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main.py
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main.py
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from __future__ import print_function
import os
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from lib.networks_cascading import define_G, define_D, GANLoss
import random
from tensorboardX import SummaryWriter
import torchvision.utils as vutils
from vgg import GeneratorLoss
import torch.nn.functional as functional
import time
from lib.utils import *
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print(opt)
if opt.use_gan:
criterionGAN = GANLoss()
feature_all_all = []
if opt.cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
print('===> Building model')
netG = define_G(opt.input_nc, opt.output_nc, opt.ngf, 'normal', 0.02, opt.gpu_ids)
if opt.use_gan:
#netD = define_D(2*opt.period_D+1, opt.ndf, 'n_layers', n_layers_D=4, norm='batch', use_sigmoid=False)
netD = define_D(opt.batchSize, opt.ndf, 'n_layers', n_layers_D=4, norm='batch', use_sigmoid=False,gpu_ids=opt.gpu_ids)
#netD2 = define_D(3 + 3, opt.ndf, 'n_layers', n_layers_D=5, norm='batch', use_sigmoid=False, gpu_ids=[0])
# setup optimizer
generator_criterion=GeneratorLoss()
torch.manual_seed(opt.seed)
if opt.cuda:
netG = netG.cuda()
netD = netD.cuda()
#netD2 = netD2.cuda()
generator_criterion = generator_criterion.cuda()
torch.cuda.manual_seed_all(opt.seed)
if opt.use_gan:
criterionGAN.cuda()
def train(epoch, lr, list_stable, list_unstable, list_feature, list_adjacent, list_affine, list_epoch):
if epoch>opt.start_gan:
with_gan=True
else:
with_gan=False
netG.train()
if opt.continue_train>0 and epoch==opt.continue_train:
net_g_model_out_path = "checkpoint/{}/netG_model_epoch_{}.pth".format(opt.dataset, opt.continue_train)
checkpoint=torch.load(net_g_model_out_path)
netG.load_state_dict(checkpoint['net'])
# netG=load_model(netG)
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(opt.beta1, 0.999))
#optimizerD = optim.Adam([{'params': netD1.parameters()}, {'params': netD2.parameters()}], lr=0.0002,betas=(opt.beta1, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(opt.beta1, 0.999))
writer = SummaryWriter(opt.dir_logs)
if opt.use_gan and with_gan:
list_epoch=list_shuffle(list_epoch)
else:
random.shuffle(list_epoch)
dataset = customData(train_files, list_epoch, list_stable, list_unstable,list_feature,list_adjacent,list_affine, with_gan)
sample = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize, shuffle=False,num_workers=opt.threads,pin_memory=True)
iter_sample = iter(sample)
batch_idxs = (int(dataset.__len__())) // opt.batchSize
for i in range(batch_idxs):
time_begin = time.time()
images1, features1, affine1, images2, features2, affine2, feature_adjacent = next(iter_sample)
images1 = images1.cuda()
images2 = images2.cuda()
features1 = features1.cuda().float()
features2 = features2.cuda().float()
affine1=affine1.cuda().float()
affine2=affine2.cuda().float()
feature_adjacent = feature_adjacent.cuda().float()
image_stable1, image_unstable1, feature_stable1, feature_unstable1 = pre_propossing(images1, features1)
image_stable2, image_unstable2, feature_stable2, feature_unstable2 = pre_propossing(images2, features2)
# real_a.data.resize_(image_unstable1.size()).copy_(image_unstable1)
# real_b.data.resize_(image_stable1.size()).copy_(image_stable1)
# fake_b, grid = netG(real_a)
fake1=[]
