Added recolor node in RGB space

.env_sample

@@ -1,3 +0,0 @@
-CLIPDROP_API_KEY=
-COMFY_PYTHON_PATH=/home/ubuntu/.conda/envs/comfy/bin/python
-PHOTOROOM_API_KEY=3603b83dfa1846bc3c7270ead7876

.gitignore

@@ -5,11 +5,5 @@ venv
 .DS_Store
 checkpoints/
 checkpoint/
-.env
-.pth
-cloth-segmentation/model/cloth_segm.pth
 
-dwpose/keypoints/
-huggingface/
-
-safetychecker/model.safetensors
+dwpose/keypoints/

atr_node/simple_extractor.py

@@ -88,29 +88,9 @@ def get_palette(num_cls):
     return palette
 
 
-def download_model_restore(model_restore_path):
-    import os
-    import gdown
-    from pathlib import Path
-
-    # Ensure the directory for the model path exists
-    os.makedirs(os.path.dirname(model_restore_path), exist_ok=True)
-    
-    # Check if the model file already exists
-    if not Path(model_restore_path).is_file():
-        print("Model file does not exist, downloading...")
-        
-        # Google Drive ID for the file
-        # file_id = '1ruJg4lqR_jgQPj-9K0PP-L2vJERYOxLP'
-        file_id="1AVVLm1LxOs3W1Fp_GLefIz6fdWfEYg88"
-        gdown.download(id=file_id, output=model_restore_path, quiet=False)
-        print("Download complete.")
-    else:
-        print("Model file already exists.")
-
-
 def main():
     args = get_arguments()
+
     gpus = [int(i) for i in args.gpu.split(',')]
     assert len(gpus) == 1
     if not args.gpu == 'None':
@@ -122,7 +102,7 @@ def main():
     print("Evaluating total class number {} with {}".format(num_classes, label))
 
     model = networks.init_model('resnet101', num_classes=num_classes, pretrained=None)
-    download_model_restore(args.model_restore)
+
     state_dict = torch.load(args.model_restore)['state_dict']
     from collections import OrderedDict
     new_state_dict = OrderedDict()

cloth-segmentation/app.py

@@ -1,47 +0,0 @@
-import PIL
-import torch
-import os
-from process import load_seg_model, get_palette, generate_mask
-
-
-
-device = 'cuda'
-
-def initialize_and_load_models():
-    checkpoint_path = 'model/cloth_segm.pth'
-    net = load_seg_model(checkpoint_path, device=device)    
-    return net
-
-net = initialize_and_load_models()
-
-def run(img, image_id, output_dir):
-    palette = get_palette(4)
-    mask0, mask1, mask2, cloth_seg = generate_mask(img, net=net, device=device, image_id=image_id, output_dir=output_dir)
-    return mask0, mask1, mask2, cloth_seg
-
-INPUT_PATH = "./input/"
-OUTPUT_PATH = "./output/"
-
-import os 
-mask0_paths = []
-mask1_paths = []
-mask2_paths = []
-cloth_paths = []
-
-for idx, cur_image in enumerate(os.listdir(INPUT_PATH)):
-    img = PIL.Image.open(INPUT_PATH + cur_image)
-    mask0, mask1, mask2, cloth_seg = run(img, image_id=idx, output_dir=OUTPUT_PATH)
-    # Save masks and cloth_seg with unique names (already saved in generate_mask)
-    mask0_path = os.path.join(OUTPUT_PATH, f"{idx}__mask0.png")
-    mask1_path = os.path.join(OUTPUT_PATH, f"{idx}__mask1.png")
-    mask2_path = os.path.join(OUTPUT_PATH, f"{idx}__mask2.png")
-    cloth_path = os.path.join(OUTPUT_PATH, f"{idx}__extracted_garment.png")
-    mask0_paths.append(mask0_path)
-    mask1_paths.append(mask1_path)
-    mask2_paths.append(mask2_path)
-    cloth_paths.append(cloth_path)
-
-print("Mask0 batch:", mask0_paths)
-print("Mask1 batch:", mask1_paths)
-print("Mask2 batch:", mask2_paths)
-print("Garment batch:", cloth_paths)

cloth-segmentation/model/ex.txt

@@ -1 +0,0 @@
-/*upload model */

cloth-segmentation/network.py

@@ -1,560 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class REBNCONV(nn.Module):
-    def __init__(self, in_ch=3, out_ch=3, dirate=1):
-        super(REBNCONV, self).__init__()
-
-        self.conv_s1 = nn.Conv2d(
-            in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate
-        )
-        self.bn_s1 = nn.BatchNorm2d(out_ch)
-        self.relu_s1 = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-
-        hx = x
-        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
-
-        return xout
-
-
-## upsample tensor 'src' to have the same spatial size with tensor 'tar'
-def _upsample_like(src, tar):
-
-    src = F.upsample(src, size=tar.shape[2:], mode="bilinear")
-
-    return src
-
-
-### RSU-7 ###
-class RSU7(nn.Module):  # UNet07DRES(nn.Module):
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU7, self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
-        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
-
-        self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
-
-        self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
-
-    def forward(self, x):
-
-        hx = x
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-        hx = self.pool4(hx4)
-
-        hx5 = self.rebnconv5(hx)
-        hx = self.pool5(hx5)
-
-        hx6 = self.rebnconv6(hx)
-
-        hx7 = self.rebnconv7(hx6)
-
-        hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
-        hx6dup = _upsample_like(hx6d, hx5)
-
-        hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
-        hx5dup = _upsample_like(hx5d, hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
-        hx4dup = _upsample_like(hx4d, hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
-
-        """
-        del hx1, hx2, hx3, hx4, hx5, hx6, hx7
-        del hx6d, hx5d, hx3d, hx2d
-        del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup
-        """
-
-        return hx1d + hxin
-
-
-### RSU-6 ###
-class RSU6(nn.Module):  # UNet06DRES(nn.Module):
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU6, self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
-        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
-
-        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
-
-        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
-
-    def forward(self, x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-        hx = self.pool4(hx4)
-
-        hx5 = self.rebnconv5(hx)
-
-        hx6 = self.rebnconv6(hx5)
-
-        hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
-        hx5dup = _upsample_like(hx5d, hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
-        hx4dup = _upsample_like(hx4d, hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
-
-        """
-        del hx1, hx2, hx3, hx4, hx5, hx6
-        del hx5d, hx4d, hx3d, hx2d
-        del hx2dup, hx3dup, hx4dup, hx5dup
-        """
-
-        return hx1d + hxin
-
-
-### RSU-5 ###
-class RSU5(nn.Module):  # UNet05DRES(nn.Module):
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU5, self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
-        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
-
-        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
-
-        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
-
-    def forward(self, x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-
-        hx5 = self.rebnconv5(hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
-        hx4dup = _upsample_like(hx4d, hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
-
-        """
-        del hx1, hx2, hx3, hx4, hx5
-        del hx4d, hx3d, hx2d
-        del hx2dup, hx3dup, hx4dup
-        """
-
-        return hx1d + hxin
-
-
-### RSU-4 ###
-class RSU4(nn.Module):  # UNet04DRES(nn.Module):
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU4, self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
-        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
-        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
-
-        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
-
-        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
-
-    def forward(self, x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-
-        hx4 = self.rebnconv4(hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
-
-        """
-        del hx1, hx2, hx3, hx4
-        del hx3d, hx2d
-        del hx2dup, hx3dup
-        """
-
-        return hx1d + hxin
-
-
-### RSU-4F ###
-class RSU4F(nn.Module):  # UNet04FRES(nn.Module):
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU4F, self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
-        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
-        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
-
-        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
-
-        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
-        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
-        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
-
-    def forward(self, x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx2 = self.rebnconv2(hx1)
-        hx3 = self.rebnconv3(hx2)
-
-        hx4 = self.rebnconv4(hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
-        hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
-        hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
-
-        """
-        del hx1, hx2, hx3, hx4
-        del hx3d, hx2d
-        """
-
-        return hx1d + hxin
-
-
-##### U^2-Net ####
-class U2NET(nn.Module):
-    def __init__(self, in_ch=3, out_ch=1):
-        super(U2NET, self).__init__()
-
-        self.stage1 = RSU7(in_ch, 32, 64)
-        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage2 = RSU6(64, 32, 128)
-        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage3 = RSU5(128, 64, 256)
-        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage4 = RSU4(256, 128, 512)
-        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage5 = RSU4F(512, 256, 512)
-        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage6 = RSU4F(512, 256, 512)
-
-        # decoder
-        self.stage5d = RSU4F(1024, 256, 512)
-        self.stage4d = RSU4(1024, 128, 256)
-        self.stage3d = RSU5(512, 64, 128)
-        self.stage2d = RSU6(256, 32, 64)
-        self.stage1d = RSU7(128, 16, 64)
-
-        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
-        self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
-        self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
-        self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
-
-        self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
-
-    def forward(self, x):
-
-        hx = x
-
-        # stage 1
-        hx1 = self.stage1(hx)
-        hx = self.pool12(hx1)
-
-        # stage 2
-        hx2 = self.stage2(hx)
-        hx = self.pool23(hx2)
-
-        # stage 3
-        hx3 = self.stage3(hx)
-        hx = self.pool34(hx3)
-
-        # stage 4
-        hx4 = self.stage4(hx)
-        hx = self.pool45(hx4)
-
-        # stage 5
-        hx5 = self.stage5(hx)
-        hx = self.pool56(hx5)
-
-        # stage 6
-        hx6 = self.stage6(hx)
-        hx6up = _upsample_like(hx6, hx5)
-
-        # -------------------- decoder --------------------
-        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
-        hx5dup = _upsample_like(hx5d, hx4)
-
-        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
-        hx4dup = _upsample_like(hx4d, hx3)
-
-        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
-
-        # side output
-        d1 = self.side1(hx1d)
-
-        d2 = self.side2(hx2d)
-        d2 = _upsample_like(d2, d1)
-
-        d3 = self.side3(hx3d)
-        d3 = _upsample_like(d3, d1)
-
-        d4 = self.side4(hx4d)
-        d4 = _upsample_like(d4, d1)
-
-        d5 = self.side5(hx5d)
-        d5 = _upsample_like(d5, d1)
-
-        d6 = self.side6(hx6)
-        d6 = _upsample_like(d6, d1)
-
-        d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
-
-        """
-        del hx1, hx2, hx3, hx4, hx5, hx6
-        del hx5d, hx4d, hx3d, hx2d, hx1d
-        del hx6up, hx5dup, hx4dup, hx3dup, hx2dup
-        """
-
-        return d0, d1, d2, d3, d4, d5, d6
-
-
-### U^2-Net small ###
-class U2NETP(nn.Module):
-    def __init__(self, in_ch=3, out_ch=1):
-        super(U2NETP, self).__init__()
-
-        self.stage1 = RSU7(in_ch, 16, 64)
-        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage2 = RSU6(64, 16, 64)
-        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage3 = RSU5(64, 16, 64)
-        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage4 = RSU4(64, 16, 64)
-        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage5 = RSU4F(64, 16, 64)
-        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
-
-        self.stage6 = RSU4F(64, 16, 64)
-
-        # decoder
-        self.stage5d = RSU4F(128, 16, 64)
-        self.stage4d = RSU4(128, 16, 64)
-        self.stage3d = RSU5(128, 16, 64)
-        self.stage2d = RSU6(128, 16, 64)
-        self.stage1d = RSU7(128, 16, 64)
-
-        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side3 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
-        self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
-
-        self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
-
-    def forward(self, x):
-
-        hx = x
-
-        # stage 1
-        hx1 = self.stage1(hx)
-        hx = self.pool12(hx1)
-
-        # stage 2
-        hx2 = self.stage2(hx)
-        hx = self.pool23(hx2)
-
-        # stage 3
-        hx3 = self.stage3(hx)
-        hx = self.pool34(hx3)
-
-        # stage 4
-        hx4 = self.stage4(hx)
-        hx = self.pool45(hx4)
-
-        # stage 5
-        hx5 = self.stage5(hx)
-        hx = self.pool56(hx5)
-
-        # stage 6
-        hx6 = self.stage6(hx)
-        hx6up = _upsample_like(hx6, hx5)
-
-        # decoder
-        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
-        hx5dup = _upsample_like(hx5d, hx4)
-
-        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
-        hx4dup = _upsample_like(hx4d, hx3)
-
-        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
-        hx3dup = _upsample_like(hx3d, hx2)
-
-        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
-        hx2dup = _upsample_like(hx2d, hx1)
-
-        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
-
-        # side output
-        d1 = self.side1(hx1d)
-
-        d2 = self.side2(hx2d)
-        d2 = _upsample_like(d2, d1)
-
-        d3 = self.side3(hx3d)
-        d3 = _upsample_like(d3, d1)
-
-        d4 = self.side4(hx4d)
-        d4 = _upsample_like(d4, d1)
-
-        d5 = self.side5(hx5d)
-        d5 = _upsample_like(d5, d1)
-
-        d6 = self.side6(hx6)
-        d6 = _upsample_like(d6, d1)
-
-        d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
-
-
-        return d0, d1, d2, d3, d4, d5, d6

