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main_flownetS_pyramid_noprevloss_dataloader.py
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main_flownetS_pyramid_noprevloss_dataloader.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
import collections
from data_loader import Data_Loader
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_sizei = shape[0]
height = shape[1]
width = shape[2]
batch_idx = tf.range(0, batch_sizei)
batch_idx = tf.reshape(batch_idx, (batch_sizei, 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))
#skip_length, stab_path, unstab_path, height, width, batch_size,
data_loader = Data_Loader([1, 9, 17, 25, 28, 29, 30, 31, 32], config.TRAIN.stab_path, config.TRAIN.unstab_path, 384, 512, batch_size, is_train = True, thread_num = 3)
data_loader_test = Data_Loader([1, 9, 17, 25, 28, 29, 30, 31, 32], config.TEST.stab_path, config.TEST.unstab_path, 384, 512, batch_size, is_train = False, thread_num = 2)
## DEFINE MODEL
# input
inputs = collections.OrderedDict()
with tf.variable_scope('input'):
#patches_in = tf.placeholder('float32', [batch_size, sample_num, h, w, 3], name = 'input_frames')
inputs['unstab_image'] = tf.placeholder('float32', [None, 384, 512, 3], name = 'input_unstab')
inputs['gtstab_image'] = tf.placeholder('float32', [None, 384, 512, 3], name = 'input_gtstab')
inputs['stab_image'] = tf.placeholder('float32', [None, 384, 512, 3*8], name = 'input_stab')
with tf.variable_scope('main_net') as scope:
outputs = flownetS_pyramid( tf.concat([inputs['stab_image'],inputs['unstab_image']],3) ,batch_size,is_train=True )
outputs_test = flownetS_pyramid(tf.concat([inputs['stab_image'],inputs['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])
print(warpedimg)
print(downsampled_stab)
print(mask)
return masked_MSE(warpedimg, downsampled_stab, mask),warpedimg
#size = [stab_image.shape[1], stab_image.shape[2]]
#flowsize = [predict_flow.shape[1],predict_flow.shape[2]]
#multiplyConst = tf.cast(tf.shape(stab_image)[1],tf.float32)/tf.cast(tf.shape(predict_flow)[1],tf.float32)
#upsampled_flow = tf.image.resize_images( tf.multiply(predict_flow,multiplyConst), size ) #downsample(gt_flow, size)
#warpedimg = tf_warp(unstab_image, upsampled_flow, stab_image.shape[1], stab_image.shape[2])
##loss6 = tl.cost.mean_squared_error(downsampled_stab6, warpedimg6, is_mean = True, name = 'loss6') #* 0.32
#mask = tf_warp(tf.ones_like(unstab_image), upsampled_flow, stab_image.shape[1], stab_image.shape[2])
#print(upsampled_flow )
#print(warpedimg)
#print(mask)
#return masked_MSE(warpedimg, stab_image, mask),warpedimg
## DEFINE LOSS
with tf.variable_scope('loss'):
loss6,warpedimg6 = lossterm(outputs['predict_flow6'],inputs['gtstab_image'],inputs['unstab_image'])
loss5,warpedimg5 = lossterm(outputs['predict_flow5'],inputs['gtstab_image'],inputs['unstab_image'])
loss4,warpedimg4 = lossterm(outputs['predict_flow4'],inputs['gtstab_image'],inputs['unstab_image'])
loss3,warpedimg3 = lossterm(outputs['predict_flow3'],inputs['gtstab_image'],inputs['unstab_image'])
loss2,warpedimg2 = lossterm(outputs['predict_flow2'],inputs['gtstab_image'],inputs['unstab_image'])
loss6_1,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss5_1,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss4_1,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss3_1,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss2_1,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss_1 = (loss6_1+loss5_1+loss4_1+loss3_1+loss2_1)*np.exp(-1/3)
loss6_2,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss5_2,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss4_2,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss3_2,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss2_2,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss_2 = (loss6_2+loss5_2+loss4_2+loss3_2+loss2_2)*np.exp(-2/3)
