# -*- coding: utf-8 -*-
import os
import cv2
import numpy as np
import scipy.ndimage
from PIL import Image
from tqdm import tqdm
import torch
import torchvision
import gc
try:
from model.modules.flow_comp_raft import RAFT_bi
from model.recurrent_flow_completion import RecurrentFlowCompleteNet
from model.propainter import InpaintGenerator
from utils.download_util import load_file_from_url
from core.utils import to_tensors
from model.misc import get_device
except:
from propainter.model.modules.flow_comp_raft import RAFT_bi
from propainter.model.recurrent_flow_completion import RecurrentFlowCompleteNet
from propainter.model.propainter import InpaintGenerator
from propainter.utils.download_util import load_file_from_url
from propainter.core.utils import to_tensors
from propainter.model.misc import get_device
import warnings
warnings.filterwarnings("ignore")
pretrain_model_url = 'https://github.com/sczhou/ProPainter/releases/download/v0.1.0/'
MaxSideThresh = 960
# resize frames
def resize_frames(frames, size=None):
if size is not None:
out_size = size
process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8)
frames = [f.resize(process_size) for f in frames]
else:
out_size = frames[0].size
process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8)
if not out_size == process_size:
frames = [f.resize(process_size) for f in frames]
return frames, process_size, out_size
# read frames from video
def read_frame_from_videos(frame_root, video_length):
if frame_root.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path
video_name = os.path.basename(frame_root)[:-4]
vframes, aframes, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec', end_pts=video_length) # RGB
frames = list(vframes.numpy())
frames = [Image.fromarray(f) for f in frames]
fps = info['video_fps']
nframes = len(frames)
else:
video_name = os.path.basename(frame_root)
frames = []
fr_lst = sorted(os.listdir(frame_root))
for fr in fr_lst:
frame = cv2.imread(os.path.join(frame_root, fr))
frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
frames.append(frame)
fps = None
nframes = len(frames)
size = frames[0].size
return frames, fps, size, video_name, nframes
def binary_mask(mask, th=0.1):
mask[mask>th] = 1
mask[mask<=th] = 0
return mask
# read frame-wise masks
def read_mask(mpath, frames_len, size, flow_mask_dilates=8, mask_dilates=5):
masks_img = []
masks_dilated = []
flow_masks = []
if mpath.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path
masks_img = [Image.open(mpath)]
elif mpath.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path
cap = cv2.VideoCapture(mpath)
if not cap.isOpened():
print("Error: Could not open video.")
exit()
idx = 0
while True:
ret, frame = cap.read()
if not ret:
break
if(idx >= frames_len):
break
masks_img.append(Image.fromarray(frame))
idx += 1
cap.release()
else:
mnames = sorted(os.listdir(mpath))
for mp in mnames:
masks_img.append(Image.open(os.path.join(mpath, mp)))
# print(mp)
for mask_img in masks_img:
if size is not None:
mask_img = mask_img.resize(size, Image.NEAREST)
mask_img = np.array(mask_img.convert('L'))
# Dilate 8 pixel so that all known pixel is trustworthy
if flow_mask_dilates > 0:
flow_mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=flow_mask_dilates).astype(np.uint8)
else:
flow_mask_img = binary_mask(mask_img).astype(np.uint8)
# Close the small holes inside the foreground objects
# flow_mask_img = cv2.morphologyEx(flow_mask_img, cv2.MORPH_CLOSE, np.ones((21, 21),np.uint8)).astype(bool)
# flow_mask_img = scipy.ndimage.binary_fill_holes(flow_mask_img).astype(np.uint8)
flow_masks.append(Image.fromarray(flow_mask_img * 255))
if mask_dilates > 0:
mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=mask_dilates).astype(np.uint8)
else:
mask_img = binary_mask(mask_img).astype(np.uint8)