fake2=[]
grid1= netG(image_unstable1[:, 0:period + 1, :, :])
for nl in range(opt.num_layer):
grid1[nl]=grid1[nl]
grid1[nl]=generate_maps(grid1[nl]).permute(0,2,3,1)
fake1.append(functional.grid_sample(image_unstable1[:, period + 1:period + 1 + 3, :, :], grid1[nl]))
fake1_gray = functional.grid_sample(image_unstable1[:, period//2 : period//2 + 1 :, :], grid1[2])
grid2 = netG(image_unstable2[:, 0:period + 1, :, :])
for nl in range(opt.num_layer):
grid2[nl] = grid2[nl]
grid2[nl] = generate_maps(grid2[nl]).permute(0, 2, 3, 1)
fake2.append(functional.grid_sample(image_unstable2[:, period + 1:period + 1 + 3, :, :], grid2[nl]))
fake2_gray = functional.grid_sample(image_unstable2[:, period // 2: period // 2 + 1:, :], grid2[2])
############################
# (1) Update D network: maximize log(D(x,y)) + log(1 - D(x,G(x)))
###########################
if opt.use_gan and with_gan:
optimizerD.zero_grad()
# train with fake
fake_ab1=fake1_gray
#fake_ab1 = torch.cat((image_stable1[:,3:3+opt.period_D,:,:], fake1_gray, image_stable1[:,3+opt.period_D+1:3+opt.period_D+1+opt.period_D,:,:]), 1)
# #
# samples = image_stable1[0,3+opt.period_D,:,:].data.cpu().numpy()
# samples = (samples + 1) * 127.5
# # samples = samples.transpose((1, 2, 0))
# # samples = samples.reshape((144, 256,-1))
# samples = np.array(samples.astype(np.uint8))
# cv2.imshow('stable_origin', samples)
#
# fake_ab1=frame_clip(fake_ab1, affine1)
#
# samples = fake_ab1[0, 3, :, :].data.cpu().numpy()
# samples = (samples + 1) * 127.5
# #samples = samples.transpose((1, 2, 0))
# # samples = samples.reshape((144, 256,-1))
# samples = np.array(samples.astype(np.uint8))
# cv2.imshow('stable',samples)
#
# samples = image_unstable1[0, 15, :, :].data.cpu().numpy()
# samples = (samples + 1) * 127.5
# # samples = samples.transpose((1, 2, 0))
# # samples = samples.reshape((144, 256,-1))
# samples = np.array(samples.astype(np.uint8))
# cv2.imshow('unstable', samples)
#
# cv2.waitKey(0)
fake_ab1 = frame_clip_batchsize(fake_ab1, affine1).permute(1,0,2,3)
pred_fake1 = netD.forward(fake_ab1.detach())
loss_d_fake1 = criterionGAN(pred_fake1, False)
fake_ab2 = fake2_gray#torch.cat((image_stable2[:,3:3+opt.period_D,:,:], fake2_gray, image_stable2[:,3+opt.period_D+1:3+opt.period_D+1+opt.period_D,:,:]), 1)
fake_ab2 = frame_clip_batchsize(fake_ab2, affine2).permute(1,0,2,3)
pred_fake2 = netD.forward(fake_ab2.detach())
loss_d_fake2 = criterionGAN(pred_fake2, False)
# train with real
real_ab1 = image_stable1[:,3+opt.period_D:3+opt.period_D+1,:,:]#image_stable1[:,3:3+opt.period_D*2+1,:,:]
real_ab1 = frame_clip_batchsize(real_ab1, affine1).permute(1,0,2,3)
pred_real1 = netD.forward(real_ab1)
loss_d_real1 = criterionGAN(pred_real1, True)
real_ab2 = image_stable2[:,3+opt.period_D:3+opt.period_D+1,:,:]#image_stable2[:,3:3+opt.period_D*2+1,:,:]
real_ab2= frame_clip_batchsize(real_ab2, affine2).permute(1,0,2,3)
pred_real2 = netD.forward(real_ab2)
loss_d_real2 = criterionGAN(pred_real2, True)
# Combined loss
loss_d = (loss_d_fake1 + loss_d_fake2 + loss_d_real1 + loss_d_real2) * 0.5
loss_d.backward()
optimizerD.step()
############################
# (2) Update G network: maximize log(D(x,G(x))) + L1(y,G(x))
##########################
optimizerG.zero_grad()
if opt.use_gan and with_gan:
