HamiguaLu
commented
1 year ago Hi, in case someone else want it too, you can try the following code By the way, it's based on https://github.com/gaomingqi/Track-Anything/blob/master/inpainter/base_inpainter.py `import os import glob from PIL import Image
import torch import yaml import cv2 import importlib import numpy as np from tqdm import tqdm import torchvision from util.tensor_util import resize_frames, resize_masks
class BaseInpainter: def init(self, E2FGVI_checkpoint, device) -> None: """ E2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support) """ net = importlib.import_module('model.e2fgvi_hq') self.model = net.InpaintGenerator().to(device) self.model.load_state_dict(torch.load(E2FGVI_checkpoint, map_location=device)) self.model.eval() self.device = device
load configurations
with open("config/config.yaml", 'r') as stream:
config = yaml.safe_load(stream)
self.neighbor_stride = config['neighbor_stride']
self.num_ref = config['num_ref']
self.step = config['step']
# config for E2FGVI with splits
self.num_subset_frames = config['num_subset_frames']
self.num_external_ref = config['num_external_ref']
# sample reference frames from the whole video
def get_ref_index(self, f, neighbor_ids, length):
ref_index = []
if self.num_ref == -1:
for i in range(0, length, self.step):
if i not in neighbor_ids:
ref_index.append(i)
else:
start_idx = max(0, f - self.step * (self.num_ref // 2))
end_idx = min(length, f + self.step * (self.num_ref // 2))
for i in range(start_idx, end_idx + 1, self.step):
if i not in neighbor_ids:
if len(ref_index) > self.num_ref:
break
ref_index.append(i)
return ref_index
def inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):
"""
Perform Inpainting for video subsets
frames: numpy array, T, H, W, 3
masks: numpy array, T, H, W
num_tcb: constant, number of temporal context before, frames
num_tca: constant, number of temporal context after, frames
dilate_radius: radius when applying dilation on masks
ratio: down-sample ratio
Output:
inpainted_frames: numpy array, T, H, W, 3
"""
#assert frames.shape[:3] == masks.shape, 'different size between frames and masks'
#assert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'
# --------------------
# pre-processing
# --------------------
masks = masks.copy()
masks = np.clip(masks, 0, 1)
kernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))
masks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)
T = frames.shape[0]
masks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1
# size: (w, h)
if ratio == 1:
size = None
binary_masks = masks
else:
print("Doesn't support ratio !!!!")
return null
# frames and binary_masks are numpy arrays
h, w = frames.shape[1:3]
video_length = T - (num_tca + num_tcb) # real video length
# convert to tensor
imgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1
masks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)
imgs, masks = imgs.to(self.device), masks.to(self.device)
comp_frames = [None] * video_length
tcb_imgs = None
tca_imgs = None
tcb_masks = None
tca_masks = None
# --------------------
# end of pre-processing
# --------------------
# separate tc frames/masks from imgs and masks
if num_tcb > 0:
tcb_imgs = imgs[:, :num_tcb]
tcb_masks = masks
if num_tca > 0:
tca_imgs = imgs[:, -num_tca:]
tca_masks = masks
end_idx = -num_tca
else:
end_idx = T
imgs = imgs[:, num_tcb:end_idx]
#masks = masks[:, num_tcb:end_idx]
#binary_masks = binary_masks[num_tcb:end_idx] # only neighbor area are involved
frames = frames[num_tcb:end_idx] # only neighbor area are involved
for f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):
neighbor_ids = [
i for i in range(max(0, f - self.neighbor_stride),
min(video_length, f + self.neighbor_stride + 1))
]
ref_ids = self.get_ref_index(f, neighbor_ids, video_length)
# selected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]
# selected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]
selected_imgs = imgs[:, neighbor_ids]
selected_masks = masks#[:, neighbor_ids]
# pad before
if tcb_imgs is not None:
selected_imgs = torch.concat([selected_imgs, tcb_imgs], dim=1)
selected_masks = torch.concat([selected_masks, tcb_masks], dim=1)
# integrate ref frames
selected_imgs = torch.concat([selected_imgs, imgs[:, ref_ids]], dim=1)
#selected_masks = torch.concat([selected_masks, masks[:, ref_ids]], dim=1)
# pad after
if tca_imgs is not None:
selected_imgs = torch.concat([selected_imgs, tca_imgs], dim=1)
#selected_masks = torch.concat([selected_masks, tca_masks], dim=1)
with torch.no_grad():
masked_imgs = selected_imgs * (1 - selected_masks)