masks_dilated.append(Image.fromarray(mask_img * 255))
if len(masks_img) == 1:
flow_masks = flow_masks * frames_len
masks_dilated = masks_dilated * frames_len
return flow_masks, masks_dilated
def get_ref_index(mid_neighbor_id, neighbor_ids, length, ref_stride=10, ref_num=-1):
ref_index = []
if ref_num == -1:
for i in range(0, length, ref_stride):
if i not in neighbor_ids:
ref_index.append(i)
else:
start_idx = max(0, mid_neighbor_id - ref_stride * (ref_num // 2))
end_idx = min(length, mid_neighbor_id + ref_stride * (ref_num // 2))
for i in range(start_idx, end_idx, ref_stride):
if i not in neighbor_ids:
if len(ref_index) > ref_num:
break
ref_index.append(i)
return ref_index
class Propainter:
def __init__(
self, propainter_model_dir, device):
self.device = device
##############################################
# set up RAFT and flow competition model
##############################################
ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'raft-things.pth'),
model_dir=propainter_model_dir, progress=True, file_name=None)
self.fix_raft = RAFT_bi(ckpt_path, device)
ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'recurrent_flow_completion.pth'),
model_dir=propainter_model_dir, progress=True, file_name=None)
self.fix_flow_complete = RecurrentFlowCompleteNet(ckpt_path)
for p in self.fix_flow_complete.parameters():
p.requires_grad = False
self.fix_flow_complete.to(device)
self.fix_flow_complete.eval()
##############################################
# set up ProPainter model
##############################################
ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'ProPainter.pth'),
model_dir=propainter_model_dir, progress=True, file_name=None)
self.model = InpaintGenerator(model_path=ckpt_path).to(device)
self.model.eval()
def forward(self, video, mask, output_path, resize_ratio=0.6, video_length=2, height=-1, width=-1,
mask_dilation=4, ref_stride=10, neighbor_length=10, subvideo_length=80,
raft_iter=20, save_fps=24, save_frames=False, fp16=True):
# Use fp16 precision during inference to reduce running memory cost
use_half = True if fp16 else False
if self.device == torch.device('cpu'):
use_half = False
################ read input video ################
frames, fps, size, video_name, nframes = read_frame_from_videos(video, video_length)
frames = frames[:nframes]
if not width == -1 and not height == -1:
size = (width, height)
longer_edge = max(size[0], size[1])
if(longer_edge > MaxSideThresh):
scale = MaxSideThresh / longer_edge
resize_ratio = resize_ratio * scale
if not resize_ratio == 1.0:
size = (int(resize_ratio * size[0]), int(resize_ratio * size[1]))
frames, size, out_size = resize_frames(frames, size)
fps = save_fps if fps is None else fps
################ read mask ################
frames_len = len(frames)
flow_masks, masks_dilated = read_mask(mask, frames_len, size,
flow_mask_dilates=mask_dilation,
mask_dilates=mask_dilation)
flow_masks = flow_masks[:nframes]
masks_dilated = masks_dilated[:nframes]
w, h = size
################ adjust input ################
frames_len = min(len(frames), len(masks_dilated))
frames = frames[:frames_len]
flow_masks = flow_masks[:frames_len]
masks_dilated = masks_dilated[:frames_len]
ori_frames_inp = [np.array(f).astype(np.uint8) for f in frames]
frames = to_tensors()(frames).unsqueeze(0) * 2 - 1
flow_masks = to_tensors()(flow_masks).unsqueeze(0)
masks_dilated = to_tensors()(masks_dilated).unsqueeze(0)
frames, flow_masks, masks_dilated = frames.to(self.device), flow_masks.to(self.device), masks_dilated.to(self.device)
##############################################
# ProPainter inference
##############################################
video_length = frames.size(1)
print(f'Priori generating: [{video_length} frames]...')