fake_ab1 = fake1_gray#torch.cat((image_stable1[:,3:3+opt.period_D,:,:], fake1_gray, image_stable1[:,3+opt.period_D+1:3+opt.period_D+1+opt.period_D,:,:]), 1)
fake_ab1 = frame_clip_batchsize(fake_ab1, affine1).permute(1,0,2,3)
pred_fake1 = netD(fake_ab1)
loss_d_fake1_g = criterionGAN(pred_fake1, True)*opt.balance_gd
fake_ab2 = fake2_gray#torch.cat((image_stable2[:,3:3+opt.period_D,:,:], fake2_gray, image_stable2[:,3+opt.period_D+1:3+opt.period_D+1+opt.period_D,:,:]), 1)
fake_ab2 = frame_clip_batchsize(fake_ab2, affine2).permute(1,0,2,3)
pred_fake2 = netD(fake_ab2)
loss_d_fake2_g = criterionGAN(pred_fake2, True)*opt.balance_gd
loss_mse=0
loss_feature=0
loss_delta=0
loss_vgg=0
loss_g2=0
loss_affine=0
for nl in range(opt.num_layer):
loss_mse1, loss_delta1, loss_feature1 = loss_calulate(grid1[nl], feature_stable1, feature_unstable1, fake1[nl], image_stable1,opt.batchSize)
loss_mse2, loss_delta2, loss_feature2 = loss_calulate(grid2[nl], feature_stable2, feature_unstable2, fake2[nl], image_stable2,opt.batchSize)
loss_mse += loss_mse1 + loss_mse2
loss_feature += loss_feature1 + loss_feature2
loss_delta += loss_delta1 + loss_delta2
loss_vgg += generator_criterion(fake1[nl], image_stable1[:,0:3,:,:])
loss_vgg += generator_criterion(fake2[nl], image_stable2[:,0:3,:,:])
# feature_adjacent = feature_adjacent.view(-1, 2, 3)
# grid = functional.affine_grid(feature_adjacent, fake1[nl].size())
#
# output2_to_output1 = functional.grid_sample(fake2[nl], grid)
# loss_g2 += torch.mean(torch.abs(output2_to_output1 - fake1[nl]))
loss_g2+=torch.mean(torch.abs(fake2[nl] - fake1[nl]))
if epoch > opt.start_loss_affine:
loss_affine = (loss_pixel(grid1[opt.num_layer-1], affine1) + loss_pixel(grid2[opt.num_layer-1], affine2)) * opt.affine_weight
# loss_g1 = loss_feature1 + loss_vgg1 + loss_feature2 + loss_vgg2 # +delta# loss_feature#+loss_mse#+10*loss_g_g#loss_g_g # + loss_g_l1
if epoch>opt.start_loss_affine:
loss_g1 = loss_affine+loss_feature.double() + loss_vgg.double()+ loss_mse.double()#+ loss_affine * 20 # (delta1+delta2)*10+loss_affine*1000
else:
loss_g1 = loss_feature.double()+loss_vgg.double() + loss_mse.double() # + loss_affine * 20 # (delta1+delta2)*10+loss_affine*1000
if opt.use_gan and with_gan:
loss_g = loss_g1 + loss_g2 * opt.lamd +(loss_d_fake1_g+loss_d_fake2_g)/2
else:
if epoch>=3:
loss_g = loss_g1 +loss_g2.double() * opt.lamd
else:
loss_g = loss_g1 + loss_g2.double() * opt.lamd
loss_g.backward()
# for item in netG.named_parameters():
# h=item[1].register_hook(lambda grad: print(grad))
optimizerG.step()
if i % 10 == 0:
writer.add_scalar('scalar/loss_g_adjacent', loss_g2, i + epoch * batch_idxs)
writer.add_scalar('scalar/loss_g_feature', loss_feature, i + epoch * batch_idxs)
writer.add_scalar('scalar/loss_vgg', loss_vgg, i + epoch * batch_idxs)
writer.add_scalar('scalar/delta', loss_delta, i + epoch * batch_idxs)
writer.add_scalar('scalar/loss_affine', loss_affine, i + epoch * batch_idxs)
writer.add_scalar('scalar/mse', loss_mse, i + epoch * batch_idxs)
if opt.use_gan and with_gan:
writer.add_scalar('scalar/loss_d_fake1_g', loss_d_fake1_g, i + epoch * batch_idxs)
writer.add_scalar('scalar/loss_d_fake1', loss_d_fake1, i + epoch * batch_idxs)
writer.add_scalar('scalar/loss_d_real1', loss_d_real1, i + epoch * batch_idxs)
if i%10==0:
