloc = curloc.astype(np.float32)
#totaloutputFrame[i] = cv2.remap(frame_unstab, curloc, None, cv2.INTER_LINEAR )
feed_dict = {inputs: curinput}
curwarpedimg = sess.run(outputs_warpedimg , feed_dict)
totaloutputFrame[i] = cv2.cvtColor(cv2.resize(np.squeeze(curwarpedimg),(512,384))*255, cv2.COLOR_RGB2BGR)
out.write(np.uint8( np.concatenate([np.uint8(cv2.resize(frame_unstab, (512, 384))), np.uint8(totaloutputFrame[i]) ],axis=1)))
out.release()
def evaluate_originalSize():
print 'Evaluation Start'
date = datetime.datetime.now().strftime('%Y_%m_%d/%H-%M')
# directories
mode_dir = config.root_dir + '{}'.format(tl.global_flag['mode'])
ckpt_dir = mode_dir + '/fixed_ckpt'
save_dir = mode_dir + '/result_video/'
tl.files.exists_or_mkdir(ckpt_dir)
tl.files.exists_or_mkdir(save_dir)
sp_config.batch_size = 1
batch_size = 1
test_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.unstab_path_testdataset, regx = '.*', printable = False)))
for k in np.arange(len(test_video_list)):
print(test_video_list[k])
# input
for k in np.arange(len(test_video_list)):
test_video_name = test_video_list[k]
print(test_video_name)
#raise
tf.reset_default_graph()
cap = cv2.VideoCapture(config.TEST.unstab_path_testdataset + test_video_name)
fps = cap.get(5)
total_frames = cap.get(7)-2
out_h = int(cap.get(4))
out_w = int(cap.get(3))
#fourcc = cv2.cv.CV_FOURCC('M','J','P','G')
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
base = os.path.basename(test_video_name)
base_name = os.path.splitext(base)[0]
out = cv2.VideoWriter(save_dir + base_name + '_out.avi', fourcc, fps, ( out_w, out_h))
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False))
with tf.variable_scope('input'):
inputs = tf.placeholder('float32', [batch_size, 384, 512, 3*9], name = 'input_frames')
resizedInput = tf.placeholder('float32', [batch_size, out_h, out_w , 3], name = 'input_frames')
ofnum = tf.placeholder('float32', [], name = 'input_OF_num')
ofsum = tf.placeholder('float32', [batch_size, out_h, out_w, 2], name = 'input_OF_sum')
# define model
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid(inputs,batch_size,is_train=False )
outflow = tf.image.resize_images(outputs['predict_flow2'],[out_h,out_w])
outflow = tf.concat([outflow[:,:,:,0:1]*out_w/512,outflow[:,:,:,1:2]*out_h/384],3)
outflow = tf.concat([tf.ones_like(outflow[:,:,:,0:1])*tf.reduce_mean(outflow[:,:,:,0],axis=[1,2]),tf.ones_like(outflow[:,:,:,1:2])*tf.reduce_mean(outflow[:,:,:,1],axis=[1,2])],3)
outflowMean = (ofsum+outflow)/ofnum
outputs_warpedimg = tf_warp(resizedInput, outflowMean , out_h,out_w)
#print(outputs_warpedimg)
#raise
# init session
tl.layers.initialize_global_variables(sess)
# load checkpoint
tl.files.load_and_assign_npz_dict(name = ckpt_dir + '/{}.npz'.format(tl.global_flag['mode']), sess = sess)
# read frame
def refine_frame(frame):
#return cv2.resize(frame / 255., (512, 384))
return cv2.resize(frame, (512, 384))
def read_frame(cap):
ref, frame = cap.read()
if ref != False:
frame = cv2.resize(frame,(out_w,out_h))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return ref, frame
totaloutputFrame = np.zeros([int(total_frames),out_h,out_w,3])
totaloutputOF = np.zeros([int(total_frames),out_h,out_w,2])
#cap.set(1,0)
#ret_unstab, frame_unstab = cap.read()
for i in range(int(total_frames)):
print(i)
starttime = time.time()
curinput = np.zeros([1,384,512,27])
#cap.set(1,i)
ret_unstab, frame_unstab = cap.read()
if i == 0:
totaloutputFrame[0] = frame_unstab
curinput[0,:,:,24:27] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
stabidxs = [1,2,3,4,7,15,23,31]
#stabidxs = [31,23,15,7,4,3,2,1]
for j in range(len(stabidxs)):
if i - stabidxs[j] < 0:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(cv2.resize(np.uint8(totaloutputFrame[0]), (512, 384)), cv2.COLOR_RGB2BGR))/255.0
else:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(cv2.resize(np.uint8(totaloutputFrame[i - stabidxs[j]]), (512, 384)), cv2.COLOR_RGB2BGR))/255.0
curflownum = min(i+1,3)
curflowsum = np.zeros([out_h,out_w,2])
for j in range(curflownum-1):
curflowsum += totaloutputOF[i-j-1]
curflowsum = np.expand_dims(curflowsum,axis=0)
feed_dict = {inputs: curinput, resizedInput: np.expand_dims(cv2.cvtColor(frame_unstab, cv2.COLOR_RGB2BGR)/255.0 ,axis=0), ofnum:curflownum, ofsum:curflowsum}
curwarpedimg = sess.run(outputs_warpedimg , feed_dict)
totaloutputOF[i] = sess.run(outflow , feed_dict)
totaloutputFrame[i] = cv2.cvtColor(np.squeeze(curwarpedimg)*255, cv2.COLOR_RGB2BGR)
print(time.time()-starttime)
out.write(np.uint8(totaloutputFrame[i]))
out.release()
def evaluate_blurNma():
print 'Evaluation Start'
date = datetime.datetime.now().strftime('%Y_%m_%d/%H-%M')
# directories
mode_dir = config.root_dir + '{}'.format(tl.global_flag['mode'])
ckpt_dir = mode_dir + '/fixed_ckpt'
save_dir = mode_dir + '/result_video/'
tl.files.exists_or_mkdir(ckpt_dir)
tl.files.exists_or_mkdir(save_dir)
sp_config.batch_size = 1
batch_size = 1
test_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.unstab_path_testdataset, regx = '.*', printable = False)))
for k in np.arange(len(test_video_list)):
print(test_video_list[k])
# input
for k in np.arange(len(test_video_list)):
test_video_name = test_video_list[k]
#if test_video_name == '18.avi' or test_video_name == '18AF.avi' or test_video_name == '6.avi':
if test_video_name != '18.avi' and test_video_name != '18AF.avi' and test_video_name != '6.avi' and test_video_name != '0.avi': # and test_video_name != '3.avi':
#if test_video_name != '3.avi' and test_video_name != '6.avi' and test_video_name != '652.avi' and test_video_name != '653.avi' and test_video_name != '654.avi':
#if test_video_name != '100.avi' and test_video_name != '101.avi' and test_video_name != '102.avi' and test_video_name != '103.avi': # and test_video_name != '3.avi':
continue
print(test_video_name)
#raise
tf.reset_default_graph()
cap = cv2.VideoCapture(config.TEST.unstab_path_testdataset + test_video_name)
fps = cap.get(5)
total_frames = cap.get(7)-2
out_h = 384 #int(cap.get(4))
out_w = 512 #int(cap.get(3))
#fourcc = cv2.cv.CV_FOURCC('M','J','P','G')
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
base = os.path.basename(test_video_name)
base_name = os.path.splitext(base)[0]
out = cv2.VideoWriter(save_dir + base_name + '_out_blurNma.avi', fourcc, fps, (2 * out_w, out_h))
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False))
with tf.variable_scope('input'):
inputs = tf.placeholder('float32', [batch_size, 384, 512, 3*9], name = 'input_frames')
inputs_prevof = tf.placeholder('float32', [batch_size, 382, 510, 2], name = 'input_prevof')
# define model
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid(inputs,batch_size,is_train=False )
unstabimg = tf.image.resize_images(inputs[:,:,:,24:27],[382,510])
of = outputs['predict_flow2']
ofx = tf.expand_dims(of[:,:,:,0],axis=3)
smoothofx = tf.nn.conv2d(ofx, tf.constant(1/(75*75.0),shape=[75,75,1,1]),[1,1,1,1], "SAME" )
ofy = tf.expand_dims(of[:,:,:,1],axis=3)
smoothofy = tf.nn.conv2d(ofy, tf.constant(1/(75*75.0),shape=[75,75,1,1]),[1,1,1,1], "SAME" )
smoothof = tf.concat([smoothofx,smoothofy],3)
print(smoothof)
outputs_warpedimg = tf_warp(unstabimg, 0.9*smoothof+0.1*inputs_prevof , 382, 510)
# init session
tl.layers.initialize_global_variables(sess)
# load checkpoint
tl.files.load_and_assign_npz_dict(name = ckpt_dir + '/{}.npz'.format(tl.global_flag['mode']), sess = sess)
# read frame
def refine_frame(frame):
#return cv2.resize(frame / 255., (512, 384))
return cv2.resize(frame, (512, 384))
def read_frame(cap):
ref, frame = cap.read()
if ref != False:
frame = cv2.resize(frame,(out_w,out_h))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return ref, frame