cloth-segmentation/options.py

@@ -1,12 +0,0 @@
-import os.path as osp
-import os
-
-
-class parser(object):
-    def __init__(self):
-        
-        self.output = "./output"  # output image folder path  
-        self.logs_dir = './logs'
-        self.device = 'cuda:0'
-
-opt = parser()

cloth-segmentation/process.py

@@ -1,257 +0,0 @@
-from network import U2NET
-
-import os
-from PIL import Image
-import cv2
-import gdown
-import argparse
-import numpy as np
-
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-
-from collections import OrderedDict
-from options import opt
-
-
-def load_checkpoint(model, checkpoint_path):
-    if not os.path.exists(checkpoint_path):
-        print("----No checkpoints at given path----")
-        return
-    model_state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
-    new_state_dict = OrderedDict()
-    for k, v in model_state_dict.items():
-        name = k[7:]  # remove `module.`
-        new_state_dict[name] = v
-
-    model.load_state_dict(new_state_dict)
-    print("----checkpoints loaded from path: {}----".format(checkpoint_path))
-    return model
-
-
-def get_palette(num_cls):
-    """ Returns the color map for visualizing the segmentation mask.
-    Args:
-        num_cls: Number of classes
-    Returns:
-        The color map
-    """
-    n = num_cls
-    palette = [0] * (n * 3)
-    for j in range(0, n):
-        lab = j
-        palette[j * 3 + 0] = 0
-        palette[j * 3 + 1] = 0
-        palette[j * 3 + 2] = 0
-        i = 0
-        while lab:
-            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
-            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
-            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
-            i += 1
-            lab >>= 3
-    return palette
-
-
-class Normalize_image(object):
-    """Normalize given tensor into given mean and standard dev
-
-    Args:
-        mean (float): Desired mean to substract from tensors
-        std (float): Desired std to divide from tensors
-    """
-
-    def __init__(self, mean, std):
-        assert isinstance(mean, (float))
-        if isinstance(mean, float):
-            self.mean = mean
-
-        if isinstance(std, float):
-            self.std = std
-
-        self.normalize_1 = transforms.Normalize(self.mean, self.std)
-        self.normalize_3 = transforms.Normalize([self.mean] * 3, [self.std] * 3)
-        self.normalize_18 = transforms.Normalize([self.mean] * 18, [self.std] * 18)
-
-    def __call__(self, image_tensor):
-        if image_tensor.shape[0] == 1:
-            return self.normalize_1(image_tensor)
-
-        elif image_tensor.shape[0] == 3:
-            return self.normalize_3(image_tensor)
-
-        elif image_tensor.shape[0] == 18:
-            return self.normalize_18(image_tensor)
-
-        else:
-            assert "Please set proper channels! Normlization implemented only for 1, 3 and 18"
-
-
-
-
-def apply_transform(img):
-    transforms_list = []
-    transforms_list += [transforms.ToTensor()]
-    transforms_list += [Normalize_image(0.5, 0.5)]
-    transform_rgb = transforms.Compose(transforms_list)
-    return transform_rgb(img)
-
-
-from PIL import Image
-
-
-def generate_mask(input_image, net, device='cpu', image_id=None, output_dir=None):
-    img = input_image
-    img_size = img.size
-    img = img.resize((768, 768), Image.BICUBIC)
-    image_tensor = apply_transform(img)
-    image_tensor = torch.unsqueeze(image_tensor, 0)
-
-    # Allow output_dir override for batch processing
-    if output_dir is None:
-        output_dir = os.path.join(opt.output, 'extracted_garment')
-    os.makedirs(output_dir, exist_ok=True)
-
-    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()
-
-    # Create and save individual masks for classes 1, 2, 3
-    classes_of_interest = [1, 2, 3]
-    mask_imgs = []
-    for idx, cls in enumerate(classes_of_interest):
-        mask = np.zeros_like(output_arr, dtype=np.uint8)
-        mask[output_arr == cls] = 255
-        if mask.ndim > 2:
-            mask = mask.squeeze()
-        if mask.ndim != 2:
-            raise ValueError(f"mask{idx} must be a 2-dimensional array")
-        mask_img = Image.fromarray(mask, mode='L').resize(img_size, Image.BICUBIC)
-        # Save with unique name if image_id is provided
-        if image_id is not None:
-            mask_path = os.path.join(output_dir, f'{image_id}__mask{idx}.png')
-        else:
-            mask_path = os.path.join(output_dir, f'mask{idx}.png')
-        mask_img.save(mask_path, format="PNG")
-        print(f"Saved mask{idx} at: {mask_path}")
-        mask_imgs.append(mask_img)
-
-    # Create a binary mask where selected classes are 1, others are 0
-    binary_mask = np.zeros_like(output_arr, dtype=np.uint8)
-    for cls in classes_of_interest:
-        binary_mask[output_arr == cls] = 255
-    if binary_mask.ndim > 2:
-        binary_mask = binary_mask.squeeze()
-    if binary_mask.ndim != 2:
-        raise ValueError("binary_mask must be a 2-dimensional array")
-    binary_mask_img = Image.fromarray(binary_mask, mode='L').resize(img_size, Image.BICUBIC)
-
-    # Create an RGBA image for the output
-    extracted_garment = Image.new("RGBA", img_size)
-    original_img = img.resize(img_size)
-    extracted_garment.paste(original_img, mask=binary_mask_img)
-
-    # Save the garment image with transparency
-    if image_id is not None:
-        garment_path = os.path.join(output_dir, f'{image_id}__extracted_garment.png')
-    else:
-        garment_path = os.path.join(output_dir, 'extracted_garment.png')
-    extracted_garment.save(garment_path, format="PNG")
-    print(f"Saved extracted garment at: {garment_path}")
-
-    return (*mask_imgs, extracted_garment)
-
-# def generate_mask(input_image, net, device='cpu'):
-#     img = input_image
-#     img_size = img.size
-#     img = img.resize((768, 768), Image.BICUBIC)
-#     image_tensor = apply_transform(img)
-#     image_tensor = torch.unsqueeze(image_tensor, 0)
-
-#     output_dir = os.path.join(opt.output, 'extracted_garment')
-#     os.makedirs(output_dir, exist_ok=True)
-
-#     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()
-
-#     # Create a binary mask where selected classes are 1, others are 0
-#     binary_mask = np.zeros_like(output_arr, dtype=np.uint8)
-#     classes_of_interest = [1, 2, 3]  # Modify this list according to your classes of interest
-#     for cls in classes_of_interest:
-#         binary_mask[output_arr == cls] = 255
-
-#     # Convert binary mask to a 3-channel image to use as a mask
-
-#     # Ensure binary_mask is 2D
-#     if binary_mask.ndim > 2:
-#         binary_mask = binary_mask.squeeze()  # Removes single-dimensional entries from the shape
-#     if binary_mask.ndim != 2:
-#         raise ValueError("binary_mask must be a 2-dimensional array")
-
-#     binary_mask_img = Image.fromarray(binary_mask, mode='L').resize(img_size, Image.BICUBIC)
-#     binary_mask_3ch = binary_mask_img.convert('RGB')  # Convert to RGB
-
-#     # Apply mask to the original image
-#     original_img = img.resize(img_size)  # Resize the processed image back to original size
-#     extracted_garment = Image.new("RGB", original_img.size)
-#     extracted_garment.paste(original_img, mask=binary_mask_img)
-
-#     # Save the garment image
-#     garment_path = os.path.join(output_dir, 'extracted_garment.png')
-#     extracted_garment.save(garment_path)
-
-#     return extracted_garment
-
-
-def check_or_download_model(file_path):
-    if not os.path.exists(file_path):
-        os.makedirs(os.path.dirname(file_path), exist_ok=True)
-        url = "https://drive.google.com/uc?export=download&id=1qVv720hAd11JSCuIVJuqfjCGolwb1H8o"
-        gdown.download(url, file_path, quiet=False)
-        print("Model downloaded successfully.")
-    else:
-        print("Model already exists.")
-
-
-
-def load_seg_model(checkpoint_path, device='cpu'):
-    net = U2NET(in_ch=3, out_ch=4)
-    check_or_download_model(checkpoint_path)
-    net = load_checkpoint(net, checkpoint_path)
-    net = net.to(device)
-    net = net.eval()
-
-    return net
-
-
-def main(args):
-
-    device = 'cuda:0' if args.cuda else 'cpu'
-
-    # Create an instance of your model
-    model = load_seg_model(args.checkpoint_path, device=device)
-
-    palette = get_palette(4)
-
-    img = Image.open(args.image).convert('RGB')
-
-    cloth_seg = generate_mask(img, net=model, palette=palette, device=device)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Help to set arguments for Cloth Segmentation.')
-    parser.add_argument('--image', type=str, help='Path to the input image')
-    parser.add_argument('--cuda', action='store_true', help='Enable CUDA (default: False)')
-    parser.add_argument('--checkpoint_path', type=str, default='model/cloth_segm.pth', help='Path to the checkpoint file')
-    args = parser.parse_args()
-
-    main(args)

distribution_reshape.py

@@ -1,189 +0,0 @@
-import cv2
-import os
-import torch
-import numpy as np
-
-
-def from_torch_image(image):
-    image = image.cpu().numpy() * 255.0
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    return image
-
-
-def to_torch_image(image):
-    image = image.astype(dtype=np.float32)
-    image /= 255.0
-    image = torch.from_numpy(image)
-    return image
-
-
-def get_histogram(array):
-    array = array.flatten().astype(dtype=np.float64)
-    hist = np.histogram(array, bins=256, range=(0, 256))
-    array = hist[0].astype(dtype=np.float64)
-    array /= len(array)
-    return array
-
-
-def get_limits(array, threshold_fraction):
-    array = get_histogram(array)
-
-    left_sum = 0
-    right_sum = 0
-
-    left_start = 0
-    right_start = len(array) - 1
-
-    for i in range(len(array)):
-
-        left_index = i
-        right_index = len(array) - i - 1
-
-        left_sum += array[left_index]
-        right_sum += array[right_index]
-
-        if left_sum < threshold_fraction:
-            left_start = left_index
-
-        if right_sum < threshold_fraction:
-            right_start = right_index
-
-        if (left_sum > threshold_fraction) and (right_sum
-                                                > threshold_fraction):
-
-            return (left_start, right_start)
-
-
-def do_rescale(x, y1, y2, x1, x2):
-
-    x = x.astype(dtype=np.float64)
-
-    if x1 > x2:
-        x1, x2 = x2, x1
-
-    if y1 > y2:
-        y1, y2 = y2, y1
-
-    epsilon = 0.0001
-
-    y = (x - x1)
-    y /= (x2 - x1 + epsilon)
-    y *= (y2 - y1)
-    y += y1
-    y = np.clip(y, y1, y2)
-
-    for iy in range(y.shape[0]):
-        for ix in range(y.shape[1]):
-            if y[iy, ix] > 255:
-                print(iy, ix)
-
-    y = y.astype(dtype=np.uint8)
-
-    return y
-
-
-class get_histogram_limits:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "luminosity_as_mask": ("MASK", ),
-                "threshold_fraction": ("FLOAT", {
-                    "default": 0.001,
-                    "min": 0.0,
-                    "max": 0.5,
-                    "step": 0.00001,
-                    "round": 0.000001,
-                    "display": "number"
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("histogram lower limit (x1) as INT",
-                    "histogram upper limit (x2) as INT")
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(self, luminosity_as_mask, threshold_fraction):
-
-        luminosity_as_mask = from_torch_image(image=luminosity_as_mask)
-
-        (left_start,
-         right_start) = get_limits(array=luminosity_as_mask[0],
-                                   threshold_fraction=threshold_fraction)
-
-        return (left_start, right_start)
-
-
-class simple_rescale_histogram:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "layer_as_mask": ("MASK", ),
-                "y1": ("INT", {
-                    "default": 100,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1,
-                    "display": "number"
-                }),
-                "y2": ("INT", {
-                    "default": 200,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1,
-                    "display": "number"
-                }),
-                "x1": ("INT", {
-                    "default": 100,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1,
-                    "display": "number"
-                }),
-                "x2": ("INT", {
-                    "default": 200,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1,
-                    "display": "number"
-                })
-            },
-        }
-
-    RETURN_TYPES = ("MASK", )
-    RETURN_NAMES = ("rescaled layer as MASK", )
-    FUNCTION = "test"
-    CATEGORY = "TRI3D"
-
-    def test(self, layer_as_mask, y1, y2, x1, x2):
-        layer_as_mask = from_torch_image(image=layer_as_mask[0])
-        layer_as_mask = do_rescale(x=layer_as_mask, y1=y1, y2=y2, x1=x1, x2=x2)
-        layer_as_mask = to_torch_image(image=layer_as_mask)
-        layer_as_mask = layer_as_mask.unsqueeze(0)
-        return (layer_as_mask, )
-
-
-NODE_CLASS_MAPPINGS = {
-    "get_histogram_limits": get_histogram_limits,
-    'simple_rescale_histogram': simple_rescale_histogram
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "get_histogram_limits": "get_histogram_limits",
-    "simple_rescale_histogram": "simple_rescale_histogram"
-}

dwpose/comfy_utils.py

@@ -274,6 +274,4 @@ def switch_to_backpose(input_keypoints, input_width):
         x,y = input_keypoints[i]
         input_keypoints[i] = [input_width - x, y]
 