writer.add_image('image/unstable',vutils.make_grid(image_unstable2[0:3, period + 1:period + 1 + 3, :, :], normalize=True,scale_each=True),i + epoch * batch_idxs)
if opt.use_gan and with_gan:
writer.add_image('image/fake1_gray',vutils.make_grid(fake2_gray[0:3,:,:,:], normalize=True,scale_each=True),i + epoch * batch_idxs)
writer.add_image('image/fake1_gray_crop',vutils.make_grid(fake_ab2[:,0:3,:,:].permute(1,0,2,3), normalize=True,scale_each=True),i + epoch * batch_idxs)
for nl in range(opt.num_layer):
writer.add_image('image/stable_fake'+str(nl+1),vutils.make_grid(fake2[nl][0:3, 0:3, :, :], normalize=True, scale_each=True),i + epoch * batch_idxs)
writer.add_image('image/stable_real',vutils.make_grid(image_stable2[0:3, 0:3, :, :], normalize=True, scale_each=True),i + epoch * batch_idxs)
time_end = time.time()
time_each=time_end-time_begin
time_left=((opt.nEpochs-epoch-1)*batch_idxs+batch_idxs-i)*time_each/3600
print("=>train--Epoch[{}]({}/{}): time(s): {:.4f} Time_left(h): {:.4f} Learning rate: {:.6f}".format(
epoch, i, batch_idxs, time_each, time_left, lr))
def test(epoch,list_stable, list_unstable, list_feature, list_adjacent, list_affine, list_epoch):
netG.eval()
with_gan = False
writer = SummaryWriter(opt.dir_logs)
random.shuffle(list_epoch)
dataset = customData(val_files, list_epoch, list_stable, list_unstable, list_feature, list_affine, list_adjacent, with_gan)
sample = torch.utils.data.DataLoader(dataset, batch_size=opt.testBatchSize, shuffle=False, num_workers=opt.threads,pin_memory=True)
iter_sample = iter(sample)
batch_idxs = (int(dataset.__len__())) // opt.testBatchSize
for i in range(batch_idxs):
images1, features1, affine1, images2, features2, affine2, feature_adjacent = next(iter_sample)
images1 = images1.cuda()
images2 = images2.cuda()
features1 = features1.cuda().float()
features2 = features2.cuda().float()
affine1=affine1.cuda().float()
affine2=affine2.cuda().float()
feature_adjacent = feature_adjacent.cuda().float()
image_stable1, image_unstable1, feature_stable1, feature_unstable1 = pre_propossing(images1, features1)
image_stable2, image_unstable2, feature_stable2, feature_unstable2 = pre_propossing(images2, features2)
# real_a.data.resize_(image_unstable1.size()).copy_(image_unstable1)
# real_b.data.resize_(image_stable1.size()).copy_(image_stable1)
# fake_b, grid = netG(real_a)
fake1 = []
fake2 = []
grid1 = netG(image_unstable1[:, 0:period + 1, :, :])
for nl in range(opt.num_layer):
grid1[nl] = grid1[nl].permute(0, 2, 3, 1)
fake1.append(functional.grid_sample(image_unstable1[:, period + 1:period + 1 + 3, :, :], grid1[nl]))
grid2 = netG(image_unstable2[:, 0:period + 1, :, :])
for nl in range(opt.num_layer):
grid2[nl] = grid2[nl].permute(0, 2, 3, 1)
fake2.append(functional.grid_sample(image_unstable2[:, period + 1:period + 1 + 3, :, :], grid2[nl]))
loss_mse = 0
loss_feature = 0
loss_delta = 0
loss_vgg = 0
loss_g2 = 0
for nl in range(opt.num_layer):
loss_mse1, loss_feature1 = loss_calulate(grid1[nl], feature_stable1, feature_unstable1,
fake1[nl], image_stable1,opt.testBatchSize)
loss_mse2, loss_feature2 = loss_calulate(grid2[nl], feature_stable2, feature_unstable2,
fake2[nl], image_stable2,opt.testBatchSize)
loss_mse += loss_mse1 + loss_mse2
loss_feature += loss_feature1 + loss_feature2
# loss_delta += loss_delta1 + loss_delta2
loss_vgg += generator_criterion(fake1[nl].detach(), image_stable1[:,0:3,:,:].detach())
