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run.py
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run.py
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#!/usr/bin/env python
import getopt
import math
import numpy
import PIL
import PIL.Image
import sys
import torch
import sepconv # the custom separable convolution layer
##########################################################
torch.set_grad_enabled(False) # make sure to not compute gradients for computational performance
torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance
##########################################################
args_strModel = 'lf'
args_strPadding = 'improved'
args_strOne = './images/one.png'
args_strTwo = './images/two.png'
args_strVideo = './videos/car-turn.mp4'
args_strOut = './out.png'
for strOption, strArg in getopt.getopt(sys.argv[1:], '', [
'model=',
'padding=',
'one=',
'two=',
'vidoe=',
'out=',
])[0]:
if strOption == '--model' and strArg != '': args_strModel = strArg # which model to use, l1 or lf, please see our paper for more details
if strOption == '--padding' and strArg != '': args_strPadding = strArg # which padding to use, the one used in the paper or the improved one
if strOption == '--one' and strArg != '': args_strOne = strArg # path to the first frame
if strOption == '--two' and strArg != '': args_strTwo = strArg # path to the second frame
if strOption == '--video' and strArg != '': args_strVideo = strArg # path to a video
if strOption == '--out' and strArg != '': args_strOut = strArg # path to where the output should be stored
# end
##########################################################
class Network(torch.nn.Module):
def __init__(self):
super().__init__()
def Basic(intInput, intOutput):
return torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intInput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
# end
def Upsample(intInput, intOutput):
return torch.nn.Sequential(
torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
# end
def Subnet():
return torch.nn.Sequential(
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=51, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
torch.nn.Conv2d(in_channels=51, out_channels=51, kernel_size=3, stride=1, padding=1)
)
# end
self.netConv1 = Basic(6, 32)
self.netConv2 = Basic(32, 64)
self.netConv3 = Basic(64, 128)
self.netConv4 = Basic(128, 256)
self.netConv5 = Basic(256, 512)
self.netDeconv5 = Basic(512, 512)
self.netDeconv4 = Basic(512, 256)
self.netDeconv3 = Basic(256, 128)
self.netDeconv2 = Basic(128, 64)
self.netUpsample5 = Upsample(512, 512)
self.netUpsample4 = Upsample(256, 256)
self.netUpsample3 = Upsample(128, 128)
self.netUpsample2 = Upsample(64, 64)
self.netVertical1 = Subnet()
self.netVertical2 = Subnet()
self.netHorizontal1 = Subnet()
self.netHorizontal2 = Subnet()
self.load_state_dict({ strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.hub.load_state_dict_from_url(url='http://content.sniklaus.com/sepconv/network-' + args_strModel + '.pytorch', file_name='sepconv-' + args_strModel).items() })
# end
def forward(self, tenOne, tenTwo):
tenConv1 = self.netConv1(torch.cat([tenOne, tenTwo], 1))
tenConv2 = self.netConv2(torch.nn.functional.avg_pool2d(input=tenConv1, kernel_size=2, stride=2, count_include_pad=False))
tenConv3 = self.netConv3(torch.nn.functional.avg_pool2d(input=tenConv2, kernel_size=2, stride=2, count_include_pad=False))
tenConv4 = self.netConv4(torch.nn.functional.avg_pool2d(input=tenConv3, kernel_size=2, stride=2, count_include_pad=False))
tenConv5 = self.netConv5(torch.nn.functional.avg_pool2d(input=tenConv4, kernel_size=2, stride=2, count_include_pad=False))
tenDeconv5 = self.netUpsample5(self.netDeconv5(torch.nn.functional.avg_pool2d(input=tenConv5, kernel_size=2, stride=2, count_include_pad=False)))
tenDeconv4 = self.netUpsample4(self.netDeconv4(tenDeconv5 + tenConv5))
tenDeconv3 = self.netUpsample3(self.netDeconv3(tenDeconv4 + tenConv4))
tenDeconv2 = self.netUpsample2(self.netDeconv2(tenDeconv3 + tenConv3))
tenCombine = tenDeconv2 + tenConv2
tenVerone = self.netVertical1(tenCombine)
tenVertwo = self.netVertical2(tenCombine)
tenHorone = self.netHorizontal1(tenCombine)
tenHortwo = self.netHorizontal2(tenCombine)
return sum([
sepconv.sepconv_func.apply(torch.nn.functional.pad(input=tenOne, pad=[int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0))], mode='replicate'), tenVerone, tenHorone),
