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from PIL import Image
import torch.nn.functional as F
import cv2
import imageio
import argparse
import sys
import torch
import clip
import warnings
import numpy as np
sys.path.append('/coc/flash6/okara7/codes/video-editing/hf-controlnet/RAFT/RAFT-master')
sys.path.append('/coc/flash6/okara7/codes/video-editing/hf-controlnet/RAFT/RAFT-master/core')
from core.raft import RAFT
from core.utils.utils import InputPadder
from skimage.metrics import structural_similarity
def video_to_pil_list(video_path):
if video_path.endswith('.mp4'):
vidcap = cv2.VideoCapture(video_path)
pil_list = []
while True:
success, image = vidcap.read()
if success:
pil_list.append(Image.fromarray(image))
else:
break
return pil_list
elif video_path.endswith('.gif'):
gif = imageio.get_reader(video_path)
pil_list = []
for frame in gif:
pil_list.append(Image.fromarray(frame))
return pil_list
def coords_grid(b, h, w, homogeneous=False, device=None):
y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]
stacks = [x, y]
if homogeneous:
ones = torch.ones_like(x) # [H, W]
stacks.append(ones)
grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]
grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]
if device is not None:
grid = grid.to(device)
return grid
def bilinear_sample(img,
sample_coords,
mode='bilinear',
padding_mode='zeros',
return_mask=False):
# img: [B, C, H, W]
# sample_coords: [B, 2, H, W] in image scale
if sample_coords.size(1) != 2: # [B, H, W, 2]
sample_coords = sample_coords.permute(0, 3, 1, 2)
b, _, h, w = sample_coords.shape
# Normalize to [-1, 1]
x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1
grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]
img = F.grid_sample(img,
grid,
mode=mode,
padding_mode=padding_mode,
align_corners=True)
if return_mask:
mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (
y_grid <= 1) # [B, H, W]
return img, mask
return img
def flow_warp_rerender(feature,
flow,
mask=False,
mode='bilinear',
padding_mode='zeros'):
b, c, h, w = feature.size()
assert flow.size(1) == 2
grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]
return bilinear_sample(feature,
grid,
mode=mode,
padding_mode=padding_mode,
return_mask=mask)
def clip_text(pil_list, text_prompt, preprocess, device, model):
text = clip.tokenize([text_prompt]).to(device)
scores = []
images = []
with torch.no_grad():
text_features = model.encode_text(text)
for pil in pil_list:
image = preprocess(pil).unsqueeze(0).to(device)
images.append(image)
image_features = model.encode_image(torch.cat(images))
scores = [torch.cosine_similarity(text_features, image_feature).item() for image_feature in image_features]
score = sum(scores) / len(scores)
return score
def clip_frame(pil_list, preprocess, device, model):
image_features = []
images = []
with torch.no_grad():
for pil in pil_list:
image = preprocess(pil).unsqueeze(0).to(device)
images.append(image)
image_features = model.encode_image(torch.cat(images))
image_features = image_features.cpu().numpy()
cosine_sim_matrix = cosine_similarity(image_features)
np.fill_diagonal(cosine_sim_matrix, 0) # set diagonal elements to 0
score = cosine_sim_matrix.sum() / (len(pil_list) * (len(pil_list)-1))
return score
def pick_score_func(frames, prompt, model, processor, device):
image_inputs = processor(images=frames, padding=True, truncation=True, max_length=77, return_tensors="pt").to(device)
text_inputs = processor(text=prompt, padding=True, truncation=True, max_length=77, return_tensors="pt").to(device)
with torch.no_grad():
image_embs = model.get_image_features(**image_inputs)
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
text_embs = model.get_text_features(**text_inputs)
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
score_per_image = model.logit_scale.exp() * (text_embs @ image_embs.T)[0]
score_per_image = score_per_image.detach().cpu().numpy()
score = score_per_image.mean()
return score
def prepare_raft_model(device):
raft_dict = {
'model': '/coc/flash6/okara7/codes/kurtkaya/RAFT/models/raft-things.pth',
'small': False,
'mixed_precision': False,
'alternate_corr': False
}
args = argparse.Namespace(**raft_dict)
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(device)
model.eval()
return model
def flow_warp(img: np.ndarray,
flow: np.ndarray,
filling_value: int = 0,
interpolate_mode: str = 'nearest'):
'''Use flow to warp img.
Args:
img (ndarray): Image to be warped.
flow (ndarray): Optical Flow.
filling_value (int): The missing pixels will be set with filling_value.
interpolate_mode (str): bilinear -> Bilinear Interpolation;
nearest -> Nearest Neighbor.
