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83039b5 | 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 | import glob
import os
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
from .u2net import load_cloth_segm_model
from .utils import NormalizeImage, naive_cutout, resize_by_bigger_index, image_resize
def segment_garment(inputs_dir, outputs_dir, cls="all"):
os.makedirs(outputs_dir, exist_ok=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform_fn = transforms.Compose(
[transforms.ToTensor(),
NormalizeImage(0.5, 0.5)]
)
net = load_cloth_segm_model(device, os.environ.get("U2NET_CLOTH_SEGM_CHECKPOINT_PATH"), in_ch=3, out_ch=4)
images_list = sorted(os.listdir(inputs_dir))
pbar = tqdm(total=len(images_list))
for image_name in images_list:
img = Image.open(os.path.join(inputs_dir, image_name)).convert('RGB')
img_size = img.size
img = img.resize((768, 768), Image.BICUBIC)
image_tensor = transform_fn(img)
image_tensor = torch.unsqueeze(image_tensor, 0)
with torch.no_grad():
output_tensor = net(image_tensor.to(device))
output_tensor = F.log_softmax(output_tensor[0], dim=1)
output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1]
output_tensor = torch.squeeze(output_tensor, dim=0)
output_arr = output_tensor.cpu().numpy()
if cls == "all":
classes_to_save = []
# Check which classes are present in the image
for cls in range(1, 4): # Exclude background class (0)
if np.any(output_arr == cls):
classes_to_save.append(cls)
elif cls == "upper":
classes_to_save = [1]
elif cls == "lower":
classes_to_save = [2]
elif cls == "dress":
classes_to_save = [3]
else:
raise ValueError(f"Unknown cls: {cls}")
for cls1 in classes_to_save:
alpha_mask = (output_arr == cls1).astype(np.uint8) * 255
alpha_mask = alpha_mask[0] # Selecting the first channel to make it 2D
alpha_mask_img = Image.fromarray(alpha_mask, mode='L')
alpha_mask_img = alpha_mask_img.resize(img_size, Image.BICUBIC)
alpha_mask_img.save(os.path.join(outputs_dir, f'{image_name.split(".")[0]}_{cls1}.jpg'))
pbar.update(1)
pbar.close()
def extract_garment(inputs_dir, outputs_dir, cls="all", resize_to_width=None):
os.makedirs(outputs_dir, exist_ok=True)
cloth_mask_dir = os.path.join(Path(outputs_dir).parent.absolute(), "cloth-mask")
os.makedirs(cloth_mask_dir, exist_ok=True)
segment_garment(inputs_dir, os.path.join(Path(outputs_dir).parent.absolute(), "cloth-mask"), cls=cls)
images_path = sorted(glob.glob(os.path.join(inputs_dir, "*")))
masks_path = sorted(glob.glob(os.path.join(cloth_mask_dir, "*")))
img = Image.open(images_path[0])
for mask_path in masks_path:
mask = Image.open(mask_path)
cutout = np.array(naive_cutout(img, mask))
cutout = resize_by_bigger_index(cutout)
canvas = np.ones((1024, 768, 3), np.uint8) * 255
y1, y2 = (canvas.shape[0] - cutout.shape[0]) // 2, (canvas.shape[0] + cutout.shape[0]) // 2
x1, x2 = (canvas.shape[1] - cutout.shape[1]) // 2, (canvas.shape[1] + cutout.shape[1]) // 2
alpha_s = cutout[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
canvas[y1:y2, x1:x2, c] = (alpha_s * cutout[:, :, c] +
alpha_l * canvas[y1:y2, x1:x2, c])
# resize image before saving
if resize_to_width:
canvas = image_resize(canvas, width=resize_to_width)
canvas = Image.fromarray(canvas)
canvas.save(os.path.join(outputs_dir, f"{os.path.basename(mask_path).split('.')[0]}.jpg"), format='JPEG')
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