loss6_3,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss5_3,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss4_3,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss3_3,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss2_3,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss_3 = (loss6_3+loss5_3+loss4_3+loss3_3+loss2_3)*np.exp(-3/3)
loss6_4,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss5_4,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss4_4,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss3_4,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss2_4,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss_4 = (loss6_4+loss5_4+loss4_4+loss3_4+loss2_4)*np.exp(-4/3)
loss6_5,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss5_5,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss4_5,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss3_5,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss2_5,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss_5 = (loss6_5+loss5_5+loss4_5+loss3_5+loss2_5)*np.exp(-7/3)
var6 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow6']))*2e-8*3
var5 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow5']))*2e-8*3
var4 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow4']))*2e-8*3
var3 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow3']))*4e-8*1.5
var2 = tf.reduce_sum(tf.image.total_variation(outputs['predict_flow2']))*4e-8*1.5
loss_main = tf.identity( loss6+loss5+loss4+loss3+loss2+var6+var5+var4+var3+var2, name = 'total')
with tf.variable_scope('loss_test'):
loss6,warpedimg6 = lossterm(outputs_test['predict_flow6'],inputs['gtstab_image'],inputs['unstab_image'])
loss5,warpedimg5 = lossterm(outputs_test['predict_flow5'],inputs['gtstab_image'],inputs['unstab_image'])
loss4,warpedimg4 = lossterm(outputs_test['predict_flow4'],inputs['gtstab_image'],inputs['unstab_image'])
loss3,warpedimg3 = lossterm(outputs_test['predict_flow3'],inputs['gtstab_image'],inputs['unstab_image'])
loss2,warpedimg2 = lossterm(outputs_test['predict_flow2'],inputs['gtstab_image'],inputs['unstab_image'])
loss6_1,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss5_1,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss4_1,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss3_1,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss2_1,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,21:24],inputs['unstab_image'])
loss_1 = (loss6_1+loss5_1+loss4_1+loss3_1+loss2_1)*np.exp(-1/3)
loss6_2,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss5_2,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss4_2,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss3_2,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss2_2,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,18:21],inputs['unstab_image'])
loss_2 = (loss6_2+loss5_2+loss4_2+loss3_2+loss2_2)*np.exp(-2/3)
loss6_3,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss5_3,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss4_3,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss3_3,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss2_3,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,15:18],inputs['unstab_image'])
loss_3 = (loss6_3+loss5_3+loss4_3+loss3_3+loss2_3)*np.exp(-3/3)
loss6_4,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss5_4,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss4_4,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss3_4,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss2_4,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,12:15],inputs['unstab_image'])
loss_4 = (loss6_4+loss5_4+loss4_4+loss3_4+loss2_4)*np.exp(-4/3)
