import gc
import os
import cv2
import imageio
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image
def get_width_and_height_from_image_and_base_resolution(image, base_resolution):
target_pixels = int(base_resolution) * int(base_resolution)
original_width, original_height = Image.open(image).size
ratio = (target_pixels / (original_width * original_height)) ** 0.5
width_slider = round(original_width * ratio)
height_slider = round(original_height * ratio)
return height_slider, width_slider
def color_transfer(sc, dc):
"""
Transfer color distribution from of sc, referred to dc.
Args:
sc (numpy.ndarray): input image to be transfered.
dc (numpy.ndarray): reference image
Returns:
numpy.ndarray: Transferred color distribution on the sc.
"""
def get_mean_and_std(img):
x_mean, x_std = cv2.meanStdDev(img)
x_mean = np.hstack(np.around(x_mean, 2))
x_std = np.hstack(np.around(x_std, 2))
return x_mean, x_std
sc = cv2.cvtColor(sc, cv2.COLOR_RGB2LAB)
s_mean, s_std = get_mean_and_std(sc)
dc = cv2.cvtColor(dc, cv2.COLOR_RGB2LAB)
t_mean, t_std = get_mean_and_std(dc)
img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
np.putmask(img_n, img_n > 255, 255)
np.putmask(img_n, img_n < 0, 0)
dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2RGB)
return dst
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=12, imageio_backend=True, color_transfer_post_process=False):
videos = rearrange(videos, "b c t h w -> t b c h w")
outputs = []
for x in videos:
x = torchvision.utils.make_grid(x, nrow=n_rows)
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
outputs.append(Image.fromarray(x))
if color_transfer_post_process:
for i in range(1, len(outputs)):
outputs[i] = Image.fromarray(color_transfer(np.uint8(outputs[i]), np.uint8(outputs[0])))
os.makedirs(os.path.dirname(path), exist_ok=True)
if imageio_backend:
if path.endswith("mp4"):
imageio.mimsave(path, outputs, fps=fps)
else:
imageio.mimsave(path, outputs, duration=(1000 * 1/fps))
else:
if path.endswith("mp4"):
path = path.replace('.mp4', '.gif')
outputs[0].save(path, format='GIF', append_images=outputs, save_all=True, duration=100, loop=0)
def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size):
if validation_image_start is not None and validation_image_end is not None:
if type(validation_image_start) is str and os.path.isfile(validation_image_start):
image_start = clip_image = Image.open(validation_image_start).convert("RGB")
image_start = image_start.resize([sample_size[1], sample_size[0]])
clip_image = clip_image.resize([sample_size[1], sample_size[0]])
else:
image_start = clip_image = validation_image_start
image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
if type(validation_image_end) is str and os.path.isfile(validation_image_end):
image_end = Image.open(validation_image_end).convert("RGB")
image_end = image_end.resize([sample_size[1], sample_size[0]])
else:
image_end = validation_image_end
image_end = [_image_end.resize([sample_size[1], sample_size[0]]) for _image_end in image_end]
if type(image_start) is list:
clip_image = clip_image[0]
start_video = torch.cat(
[torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
dim=2
)
input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
input_video[:, :, :len(image_start)] = start_video
input_video_mask = torch.zeros_like(input_video[:, :1])
input_video_mask[:, :, len(image_start):] = 255
else:
input_video = torch.tile(
torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
[1, 1, video_length, 1, 1]
)
input_video_mask = torch.zeros_like(input_video[:, :1])
input_video_mask[:, :, 1:] = 255
if type(image_end) is list:
image_end = [_image_end.resize(image_start[0].size if type(image_start) is list else image_start.size) for _image_end in image_end]
end_video = torch.cat(
[torch.from_numpy(np.array(_image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_end in image_end],
dim=2
)
input_video[:, :, -len(end_video):] = end_video
input_video_mask[:, :, -len(image_end):] = 0
else:
image_end = image_end.resize(image_start[0].size if type(image_start) is list else image_start.size)
input_video[:, :, -1:] = torch.from_numpy(np.array(image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0)
input_video_mask[:, :, -1:] = 0
input_video = input_video / 255
elif validation_image_start is not None:
if type(validation_image_start) is str and os.path.isfile(validation_image_start):
image_start = clip_image = Image.open(validation_image_start).convert("RGB")
image_start = image_start.resize([sample_size[1], sample_size[0]])
clip_image = clip_image.resize([sample_size[1], sample_size[0]])
else:
image_start = clip_image = validation_image_start
image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
image_end = None
if type(image_start) is list:
clip_image = clip_image[0]
start_video = torch.cat(
[torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
dim=2
)
input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
input_video[:, :, :len(image_start)] = start_video
input_video = input_video / 255
input_video_mask = torch.zeros_like(input_video[:, :1])
input_video_mask[:, :, len(image_start):] = 255
else:
input_video = torch.tile(
torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
[1, 1, video_length, 1, 1]
) / 255
input_video_mask = torch.zeros_like(input_video[:, :1])
input_video_mask[:, :, 1:, ] = 255
else:
image_start = None
image_end = None
input_video = torch.zeros([1, 3, video_length, sample_size[0], sample_size[1]])
input_video_mask = torch.ones([1, 1, video_length, sample_size[0], sample_size[1]]) * 255
clip_image = None
del image_start
del image_end
gc.collect()
return input_video, input_video_mask, clip_image
def get_video_to_video_latent(input_video_path, video_length, sample_size, fps=None, validation_video_mask=None, ref_image=None):
if input_video_path is not None:
if isinstance(input_video_path, str):
cap = cv2.VideoCapture(input_video_path)
input_video = []
original_fps = cap.get(cv2.CAP_PROP_FPS)
frame_skip = 1 if fps is None else int(original_fps // fps)
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
if frame_count % frame_skip == 0:
frame = cv2.resize(frame, (sample_size[1], sample_size[0]))
input_video.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
frame_count += 1
cap.release()
else:
input_video = input_video_path
input_video = torch.from_numpy(np.array(input_video))[:video_length]
input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255
if validation_video_mask is not None:
validation_video_mask = Image.open(validation_video_mask).convert('L').resize((sample_size[1], sample_size[0]))
input_video_mask = np.where(np.array(validation_video_mask) < 240, 0, 255)
input_video_mask = torch.from_numpy(np.array(input_video_mask)).unsqueeze(0).unsqueeze(-1).permute([3, 0, 1, 2]).unsqueeze(0)
input_video_mask = torch.tile(input_video_mask, [1, 1, input_video.size()[2], 1, 1])
input_video_mask = input_video_mask.to(input_video.device, input_video.dtype)
else:
input_video_mask = torch.zeros_like(input_video[:, :1])
input_video_mask[:, :, :] = 255
else:
input_video, input_video_mask = None, None
if ref_image is not None:
if isinstance(ref_image, str):
ref_image = Image.open(ref_image).convert("RGB")
ref_image = ref_image.resize((sample_size[1], sample_size[0]))
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255
else:
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255
return input_video, input_video_mask, ref_image
def get_image_latent(ref_image=None, sample_size=None):
if ref_image is not None:
if isinstance(ref_image, str):
ref_image = Image.open(ref_image).convert("RGB")
ref_image = ref_image.resize((sample_size[1], sample_size[0]))
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255
else:
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255
return ref_image