loss_vgg += generator_criterion(fake2[nl].detach(), image_stable2[:,0:3,:,:].detach())
loss_g2 += torch.mean(torch.abs(fake2[nl] - fake1[nl]))
loss_g1 = loss_feature + loss_vgg + loss_mse # + loss_affine * 20 # (delta1+delta2)*10+loss_affine*1000
loss_g = loss_g1 + loss_g2 * opt.lamd
print("===> val----Epoch[{}]({}/{})".format(epoch, i, batch_idxs))
if i % 10 == 0:
writer.add_scalar('scalar/val_loss_g_adjacent', loss_g2, i + epoch * batch_idxs)
writer.add_scalar('scalar/val_loss_g_feature', loss_feature, i + epoch * batch_idxs)
writer.add_scalar('scalar/val_loss_vgg', loss_vgg, i + epoch * batch_idxs)
# writer.add_scalar('scalar/val_delta', loss_delta, i + epoch * batch_idxs)
#writer.add_scalar('scalar/loss_affine', loss_affine, i + epoch * batch_idxs)
writer.add_scalar('scalar/val_mse', loss_mse, i + epoch * batch_idxs)
# writer.add_scalar('scalar/loss', lossG, i + epoch * batch_idxs)
writer.add_image('image/val_unstable',
vutils.make_grid(image_unstable1[0:3, period + 1:period + 1 + 3, :, :], normalize=True,
scale_each=True),
i + epoch * batch_idxs)
for nl in range(opt.num_layer):
writer.add_image('image/val_stable_fake'+str(nl+1),
vutils.make_grid(fake1[nl][0:3, :, :, :], normalize=True, scale_each=True),
i + epoch * batch_idxs)
writer.add_image('image/val_stable_real',
vutils.make_grid(image_stable1[0:3, 0:3, :, :], normalize=True, scale_each=True),
i + epoch * batch_idxs)
def checkpoint(epoch):
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
if not os.path.exists(os.path.join("checkpoint", opt.dataset)):
os.mkdir(os.path.join("checkpoint", opt.dataset))
state = {'net':netG.state_dict(), 'epoch':epoch}
net_g_model_out_path = "checkpoint/{}/netG_model_epoch_{}.pth".format(opt.dataset, epoch)
torch.save(state, net_g_model_out_path)
print("Checkpoint saved to {}".format("checkpoint" + opt.dataset))
def process():
"""Test pix2pix"""
# record all videos
class_name = ['lowquality','Regular', 'QuickRotation', 'Running', 'Parallax', 'Crowd', 'Zooming']
crop_ratio = 0.2
num_sample = 20
stride_sample = 15
threshold = 0
rate = 0.5
for ii in range(1,len(class_name)):
path = '/data/zmd/demo/datas/' + class_name[ii] + '/'
# path = '/data/zmd/DeepStab/DeepStab/unstable_test/'
list_videos = os.listdir(path)
# index_sample_test = np.arange(30, 0, -1)
index_sample_test = np.append(np.arange(-period // 2, 0, 1), np.arange(0, period // 2 + 1, 1))
# index_sample_test = [32, 16, 8, 4, 2, 1]#[5, 10, 15, 20, 25, 30]#[1, 2, 4, 8, 16, 32]
net_g_model_out_path = "checkpoint/{}/netG_model_epoch_{}.pth".format(opt.dataset, opt.continue_train)
checkpoint = torch.load(net_g_model_out_path)
netG.load_state_dict(checkpoint['net'])
#netG = torch.load('./checkpoint/unet_512_kalman_10_period_30-1515/netG_model_epoch_35.pth')
netG.eval()
for video_id in range(0,len(list_videos)):
###calculate cropping
'''
cap = cv2.VideoCapture(path + list_videos[video_id])
num_frame = cap.get(7)
fps = cap.get(5)
size_origin = (int(cap.get(3)), int(cap.get(4)))
size = (256, 256)
history_frame = []
ret, img_init = cap.read()
# img_init_resize = cv2.resize(img_init, size, interpolation=cv2.INTER_AREA)
# img_init_resize_gray = cv2.cvtColor(img_init_resize, cv2.COLOR_BGR2GRAY)
for i in range(period // 2 + 1):
history_frame.append(img_init)
# for i in range(index_sample_test[0]):
for i in range(period // 2):
ret, img_unstable = cap.read()