sepconv.sepconv_func.apply(torch.nn.functional.pad(input=tenTwo, pad=[int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0))], mode='replicate'), tenVertwo, tenHortwo)
])
# end
# end
netNetwork = None
##########################################################
def estimate(tenOne, tenTwo):
global netNetwork
if netNetwork is None:
netNetwork = Network().cuda().eval()
# end
assert(tenOne.shape[1] == tenTwo.shape[1])
assert(tenOne.shape[2] == tenTwo.shape[2])
intWidth = tenOne.shape[2]
intHeight = tenOne.shape[1]
assert(intWidth <= 1280) # while our approach works with larger images, we do not recommend it unless you are aware of the implications
assert(intHeight <= 720) # while our approach works with larger images, we do not recommend it unless you are aware of the implications
tenPreprocessedOne = tenOne.cuda().view(1, 3, intHeight, intWidth)
tenPreprocessedTwo = tenTwo.cuda().view(1, 3, intHeight, intWidth)
if args_strPadding == 'paper':
intPaddingLeft, intPaddingTop, intPaddingBottom, intPaddingRight = int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)) ,int(math.floor(51 / 2.0))
elif args_strPadding == 'improved':
intPaddingLeft, intPaddingTop, intPaddingBottom, intPaddingRight = 0, 0, 0, 0
# end
intPreprocessedWidth = intPaddingLeft + intWidth + intPaddingRight
intPreprocessedHeight = intPaddingTop + intHeight + intPaddingBottom
if intPreprocessedWidth != ((intPreprocessedWidth >> 7) << 7):
intPreprocessedWidth = (((intPreprocessedWidth >> 7) + 1) << 7) # more than necessary
# end
if intPreprocessedHeight != ((intPreprocessedHeight >> 7) << 7):
intPreprocessedHeight = (((intPreprocessedHeight >> 7) + 1) << 7) # more than necessary
# end
intPaddingRight = intPreprocessedWidth - intWidth - intPaddingLeft
intPaddingBottom = intPreprocessedHeight - intHeight - intPaddingTop
tenPreprocessedOne = torch.nn.functional.pad(input=tenPreprocessedOne, pad=[intPaddingLeft, intPaddingRight, intPaddingTop, intPaddingBottom], mode='replicate')
tenPreprocessedTwo = torch.nn.functional.pad(input=tenPreprocessedTwo, pad=[intPaddingLeft, intPaddingRight, intPaddingTop, intPaddingBottom], mode='replicate')
return torch.nn.functional.pad(input=netNetwork(tenPreprocessedOne, tenPreprocessedTwo), pad=[0 - intPaddingLeft, 0 - intPaddingRight, 0 - intPaddingTop, 0 - intPaddingBottom], mode='replicate')[0, :, :, :].cpu()
# end
##########################################################
if __name__ == '__main__':
if args_strOut.split('.')[-1] in ['bmp', 'jpg', 'jpeg', 'png']:
tenOne = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(args_strOne))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
tenTwo = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(args_strTwo))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
tenOutput = estimate(tenOne, tenTwo)
PIL.Image.fromarray((tenOutput.clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8)).save(args_strOut)
elif args_strOut.split('.')[-1] in ['avi', 'mp4', 'webm', 'wmv']:
import moviepy
import moviepy.editor
import moviepy.video.io.ffmpeg_writer
objVideoreader = moviepy.editor.VideoFileClip(filename=args_strVideo)
intWidth = objVideoreader.w
intHeight = objVideoreader.h
tenFrames = [None, None, None, None, None]
with moviepy.video.io.ffmpeg_writer.FFMPEG_VideoWriter(filename=args_strOut, size=(intWidth, intHeight), fps=objVideoreader.fps) as objVideowriter:
for npyFrame in objVideoreader.iter_frames():
tenFrames[4] = torch.FloatTensor(numpy.ascontiguousarray(npyFrame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
if tenFrames[0] is not None:
tenFrames[2] = estimate(tenFrames[0], tenFrames[4])
tenFrames[1] = estimate(tenFrames[0], tenFrames[2])
tenFrames[3] = estimate(tenFrames[2], tenFrames[4])
objVideowriter.write_frame((tenFrames[0].clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8))
objVideowriter.write_frame((tenFrames[1].clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8))
objVideowriter.write_frame((tenFrames[2].clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8))
objVideowriter.write_frame((tenFrames[3].clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8))
# end
tenFrames[0] = torch.FloatTensor(numpy.ascontiguousarray(npyFrame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
# end
# end
# end
# end