-    return input_keypoints
-
-
+    return input_keypoints

image_stack.py

@@ -1,203 +0,0 @@
-#!/usr/bin/python3
-
-from PIL import Image, ImageOps, ImageSequence, ImageFile
-from PIL.PngImagePlugin import PngInfo
-import cv2
-import hashlib
-import json
-import logging
-import math
-import numpy as np
-import os
-import random
-import safetensors.torch
-import sys
-import time
-import torch
-import traceback
-
-
-def load_image(path):
-
-    return torch.from_numpy(cv2.imread(
-        path, cv2.IMREAD_COLOR)).to(dtype=torch.float32) / 255.0
-
-
-def do_stack(img1, img2):
-
-    dim = max(max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1])
-
-    out = torch.zeros((dim, dim, 3), dtype=img1.dtype, device=img1.device) + 1
-
-    diff1 = (out.shape[0] - img1.shape[0]) // 2
-    diff2 = (out.shape[0] - img2.shape[0]) // 2
-
-    part0 = 0
-    part1 = img1.shape[1]
-    part2 = img2.shape[1] + img1.shape[1]
-
-    out[diff1:diff1 + img1.shape[0], part0:part1, :] = img1
-    out[diff2:diff2 + img2.shape[0], part1:part2, :] = img2
-
-    return out
-
-
-def save_image(image, outpath):
-
-    cv2.imwrite(outpath,
-                (image * 255).to(dtype=torch.uint8).detach().cpu().numpy())
-
-
-class H_Stack_Images:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image_L": ("IMAGE", ),
-                "image_R": ("IMAGE", ),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", )
-    FUNCTION = "test"
-    CATEGORY = "TRI3D"
-
-    def test(self, image_L, image_R):
-
-        return (do_stack(img1=image_L[0], img2=image_R[0]).unsqueeze(0), )
-
-
-class SaveImage_absolute:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE", {
-                    "tooltip": "The images to save."
-                }),
-                "absolute_filename": ("STRING", {
-                    "default":
-                    "image.png",
-                    "tooltip":
-                    "The absolute path to the file to save."
-                })
-            },
-        }
-
-    RETURN_TYPES = ("STRING", )
-    RETURN_NAMES = ("text to control order", )
-    FUNCTION = "save_images"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "image"
-    DESCRIPTION = "Saves the input images to an absolute path."
-
-    def save_images(self, images, absolute_filename):
-        i = 255.0 * images[0].cpu().numpy()
-        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-        img.save(absolute_filename)
-
-        return (absolute_filename, )
-
-
-class SaveText_absolute:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "text": ("STRING", {
-                    "multiline": True,
-                    "dynamicPrompts": True,
-                    "tooltip": "Text to be saved to the file."
-                }),
-                "absolute_filename": ("STRING", {
-                    "default":
-                    "image.txt",
-                    "tooltip":
-                    "The absolute path to the file to save."
-                })
-            },
-            "optional": {
-                "text_opt": ("STRING", {
-                    "multiline":
-                    True,
-                    "dynamicPrompts":
-                    True,
-                    "tooltip":
-                    "Text to provide order when necessary (to create work files after txt files)."
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("STRING", )
-    RETURN_NAMES = ("same text as input", )
-    FUNCTION = "save_text"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "text"
-    DESCRIPTION = "Saves the input text to an absolute path."
-
-    def save_text(self, text, absolute_filename, text_opt=''):
-        open(absolute_filename, "w").write(text)
-        return (text, )
-
-
-class Wait_And_Read_File:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "absolute_filename": ("STRING", {
-                    "default":
-                    "image.txt",
-                    "tooltip":
-                    "The absolute path to the file to read."
-                })
-            },
-            "optional": {
-                "text": ("STRING", {
-                    "multiline":
-                    True,
-                    "dynamicPrompts":
-                    True,
-                    "tooltip":
-                    "Text to provide order when necessary (to wait on done file)."
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("STRING", )
-    RETURN_NAMES = ("text from file", )
-    FUNCTION = "read_text"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "text"
-    DESCRIPTION = "Saves the input text to an absolute path."
-
-    def read_text(self, absolute_filename, text=''):
-        while not os.path.exists(absolute_filename):
-            time.sleep(0.1)
-
-        res = open(absolute_filename, "r").read()
-        os.unlink(absolute_filename)
-
-        return (res, )

light_layer.py

@@ -1,202 +0,0 @@
-import numpy as np
-import cv2
-import math
-import torch
-
-
-def from_torch_image(image):
-    image = image.cpu().numpy() * 255.0
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    return image
-
-
-def to_torch_image(image):
-    image = image.astype(dtype=np.float32)
-    image /= 255.0
-    image = torch.from_numpy(image)
-    return image
-
-
-def smooth_step_plain(x):
-
-    if x < -1:
-        return -1
-    elif x <= 1:
-        return math.sin(x * np.pi / 2.0)
-    else:
-        return 1
-
-
-def smooth_step_np(x):
-    truths = np.logical_and(-1 < x, x < 1).astype(np.float32)
-    x1 = np.clip(x, -1, 1)
-    x2 = np.sin(x * np.pi / 2.0)
-    ret = (truths * x2) + ((1 - truths) * x1)
-    return ret
-
-
-def smooth_step_stretch(x, a, b):
-
-    if b < a:
-        tmp = b
-        b = a
-        a = tmp
-
-    if a < 0:
-        a = 0
-
-    if b > 1:
-        b = 1
-
-    if a == b:
-        a = 0
-        b = 1
-
-    return smooth_step_np((2 * (x - a) / (b - a)) - 1)
-
-
-def get_light_layer(image,
-                    ref_r=255,
-                    ref_g=255,
-                    ref_b=255,
-                    do_scale=True,
-                    scale_a=0.0,
-                    scale_b=1.0):
-
-    sqmax = 3 * 255 * 255
-    scalemax = math.sqrt(sqmax)
-
-    b = image[:, :, 0].astype(dtype=np.float32)
-    g = image[:, :, 1].astype(dtype=np.float32)
-    r = image[:, :, 2].astype(dtype=np.float32)
-
-    b2 = b * b
-    g2 = g * g
-    r2 = r * r
-
-    d2 = np.zeros(b2.shape, dtype=np.float32)
-    d2 += sqmax - b2 - g2 - r2
-    d = np.sqrt(d2)
-
-    ref_r2 = ref_r * ref_r
-    ref_g2 = ref_g * ref_g
-    ref_b2 = ref_b * ref_b
-    ref_d2 = sqmax - ref_r2 - ref_g2 - ref_b2
-
-    ref_d = math.sqrt(ref_d2)
-
-    dot = (b * ref_b) + (g * ref_g) + (r * ref_r) + (d * ref_d)
-    dot /= sqmax
-
-    if do_scale:
-        dot = smooth_step_stretch(x=dot, a=scale_a, b=scale_b)
-
-    dot *= 255
-    dot = dot.astype(np.uint8)
-
-    return dot
-
-
-class main_light_layer():
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "ref_r": ("INT", {
-                    "default": 255,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1
-                }),
-                "ref_g": ("INT", {
-                    "default": 255,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1
-                }),
-                "ref_b": ("INT", {
-                    "default": 255,
-                    "min": 0,
-                    "max": 255,
-                    "step": 1
-                }),
-                "do_scale": (["enable", "disable"], ),
-                "thresh_low": ("FLOAT", {
-                    "default": 0.6,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.01
-                }),
-                "thresh_high": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.01
-                }),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("MASK", )
-    CATEGORY = "HackNode"
-
-    def run(
-        self,
-        image,
-        ref_r,
-        ref_g,
-        ref_b,
-        do_scale,
-        thresh_low,
-        thresh_high,
-    ):
-
-        do_scale = (do_scale == "enable")
-        print('do_scale', do_scale)
-
-        image = from_torch_image(image)
-        print('image.shape', image.shape)
-
-        batch_size = image.shape[0]
-        print('batch_size', batch_size)
-
-        mask = []
-
-        for i in range(batch_size):
-
-            tmp_img = image[i]
-            print('tmp_img.shape', tmp_img.shape)
-
-            tmp_mask = get_light_layer(
-                tmp_img,
-                ref_b,
-                ref_g,
-                ref_r,
-                do_scale,
-                scale_a=thresh_low,
-                scale_b=thresh_high,
-            )
-            print('tmp_mask.shape', tmp_mask.shape)
-
-            mask.append(tmp_mask)
-
-        mask = np.array(mask)
-
-        mask = to_torch_image(mask)
-        print(mask.shape)
-
-        return (mask, )
-
-
-NODE_CLASS_MAPPINGS = {
-    'main_light_layer': main_light_layer,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    'main_light_layer': 'main_light_layer',
-}