loss6_5,_ = lossterm(outputs['predict_flow6'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss5_5,_ = lossterm(outputs['predict_flow5'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss4_5,_ = lossterm(outputs['predict_flow4'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss3_5,_ = lossterm(outputs['predict_flow3'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss2_5,_ = lossterm(outputs['predict_flow2'],inputs['stab_image'][:,:,:,9:12],inputs['unstab_image'])
loss_5 = (loss6_5+loss5_5+loss4_5+loss3_5+loss2_5)*np.exp(-7/3)
var6 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow6']))*2e-8*3
var5 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow5']))*2e-8*3
var4 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow4']))*2e-8*3
var3 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow3']))*4e-8*1.5
var2 = tf.reduce_sum(tf.image.total_variation(outputs_test['predict_flow2']))*4e-8*1.5
loss_main_test = tf.identity( loss6+loss5+loss4+loss3+loss2+var6+var5+var4+var3+var2 , 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(inputs['unstab_image'], 1)))
image_sum_list.append(tf.summary.image('stab_image', fix_image_tf(inputs['gtstab_image'], 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/main_flownetS_pyramid_noprevloss_dataloader/fixed_ckpt/main_flownetS_pyramid_noprevloss_dataloader_prevloss_29k.npz', sess = sess)
## START TRAINING
print('*****************************************')
print(' TRAINING START')
print('*****************************************')
data_loader.init_data_loader(inputs)
data_loader_test.init_data_loader(inputs)
global_step = 0
for epoch in range(0, n_epoch + 1):
epoch_time = time.time()
total_loss, n_iter = 0, 0
# 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))
idx = 0
while 1:
step_time = time.time()
print('==============================================================')
feed_dict, is_end, is_not_batchsize = data_loader.feed_the_network()
if is_end: break;
if is_not_batchsize: continue;
_ = sess.run(optim_main, feed_dict)
# print(status)
err_main, lr = \
sess.run([loss_main, learning_rate], feed_dict)
print('[%s] global step: %d %4d/%4d time: %4.2fs, err[main: %1.2e], lr: %1.2e' % \
(tl.global_flag['mode'],global_step, idx, data_loader.num_itr, 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)
idx += 1
global_step += 1
# test
idx = 0
testsummary_loss = 0
while 1:
print(idx)
feed_dict, is_end, is_not_batchsize = data_loader_test.feed_the_network()
if is_end: break;
if is_not_batchsize: continue;
testsummary_loss = sess.run(loss_sum_test_main, feed_dict)
writer_scalar.add_summary(testsummary_loss, global_step+idx)
idx += 1
# 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_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]
if test_video_name != '0.avi' and test_video_name != '1.avi' and test_video_name != '2.avi' and test_video_name != '3.avi' and test_video_name != '6.avi'and test_video_name != '18.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 = 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')
# 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.image.resize_images(outputs['predict_flow2']*384.0/outputs['predict_flow2'].shape.as_list()[1],[out_h,out_w])
outflow = tf.concat([outflow[:,:,:,0:1]*out_w/512,outflow[:,:,:,1:2]*out_h/384],3)
#filtersize = 41
#ofx = outflow[0,:,:,0]
#for _ in range(filtersize/2):
# ofx = tf.pad( ofx, [ [ 1, 1 ], [ 1, 1 ] ], "SYMMETRIC" )
#ofx = tf.expand_dims(tf.expand_dims(ofx,axis=0),axis=3)
#smoothofx = tf.nn.conv2d(ofx, tf.constant(1.0/(filtersize*filtersize),shape=[filtersize,filtersize,1,1]),[1,1,1,1], "VALID" )
#ofy = outflow[0,:,:,1]
#for _ in range(filtersize/2):
# ofy = tf.pad( ofy, [ [ 1, 1 ], [ 1, 1 ] ], "SYMMETRIC" )
#ofy = tf.expand_dims(tf.expand_dims(ofy,axis=0),axis=3)
#smoothofy = tf.nn.conv2d(ofy, tf.constant(1.0/(filtersize*filtersize),shape=[filtersize,filtersize,1,1]),[1,1,1,1], "VALID" )
#smoothoutflow = tf.concat([smoothofx,smoothofy],3)
outputs_warpedimg = tf_warp(resizedInput, outflow , out_h,out_w)