# img_init_resize = cv2.resize(img_unstable, size, interpolation=cv2.INTER_AREA)
history_frame.append(img_unstable)
# last=img_init_resize
for i in range(int(num_frame)):
print('calculating cropping::: video_index: ' + list_videos[video_id] + ' frame_index: ' + str(i))
list_unstable = []
# list_stable=[]
for j in range(len(index_sample_test)):
list_unstable.append(
cv2.resize(cv2.cvtColor(history_frame[period // 2 + index_sample_test[j]], cv2.COLOR_BGR2GRAY),
size, interpolation=cv2.INTER_AREA))
if (i >= int(num_frame) - period // 2):
img_unstable = history_frame[-1]
else:
ret, img_unstable = cap.read()
if ret == False:
continue
# img_unstable = cv2.resize(img_unstable, size, interpolation=cv2.INTER_AREA)
# img_unstable_resize=cv2.resize(img_unstable,size,interpolation=cv2.INTER_AREA)
# img_unstable_resize = cv2.cvtColor(img_unstable_resize, cv2.COLOR_BGR2RGB)
# list_unstable.append(img_unstable_resize)
now = cv2.cvtColor(history_frame[period // 2], cv2.COLOR_BGR2RGB)
now = now[np.newaxis, :, :, :]
now = torch.from_numpy(now)
now = now.cuda().float()
now = now.float() * (1. / 255) * 2 - 1
now = now.permute(0, 3, 1, 2)
# np1 = np.array(list_stable)
# np2 = np.array(list_unstable).transpose(0, 3, 1, 2).squeeze()
# npp = np.concatenate((np1, np2), axis=0)
npp = np.array(list_unstable)
images = npp[np.newaxis, :, :, :]
images = torch.from_numpy(images)
images = images.cuda().float()
images = images.float() * (1. / 255) * 2 - 1
grid_whole = netG(images)
grid = grid_whole[2]
m = torch.nn.Upsample(scale_factor=(size_origin[1] / 256, size_origin[0] / 256), mode='bilinear')
grid_resize = m(grid)
grid_resize = grid_resize.permute(0, 2, 3, 1)
pts1 = []
pts2 = []
for sample_height in range(num_sample):
for sample_width in range(num_sample):
pts1.append([grid_resize[
0, size_origin[1] // 2 + (sample_height - num_sample // 2) * stride_sample,
size_origin[0] // 2 + (
sample_width - num_sample // 2) * stride_sample * 2, 0].cpu().detach().numpy(),
grid_resize[
0, size_origin[1] // 2 + (sample_height - num_sample // 2) * stride_sample,
size_origin[0] // 2 + (
sample_width - num_sample // 2) * stride_sample * 2, 1].cpu().detach().numpy()])
pts2.append([(size_origin[0] / 2 + (sample_width - num_sample / 2) * stride_sample * 2) /
size_origin[0] * 2 - 1,
(size_origin[1] // 2 + (sample_height - num_sample // 2) * stride_sample) /
size_origin[1] * 2 - 1])
pts1 = np.array(pts1, dtype=np.float32)
pts2 = np.array(pts2, dtype=np.float32)
M, mask = cv2.findHomography(pts1, pts2, cv2.RANSAC, 3.0)
pts = np.float32([[-1, -1], [-1, 0.992], [0.992, 0.992], [0.992, -1]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
if (i == 0):
y_start = 0
x_start = 0
y_end = size_origin[1]
x_end = size_origin[0]
else:
if (i > period // 2 and i < num_frame - period // 2):
y_start = (y_start + (max((dst[0, 0, 1] + 1) * size_origin[1] / 2,(dst[3, 0, 1] + 1) * size_origin[1] / 2) - y_start) * rate if y_start < max((dst[0, 0, 1] + 1) * size_origin[1] / 2,(dst[3, 0, 1] + 1) * size_origin[1] / 2) else y_start)
y_end = (y_end - (y_end - min(y_end, min((dst[1, 0, 1] + 1) * size_origin[1] / 2,
(dst[2, 0, 1] + 1) * size_origin[
1] / 2))) * rate if y_end > min(
(dst[1, 0, 1] + 1) * size_origin[1] / 2,
(dst[2, 0, 1] + 1) * size_origin[1] / 2) else y_end)
x_start = (x_start + (max((dst[0, 0, 0] + 1) * size_origin[0] / 2,