totaloutputFrame = np.zeros([int(total_frames),out_h,out_w,3])
#cap.set(1,0)
#ret_unstab, frame_unstab = cap.read()
prevof = np.zeros([382,510,2]);
for i in range(int(total_frames)):
print(i)
curinput = np.zeros([1,out_h,out_w,27])
#cap.set(1,i)
ret_unstab, frame_unstab = cap.read()
if i == 0:
totaloutputFrame[0] = cv2.resize(frame_unstab, (512, 384))
curinput[0,:,:,24:27] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
stabidxs = [1,2,3,4,7,15,23,31]
#stabidxs = [31,23,15,7,4,3,2,1]
for j in range(len(stabidxs)):
if i - stabidxs[j] < 0:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[0]), cv2.COLOR_RGB2BGR))/255.0
else:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[i - stabidxs[j]]), cv2.COLOR_RGB2BGR))/255.0
#feed_dict = {inputs: curinput}
#curflow = sess.run(outputs , feed_dict)
#curflow = curflow['predict_flow2']
#curloc = np.dstack(np.meshgrid(np.arange(out_w), np.arange(out_h)))
#curloc = curloc + cv2.resize(np.squeeze(curflow),(512,384))
#curloc = curloc.astype(np.float32)
#totaloutputFrame[i] = cv2.remap(frame_unstab, curloc, None, cv2.INTER_LINEAR )
feed_dict = {inputs: curinput, inputs_prevof: np.expand_dims(prevof,axis=0)}
curwarpedimg,curof = sess.run((outputs_warpedimg,of) , feed_dict)
prevof = 0.9*prevof + 0.1*np.squeeze(curof)
totaloutputFrame[i] = cv2.cvtColor(cv2.resize(np.squeeze(curwarpedimg),(512,384))*255, cv2.COLOR_RGB2BGR)
out.write(np.uint8( np.concatenate([np.uint8(cv2.resize(frame_unstab, (512, 384))), np.uint8(totaloutputFrame[i]) ],axis=1)))
out.release()
def evaluate_medianNma():
print 'Evaluation Start'
date = datetime.datetime.now().strftime('%Y_%m_%d/%H-%M')
# directories
mode_dir = config.root_dir + '{}'.format(tl.global_flag['mode'])
ckpt_dir = mode_dir + '/fixed_ckpt'
save_dir = mode_dir + '/result_video/'
tl.files.exists_or_mkdir(ckpt_dir)
tl.files.exists_or_mkdir(save_dir)
sp_config.batch_size = 1
batch_size = 1
test_video_list = np.array(sorted(tl.files.load_file_list(path = config.TEST.unstab_path_testdataset, regx = '.*', printable = False)))
for k in np.arange(len(test_video_list)):
print(test_video_list[k])
# input
for k in np.arange(len(test_video_list)):
test_video_name = test_video_list[k]
#if test_video_name == '18.avi' or test_video_name == '18AF.avi' or test_video_name == '6.avi':
if test_video_name != '18.avi' and test_video_name != '18AF.avi' and test_video_name != '6.avi' and test_video_name != '0.avi': # and test_video_name != '3.avi':
#if test_video_name != '3.avi' and test_video_name != '6.avi' and test_video_name != '652.avi' and test_video_name != '653.avi' and test_video_name != '654.avi':
#if test_video_name != '100.avi' and test_video_name != '101.avi' and test_video_name != '102.avi' and test_video_name != '103.avi': # and test_video_name != '3.avi':
continue
print(test_video_name)
#raise
tf.reset_default_graph()
cap = cv2.VideoCapture(config.TEST.unstab_path_testdataset + test_video_name)
fps = cap.get(5)
total_frames = cap.get(7)-2
out_h = 384 #int(cap.get(4))
out_w = 512 #int(cap.get(3))
#fourcc = cv2.cv.CV_FOURCC('M','J','P','G')
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
base = os.path.basename(test_video_name)
base_name = os.path.splitext(base)[0]
out = cv2.VideoWriter(save_dir + base_name + '_out_mdeianNma.avi', fourcc, fps, (2 * out_w, out_h))
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False))
with tf.variable_scope('input'):
inputs = tf.placeholder('float32', [batch_size, 384, 512, 3*9], name = 'input_frames')
# define model
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid(inputs,batch_size,is_train=False )
unstabimg = tf.image.resize_images(inputs[:,:,:,24:27],[382,510])
of = outputs['predict_flow2']
with tf.variable_scope('input_warp'):
inputs2 = tf.placeholder('float32', [batch_size, 384, 512, 3*9], name = 'input_frames')