# 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)
print(test_video_name)
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
#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, resizedInput: np.expand_dims(cv2.cvtColor(frame_unstab, cv2.COLOR_RGB2BGR)/255.0 ,axis=0)}
curwarpedimg = sess.run(outputs_warpedimg , feed_dict)
'''orb = cv2.ORB_create()
curframe = cv2.cvtColor(np.squeeze(curwarpedimg)*255, cv2.COLOR_RGB2BGR).astype(np.uint8)
preframe = np.squeeze(totaloutputFrame[np.int32(max(i-1,0))]).astype(np.uint8)
curframeKeypoint, curframeDescriptors = orb.detectAndCompute(curframe, None)
prevframeKeypoint, prevframeDescriptors = orb.detectAndCompute(preframe, None)
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(curframeDescriptors, prevframeDescriptors, None)
matches.sort(key=lambda x: x.distance, reverse=False)
numGoodMatches = int(len(matches) * 0.6)
matches = matches[:numGoodMatches]
validmatchesNum = 0
validmatchesIdx = []
for k, match in enumerate(matches):
is_boundary = False
for ix in range(-7,7):
for iy in range(-7,7):
curframelocx = np.int32(min(max(curframeKeypoint[match.queryIdx].pt[1]+ix,0),out_w-1))
curframelocy = np.int32(min(max(curframeKeypoint[match.queryIdx].pt[0]+iy,0),out_h-1))
prevframelocx = np.int32(min(max(prevframeKeypoint[match.queryIdx].pt[1]+ix,0),out_w-1))
prevframelocy = np.int32(min(max(prevframeKeypoint[match.queryIdx].pt[0]+iy,0),out_h-1))
if curframe[curframelocy,curframelocx,0] == 0 and curframe[curframelocy,curframelocx,1] == 0 and curframe[curframelocy,curframelocx,2] == 0 \
and preframe[prevframelocy,prevframelocx,0] == 0 and preframe[prevframelocy,prevframelocx,1] == 0 and preframe[prevframelocy,prevframelocx,2] == 0 :
is_boundary = True
if not is_boundary:
validmatchesNum += 1
validmatchesIdx.append(k)
curframePoints = np.zeros((validmatchesNum, 2), dtype=np.float32)
prevframePoints = np.zeros((validmatchesNum, 2), dtype=np.float32)
idx = 0
for k, match in enumerate(matches):
if k in validmatchesIdx:
curframePoints[idx, :] = curframeKeypoint[match.queryIdx].pt
prevframePoints[idx, :] = prevframeKeypoint[match.trainIdx].pt
idx += 1
distanceBtwPoints = np.zeros((validmatchesNum), dtype=np.float32)
for k in range(validmatchesNum):
distanceBtwPoints[k] = np.sqrt( (curframePoints[k,0]-prevframePoints[k,0])*(curframePoints[k,0]-prevframePoints[k,0]) + (curframePoints[k,1]-prevframePoints[k,1])*(curframePoints[k,1]-prevframePoints[k,1]) )
distanceBtwPointsSortedIdx = np.argsort(distanceBtwPoints)
curframePointsFiltered = curframePoints[distanceBtwPointsSortedIdx[0:max(np.int32(validmatchesNum*0.6),4)],:]
prevframePointsFiltered = prevframePoints[distanceBtwPointsSortedIdx[0:max(np.int32(validmatchesNum*0.6),4)],:]
h, mask = cv2.findHomography(curframePointsFiltered, prevframePointsFiltered, cv2.RANSAC)
curframeHomo = cv2.warpPerspective(curframe, h, (out_w, out_h))
#h = cv2.getAffineTransform(np.float32(curframePointsFiltered), np.float32(prevframePointsFiltered))
#curframeHomo= cv2.warpAffine(curframe, h, (out_w, out_h))'''
'''curinput[0,:,:,24:27] = cv2.resize(cv2.cvtColor(np.squeeze(curwarpedimg), cv2.COLOR_RGB2BGR), (512, 384))
for j in range(8):
curinput[0,:,:,j*3:(j+1)*3] = np.float32(cv2.cvtColor(cv2.resize(np.uint8(totaloutputFrame[max(i-1,0)]), (512, 384)), cv2.COLOR_RGB2BGR))/255.0
feed_dict = {inputs: curinput, resizedInput: np.expand_dims(cv2.cvtColor(np.squeeze(curwarpedimg), cv2.COLOR_RGB2BGR),axis=0)}
curwarpedimgTwice = sess.run(outputs_warpedimg , feed_dict)'''
#totaloutputFrame[i] = np.squeeze(curwarpedimgTwice)*255
totaloutputFrame[i] = cv2.cvtColor(np.squeeze(curwarpedimg)*255, cv2.COLOR_RGB2BGR)
#totaloutputFrame[i] = curframeHomo
print(time.time()-starttime)
#out.write(np.concatenate( (preframe, curframe, np.uint8(totaloutputFrame[i])),axis=1))