(dst[1, 0, 0] + 1) * size_origin[
0] / 2) - x_start) * rate if x_start < max(
(dst[0, 0, 0] + 1) * size_origin[0] / 2,
(dst[1, 0, 0] + 1) * size_origin[0] / 2) else x_start)
# x_start = max(x_start,max((dst[0, 0, 0] + 1) * size_origin[0] / 2, (dst[1, 0, 0] + 1) * size_origin[0] / 2))
x_end = (x_end - (x_end - min(x_end, min((dst[2, 0, 0] + 1) * size_origin[0] / 2,
(dst[3, 0, 0] + 1) * size_origin[
0] / 2))) * rate if x_end > min(
(dst[2, 0, 0] + 1) * size_origin[0] / 2,
(dst[3, 0, 0] + 1) * size_origin[0] / 2) else x_end)
# x_end = min(x_end,min((dst[2, 0, 0] + 1) * size_origin[0] / 2, (dst[3, 0, 0] + 1) * size_origin[0] / 2))
history_frame.pop(0)
history_frame.append(img_unstable)
'''
#################calculate cropping##########
x_start = 0
x_end = 640
y_start = 0
y_end = 360
cap = cv2.VideoCapture(path + list_videos[video_id])
num_frame = cap.get(7)
fps = cap.get(5)
size_origin = (int(cap.get(3)), int(cap.get(4)))
# size_crop=(int(cap.get(3)*(1-crop_ratio)),int(cap.get(4)*(1-crop_ratio*1.5)))
size = (256, 256)
size_crop = (int(x_end) - int(x_start) - threshold * 2, int(y_end) - int(y_start) - threshold * 2)
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
videoWriter = cv2.VideoWriter(
'./test/stable_diff_frequency_0.1/' + class_name[ii] + '/' + str(list_videos[video_id]) + '.avi',
fourcc, int(fps), size_crop)
history_frame = []
ret, img_init = cap.read()
# img_init_resize = cv2.resize(img_init, size, interpolation=cv2.INTER_AREA)
# img_init_resize_gray = cv2.cvtColor(img_init_resize, cv2.COLOR_BGR2GRAY)
for i in range(period // 2 + 1):
history_frame.append(img_init)
# for i in range(index_sample_test[0]):
for i in range(period // 2):
ret, img_unstable = cap.read()
# img_init_resize = cv2.resize(img_unstable, size, interpolation=cv2.INTER_AREA)
history_frame.append(img_unstable)
# last=img_init_resize
for i in range(int(num_frame)):
print('video_index: ' + list_videos[video_id] + ' frame_index: ' + str(i) + ' cropping :' + str(
x_start) + ' ' + str(x_end) + ' ' + str(y_start) + ' ' + str(y_end))
list_unstable = []
# list_stable=[]
time_sum1 = time.time()
if i == 0:
for j in range(len(index_sample_test)):
list_unstable.append(cv2.resize(cv2.cvtColor(history_frame[period // 2 + index_sample_test[j]], cv2.COLOR_BGR2GRAY),size,interpolation=cv2.INTER_AREA))
npp = np.array(list_unstable)
images = npp[np.newaxis, :, :, :]
images = torch.from_numpy(images)
images = images.cuda().float()
images = images.float() / 255 * 2 - 1
else:
if (i >= int(num_frame) - period // 2):
img_unstable = history_frame[-1]
img_unstable_resize = cv2.resize(cv2.cvtColor(img_unstable, cv2.COLOR_BGR2GRAY), size,interpolation=cv2.INTER_AREA)
img_unstable_tensor = torch.from_numpy(img_unstable_resize[np.newaxis, np.newaxis, :, :]).cuda().float() / 255 * 2 - 1
images = torch.cat((images[:, 1:, :, :], img_unstable_tensor), 1)
history_frame.pop(0)
history_frame.append(img_unstable)
else:
ret, img_unstable = cap.read()
if ret == False:
continue
img_unstable_resize = cv2.resize(cv2.cvtColor(img_unstable, cv2.COLOR_BGR2GRAY), size,
interpolation=cv2.INTER_AREA)
img_unstable_tensor = torch.from_numpy(img_unstable_resize[np.newaxis, np.newaxis, :, :]).cuda().float() / 255 * 2 - 1
images = torch.cat((images[:, 1:, :, :], img_unstable_tensor), 1)
history_frame.pop(0)
history_frame.append(img_unstable)