nsfw.py

@@ -1,166 +0,0 @@
-from __future__ import annotations
-from weakref import ref as WeakRef
-from pathlib import Path
-from tqdm import tqdm
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from transformers import CLIPImageProcessor, CLIPConfig, CLIPVisionModel, PreTrainedModel
-from kornia.filters import box_blur
-
-
-
-def cosine_similarity(image_embeds: Tensor, text_embeds: Tensor):
-    if image_embeds.dim() == 2 and text_embeds.dim() == 2:
-        image_embeds = image_embeds.unsqueeze(1)
-    return F.cosine_similarity(image_embeds, text_embeds, dim=-1)
-
-
-class CLIPSafetyChecker(PreTrainedModel):
-    # https://huggingface.co/CompVis/stable-diffusion-safety-checker
-    # Adapted from:
-    # https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
-
-    config_class = CLIPConfig
-    _no_split_modules = ["CLIPEncoderLayer"]
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__(config)
-        projdim = config.projection_dim
-
-        self.vision_model = CLIPVisionModel(config.vision_config)
-        self.visual_projection = nn.Linear(config.vision_config.hidden_size, projdim, bias=False)
-
-        self.concept_embeds = nn.Parameter(torch.ones(17, projdim), requires_grad=False)
-        self.special_care_embeds = nn.Parameter(torch.ones(3, projdim), requires_grad=False)
-        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
-        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
-
-    def forward(self, clip_input, images: Tensor, sensitivity: float, alternate_image: Tensor):
-        with torch.no_grad():
-            image_batch = self.vision_model(clip_input)[1]
-            image_embeds = self.visual_projection(image_batch)
-            sensitivity = -0.1 + 0.14 * sensitivity
-
-            special_cos_dist = cosine_similarity(image_embeds, self.special_care_embeds)
-            special_scores_threshold = self.special_care_embeds_weights.unsqueeze(0)
-            special_scores = special_cos_dist - special_scores_threshold + sensitivity
-
-            if torch.any(special_scores > 0):
-                sensitivity = sensitivity + 0.01
-
-            cos_dist = cosine_similarity(image_embeds, self.concept_embeds)
-            concept_threshold = self.concept_embeds_weights.unsqueeze(0)
-            concept_scores = cos_dist - concept_threshold + sensitivity
-
-            is_nsfw = [torch.any(concept_scores[i] > 0) for i in range(concept_scores.shape[0])]
-            is_nsfw = [x.item() for x in is_nsfw]
-            return self.filter_images(images, alternate_image, is_nsfw)
-
-    def filter_images(self, images: Tensor, alternate_image: Tensor, is_nsfw: list[bool]):
-        if not any(is_nsfw):
-            return images
-
-        images = images.clone()
-        for idx, nsfw in enumerate(is_nsfw):
-            if nsfw:
-                # Resize alternate image to match original image dimensions
-                resized_alternate = F.interpolate(
-                    alternate_image[idx:idx+1],  # Add batch dimension
-                    size=(images[idx].shape[1], images[idx].shape[2]),  # Target height, width
-                    mode='bilinear',
-                    align_corners=False
-                )
-                images[idx] = resized_alternate.squeeze(0)  # Remove batch dimension
-        return images
-
-
-class CachedModels:
-    _instance: WeakRef | None = None
-
-    def __init__(self):
-        model_dir = Path(__file__).parent / "safetychecker"
-        model_file = model_dir / "model.safetensors"
-        if not model_file.exists():
-            self.download(
-                "https://huggingface.co/CompVis/stable-diffusion-safety-checker/resolve/refs%2Fpr%2F41/model.safetensors",
-                target=model_file,
-            )
-        self.feature_extractor = CLIPImageProcessor.from_pretrained(model_dir)
-        self.safety_checker = CLIPSafetyChecker.from_pretrained(model_dir)
-
-    @classmethod
-    def load(cls):
-        models = cls._instance and cls._instance()
-        if models is None:
-            models = cls()
-            cls._instance = WeakRef(models)
-        return models
-
-    def download(self, url: str, target: Path):
-        import requests
-
-        try:
-            target_temp = target.with_suffix(".download")
-            with requests.get(url, stream=True) as response:
-                text = "NSFWFilter model download"
-                total = int(response.headers.get("content-length", 0))
-                pbar = tqdm(None, total=total, unit="b", unit_scale=True, desc=text)
-                with open(target_temp, "wb") as f:
-                    for chunk in response.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                        pbar.update(len(chunk))
-                pbar.close()
-            target_temp.rename(target)
-        except Exception as e:
-            raise RuntimeError(
-                f"NSFWFilter: Failed to download safety-checker model from {url} to target location {target}: {e}"
-            ) from e
-
-
-
-def to_bchw(image: torch.Tensor):
-    if image.ndim == 3:
-        image = image.unsqueeze(0)
-    return image.movedim(-1, 1)
-
-
-def to_bhwc(image: torch.Tensor):
-    return image.movedim(1, -1)
-
-
-def mask_batch(mask: torch.Tensor):
-    if mask.ndim == 2:
-        mask = mask.unsqueeze(0)
-    return mask
-
-
-class TRI3DNSFWFilter:
-    models: CachedModels
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "alternate_image": ("IMAGE",),
-                "sensitivity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.10}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "check"
-    CATEGORY = "TRI3D NSFW"
-
-    def __init__(self):
-        self.models = CachedModels.load()
-
-    def check(self, image, alternate_image,sensitivity):
-        image = to_bchw(image)
-        alternate_image = to_bchw(alternate_image)
-        input = self.models.feature_extractor(image, do_rescale=False, return_tensors="pt")
-        filtered = self.models.safety_checker(
-            images=image, clip_input=input.pixel_values, sensitivity=sensitivity, alternate_image=alternate_image
-        )
-        return (to_bhwc(filtered),)

photoroom.py

@@ -1,131 +0,0 @@
-import http.client
-import mimetypes
-import os
-import uuid
-import requests
-import numpy as np
-import torch
-import cv2
-from PIL import Image
-import io
-
-class TRI3D_photoroom_bgremove_api:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE", ),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def run(self, images):
-        import http.client
-        import mimetypes
-        import os
-        import uuid
-        import dotenv
-
-        dotenv.load_dotenv()
-
-        # Read the API key from the environment variable
-        PHOTOROOM_API_KEY = os.getenv('PHOTOROOM_API_KEY','EMPTY')
-        if PHOTOROOM_API_KEY == 'EMPTY':
-            return images
-        def tensor_to_cv2_img(tensor, remove_alpha=False):
-            i = 255. * tensor.cpu().numpy()  # This will give us (H, W, C)
-            img = np.clip(i, 0, 255).astype(np.uint8)
-            return img
-
-        def cv2_img_to_tensor(img):
-            img = img.astype(np.float32) / 255.0
-            img = torch.from_numpy(img)[
-                None,
-            ]
-            return img
-        
-
-        # Please replace with your own apiKey
-
-        def remove_background(input_image_path, output_image_path,apiKey):
-            # Define multipart boundary
-            boundary = '----------{}'.format(uuid.uuid4().hex)
-
-            # Get mimetype of image
-            content_type, _ = mimetypes.guess_type(input_image_path)
-            if content_type is None:
-                content_type = 'application/octet-stream'  # Default type if guessing fails
-
-            # Prepare the POST data
-            with open(input_image_path, 'rb') as f:
-                image_data = f.read()
-            filename = os.path.basename(input_image_path)
-
-            body = (
-            f"--{boundary}\r\n"
-            f"Content-Disposition: form-data; name=\"image_file\"; filename=\"{filename}\"\r\n"
-            f"Content-Type: {content_type}\r\n\r\n"
-            ).encode('utf-8') + image_data + f"\r\n--{boundary}--\r\n".encode('utf-8')
-            
-            # Set up the HTTP connection and headers
-            conn = http.client.HTTPSConnection('sdk.photoroom.com')
-
-            headers = {
-                'Content-Type': f'multipart/form-data; boundary={boundary}',
-                'x-api-key': apiKey
-            }
-
-            # Make the POST request
-            conn.request('POST', '/v1/segment', body=body, headers=headers)
-            response = conn.getresponse()
-
-            # Handle the response
-            if response.status == 200:
-                response_data = response.read()
-                with open(output_image_path, 'wb') as out_f:
-                    out_f.write(response_data)
-                print("Image saved to", output_image_path)
-            else:
-                print(f"Error: {response.status} - {response.reason}")
-                print(response.read())
-
-            # Close the connection
-            conn.close()
-
-        
-
-        OUTPUT_FOLDER = "output/"
-        batch_results = []
-        for i in range(images.shape[0]):
-            image = images[i]
-            cv2_image = tensor_to_cv2_img(image)
-            cv2_image = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
-            import random 
-            random_number = random.randint(0, 100000)
-            output_path = OUTPUT_FOLDER + f"output{i}_{random_number}.png"
-            input_path = OUTPUT_FOLDER + f"input{i}_{random_number}.png"
-            cv2.imwrite(input_path, cv2_image)
-            remove_background(input_path, output_path, PHOTOROOM_API_KEY)
-
-            print(input_path, output_path)
-            cv2_segm = cv2.imread(output_path, cv2.IMREAD_UNCHANGED)
-            cv2_segm = cv2.cvtColor(cv2_segm, cv2.COLOR_BGRA2RGBA)
-            b_tensor_img = cv2_img_to_tensor(cv2_segm)
-            batch_results.append(b_tensor_img.squeeze(0))
-
-        batch_results = torch.stack(batch_results)
-
-        return (batch_results,)
-            
-
-
-        
-      
-

