Spaces:
Runtime error
Runtime error
File size: 7,877 Bytes
4d1ebf3 53a8438 bb879e5 4d1ebf3 23d6e96 4d1ebf3 23d6e96 4d1ebf3 bb879e5 4d1ebf3 23d6e96 4d1ebf3 23d6e96 4d1ebf3 3c7c9f9 4d1ebf3 3c7c9f9 4d1ebf3 53a8438 3c7c9f9 4d1ebf3 53a8438 4d1ebf3 3c7c9f9 4d1ebf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import os
import glob
from PIL import Image
import torch
import yaml
import cv2
import importlib
import numpy as np
from tqdm import tqdm
from inpainter.util.tensor_util import resize_frames, resize_masks
def read_image_from_userfolder(image_path):
# if type:
image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
# else:
# image = cv2.cvtColor(cv2.imread("/tmp/{}/paintedimages/{}/{:08d}.png".format(username, video_state["video_name"], index+ ".png")), cv2.COLOR_BGR2RGB)
return image
def save_image_to_userfolder(video_state, index, image, type:bool):
if type:
image_path = "/tmp/{}/originimages/{}/{:08d}.png".format(video_state["user_name"], video_state["video_name"], index)
else:
image_path = "/tmp/{}/paintedimages/{}/{:08d}.png".format(video_state["user_name"], video_state["video_name"], index)
cv2.imwrite(image_path, image)
return image_path
class BaseInpainter:
def __init__(self, E2FGVI_checkpoint, device) -> None:
"""
E2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)
"""
net = importlib.import_module('inpainter.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("inpainter/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']
# 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(self, frames_path, 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
"""
frames = []
for file in frames_path:
frames.append(read_image_from_userfolder(file))
frames = np.asarray(frames)
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)]
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
inpainted_frames = np.stack(comp_frames, 0)
return inpainted_frames.astype(np.uint8)
if __name__ == '__main__':
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()
save_path = '/ssd1/gaomingqi/results/inpainting/parkour'
if not os.path.exists(save_path):
os.mkdir(save_path)
frames = []
masks = []
for fid, mid in zip(frame_path, mask_path):
frames.append(Image.open(fid).convert('RGB'))
masks.append(Image.open(mid).convert('P'))
frames = np.stack(frames, 0)
masks = np.stack(masks, 0)
# ----------------------------------------------
# how to use
# ----------------------------------------------
# 1/3: set checkpoint and device
checkpoint = '/ssd1/gaomingqi/checkpoints/E2FGVI-HQ-CVPR22.pth'
device = 'cuda:6'
# 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, ratio=0.01) # numpy array, T, H, W, 3
# ----------------------------------------------
# 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'))
|