with torch.no_grad():
# ---- compute flow ----
new_longer_edge = max(frames.size(-1), frames.size(-2))
if new_longer_edge <= 640:
short_clip_len = 12
elif new_longer_edge <= 720:
short_clip_len = 8
elif new_longer_edge <= 1280:
short_clip_len = 4
else:
short_clip_len = 2
# use fp32 for RAFT
if frames.size(1) > short_clip_len:
gt_flows_f_list, gt_flows_b_list = [], []
for f in range(0, video_length, short_clip_len):
end_f = min(video_length, f + short_clip_len)
if f == 0:
flows_f, flows_b = self.fix_raft(frames[:,f:end_f], iters=raft_iter)
else:
flows_f, flows_b = self.fix_raft(frames[:,f-1:end_f], iters=raft_iter)
gt_flows_f_list.append(flows_f)
gt_flows_b_list.append(flows_b)
torch.cuda.empty_cache()
gt_flows_f = torch.cat(gt_flows_f_list, dim=1)
gt_flows_b = torch.cat(gt_flows_b_list, dim=1)
gt_flows_bi = (gt_flows_f, gt_flows_b)
else:
gt_flows_bi = self.fix_raft(frames, iters=raft_iter)
torch.cuda.empty_cache()
torch.cuda.empty_cache()
gc.collect()
if use_half:
frames, flow_masks, masks_dilated = frames.half(), flow_masks.half(), masks_dilated.half()
gt_flows_bi = (gt_flows_bi[0].half(), gt_flows_bi[1].half())
self.fix_flow_complete = self.fix_flow_complete.half()
self.model = self.model.half()
# ---- complete flow ----
flow_length = gt_flows_bi[0].size(1)
if flow_length > subvideo_length:
pred_flows_f, pred_flows_b = [], []
pad_len = 5
for f in range(0, flow_length, subvideo_length):
s_f = max(0, f - pad_len)
e_f = min(flow_length, f + subvideo_length + pad_len)
pad_len_s = max(0, f) - s_f
pad_len_e = e_f - min(flow_length, f + subvideo_length)
pred_flows_bi_sub, _ = self.fix_flow_complete.forward_bidirect_flow(
(gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]),
flow_masks[:, s_f:e_f+1])
pred_flows_bi_sub = self.fix_flow_complete.combine_flow(
(gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]),
pred_flows_bi_sub,
flow_masks[:, s_f:e_f+1])
pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e])
pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e])
torch.cuda.empty_cache()
pred_flows_f = torch.cat(pred_flows_f, dim=1)
pred_flows_b = torch.cat(pred_flows_b, dim=1)
pred_flows_bi = (pred_flows_f, pred_flows_b)
else:
pred_flows_bi, _ = self.fix_flow_complete.forward_bidirect_flow(gt_flows_bi, flow_masks)
pred_flows_bi = self.fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, flow_masks)
torch.cuda.empty_cache()
torch.cuda.empty_cache()
gc.collect()
masks_dilated_ori = masks_dilated.clone()
# ---- Pre-propagation ----
subvideo_length_img_prop = min(100, subvideo_length) # ensure a minimum of 100 frames for image propagation
if(len(frames[0]))>subvideo_length_img_prop: # perform propagation only when length of frames is larger than subvideo_length_img_prop
sample_rate = len(frames[0])//(subvideo_length_img_prop//2)
index_sample = list(range(0, len(frames[0]), sample_rate))
sample_frames = torch.stack([frames[0][i].to(torch.float32) for i in index_sample]).unsqueeze(0) # use fp32 for RAFT
sample_masks_dilated = torch.stack([masks_dilated[0][i] for i in index_sample]).unsqueeze(0)
sample_flow_masks = torch.stack([flow_masks[0][i] for i in index_sample]).unsqueeze(0)
## recompute flow for sampled frames
# use fp32 for RAFT
sample_video_length = sample_frames.size(1)
if sample_frames.size(1) > short_clip_len:
gt_flows_f_list, gt_flows_b_list = [], []
for f in range(0, sample_video_length, short_clip_len):
end_f = min(sample_video_length, f + short_clip_len)
if f == 0:
flows_f, flows_b = self.fix_raft(sample_frames[:,f:end_f], iters=raft_iter)
else:
flows_f, flows_b = self.fix_raft(sample_frames[:,f-1:end_f], iters=raft_iter)
gt_flows_f_list.append(flows_f)
gt_flows_b_list.append(flows_b)