requirements.txt

@@ -3,9 +3,4 @@ ninja
 pillow
 torch
 torchvision
-transparent-background
-wget
-gdown
-matplotlib
-python-dotenv
-git+https://github.com/FacePerceiver/facer.git@main
+gdown

safetychecker/config.json

@@ -1,171 +0,0 @@
-{
-  "_name_or_path": "clip-vit-large-patch14/",
-  "architectures": [
-    "SafetyChecker"
-  ],
-  "initializer_factor": 1.0,
-  "logit_scale_init_value": 2.6592,
-  "model_type": "clip",
-  "projection_dim": 768,
-  "text_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
-    "architectures": null,
-    "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "bos_token_id": 0,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
-    "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": 2,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
-    "hidden_act": "quick_gelu",
-    "hidden_size": 768,
-	"id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
-    "initializer_factor": 1.0,
-    "initializer_range": 0.02,
-    "intermediate_size": 3072,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
-    "layer_norm_eps": 1e-05,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "max_position_embeddings": 77,
-    "min_length": 0,
-    "model_type": "clip_text_model",
-    "no_repeat_ngram_size": 0,
-    "num_attention_heads": 12,
-    "num_beam_groups": 1,
-    "num_beams": 1,
-    "num_hidden_layers": 12,
-    "num_return_sequences": 1,
-    "output_attentions": false,
-    "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": 1,
-    "prefix": null,
-    "problem_type": null,
-    "pruned_heads": {},
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
-    "return_dict": true,
-    "return_dict_in_generate": false,
-    "sep_token_id": null,
-    "task_specific_params": null,
-    "temperature": 1.0,
-    "tie_encoder_decoder": false,
-    "tie_word_embeddings": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": 1.0,
-    "torch_dtype": null,
-    "torchscript": false,
-    "transformers_version": "4.21.0.dev0",
-    "typical_p": 1.0,
-    "use_bfloat16": false,
-    "vocab_size": 49408
-  },
-  "text_config_dict": {
-    "hidden_size": 768,
-    "intermediate_size": 3072,
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12
-  },
-  "torch_dtype": "float32",
-  "transformers_version": null,
-  "vision_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
-    "architectures": null,
-    "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "bos_token_id": null,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
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samples/3.1_resize_extract_parts/test_workflow.json

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samples/back_poses/backpose_kidold.json

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samples/back_poses/left_backpose.json

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samples/back_poses/right_backpose.json

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-    [
-      232,
-      291
-    ],
-    [
-      214,
-      281
-    ],
-    [
-      221,
-      288
-    ],
-    [
-      223,
-      291
-    ],
-    [
-      229,
-      291
-    ],
-    [
-      211,
-      281
-    ],
-    [
-      217,
-      286
-    ],
-    [
-      223,
-      288
-    ],
-    [
-      223,
-      291
-    ],
-    [
-      211,
-      281
-    ],
-    [
-      214,
-      286
-    ],
-    [
-      217,
-      288
-    ],
-    [
-      221,
-      288
-    ],
-    [
-      314,
-      257
-    ],
-    [
-      308,
-      262
-    ],
-    [
-      306,
-      266
-    ],
-    [
-      303,
-      270
-    ],
-    [
-      301,
-      276
-    ],
-    [
-      312,
-      279
-    ],
-    [
-      311,
-      284
-    ],
-    [
-      308,
-      290
-    ],
-    [
-      303,
-      292
-    ],
-    [
-      313,
-      279
-    ],
-    [
-      312,
-      284
-    ],
-    [
-      311,
-      290
-    ],
-    [
-      307,
-      292
-    ],
-    [
-      314,
-      279
-    ],
-    [
-      313,
-      284
-    ],
-    [
-      312,
-      289
-    ],
-    [
-      311,
-      293
-    ],
-    [
-      317,
-      279
-    ],
-    [
-      314,
-      283
-    ],
-    [
-      313,
-      287
-    ],
-    [
-      312,
-      290
-    ]
-  ]
-}

