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video_to_slomo.py
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video_to_slomo.py
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#!/usr/bin/env python3
import argparse
import os
import os.path
import subprocess
import ctypes
from shutil import rmtree, move
from PIL import Image
import torch
import torchvision.transforms as transforms
import torch.nn.functional as F
import model
import dataloader
import platform
from tqdm import tqdm
# For parsing commandline arguments
parser = argparse.ArgumentParser()
parser.add_argument("--ffmpeg_dir", type=str, default="", help='path to ffmpeg.exe')
parser.add_argument("--video", type=str, required=True, help='path of video to be converted')
parser.add_argument("--checkpoint", type=str, required=True, help='path of checkpoint for pretrained model')
parser.add_argument("--fps", type=float, default=30, help='specify fps of output video. Default: 30.')
parser.add_argument("--sf", type=int, required=True,
help='specify the slomo factor N. This will increase the frames by Nx. Example sf=2 ==> 2x frames')
parser.add_argument("--batch_size", type=int, default=1,
help='Specify batch size for faster conversion. This will depend on your cpu/gpu memory. Default: 1')
parser.add_argument("--output", type=str, default="output.mkv", help='Specify output file name. Default: output.mp4')
args = parser.parse_args()
def check():
"""
Checks the validity of commandline arguments.
Parameters
----------
None
Returns
-------
error : string
Error message if error occurs otherwise blank string.
"""
error = ""
if (args.sf < 2):
error = "Error: --sf/slomo factor has to be atleast 2"
if (args.batch_size < 1):
error = "Error: --batch_size has to be atleast 1"
if (args.fps < 1):
error = "Error: --fps has to be atleast 1"
if ".mkv" not in args.output:
error = "output needs to have mkv container"
return error
def extract_frames(video, outDir):
"""
Converts the `video` to images.
Parameters
----------
video : string
full path to the video file.
outDir : string
path to directory to output the extracted images.
Returns
-------
error : string
Error message if error occurs otherwise blank string.
"""
print('{} -i {} -vsync 0 {}/%06d.png'.format(os.path.join(args.ffmpeg_dir, "ffmpeg"), video, outDir))
try:
subprocess.check_call(
'{} -i "{}" -vsync 0 {}/%06d.png'.format(os.path.join(args.ffmpeg_dir, "ffmpeg"), video, outDir))
except CalledProcessError:
return "Error converting file:{}. Exiting.".format(video)
def create_video(dir):
error = ""
print('{} -r {} -i {}/%d.png -vcodec ffvhuff {}'.format(os.path.join(args.ffmpeg_dir, "ffmpeg"), args.fps, dir,
args.output))
try:
subprocess.check_call(
'{} -r {} -i {}/%d.png -vcodec ffvhuff "{}"'.format(os.path.join(args.ffmpeg_dir, "ffmpeg"), args.fps, dir,
args.output))
except CalledProcessError:
return "Error creating output video. Exiting."
def main():
# Check if arguments are okay
error = check()
if error:
print(error)
exit(1)
# Create extraction folder and extract frames
IS_WINDOWS = 'Windows' == platform.system()
extractionDir = "tmpSuperSloMo"
if not IS_WINDOWS:
# Assuming UNIX-like system where "." indicates hidden directories
extractionDir = "." + extractionDir
if os.path.isdir(extractionDir):
rmtree(extractionDir)
os.mkdir(extractionDir)
if IS_WINDOWS:
FILE_ATTRIBUTE_HIDDEN = 0x02
# ctypes.windll only exists on Windows
ctypes.windll.kernel32.SetFileAttributesW(extractionDir, FILE_ATTRIBUTE_HIDDEN)
extractionPath = os.path.join(extractionDir, "input")
outputPath = os.path.join(extractionDir, "output")
os.mkdir(extractionPath)
os.mkdir(outputPath)
error = extract_frames(args.video, extractionPath)
if error:
print(error)
exit(1)
# Initialize transforms
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
mean = [0.429, 0.431, 0.397]
std = [1, 1, 1]
normalize = transforms.Normalize(mean=mean,
std=std)
negmean = [x * -1 for x in mean]
revNormalize = transforms.Normalize(mean=negmean, std=std)