Returns:
ndarray: Warped image with the same shape of img
'''
warnings.warn('This function is just for prototyping and cannot '
'guarantee the computational efficiency.')
assert flow.ndim == 3, 'Flow must be in 3D arrays.'
height = flow.shape[0]
width = flow.shape[1]
channels = img.shape[2]
output = np.ones(
(height, width, channels), dtype=img.dtype) * filling_value
grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2)
dx = grid[:, :, 0] + flow[:, :, 1]
dy = grid[:, :, 1] + flow[:, :, 0]
sx = np.floor(dx).astype(int)
sy = np.floor(dy).astype(int)
valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1)
if interpolate_mode == 'nearest':
output[valid, :] = img[dx[valid].round().astype(int),
dy[valid].round().astype(int), :]
elif interpolate_mode == 'bilinear':
# dirty walkround for integer positions
eps_ = 1e-6
dx, dy = dx + eps_, dy + eps_
left_top_ = img[np.floor(dx[valid]).astype(int),
np.floor(dy[valid]).astype(int), :] * (
np.ceil(dx[valid]) - dx[valid])[:, None] * (
np.ceil(dy[valid]) - dy[valid])[:, None]
left_down_ = img[np.ceil(dx[valid]).astype(int),
np.floor(dy[valid]).astype(int), :] * (
dx[valid] - np.floor(dx[valid]))[:, None] * (
np.ceil(dy[valid]) - dy[valid])[:, None]
right_top_ = img[np.floor(dx[valid]).astype(int),
np.ceil(dy[valid]).astype(int), :] * (
np.ceil(dx[valid]) - dx[valid])[:, None] * (
dy[valid] - np.floor(dy[valid]))[:, None]
right_down_ = img[np.ceil(dx[valid]).astype(int),
np.ceil(dy[valid]).astype(int), :] * (
dx[valid] - np.floor(dx[valid]))[:, None] * (
dy[valid] - np.floor(dy[valid]))[:, None]
output[valid, :] = left_top_ + left_down_ + right_top_ + right_down_
else:
raise NotImplementedError(
'We only support interpolation modes of nearest and bilinear, '
f'but got {interpolate_mode}.')
return output.astype(img.dtype)
def calculate_flow(pil_list, model, DEVICE):
def load_image(imfile, DEVICE):
img = np.array(imfile).astype(np.uint8)
img = torch.from_numpy(img).permute(2, 0, 1).float()
return img[None].to(DEVICE)
flow_up_list = []
with torch.no_grad():
images = pil_list.copy()
for imfile1, imfile2 in zip(images[:-1], images[1:]):
image1 = load_image(imfile1, DEVICE)
image2 = load_image(imfile2, DEVICE)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
_, flow_up = model(image1, image2, iters=20, test_mode=True)
flow_up_list.append(flow_up.detach().squeeze().permute(1,2,0).cpu().numpy())
return flow_up_list
def rerender_warp(img, flow, mode='bilinear'):
expand = False
if len(img.shape) == 2:
expand = True
img = np.expand_dims(img, 2)
img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0)
dtype = img.dtype
img = img.to(torch.float)
flow = torch.from_numpy(flow).permute(2, 0, 1).unsqueeze(0)
res = flow_warp_rerender(img, flow, mode=mode)
res = res.to(dtype)
res = res[0].cpu().permute(1, 2, 0).numpy()
if expand:
res = res[:, :, 0]
return res
def opencv_warp(img, flow):
h, w = flow.shape[:2]
flow[:,:,0] += np.arange(w)
flow[:,:,1] += np.arange(h)[:,np.newaxis]
warped_img = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
return warped_img
rearrange = lambda x: (np.array(x)/255).reshape(-1,1)
def warp_video(edit_pil_list, source_pil_list, raft_model, device, distance_func):
# print('source size', source_pil_list[0].size)
flow_up_list = calculate_flow(source_pil_list, raft_model, device)
res_list = [edit_pil_list[0]]
for i,pil_img in enumerate(edit_pil_list[:-1]):
warped = opencv_warp(np.array(pil_img), flow_up_list[i])
pil_warped = Image.fromarray(warped)
# pil_warped.save(f'warped_{i}.png')
res_list.append(pil_warped)
# res_list[0].save('warped.gif', save_all=True, append_images=res_list[1:], duration=100, loop=0)
# print('size of video', res_list[0].size)
if distance_func == structural_similarity:
return np.mean(np.array([distance_func(np.array(edit_pil_list[i]), np.array(res_list[i]), channel_axis=2) for i in range(len(res_list))]))
else:
return np.mean(np.array([distance_func(edit_pil_list[i], res_list[i]) for i in range(len(res_list))]))
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