mod_size_h = 60
mod_size_w = 108
h_pad = (mod_size_h - h % mod_size_h) % mod_size_h
w_pad = (mod_size_w - w % mod_size_w) % mod_size_w
masked_imgs = torch.cat(
[masked_imgs, torch.flip(masked_imgs, [3])],
3)[:, :, :, :h + h_pad, :]
masked_imgs = torch.cat(
[masked_imgs, torch.flip(masked_imgs, [4])],
4)[:, :, :, :, :w + w_pad]
pred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))
pred_imgs = pred_imgs[:, :, :h, :w]
pred_imgs = (pred_imgs + 1) / 2
pred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255
for i in range(len(neighbor_ids)):
idx = neighbor_ids[i]
img = pred_imgs[i].astype(np.uint8) * binary_masks[0] + frames[idx] * (
1 - binary_masks[0])
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
torch.cuda.empty_cache()
inpainted_frames = np.stack(comp_frames, 0)
return inpainted_frames.astype(np.uint8)
def inpaint(self, frames, masks, dilate_radius=15, ratio=1):
"""
Perform Inpainting for video subsets
frames: numpy array, T, H, W, 3
masks: numpy array, T, H, W
dilate_radius: radius when applying dilation on masks
ratio: down-sample ratio
Output:
inpainted_frames: numpy array, T, H, W, 3
"""
assert masks.shape[0] == 1, 'different size between frames and masks'
assert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'
# set num_subset_frames
num_subset_frames = self.num_subset_frames
# split frames into subsets
video_length = len(frames)
num_splits = video_length // num_subset_frames
id_splits = [[i*num_subset_frames, (i+1)*num_subset_frames] for i in range(num_splits)] # id splits
if num_splits == 0:
id_splits = [[0, video_length]]
# if remaining split > num_subset_frames/2, add a new split, else, append to the last split
if video_length - id_splits[-1][-1] > num_subset_frames / 3:
id_splits.append([num_splits*num_subset_frames, video_length])
else:
diff = video_length - id_splits[-1][-1]
id_splits = [[ids[0]+diff, ids[1]+diff] for ids in id_splits]
id_splits[0][0] = 0 # if OOM, let it happen at the begining :D
# if appending, convert the appended split to the FIRST one, avoiding OOM at last
# perform inpainting for each split
inpainted_splits = []
for id_split in id_splits:
video_split = frames[id_split[0]:id_split[1]]
#mask_split = masks[id_split[0]:id_split[1]]
# | id_before | ----- | id_split[0] | ----- | id_split[1] | ----- | id_after |
# for each split, consider its temporal context [-context_range] frames and [context_range] frames
id_before = max(0, id_split[0] - self.step * self.num_external_ref)
try:
tcb_frames = np.stack([frames[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)
tcb_masks = np.stack([masks[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)
num_tcb = len(tcb_frames)
except:
num_tcb = 0
id_after = min(video_length, id_split[1] + self.step * self.num_external_ref + 1)
try:
tca_frames = np.stack([frames[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)
tca_masks = np.stack([masks[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)
num_tca = len(tca_frames)
except:
num_tca = 0
# concatenate temporal context frames/masks with input frames/masks (for parallel pre-processing)
if num_tcb > 0:
video_split = np.concatenate([tcb_frames, video_split], 0)
#mask_split = np.concatenate([tcb_masks, mask_split], 0)
if num_tca > 0:
video_split = np.concatenate([video_split, tca_frames], 0)
#mask_split = np.concatenate([mask_split, tca_masks], 0)
torch.cuda.empty_cache()
# inpaint each split
inpainted_splits.append(self.inpaint_efficient(video_split, masks, num_tcb, num_tca, dilate_radius, ratio))
torch.cuda.empty_cache()
inpainted_frames = np.concatenate(inpainted_splits, 0)
return inpainted_frames.astype(np.uint8)
def inpaint_ori(self, frames, masks, dilate_radius=15, ratio=1):
"""
frames: numpy array, T, H, W, 3
masks: numpy array, T, H, W
dilate_radius: radius when applying dilation on masks
ratio: down-sample ratio
Output:
inpainted_frames: numpy array, T, H, W, 3
"""
assert frames.shape[:3] == masks.shape, 'different size between frames and masks'
assert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'
masks = masks.copy()
masks = np.clip(masks, 0, 1)
kernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))
masks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)
T, H, W = masks.shape
masks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1
# size: (w, h)
if ratio == 1:
size = None
binary_masks = masks
else:
size = [int(W*ratio), int(H*ratio)]
size = [si+1 if si%2>0 else si for si in size] # only consider even values
# shortest side should be larger than 50
if min(size) < 50:
ratio = 50. / min(H, W)
size = [int(W*ratio), int(H*ratio)]
size = [160, 120]
binary_masks = resize_masks(masks, tuple(size))
frames = resize_frames(frames, tuple(size)) # T, H, W, 3
# frames and binary_masks are numpy arrays
h, w = frames.shape[1:3]
video_length = T
# convert to tensor
imgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1
masks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)
imgs, masks = imgs.to(self.device), masks.to(self.device)
comp_frames = [None] * video_length
for f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):
neighbor_ids = [
i for i in range(max(0, f - self.neighbor_stride),
min(video_length, f + self.neighbor_stride + 1))
]
ref_ids = self.get_ref_index(f, neighbor_ids, video_length)
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)
mod_size_h = 60
mod_size_w = 108
h_pad = (mod_size_h - h % mod_size_h) % mod_size_h
w_pad = (mod_size_w - w % mod_size_w) % mod_size_w
masked_imgs = torch.cat(
[masked_imgs, torch.flip(masked_imgs, [3])],
3)[:, :, :, :h + h_pad, :]
masked_imgs = torch.cat(
[masked_imgs, torch.flip(masked_imgs, [4])],
4)[:, :, :, :, :w + w_pad]
pred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))
pred_imgs = pred_imgs[:, :, :h, :w]
pred_imgs = (pred_imgs + 1) / 2
pred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255
for i in range(len(neighbor_ids)):
idx = neighbor_ids[i]
img = pred_imgs[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
torch.cuda.empty_cache()
inpainted_frames = np.stack(comp_frames, 0)
return inpainted_frames.astype(np.uint8)def read_mask(mpath): masks = [] mnames = os.listdir(mpath) mnames.sort() for mp in mnames: m = Image.open(os.path.join(mpath, mp))
m = m.resize(size, 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))
masks = np.stack(masks)
return masksread frames from video
def read_frame_from_videos(vname, use_mp4 = False):
frames = []
if 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)
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)
return framesgenerate video after vos inference
def generate_video_from_frames(frames, output_path, fps=30): """ Generates a video from a list of frames.
Args:
frames (list of numpy arrays): The frames to include in the video.
output_path (str): The path to save the generated video.
fps (int, optional): The frame rate of the output video. Defaults to 30.
"""
# height, width, layers = frames[0].shape
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
# video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
# print(output_path)
# for frame in frames:
# video.write(frame)
# video.release()
frames = torch.from_numpy(np.asarray(frames))
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
return output_pathif name == 'main':
# # davis-2017
# frame_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/parkour', '*.jpg'))
# frame_path.sort()
# mask_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/Annotations/480p/parkour', "*.png"))
# mask_path.sort()
# long and large video
frames = read_frame_from_videos("examples/schoolgirls.mp4",True)
video_length = len(frames)
frames = [np.array(f).astype(np.uint8) for f in frames]
masks = read_mask("examples/schoolgirls_mask")
frames = np.array(frames) # Convert list to numpy array
masks = np.array(masks) # Convert list to numpy array
# ----------------------------------------------
# how to use
# ----------------------------------------------
# 1/3: set checkpoint and device
checkpoint = 'release_model\\E2FGVI-HQ-CVPR22.pth'
device = 'cuda:0'
# 2/3: initialise inpainter
base_inpainter = BaseInpainter(checkpoint, device)
# 3/3: inpainting (frames: numpy array, T, H, W, 3; masks: numpy array, T, H, W)
# ratio: (0, 1], ratio for down sample, default value is 1
inpainted_frames = base_inpainter.inpaint(frames, masks) # numpy array, T, H, W, 3
generate_video_from_frames(inpainted_frames,"result/test.mp4")
# save
torch.cuda.empty_cache()
print('switch to ori')
# inpainted_frames = base_inpainter.inpaint_ori(frames[:50], masks[:50], ratio=0.1)
# save_path = '/ssd1/gaomingqi/results/inpainting/avengers'
# # ----------------------------------------------
# # end
# # ----------------------------------------------
# # save
# for ti, inpainted_frame in enumerate(inpainted_frames):
# frame = Image.fromarray(inpainted_frame).convert('RGB')
# frame.save(os.path.join(save_path, f'{ti:05d}.jpg'))`
I want to remove wartermark from the video, the watermark is at the fixed postion, so can I use only one mask file for all the frames? if so how
Thanks in advance