# img_unstable = cv2.resize(img_unstable, size, interpolation=cv2.INTER_AREA)
# img_unstable_resize=cv2.resize(img_unstable,size,interpolation=cv2.INTER_AREA)
# img_unstable_resize = cv2.cvtColor(img_unstable_resize, cv2.COLOR_BGR2RGB)
# list_unstable.append(img_unstable_resize)
now = cv2.cvtColor(history_frame[period // 2], cv2.COLOR_BGR2RGB)
now = now[np.newaxis, :, :, :]
now = torch.from_numpy(now)
now = now.cuda().float()
now = now.float() / 255 * 2 - 1
now = now.permute(0, 3, 1, 2)
# np1 = np.array(list_stable)
# np2 = np.array(list_unstable).transpose(0, 3, 1, 2).squeeze()
# npp = np.concatenate((np1, np2), axis=0)
# npp = np.array(list_unstable)
# images = npp[np.newaxis, :, :, :]
# images = torch.from_numpy(images)
# images = images.cuda().float()
# images = images.float() / 255 * 2 - 1
torch.cuda.synchronize()
time1 = time.time()
grid= netG(images, False)
torch.cuda.synchronize()
time2 = time.time()
grid = generate_maps(grid, 1)
m = torch.nn.Upsample(scale_factor=(size_origin[1] / 256, size_origin[0] / 256), mode='bilinear')
grid_resize = m(grid)
grid_resize = grid_resize.permute(0, 2, 3, 1)
fake = functional.grid_sample(now, grid_resize)
# update history_frame
# fake_gray=cv2.cvtColor(fake[0,:,:,:].data.cpu().numpy(),cv2.COLOR_BGR2GRAY)
# history_frame.pop(0)
# history_frame.append(fake_gray)
samples = fake[0, :, :, :].data.cpu().numpy()
samples = (samples + 1) * 127.5
samples = samples.transpose((1, 2, 0))
# samples = samples.reshape((144, 256,-1))
samples = np.array(samples.astype(np.uint8))
# update history_frame
# fake_gray=cv2.cvtColor(samples,cv2.COLOR_BGR2GRAY)
samples = cv2.resize(samples, (640, 360), interpolation=cv2.INTER_AREA)
samples = cv2.cvtColor(samples, cv2.COLOR_BGR2RGB)
# samples_resize = samples[int(cap.get(4) * crop_ratio):int(cap.get(4) * crop_ratio ) + int(size_crop[1]),int(cap.get(3) * crop_ratio / 2):int(cap.get(3) * crop_ratio / 2) + int(size_crop[0]), :]
samples_resize = samples[int(y_start) + threshold:int(y_end) - threshold,
int(x_start) + threshold:int(x_end) - threshold, :]
samples_resize = cv2.GaussianBlur(samples_resize, (3, 3), 0.2, 0)
unstable_resize = cv2.resize(history_frame[period // 2], (640, 360), interpolation=cv2.INTER_AREA)
cv2.imshow('show_unstable.jpg', unstable_resize)
cv2.imshow('show_stable.jpg', samples_resize)
# cv2.imshow('show_crop.jpg',samples_resize)
cv2.waitKey(1)
videoWriter.write(samples_resize) # 写视频帧
time_sum2 = time.time()
print(str(int(1/(time2 - time1)))+' FPS')
videoWriter.release()
def main():
if opt.train:
list_stable,list_unstable,list_feature,list_adjacent,list_affine=image_store(train_files)
list_stable_val,list_unstable_val,list_feature_val,list_adjacent_val,list_affine_val=image_store(val_files)
for epoch in range(opt.continue_train, opt.nEpochs):
if epoch> opt.start_gan:
list_epoch = list_random_batchsize(train_files)
list_epoch_val = list_random_batchsize(val_files)
else:
list_epoch = list_random(train_files)
list_epoch_val = list_random(val_files)
if epoch % 5 == 4:
# test(epoch, list_stable_val, list_unstable_val, list_feature_val, list_adjacent_val, list_affine_val,list_epoch_val)
checkpoint(epoch)
lr = opt.lr * 0.1 ** int((epoch) / 10)
train(epoch, lr,list_stable,list_unstable,list_feature,list_adjacent,list_affine,list_epoch)
else:
process()
if __name__ == '__main__':
main()
'''
process()
'''