torch.cuda.empty_cache()
gt_flows_f = torch.cat(gt_flows_f_list, dim=1)
gt_flows_b = torch.cat(gt_flows_b_list, dim=1)
sample_gt_flows_bi = (gt_flows_f, gt_flows_b)
else:
sample_gt_flows_bi = self.fix_raft(sample_frames, iters=raft_iter)
torch.cuda.empty_cache()
torch.cuda.empty_cache()
gc.collect()
if use_half:
sample_frames, sample_flow_masks, sample_masks_dilated = sample_frames.half(), sample_flow_masks.half(), sample_masks_dilated.half()
sample_gt_flows_bi = (sample_gt_flows_bi[0].half(), sample_gt_flows_bi[1].half())
# ---- complete flow ----
flow_length = sample_gt_flows_bi[0].size(1)
if flow_length > subvideo_length:
pred_flows_f, pred_flows_b = [], []
pad_len = 5
for f in range(0, flow_length, subvideo_length):
s_f = max(0, f - pad_len)
e_f = min(flow_length, f + subvideo_length + pad_len)
pad_len_s = max(0, f) - s_f
pad_len_e = e_f - min(flow_length, f + subvideo_length)
pred_flows_bi_sub, _ = self.fix_flow_complete.forward_bidirect_flow(
(sample_gt_flows_bi[0][:, s_f:e_f], sample_gt_flows_bi[1][:, s_f:e_f]),
sample_flow_masks[:, s_f:e_f+1])
pred_flows_bi_sub = self.fix_flow_complete.combine_flow(
(sample_gt_flows_bi[0][:, s_f:e_f], sample_gt_flows_bi[1][:, s_f:e_f]),
pred_flows_bi_sub,
sample_flow_masks[:, s_f:e_f+1])
pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e])
pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e])
torch.cuda.empty_cache()
pred_flows_f = torch.cat(pred_flows_f, dim=1)
pred_flows_b = torch.cat(pred_flows_b, dim=1)
sample_pred_flows_bi = (pred_flows_f, pred_flows_b)
else:
sample_pred_flows_bi, _ = self.fix_flow_complete.forward_bidirect_flow(sample_gt_flows_bi, sample_flow_masks)
sample_pred_flows_bi = self.fix_flow_complete.combine_flow(sample_gt_flows_bi, sample_pred_flows_bi, sample_flow_masks)
torch.cuda.empty_cache()
torch.cuda.empty_cache()
gc.collect()
masked_frames = sample_frames * (1 - sample_masks_dilated)
if sample_video_length > subvideo_length_img_prop:
updated_frames, updated_masks = [], []
pad_len = 10
for f in range(0, sample_video_length, subvideo_length_img_prop):
s_f = max(0, f - pad_len)
e_f = min(sample_video_length, f + subvideo_length_img_prop + pad_len)
pad_len_s = max(0, f) - s_f
pad_len_e = e_f - min(sample_video_length, f + subvideo_length_img_prop)
b, t, _, _, _ = sample_masks_dilated[:, s_f:e_f].size()
pred_flows_bi_sub = (sample_pred_flows_bi[0][:, s_f:e_f-1], sample_pred_flows_bi[1][:, s_f:e_f-1])
prop_imgs_sub, updated_local_masks_sub = self.model.img_propagation(masked_frames[:, s_f:e_f],
pred_flows_bi_sub,
sample_masks_dilated[:, s_f:e_f],
'nearest')
updated_frames_sub = sample_frames[:, s_f:e_f] * (1 - sample_masks_dilated[:, s_f:e_f]) + \
prop_imgs_sub.view(b, t, 3, h, w) * sample_masks_dilated[:, s_f:e_f]
updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w)
updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e])
updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e])
torch.cuda.empty_cache()
updated_frames = torch.cat(updated_frames, dim=1)
updated_masks = torch.cat(updated_masks, dim=1)
else:
b, t, _, _, _ = sample_masks_dilated.size()
prop_imgs, updated_local_masks = self.model.img_propagation(masked_frames, sample_pred_flows_bi, sample_masks_dilated, 'nearest')
updated_frames = sample_frames * (1 - sample_masks_dilated) + prop_imgs.view(b, t, 3, h, w) * sample_masks_dilated
updated_masks = updated_local_masks.view(b, t, 1, h, w)
torch.cuda.empty_cache()
## replace input frames/masks with updated frames/masks
for i,index in enumerate(index_sample):
frames[0][index] = updated_frames[0][i]
masks_dilated[0][index] = updated_masks[0][i]
# ---- frame-by-frame image propagation ----
masked_frames = frames * (1 - masks_dilated)