inputs_prevof = tf.placeholder('float32', [batch_size, 382, 510, 2], name = 'input_prevof')
medianof = tf.placeholder('float32', [batch_size, 382, 510, 2], name = 'input_medianof')
with tf.variable_scope('main_net_warp') as scope:
unstabimg = tf.image.resize_images(inputs2[:,:,:,24:27],[382,510])
outputs_warpedimg = tf_warp(unstabimg, 0.9*medianof+0.1*inputs_prevof , 382, 510)
# init session
tl.layers.initialize_global_variables(sess)
# load checkpoint
tl.files.load_and_assign_npz_dict(name = ckpt_dir + '/{}.npz'.format(tl.global_flag['mode']), sess = sess)
# read frame
def refine_frame(frame):
#return cv2.resize(frame / 255., (512, 384))
return cv2.resize(frame, (512, 384))
def read_frame(cap):
ref, frame = cap.read()
if ref != False:
frame = cv2.resize(frame,(out_w,out_h))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return ref, frame
totaloutputFrame = np.zeros([int(total_frames),out_h,out_w,3])
#cap.set(1,0)
#ret_unstab, frame_unstab = cap.read()
prevof = np.zeros([382,510,2]);
for i in range(int(total_frames)):
print(i)
curinput = np.zeros([1,out_h,out_w,27])
#cap.set(1,i)
ret_unstab, frame_unstab = cap.read()
if i == 0:
totaloutputFrame[0] = cv2.resize(frame_unstab, (512, 384))
curinput[0,:,:,24:27] = cv2.cvtColor(cv2.resize(frame_unstab, (512, 384)), cv2.COLOR_RGB2BGR)/255.0
stabidxs = [1,2,3,4,7,15,23,31]
#stabidxs = [31,23,15,7,4,3,2,1]
for j in range(len(stabidxs)):
if i - stabidxs[j] < 0:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[0]), cv2.COLOR_RGB2BGR))/255.0
else:
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(np.uint8(totaloutputFrame[i - stabidxs[j]]), cv2.COLOR_RGB2BGR))/255.0
feed_dict = {inputs: curinput}
curof = sess.run(of , feed_dict)
#curmedianof = np.concatenate( ( np.expand_dims(cv2.medianBlur(curof[:,:,0], 101),2),np.expand_dims(cv2.medianBlur(curof[:,:,1], 101),2) ) , axis=2)
#curof = scipy.ndimage.filters.median_filter(curof,31)
curmedianof = scipy.signal.medfilt(np.squeeze(curof),5)
print(curmedianof.shape)
feed_dict = {inputs: curinput, inputs_prevof: np.expand_dims(prevof,axis=0),medianof: curmedianof}
curwarpedimg = sess.run(outputs_warpedimg , feed_dict)
prevof = 0.9*prevof + 0.1*np.squeeze(curmedianof)
totaloutputFrame[i] = cv2.cvtColor(cv2.resize(np.squeeze(curwarpedimg),(512,384))*255, cv2.COLOR_RGB2BGR)
out.write(np.uint8( np.concatenate([np.uint8(cv2.resize(frame_unstab, (512, 384))), np.uint8(totaloutputFrame[i]) ],axis=1)))
out.release()
# imported from flownet
def flow_to_image(flow):
"""
Convert flow into middlebury color code image
:param flow: optical flow map
:return: optical flow image in middlebury color
"""
u = flow[:, :, 0]
v = flow[:, :, 1]
maxu = -999.
maxv = -999.
minu = 999.
minv = 999.
idxUnknow = (abs(u) > 1e7) | (abs(v) > 1e7)
u[idxUnknow] = 0
v[idxUnknow] = 0
maxu = max(maxu, np.max(u))
minu = min(minu, np.min(u))
maxv = max(maxv, np.max(v))
minv = min(minv, np.min(v))
rad = np.sqrt(u ** 2 + v ** 2)
maxrad = max(-1, np.max(rad))
#print("max flow: %.4f\nflow range:\nu = %.3f .. %.3f\nv = %.3f .. %.3f" % (maxrad, minu,maxu, minv, maxv))
u = u/(maxrad + np.finfo(float).eps)
v = v/(maxrad + np.finfo(float).eps)
img = compute_color(u, v)
idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
img[idx] = 0
anglemap = np.expand_dims(np.uint8(np.arctan2(-v, -u) / np.pi * 127+ 128),axis=2)
anglemap = np.concatenate([anglemap,anglemap,anglemap],axis=2)
return np.uint8(img), anglemap
# imported from flownet
def compute_color(u, v):
"""
compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code
"""
[h, w] = u.shape
img = np.zeros([h, w, 3])
nanIdx = np.isnan(u) | np.isnan(v)
u[nanIdx] = 0
v[nanIdx] = 0
colorwheel = make_color_wheel()
ncols = np.size(colorwheel, 0)
rad = np.sqrt(u**2+v**2)
a = np.arctan2(-v, -u) / np.pi
fk = (a+1) / 2 * (ncols - 1) + 1