Spaces:
Sleeping
Sleeping
File size: 6,658 Bytes
b213d84 |
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 |
import logging
from typing import Any, Dict
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from PIL import Image
from torch import nn
logger: logging.Logger = logging.getLogger(__name__)
class LeffaTransform(nn.Module):
def __init__(
self,
height: int = 1024,
width: int = 768,
dataset: str = "virtual_tryon", # virtual_tryon or pose_transfer
):
super().__init__()
self.height = height
self.width = width
self.dataset = dataset
self.vae_processor = VaeImageProcessor(vae_scale_factor=8)
self.mask_processor = VaeImageProcessor(
vae_scale_factor=8,
do_normalize=False,
do_binarize=True,
do_convert_grayscale=True,
)
def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
batch_size = len(batch["src_image"])
src_image_list = []
ref_image_list = []
mask_list = []
densepose_list = []
for i in range(batch_size):
# 1. get original data
src_image = batch["src_image"][i]
ref_image = batch["ref_image"][i]
mask = batch["mask"][i]
densepose = batch["densepose"][i]
# 3. process data
src_image = self.vae_processor.preprocess(
src_image, self.height, self.width)[0]
ref_image = self.vae_processor.preprocess(
ref_image, self.height, self.width)[0]
mask = self.mask_processor.preprocess(
mask, self.height, self.width)[0]
if self.dataset in ["pose_transfer"]:
densepose = densepose.resize(
(self.width, self.height), Image.NEAREST)
else:
densepose = self.vae_processor.preprocess(
densepose, self.height, self.width
)[0]
src_image = self.prepare_image(src_image)
ref_image = self.prepare_image(ref_image)
mask = self.prepare_mask(mask)
if self.dataset in ["pose_transfer"]:
densepose = self.prepare_densepose(densepose)
else:
densepose = self.prepare_image(densepose)
src_image_list.append(src_image)
ref_image_list.append(ref_image)
mask_list.append(mask)
densepose_list.append(densepose)
src_image = torch.cat(src_image_list, dim=0)
ref_image = torch.cat(ref_image_list, dim=0)
mask = torch.cat(mask_list, dim=0)
densepose = torch.cat(densepose_list, dim=0)
batch["src_image"] = src_image
batch["ref_image"] = ref_image
batch["mask"] = mask
batch["densepose"] = densepose
return batch
@staticmethod
def prepare_image(image):
if isinstance(image, torch.Tensor):
# Batch single image
if image.ndim == 3:
image = image.unsqueeze(0)
image = image.to(dtype=torch.float32)
else:
# preprocess image
if isinstance(image, (Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], Image.Image):
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(
dtype=torch.float32) / 127.5 - 1.0
return image
@staticmethod
def prepare_mask(mask):
if isinstance(mask, torch.Tensor):
if mask.ndim == 2:
# Batch and add channel dim for single mask
mask = mask.unsqueeze(0).unsqueeze(0)
elif mask.ndim == 3 and mask.shape[0] == 1:
# Single mask, the 0'th dimension is considered to be
# the existing batch size of 1
mask = mask.unsqueeze(0)
elif mask.ndim == 3 and mask.shape[0] != 1:
# Batch of mask, the 0'th dimension is considered to be
# the batching dimension
mask = mask.unsqueeze(1)
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
else:
# preprocess mask
if isinstance(mask, (Image.Image, np.ndarray)):
mask = [mask]
if isinstance(mask, list) and isinstance(mask[0], Image.Image):
mask = np.concatenate(
[np.array(m.convert("L"))[None, None, :] for m in mask],
axis=0,
)
mask = mask.astype(np.float32) / 255.0
elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
return mask
@staticmethod
def prepare_densepose(densepose):
"""
For internal (meta) densepose, the first and second channel should be normalized to 0~1 by 255.0,
and the third channel should be normalized to 0~1 by 24.0
"""
if isinstance(densepose, torch.Tensor):
# Batch single densepose
if densepose.ndim == 3:
densepose = densepose.unsqueeze(0)
densepose = densepose.to(dtype=torch.float32)
else:
# preprocess densepose
if isinstance(densepose, (Image.Image, np.ndarray)):
densepose = [densepose]
if isinstance(densepose, list) and isinstance(
densepose[0], Image.Image
):
densepose = [np.array(i.convert("RGB"))[None, :]
for i in densepose]
densepose = np.concatenate(densepose, axis=0)
elif isinstance(densepose, list) and isinstance(densepose[0], np.ndarray):
densepose = np.concatenate(
[i[None, :] for i in densepose], axis=0)
densepose = densepose.transpose(0, 3, 1, 2)
densepose = densepose.astype(np.float32)
densepose[:, 0:2, :, :] /= 255.0
densepose[:, 2:3, :, :] /= 24.0
densepose = torch.from_numpy(densepose).to(
dtype=torch.float32) * 2.0 - 1.0
return densepose
|