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import random
import json
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.transforms.functional import crop
from stablevideo.atlas_utils import (
load_neural_atlases_models,
get_frames_data,
get_high_res_atlas,
get_atlas_crops,
reconstruct_video_layer,
create_uv_mask,
get_masks_boundaries,
get_random_crop_params,
get_atlas_bounding_box,
load_video
)
class AtlasData():
def __init__(self, video_name):
with open(f"data/{video_name}/config.json", "r") as f:
json_dict = json.load(f)
try:
maximum_number_of_frames = json_dict["number_of_frames"]
except:
maximum_number_of_frames = json_dict["maximum_number_of_frames"]
config = {
"device": "cpu",
"checkpoint_path": f"data/{video_name}/checkpoint.ckpt",
"resx": json_dict["resx"],
"resy": json_dict["resy"],
"maximum_number_of_frames": maximum_number_of_frames,
"return_atlas_alpha": False,
"grid_atlas_resolution": 2000,
"num_scales": 7,
"masks_border_expansion": 30,
"mask_alpha_threshold": 0.99, # 0.95
"align_corners": False
}
self.config = config
self.device = config["device"]
self.min_size = min(self.config["resx"], self.config["resy"])
self.max_size = max(self.config["resx"], self.config["resy"])
data_folder = f"data/{video_name}/{video_name}"
self.original_video = load_video(
data_folder,
resize=(self.config["resy"], self.config["resx"]),
num_frames=self.config["maximum_number_of_frames"],
)
self.original_video = self.original_video.to(self.device) # tensor
(
foreground_mapping,
background_mapping,
foreground_atlas_model,
background_atlas_model,
alpha_model,
) = load_neural_atlases_models(config)
(
original_background_all_uvs,
original_foreground_all_uvs,
self.all_alpha,
foreground_atlas_alpha,
) = get_frames_data(
config,
foreground_mapping,
background_mapping,
alpha_model,
)
self.background_reconstruction = reconstruct_video_layer(original_background_all_uvs, background_atlas_model)
# using original video for the foreground layer
self.foreground_reconstruction = self.original_video * self.all_alpha
(
self.background_all_uvs,
self.scaled_background_uvs,
self.background_min_u,
self.background_min_v,
self.background_max_u,
self.background_max_v,
) = self.preprocess_uv_values(
original_background_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="background"
)
(
self.foreground_all_uvs,
self.scaled_foreground_uvs,
self.foreground_min_u,
self.foreground_min_v,
self.foreground_max_u,
self.foreground_max_v,
) = self.preprocess_uv_values(
original_foreground_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="foreground"
)
self.background_uv_mask = create_uv_mask(
config,
background_mapping,
self.background_min_u,
self.background_min_v,
self.background_max_u,
self.background_max_v,
uv_shift=-0.5,
resolution_shift=1,
)
self.foreground_uv_mask = create_uv_mask(
config,
foreground_mapping,
self.foreground_min_u,
self.foreground_min_v,
self.foreground_max_u,
self.foreground_max_v,
uv_shift=0.5,
resolution_shift=0,
)
self.background_grid_atlas = get_high_res_atlas(
background_atlas_model,
self.background_min_v,
self.background_min_u,
self.background_max_v,
self.background_max_u,
config["grid_atlas_resolution"],
device=config["device"],
layer="background",
)
self.foreground_grid_atlas = get_high_res_atlas(
foreground_atlas_model,
self.foreground_min_v,
self.foreground_min_u,
self.foreground_max_v,
self.foreground_max_u,
config["grid_atlas_resolution"],
device=config["device"],
layer="foreground",
)
if config["return_atlas_alpha"]:
self.foreground_atlas_alpha = foreground_atlas_alpha # used for visualizations
self.cnn_min_crop_size = 2 ** self.config["num_scales"] + 1
self.mask_boundaries = get_masks_boundaries(
alpha_video=self.all_alpha.cpu(),
border=self.config["masks_border_expansion"],
threshold=self.config["mask_alpha_threshold"],
min_crop_size=self.cnn_min_crop_size,
)
self.cropped_foreground_atlas, self.foreground_atlas_bbox = get_atlas_bounding_box(
self.mask_boundaries, self.foreground_grid_atlas, self.foreground_all_uvs
)
self.step = -1
self.edited_atlas_dict, self.edit_dict, self.uv_mask = {}, {}, {}
@staticmethod
def preprocess_uv_values(layer_uv_values, resolution, device="cuda", layer="background"):
if layer == "background":
shift = 1
else:
shift = 0
uv_values = (layer_uv_values + shift) * resolution