subvideo_length_img_prop = min(100, subvideo_length) # ensure a minimum of 100 frames for image propagation
if video_length > subvideo_length_img_prop:
updated_frames, updated_masks = [], []
pad_len = 10
for f in range(0, video_length, subvideo_length_img_prop):
s_f = max(0, f - pad_len)
e_f = min(video_length, f + subvideo_length_img_prop + pad_len)
pad_len_s = max(0, f) - s_f
pad_len_e = e_f - min(video_length, f + subvideo_length_img_prop)
b, t, _, _, _ = masks_dilated[:, s_f:e_f].size()
pred_flows_bi_sub = (pred_flows_bi[0][:, s_f:e_f-1], pred_flows_bi[1][:, s_f:e_f-1])
prop_imgs_sub, updated_local_masks_sub = self.model.img_propagation(masked_frames[:, s_f:e_f],
pred_flows_bi_sub,
masks_dilated[:, s_f:e_f],
'nearest')
updated_frames_sub = frames[:, s_f:e_f] * (1 - masks_dilated[:, s_f:e_f]) + \
prop_imgs_sub.view(b, t, 3, h, w) * masks_dilated[:, s_f:e_f]
updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w)
updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e])
updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e])
torch.cuda.empty_cache()
updated_frames = torch.cat(updated_frames, dim=1)
updated_masks = torch.cat(updated_masks, dim=1)
else:
b, t, _, _, _ = masks_dilated.size()
prop_imgs, updated_local_masks = self.model.img_propagation(masked_frames, pred_flows_bi, masks_dilated, 'nearest')
updated_frames = frames * (1 - masks_dilated) + prop_imgs.view(b, t, 3, h, w) * masks_dilated
updated_masks = updated_local_masks.view(b, t, 1, h, w)
torch.cuda.empty_cache()
comp_frames = [None] * video_length
neighbor_stride = neighbor_length // 2
if video_length > subvideo_length:
ref_num = subvideo_length // ref_stride
else:
ref_num = -1
torch.cuda.empty_cache()
# ---- feature propagation + transformer ----
for f in tqdm(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, ref_stride, ref_num)
selected_imgs = updated_frames[:, neighbor_ids + ref_ids, :, :, :]
selected_masks = masks_dilated[:, neighbor_ids + ref_ids, :, :, :]
selected_update_masks = updated_masks[:, neighbor_ids + ref_ids, :, :, :]
selected_pred_flows_bi = (pred_flows_bi[0][:, neighbor_ids[:-1], :, :, :], pred_flows_bi[1][:, neighbor_ids[:-1], :, :, :])
with torch.no_grad():
# 1.0 indicates mask
l_t = len(neighbor_ids)
# pred_img = selected_imgs # results of image propagation
pred_img = self.model(selected_imgs, selected_pred_flows_bi, selected_masks, selected_update_masks, l_t)
pred_img = pred_img.view(-1, 3, h, w)
## compose with input frames
pred_img = (pred_img + 1) / 2
pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255
binary_masks = masks_dilated_ori[0, neighbor_ids, :, :, :].cpu().permute(
0, 2, 3, 1).numpy().astype(np.uint8) # use original mask
for i in range(len(neighbor_ids)):
idx = neighbor_ids[i]
img = np.array(pred_img[i]).astype(np.uint8) * binary_masks[i] \
+ ori_frames_inp[idx] * (1 - binary_masks[i])
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
comp_frames[idx] = comp_frames[idx].astype(np.uint8)
torch.cuda.empty_cache()
##save composed video##
comp_frames = [cv2.resize(f, out_size) for f in comp_frames]
writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"),
fps, (comp_frames[0].shape[1],comp_frames[0].shape[0]))
for f in range(video_length):
frame = comp_frames[f].astype(np.uint8)
writer.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
writer.release()
torch.cuda.empty_cache()
return output_path
if __name__ == '__main__':
device = get_device()
propainter_model_dir = "weights/propainter"
propainter = Propainter(propainter_model_dir, device=device)
video = "examples/example1/video.mp4"
mask = "examples/example1/mask.mp4"
output = "results/priori.mp4"
res = propainter.forward(video, mask, output)