scaled_paste.py

@@ -1,316 +0,0 @@
-#!/usr/bin/python3
-import torch
-import cv2
-import numpy as np
-
-
-#!/usr/bin/python3
-def from_torch_image(image):
-    image = image.cpu().numpy() * 255.0
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    return image
-
-
-def to_torch_image(image):
-    image = image.astype(dtype=np.float32)
-    image /= 255.0
-    image = torch.from_numpy(image)
-    return image
-
-
-def scaled_paste_2(
-    image_background,
-    image_foreground,
-    mask_foreground,
-    scale_factor,
-    height_factor=1.2,
-):
-
-    print('DEBUG scaled_paste 0 ', image_background.shape,
-          image_foreground.shape, mask_foreground.shape, scale_factor,
-          height_factor)
-
-    height = image_foreground.shape[0] * height_factor
-
-    print('DEBUG scaled_paste 1 ', height)
-
-    max_0 = max(image_background.shape[0], height)
-    max_1 = max(image_background.shape[1], image_foreground.shape[1])
-
-    print('DEBUG scaled_paste 2 ', max_0, max_1)
-
-    ratio_0 = max_0 / image_background.shape[0]
-    ratio_1 = max_1 / image_background.shape[1]
-    ratio_max = max(ratio_0, ratio_1) * scale_factor
-
-    print('DEBUG scaled_paste 2 ', ratio_0, ratio_1, ratio_max)
-
-    size_0 = int(image_background.shape[0] * scale_factor)
-    size_1 = int(image_background.shape[1] * scale_factor)
-
-    print('DEBUG scaled_paste 3 ', size_0, size_1)
-
-    image_background = cv2.resize(image_background, (size_1, size_0),
-                                  cv2.INTER_CUBIC)
-
-    print('DEBUG scaled_paste 4 ', image_background.shape)
-
-    bg_h = image_background.shape[0]
-    fg_h = image_foreground.shape[0]
-    
-    end_0 = int(bg_h - (bg_h * (height_factor-1)))    
-    # end_0 = int(image_background.shape[0])
-    begin_0 = max(0, int(end_0 - fg_h))
-    # end_0 = int(begin_0 + image_foreground.shape[0])
-
-    fg_start_height = fg_h - (end_0 - begin_0)
-    
-    print('DEBUG scaled_paste 5 ', begin_0, end_0, fg_start_height)
-
-    end_1 = image_background.shape[1]
-    begin_1 = end_1 - image_foreground.shape[1]
-    begin_1 = int(begin_1 / 2)
-    end_1 = int(begin_1 + image_foreground.shape[1])
-
-    print('DEBUG scaled_paste 6 ', begin_1, end_1)
-
-    image_reference = image_background[begin_0:end_0, begin_1:end_1, :]
-    
-    image_foreground = image_foreground[fg_start_height:,:,:]
-    mask_foreground = mask_foreground[fg_start_height:,:]
-
-    print('DEBUG scaled_paste 7 ', image_reference.shape, image_foreground.shape, mask_foreground.shape)
-    for i in range(3):
-        image_reference[:, :,
-                        i] = (mask_foreground * image_foreground[:, :, i]) + (
-                            (1 - mask_foreground) * image_reference[:, :, i])
-
-    return image_background
-
-
-def scaled_paste(
-    image_background,
-    image_foreground,
-    mask_foreground,
-    scale_factor,
-    height_factor=1.2,
-):
-
-    print('DEBUG scaled_paste 0 ', image_background.shape,
-          image_foreground.shape, mask_foreground.shape, scale_factor,
-          height_factor)
-
-    height = image_foreground.shape[0] * height_factor
-
-    print('DEBUG scaled_paste 1 ', height)
-
-    max_0 = max(image_background.shape[0], height)
-    max_1 = max(image_background.shape[1], image_foreground.shape[1])
-
-    print('DEBUG scaled_paste 2 ', max_0, max_1)
-
-    ratio_0 = max_0 / image_background.shape[0]
-    ratio_1 = max_1 / image_background.shape[1]
-    ratio_max = max(ratio_0, ratio_1) * scale_factor
-
-    print('DEBUG scaled_paste 2 ', ratio_0, ratio_1, ratio_max)
-
-    size_0 = int(image_background.shape[0] * ratio_max) + 1
-    size_1 = int(image_background.shape[1] * ratio_max) + 1
-
-    print('DEBUG scaled_paste 3 ', size_0, size_1)
-
-    image_background = cv2.resize(image_background, (size_1, size_0),
-                                  cv2.INTER_CUBIC)
-
-    print('DEBUG scaled_paste 4 ', image_background.shape)
-
-    end_0 = int(image_background.shape[0])
-    begin_0 = int(end_0 - height)
-    end_0 = int(begin_0 + image_foreground.shape[0])
-
-    print('DEBUG scaled_paste 5 ', begin_0, end_0)
-
-    end_1 = image_background.shape[1]
-    begin_1 = end_1 - image_foreground.shape[1]
-    begin_1 = int(begin_1 / 2)
-    end_1 = int(begin_1 + image_foreground.shape[1])
-
-    print('DEBUG scaled_paste 6 ', begin_1, end_1)
-
-    image_reference = image_background[begin_0:end_0, begin_1:end_1, :]
-
-    for i in range(3):
-        image_reference[:, :,
-                        i] = (mask_foreground * image_foreground[:, :, i]) + (
-                            (1 - mask_foreground) * image_reference[:, :, i])
-
-    return image_background
-
-
-#!/usr/bin/python3
-
-
-class main_scaled_paste_2():
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image_background": ("IMAGE", ),
-                "image_foreground": ("IMAGE", ),
-                "mask_foreground": ("MASK", ),
-                "scale_factor": ("FLOAT", {
-                    "default": 1.2,
-                    "min": 1,
-                    "max": 10,
-                    "step": 0.05
-                }),
-                "height_factor": ("FLOAT", {
-                    "default": 1.01,
-                    "min": 1,
-                    "max": 8,
-                    "step": 0.05
-                }),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def run(
-        self,
-        image_background,
-        image_foreground,
-        mask_foreground,
-        scale_factor,
-        height_factor,
-    ):
-
-        print('DEBUG 0 ', image_background.shape, image_foreground.shape,
-              mask_foreground.shape)
-
-        image_background = from_torch_image(image_background)
-        image_foreground = from_torch_image(image_foreground)
-        mask_foreground = mask_foreground.cpu().numpy()
-
-        image_output = scaled_paste_2(
-            image_background[0],
-            image_foreground[0],
-            mask_foreground[0],
-            scale_factor,
-            height_factor,
-        )
-
-        print('DEBUG 1 ', image_output.shape)
-
-        image_output = to_torch_image(image=image_output)
-
-        print('DEBUG 2 ', image_output.shape)
-
-        image_output = image_output.unsqueeze(0)
-
-        print('DEBUG 3 ', image_output.shape)
-
-        return (image_output, )
-
-
-class main_scaled_paste():
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image_background": ("IMAGE", ),
-                "image_foreground": ("IMAGE", ),
-                "mask_foreground": ("MASK", ),
-                "scale_factor": ("FLOAT", {
-                    "default": 1.2,
-                    "min": 1,
-                    "max": 10,
-                    "step": 0.05
-                }),
-                "height_factor": ("FLOAT", {
-                    "default": 1.01,
-                    "min": 1,
-                    "max": 8,
-                    "step": 0.05
-                }),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def run(
-        self,
-        image_background,
-        image_foreground,
-        mask_foreground,
-        scale_factor,
-        height_factor,
-    ):
-
-        print('DEBUG 0 ', image_background.shape, image_foreground.shape,
-              mask_foreground.shape)
-
-        image_background = from_torch_image(image_background)
-        image_foreground = from_torch_image(image_foreground)
-        mask_foreground = mask_foreground.cpu().numpy()
-
-        image_output = scaled_paste(
-            image_background[0],
-            image_foreground[0],
-            mask_foreground[0],
-            scale_factor,
-            height_factor,
-        )
-
-        print('DEBUG 1 ', image_output.shape)
-
-        image_output = to_torch_image(image=image_output)
-
-        print('DEBUG 2 ', image_output.shape)
-
-        image_output = image_output.unsqueeze(0)
-
-        print('DEBUG 3 ', image_output.shape)
-
-        return (image_output, )
-
-
-#!/usr/bin/python3
-# mask = cv2.imread('/home/asd/DATASETS/BG_SWAP_HACK_TEST/FOREGROUND_MASK.png',
-#                   cv2.IMREAD_GRAYSCALE)
-
-# mask = mask.astype(dtype=np.float32) / 255.0
-
-# image_background = scaled_paste(
-#     image_background=cv2.imread(
-#         '/home/asd/DATASETS/BG_SWAP_HACK_TEST/BACKGROUND_DEPTH.png',
-#         cv2.IMREAD_COLOR),
-#     image_foreground=cv2.imread(
-#         '/home/asd/DATASETS/BG_SWAP_HACK_TEST/FOREGROUND_DEPTH.png',
-#         cv2.IMREAD_COLOR),
-#     mask_foreground=mask,
-#     scale_factor=2,
-#     height_factor=1.05,
-# )
-
-# cv2.imwrite('tmp.png', image_background)
-
-NODE_CLASS_MAPPINGS = {
-    'main_scaled_paste': main_scaled_paste,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    'main_scaled_paste': 'main_scaled_paste',
-}