min_u, min_v = uv_values.reshape(-1, 2).min(dim=0).values.long()
uv_values -= torch.tensor([min_u, min_v], device=device)
max_u, max_v = uv_values.reshape(-1, 2).max(dim=0).values.ceil().long()
edge_size = torch.tensor([max_u, max_v], device=device)
scaled_uv_values = ((uv_values.reshape(-1, 2) / edge_size) * 2 - 1).unsqueeze(1).unsqueeze(0)
return uv_values, scaled_uv_values, min_u, min_v, max_u, max_v
def get_random_crop_data(self, crop_size):
t = random.randint(0, self.config["maximum_number_of_frames"] - 1)
y_start, x_start, h_crop, w_crop = get_random_crop_params((self.config["resx"], self.config["resy"]), crop_size)
return y_start, x_start, h_crop, w_crop, t
def get_global_crops_multi(self, keyframes, res):
foreground_atlas_crops = []
background_atlas_crops = []
foreground_uvs = []
background_uvs = []
background_alpha_crops = []
foreground_alpha_crops = []
original_background_crops = []
original_foreground_crops = []
output_dict = {}
self.config["crops_min_cover"] = 0.95
self.config["grid_atlas_resolution"] = res
for cur_frame in keyframes:
y_start, x_start, frame_h, frame_w = self.mask_boundaries[cur_frame].tolist()
crop_size = (
max(
random.randint(round(self.config["crops_min_cover"] * frame_h), frame_h),
self.cnn_min_crop_size,
),
max(
random.randint(round(self.config["crops_min_cover"] * frame_w), frame_w),
self.cnn_min_crop_size,
),
)
y_crop, x_crop, h_crop, w_crop = get_random_crop_params((frame_w, frame_h), crop_size)
foreground_uv = self.foreground_all_uvs[
cur_frame,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
alpha = self.all_alpha[
[cur_frame],
:,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
original_foreground_crop = self.foreground_reconstruction[
[cur_frame],
:,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
foreground_alpha_crops.append(alpha)
foreground_uvs.append(foreground_uv) # not scaled
original_foreground_crops.append(original_foreground_crop)
foreground_max_vals = torch.tensor(
[self.config["grid_atlas_resolution"]] * 2, device=self.device, dtype=torch.long
)
foreground_min_vals = torch.tensor([0] * 2, device=self.device, dtype=torch.long)
for uv_values in foreground_uvs:
min_uv = uv_values.amin(dim=[0, 1]).long()
max_uv = uv_values.amax(dim=[0, 1]).ceil().long()
foreground_min_vals = torch.minimum(foreground_min_vals, min_uv)
foreground_max_vals = torch.maximum(foreground_max_vals, max_uv)
h_v = foreground_max_vals[1] - foreground_min_vals[1]
w_u = foreground_max_vals[0] - foreground_min_vals[0]
foreground_atlas_crop = crop(
self.foreground_grid_atlas,
foreground_min_vals[1],
foreground_min_vals[0],
h_v,
w_u,
)
foreground_atlas_crops.append(foreground_atlas_crop)
for i, uv_values in enumerate(foreground_uvs):
foreground_uvs[i] = (
2 * (uv_values - foreground_min_vals) / (foreground_max_vals - foreground_min_vals) - 1
).unsqueeze(0)
crop_size = (
random.randint(round(self.config["crops_min_cover"] * self.min_size), self.min_size),
random.randint(round(self.config["crops_min_cover"] * self.max_size), self.max_size),
)
crop_data = self.get_random_crop_data(crop_size)
y, x, h, w, _ = crop_data
background_uv = self.background_all_uvs[keyframes, y : y + h, x : x + w]
original_background_crop = self.background_reconstruction[
keyframes, :, y : y + h, x : x + w
]
alpha = self.all_alpha[keyframes, :, y : y + h, x : x + w]
original_background_crops = [el.unsqueeze(0) for el in original_background_crop]
background_alpha_crops = [el.unsqueeze(0) for el in alpha]
background_atlas_crop, background_min_vals, background_max_vals = get_atlas_crops(
background_uv,
self.background_grid_atlas,
)
background_uv = 2 * (background_uv - background_min_vals) / (background_max_vals - background_min_vals) - 1
background_atlas_crops = [el.unsqueeze(0) for el in background_atlas_crop]
background_uvs = [el.unsqueeze(0) for el in background_uv]
output_dict["foreground_alpha"] = foreground_alpha_crops
output_dict["foreground_uvs"] = foreground_uvs
output_dict["original_foreground_crops"] = original_foreground_crops
output_dict["foreground_atlas_crops"] = foreground_atlas_crops
output_dict["background_alpha"] = background_alpha_crops
output_dict["background_uvs"] = background_uvs
output_dict["original_background_crops"] = original_background_crops
output_dict["background_atlas_crops"] = background_atlas_crops
return output_dict |