# Temporary fix for issue #7 https://github.com/avinashpaliwal/Super-SloMo/issues/7 -
# - Removed per channel mean subtraction for CPU.
if (device == "cpu"):
transform = transforms.Compose([transforms.ToTensor()])
TP = transforms.Compose([transforms.ToPILImage()])
else:
transform = transforms.Compose([transforms.ToTensor(), normalize])
TP = transforms.Compose([revNormalize, transforms.ToPILImage()])
# Load data
videoFrames = dataloader.Video(root=extractionPath, transform=transform)
videoFramesloader = torch.utils.data.DataLoader(videoFrames, batch_size=args.batch_size, shuffle=False)
# Initialize model
flowComp = model.UNet(6, 4)
flowComp.to(device)
for param in flowComp.parameters():
param.requires_grad = False
ArbTimeFlowIntrp = model.UNet(20, 5)
ArbTimeFlowIntrp.to(device)
for param in ArbTimeFlowIntrp.parameters():
param.requires_grad = False
flowBackWarp = model.backWarp(videoFrames.dim[0], videoFrames.dim[1], device)
flowBackWarp = flowBackWarp.to(device)
dict1 = torch.load(args.checkpoint, map_location='cpu')
ArbTimeFlowIntrp.load_state_dict(dict1['state_dictAT'])
flowComp.load_state_dict(dict1['state_dictFC'])
# Interpolate frames
frameCounter = 1
with torch.no_grad():
for _, (frame0, frame1) in enumerate(tqdm(videoFramesloader), 0):
I0 = frame0.to(device)
I1 = frame1.to(device)
flowOut = flowComp(torch.cat((I0, I1), dim=1))
F_0_1 = flowOut[:, :2, :, :]
F_1_0 = flowOut[:, 2:, :, :]
# Save reference frames in output folder
for batchIndex in range(args.batch_size):
(TP(frame0[batchIndex].detach())).resize(videoFrames.origDim, Image.BILINEAR).save(
os.path.join(outputPath, str(frameCounter + args.sf * batchIndex) + ".png"))
frameCounter += 1
# Generate intermediate frames
for intermediateIndex in range(1, args.sf):
t = float(intermediateIndex) / args.sf
temp = -t * (1 - t)
fCoeff = [temp, t * t, (1 - t) * (1 - t), temp]
F_t_0 = fCoeff[0] * F_0_1 + fCoeff[1] * F_1_0
F_t_1 = fCoeff[2] * F_0_1 + fCoeff[3] * F_1_0
g_I0_F_t_0 = flowBackWarp(I0, F_t_0)
torch.cuda.empty_cache()
g_I1_F_t_1 = flowBackWarp(I1, F_t_1)
torch.cuda.empty_cache()
intrpOut = ArbTimeFlowIntrp(
torch.cat((I0, I1, F_0_1, F_1_0, F_t_1, F_t_0, g_I1_F_t_1, g_I0_F_t_0), dim=1))
torch.cuda.empty_cache()
F_t_0_f = intrpOut[:, :2, :, :] + F_t_0
F_t_1_f = intrpOut[:, 2:4, :, :] + F_t_1
V_t_0 = torch.sigmoid(intrpOut[:, 4:5, :, :])
V_t_1 = 1 - V_t_0
g_I0_F_t_0_f = flowBackWarp(I0, F_t_0_f)
torch.cuda.empty_cache()
g_I1_F_t_1_f = flowBackWarp(I1, F_t_1_f)
torch.cuda.empty_cache()
wCoeff = [1 - t, t]
Ft_p = (wCoeff[0] * V_t_0 * g_I0_F_t_0_f + wCoeff[1] * V_t_1 * g_I1_F_t_1_f) / (
wCoeff[0] * V_t_0 + wCoeff[1] * V_t_1)
# Save intermediate frame
for batchIndex in range(args.batch_size):
(TP(Ft_p[batchIndex].cpu().detach())).resize(videoFrames.origDim, Image.BILINEAR).save(
os.path.join(outputPath, str(frameCounter + args.sf * batchIndex) + ".png"))
frameCounter += 1
# Set counter accounting for batching of frames
frameCounter += args.sf * (args.batch_size - 1)
# Generate video from interpolated frames
create_video(outputPath)
# Remove temporary files
rmtree(extractionDir)
exit(0)
main()