#out.write(np.concatenate(( np.uint8(cv2.resize(np.squeeze(curinput[0,:,:,24:27])*255, (out_w, out_h))) ,np.uint8(totaloutputFrame[i])),axis=1))
out.write(np.uint8(totaloutputFrame[i]))
out.release()
def evaluate_originalSize_homo():
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)
if test_video_name != '0.avi' and test_video_name != '1.avi' and test_video_name != '2.avi' and test_video_name != '3.avi' and test_video_name != '6.avi'and test_video_name != '18.avi':
continue
#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')
# 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.image.resize_images(outputs['predict_flow3']*out_h/outputs['predict_flow3'].shape.as_list()[1],[out_h,out_w])
outflow = tf.concat([outflow[:,:,:,0:1]*out_w/512,outflow[:,:,:,1:2]*out_h/384],3)
outputs_warpedimg = tf_warp(resizedInput, outflow , out_h,out_w)
# 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)
print(test_video_name)
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
feed_dict = {inputs: curinput, resizedInput: np.expand_dims(cv2.cvtColor(frame_unstab, cv2.COLOR_RGB2BGR)/255.0 ,axis=0)}
curoutflow = sess.run(outflow , feed_dict)
curoutputs_warpedimg = sess.run(outputs_warpedimg , feed_dict)
curoutflow = np.squeeze(curoutflow)
#curoutflow = np.concatenate((curoutflow[:,:,1:2],curoutflow[:,:,0:1]),axis=2)
xv,yv = np.meshgrid(np.linspace(0,out_w-1,out_w),np.linspace(0,out_h-1,out_h))
gridmesh = np.concatenate((np.expand_dims(xv,2),np.expand_dims(yv,2)),axis=2)
gridmeshOF = gridmesh - curoutflow
#print(gridmesh.shape)
#print(np.reshape(gridmesh,(out_w*out_h*2)).shape)
#h, mask = cv2.findHomography(np.reshape(gridmesh,(out_w*out_h,2)), np.reshape(gridmeshOF,(out_w*out_h,2)), cv2.RANSAC)
h, mask = cv2.findHomography(np.reshape(gridmesh,(out_w*out_h,2)), np.reshape(gridmeshOF,(out_w*out_h,2)), cv2.RANSAC)
curframeHomo = cv2.warpPerspective(frame_unstab, h, (out_w, out_h))
#totaloutputFrame[i] = np.squeeze(curwarpedimgTwice)*255
#totaloutputFrame[i] = curframeHomo
totaloutputFrame[i] = cv2.cvtColor(np.squeeze(curoutputs_warpedimg)*255, cv2.COLOR_RGB2BGR)
print(time.time()-starttime)
out.write(np.uint8(curframeHomo))
#out.write(np.uint8(totaloutputFrame[i]))
#out.write(np.concatenate( (np.uint8(np.squeeze(curoutputs_warpedimg*255)), np.uint8(totaloutputFrame[i])),axis=1))
out.release()
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)
starttime = time.time()
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)
print(time.time()-starttime)
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
k0 = np.floor(fk).astype(int)
k1 = k0 + 1
k1[k1 == ncols+1] = 1
f = fk - k0
for i in range(0, np.size(colorwheel,1)):
tmp = colorwheel[:, i]
col0 = tmp[k0-1] / 255
col1 = tmp[k1-1] / 255
col = (1-f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1-rad[idx]*(1-col[idx])
notidx = np.logical_not(idx)
col[notidx] *= 0.75
img[:, :, i] = np.uint8(np.floor(255 * col*(1-nanIdx)))
return img
def make_color_wheel():
"""
Generate color wheel according Middlebury color code
:return: Color wheel
"""
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
ncols = RY + YG + GC + CB + BM + MR
colorwheel = np.zeros([ncols, 3])
col = 0
# RY
colorwheel[0:RY, 0] = 255
colorwheel[0:RY, 1] = np.transpose(np.floor(255*np.arange(0, RY) / RY))
col += RY
# YG
colorwheel[col:col+YG, 0] = 255 - np.transpose(np.floor(255*np.arange(0, YG) / YG))
colorwheel[col:col+YG, 1] = 255
col += YG
# GC
colorwheel[col:col+GC, 1] = 255
colorwheel[col:col+GC, 2] = np.transpose(np.floor(255*np.arange(0, GC) / GC))
col += GC
# CB
colorwheel[col:col+CB, 1] = 255 - np.transpose(np.floor(255*np.arange(0, CB) / CB))
colorwheel[col:col+CB, 2] = 255
col += CB
# BM
colorwheel[col:col+BM, 2] = 255
colorwheel[col:col+BM, 0] = np.transpose(np.floor(255*np.arange(0, BM) / BM))
col += + BM
# MR
colorwheel[col:col+MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
colorwheel[col:col+MR, 0] = 255