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test.py
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test.py
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# -*- coding: utf-8 -*-
import cv2
from PIL import Image
import numpy as np
import importlib
import os
import argparse
import torch
import torch.nn as nn
from torchvision import transforms
from core.utils import Stack, ToTorchFormatTensor
parser = argparse.ArgumentParser(description="DSTT")
parser.add_argument("-v", "--video", type=str, required=True)
parser.add_argument("-m", "--mask", type=str, required=True)
parser.add_argument("-c", "--ckpt", type=str, required=True)
parser.add_argument("--model", type=str, default='DSTT')
parser.add_argument("--width", type=int, default=432)
parser.add_argument("--height", type=int, default=240)
parser.add_argument("--outw", type=int, default=432)
parser.add_argument("--outh", type=int, default=240)
parser.add_argument("--step", type=int, default=10)
parser.add_argument("--num_ref", type=int, default=-1)
parser.add_argument("--neighbor_stride", type=int, default=5)
parser.add_argument("--savefps", type=int, default=24)
parser.add_argument("--use_mp4", action='store_true')
args = parser.parse_args()
w, h = args.width, args.height
ref_length = args.step # ref_step
num_ref = args.num_ref
neighbor_stride = args.neighbor_stride
default_fps = args.savefps
_to_tensors = transforms.Compose([
Stack(),
ToTorchFormatTensor()])
# sample reference frames from the whole video
def get_ref_index(f, neighbor_ids, length):
ref_index = []
if num_ref == -1:
for i in range(0, length, ref_length):
if not i in neighbor_ids:
ref_index.append(i)
else:
start_idx = max(0, f - ref_length * (num_ref//2))
end_idx = min(length, f + ref_length * (num_ref//2))
for i in range(start_idx, end_idx+1, ref_length):
if not i in neighbor_ids:
if len(ref_index) > num_ref:
#if len(ref_index) >= 5-len(neighbor_ids):
break
ref_index.append(i)
return ref_index
# read frame-wise masks
def read_mask(mpath):
masks = []
mnames = os.listdir(mpath)
mnames.sort()
for m in mnames:
m = Image.open(os.path.join(mpath, m))
m = m.resize((w, h), Image.NEAREST)
m = np.array(m.convert('L'))
m = np.array(m > 0).astype(np.uint8)
m = cv2.dilate(m, cv2.getStructuringElement(
cv2.MORPH_CROSS, (3, 3)), iterations=4)
masks.append(Image.fromarray(m*255))
return masks
# read frames from video
def read_frame_from_videos(args):
vname = args.video
frames = []
if args.use_mp4:
vidcap = cv2.VideoCapture(vname)
success, image = vidcap.read()
count = 0
while success:
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
frames.append(image.resize((w,h)))
success, image = vidcap.read()
count += 1
else:
lst = os.listdir(vname)
lst.sort()
fr_lst = [vname+'/'+name for name in lst]
for fr in fr_lst:
image = cv2.imread(fr)
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
frames.append(image.resize((w,h)))
return frames
def main_worker():
# set up models
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = importlib.import_module('model.' + args.model)
model = net.InpaintGenerator().to(device)
model_path = args.ckpt
data = torch.load(args.ckpt, map_location=device)
model.load_state_dict(data['netG'])
print('loading from: {}'.format(args.ckpt))
model.eval()
# prepare datset, encode all frames into deep space
frames = read_frame_from_videos(args)
video_length = len(frames)
imgs = _to_tensors(frames).unsqueeze(0)*2-1
frames = [np.array(f).astype(np.uint8) for f in frames]
masks = read_mask(args.mask)
binary_masks = [np.expand_dims((np.array(m) != 0).astype(np.uint8), 2) for m in masks]
masks = _to_tensors(masks).unsqueeze(0)
imgs, masks = imgs.to(device), masks.to(device)
comp_frames = [None]*video_length
print('loading videos and masks from: {}'.format(args.video))
# completing holes by spatial-temporal transformers
for f in range(0, video_length, neighbor_stride):
neighbor_ids = [i for i in range(max(0, f-neighbor_stride), min(video_length, f+neighbor_stride+1))]
ref_ids = get_ref_index(f, neighbor_ids, video_length)
print(f, len(neighbor_ids), len(ref_ids))
len_temp = len(neighbor_ids) + len(ref_ids)
selected_imgs = imgs[:1, neighbor_ids+ref_ids, :, :, :]
selected_masks = masks[:1, neighbor_ids+ref_ids, :, :, :]
with torch.no_grad():
masked_imgs = selected_imgs*(1-selected_masks)
pred_img = model(masked_imgs)
pred_img = (pred_img + 1) / 2
pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy()*255
for i in range(len(neighbor_ids)):
idx = neighbor_ids[i]
img = np.array(pred_img[i]).astype(
np.uint8)*binary_masks[idx] + frames[idx] * (1-binary_masks[idx])
if comp_frames[idx] is None:
comp_frames[idx] = img
else:
comp_frames[idx] = comp_frames[idx].astype(
np.float32)*0.5 + img.astype(np.float32)*0.5
aa, bb, cc = args.ckpt.split('/')
#bb, cc = args.ckpt.split('/')
name = bb + '_' + cc.split('.')[0] + '_' + args.mask.split('/')[-1]
writer = cv2.VideoWriter(f"{name}_result.mp4", cv2.VideoWriter_fourcc(*"mp4v"), default_fps, (args.outw, args.outh))
for f in range(video_length):
comp = np.array(comp_frames[f]).astype(
np.uint8)*binary_masks[f] + frames[f] * (1-binary_masks[f])
if w != args.outw:
comp = cv2.resize(comp, (args.outw, args.outh), interpolation=cv2.INTER_LINEAR)
writer.write(cv2.cvtColor(np.array(comp).astype(np.uint8), cv2.COLOR_BGR2RGB))
writer.release()
print('Finish in {}'.format(f"{name}_result.mp4"))
if __name__ == '__main__':
main_worker()