simple_bg_swap.py

@@ -1,610 +0,0 @@
-#!/usr/bin/python3
-import cv2
-import math
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-
-#!/usr/bin/python3
-def from_torch_image(image):
-    image = image.cpu().numpy() * 255.0
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    return image
-
-
-def to_torch_image(image):
-    image = image.astype(dtype=np.float32)
-    image /= 255.0
-    image = torch.from_numpy(image)
-    return image
-
-
-def do_custom_threshhold(image, value):
-    image = image.astype(dtype=np.float64)
-    image = 255 * (image - value) / (255 - value)
-    image = np.clip(image, 0, 255)
-    image = image.astype(dtype=np.uint8)
-    return image
-
-
-def scaled_paste(
-    image_background,
-    image_foreground,
-    mask_foreground,
-    scale_factor=1.2,
-    height_factor=1.05,
-):
-
-    height = image_foreground.shape[0] * height_factor
-
-    max_0 = max(image_background.shape[0], height)
-    max_1 = max(image_background.shape[1], image_foreground.shape[1])
-
-    ratio_0 = max_0 / image_background.shape[0]
-    ratio_1 = max_1 / image_background.shape[1]
-    ratio_max = max(ratio_0, ratio_1) * scale_factor
-
-    size_0 = int(image_background.shape[0] * ratio_max) + 1
-    size_1 = int(image_background.shape[1] * ratio_max) + 1
-
-    image_background = cv2.resize(image_background, (size_1, size_0),
-                                  cv2.INTER_CUBIC)
-
-    end_0 = int(image_background.shape[0])
-    begin_0 = int(end_0 - height)
-    end_0 = int(begin_0 + image_foreground.shape[0])
-
-    end_1 = image_background.shape[1]
-    begin_1 = end_1 - image_foreground.shape[1]
-    begin_1 = int(begin_1 / 2)
-    end_1 = int(begin_1 + image_foreground.shape[1])
-
-    image_reference = image_background[begin_0:end_0, begin_1:end_1, :]
-
-    for i in range(3):
-        image_reference[:, :,
-                        i] = (mask_foreground * image_foreground[:, :, i]) + (
-                            (1 - mask_foreground) * image_reference[:, :, i])
-
-    return image_background
-
-
-def do_bg_swap(
-    bkg_image,
-    subject_image,
-    mask_image,
-    threshhold_hist,
-    scale_factor=1.2,
-    height_factor=1.05,
-):
-
-    blank_subject_image = np.zeros(subject_image.shape, dtype=np.uint8)
-    blank_subject_image += 255
-
-    blank_background_image = np.zeros(bkg_image.shape, dtype=np.uint8)
-
-    blank_subject_mask = np.zeros(
-        (subject_image.shape[0], subject_image.shape[1]), dtype=np.float64)
-
-    blank_subject_mask += 1
-
-    mask_image_3channel = subject_image.copy()
-    for i in range(3):
-        mask_image_3channel[:, :, i] = mask_image
-
-    mask_image = mask_image.astype(dtype=np.float64)
-    mask_image /= 255.0
-
-    result_image = scaled_paste(
-        image_background=bkg_image,
-        image_foreground=subject_image,
-        mask_foreground=mask_image,
-        scale_factor=scale_factor,
-        height_factor=height_factor,
-    )
-
-    luminosity_image = scaled_paste(
-        image_background=blank_background_image + 255,
-        image_foreground=subject_image,
-        mask_foreground=blank_subject_mask,
-        scale_factor=scale_factor,
-        height_factor=height_factor,
-    )
-
-    final_mask = scaled_paste(
-        image_background=blank_background_image,
-        image_foreground=mask_image_3channel,
-        mask_foreground=mask_image,
-        scale_factor=scale_factor,
-        height_factor=height_factor,
-    )
-
-    result_image_lab = cv2.cvtColor(src=result_image, code=cv2.COLOR_RGB2LAB)
-
-    luminosity_image_lab = cv2.cvtColor(src=luminosity_image,
-                                        code=cv2.COLOR_RGB2LAB)[:, :, 0]
-
-    luminosity_image_lab_flip = 255 - luminosity_image_lab
-
-    luminosity_image_lab_flip = do_custom_threshhold(
-        image=luminosity_image_lab_flip, value=threshhold_hist)
-
-    luminosity_image_lab_flip_full = luminosity_image_lab_flip.copy()
-
-    luminosity_image_lab_flip *= 1 - (final_mask[:, :, 0]
-                                      > 127.5).astype(dtype=np.uint8)
-
-    for i in range(3):
-        result_image[:, :,
-                     i] = (result_image[:, :, i] *
-                           (1 - (luminosity_image_lab_flip / 255.0))).astype(
-                               dtype=np.uint8)
-
-    return (result_image, luminosity_image_lab_flip_full)
-
-
-def find_threshold(image_input, threshold=0.0001):
-
-    image_input_L = cv2.cvtColor(image_input, cv2.COLOR_RGB2LAB)[:, :,
-                                                                 0].flatten()
-    image_input_L = 255 - image_input_L
-
-    hist = np.histogram(image_input_L, range(0, 256, 1))
-
-    values = hist[0]
-    values = values.astype(dtype=np.float64)
-    values /= len(image_input_L)
-
-    for i in range(0, values.shape[0], 1):
-
-        lhd = 0
-        rhd = 0
-
-        if i > 0:
-            lhd = values[i] - values[i - 1]
-
-        if i < values.shape[0] - 1:
-            rhd = values[i + 1] - values[i]
-
-        print(lhd, rhd)
-
-        if max(lhd, rhd) > threshold:
-            return i
-
-
-def get_mu_sigma(array_input, mask_input):
-
-    array_input = array_input.astype(dtype=np.float32).flatten()
-    mask_input = mask_input.astype(dtype=np.float32).flatten()
-
-    sum = np.sum(mask_input)
-    mean = np.sum(array_input * mask_input) / sum
-
-    array_input -= mean
-    array_input *= mask_input
-    sigma = math.sqrt(np.sum(np.square(array_input)) / sum)
-
-    return mean, sigma
-
-
-def renormalize_array_main(array_input, mask_input, mu, sigma):
-    array_input_original = array_input.copy()
-    in_mu, in_sigma = get_mu_sigma(array_input, mask_input)
-    array_input = (((array_input - in_mu) / in_sigma) * sigma) + mu
-
-    array_input_original = (array_input_original *
-                            (1 - mask_input)) + (array_input * mask_input)
-
-    return array_input_original
-
-
-#!/usr/bin/python3
-class simple_bg_swap:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "bkg_image": ("IMAGE", ),
-                "subject_image": ("IMAGE", ),
-                "subject_mask": ("MASK", ),
-                "threshhold_hist": (
-                    "INT",
-                    {
-                        "default": 150,
-                        "min": 0,  #Minimum value
-                        "max": 255,  #Maximum value
-                        "step": 1,  #Slider's step
-                        "display":
-                        "number"  # Cosmetic only: display as "number" or "slider"
-                    }),
-                "scale_factor": (
-                    "FLOAT",
-                    {
-                        "default": 1.2,
-                        "min": 0.0,
-                        "max": 10.0,
-                        "step": 0.01,
-                        "round":
-                        0.001,  #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                        "display": "number"
-                    }),
-                "height_factor": (
-                    "FLOAT",
-                    {
-                        "default": 1.05,
-                        "min": 1.0,
-                        "max": 8.0,
-                        "step": 0.01,
-                        "round":
-                        0.001,  #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                        "display": "number"
-                    }),
-            },
-        }
-
-    RETURN_TYPES = (
-        "IMAGE",
-        "MASK",
-    )
-
-    RETURN_NAMES = (
-        "output bg swapped image",
-        "shadow layer",
-    )
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(
-        self,
-        bkg_image,
-        subject_image,
-        subject_mask,
-        threshhold_hist,
-        scale_factor,
-        height_factor,
-    ):
-
-        mask_image = subject_mask
-
-        bkg_image = from_torch_image(image=bkg_image)
-        subject_image = from_torch_image(image=subject_image)
-        mask_image = from_torch_image(image=mask_image)
-
-        batch_size = bkg_image.shape[0]
-
-        ret = []
-        ret_lum = []
-
-        if (subject_image.shape[0] == batch_size) and (mask_image.shape[0]
-                                                       == batch_size):
-
-            for i in range(batch_size):
-
-                result, luminosity = do_bg_swap(
-                    bkg_image[i],
-                    subject_image[i],
-                    mask_image[i],
-                    threshhold_hist,
-                    scale_factor,
-                    height_factor,
-                )
-
-                result = to_torch_image(result)
-                result = result.unsqueeze(0)
-                ret.append(result)
-
-                luminosity = to_torch_image(luminosity)
-                luminosity = luminosity.unsqueeze(0)
-                ret_lum.append(luminosity)
-
-        else:
-
-            print(
-                'input format is not correct, got different batch sizes for each input image'
-            )
-
-        ret = torch.cat(ret, dim=0)
-        ret_lum = torch.cat(ret_lum, dim=0)
-
-        return (
-            ret,
-            ret_lum,
-        )
-
-
-class get_threshold_for_bg_swap:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "subject_image": ("IMAGE", ),
-                "gradient_threshold": (
-                    "FLOAT",
-                    {
-                        "default": 0.0001,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.00001,
-                        "round":
-                        0.000001,  #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                        "display": "number"
-                    }),
-            },
-        }
-
-    RETURN_TYPES = ("INT", )
-    RETURN_NAMES = ("output histogram threshold", )
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(
-        self,
-        subject_image,
-        gradient_threshold,
-    ):
-
-        subject_image = from_torch_image(image=subject_image)
-        return (find_threshold(subject_image[0],
-                               threshold=gradient_threshold), )
-
-
-class RGB_2_LAB:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_RGB_image": ("IMAGE", ),
-            },
-        }
-
-    RETURN_TYPES = ("MASK", "MASK", "MASK")
-    RETURN_NAMES = ("L", "A", "B")
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(self, input_RGB_image):
-        print('input_RGB_image.shape', input_RGB_image.shape)
-        input_RGB_image = from_torch_image(image=input_RGB_image)
-        ret_L = []
-        ret_A = []
-        ret_B = []
-        for i in range(input_RGB_image.shape[0]):
-            tmp = cv2.cvtColor(input_RGB_image[i], cv2.COLOR_RGB2LAB)
-            ret_L.append(to_torch_image(image=tmp[:, :, 0]).unsqueeze(0))
-            ret_A.append(to_torch_image(image=tmp[:, :, 1]).unsqueeze(0))
-            ret_B.append(to_torch_image(image=tmp[:, :, 2]).unsqueeze(0))
-
-        ret_L = torch.cat(ret_L, dim=0)
-        ret_A = torch.cat(ret_A, dim=0)
-        ret_B = torch.cat(ret_B, dim=0)
-
-        print(
-            'LAB output',
-            ret_L.shape,
-            ret_A.shape,
-            ret_B.shape,
-        )
-
-        return (ret_L, ret_A, ret_B)
-
-
-class LAB_2_RGB:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_L": ("MASK", ),
-                "input_A": ("MASK", ),
-                "input_B": ("MASK", ),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", )
-    RETURN_NAMES = ("Output RGB image", )
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(self, input_L, input_A, input_B):
-
-        batch_size = input_L.shape[0]
-        print(input_L.shape, input_A.shape, input_B.shape)
-
-        ret = []
-
-        if (input_A.shape[0] == batch_size) and (input_B.shape[0]
-                                                 == batch_size):
-
-            for i in range(batch_size):
-
-                input_L_NP = from_torch_image(image=input_L[i])
-                input_A_NP = from_torch_image(image=input_A[i])
-                input_B_NP = from_torch_image(image=input_B[i])
-
-                Y_MAX = input_L_NP.shape[0]
-                X_MAX = input_L_NP.shape[1]
-
-                if (input_A_NP.shape[0]
-                        == Y_MAX) and (input_B_NP.shape[0] == Y_MAX) and (
-                            (input_A_NP.shape[1] == X_MAX) and
-                            (input_B_NP.shape[1] == X_MAX)):
-
-                    image = np.zeros((Y_MAX, X_MAX, 3), dtype=np.uint8)
-
-                    image[:, :, 0] = input_L_NP
-                    image[:, :, 1] = input_A_NP
-                    image[:, :, 2] = input_B_NP
-
-                    image = cv2.cvtColor(image, cv2.COLOR_LAB2RGB)
-                    image = to_torch_image(image).unsqueeze(0)
-                    print('image.shape')
-                    print(image.shape)
-                    ret.append(image)
-
-                else:
-
-                    print('Resolution of different layers donot match')
-
-        else:
-
-            print('batch size of different layers donot match')
-
-        ret = torch.cat(ret, dim=0)
-
-        print('ret.shape', ret.shape)
-
-        return (ret, )
-
-
-class get_mean_and_standard_deviation:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_array": ("MASK", ),
-                "input_mask": ("MASK", ),
-            },
-        }
-
-    RETURN_TYPES = (
-        "FLOAT",
-        "FLOAT",
-    )
-
-    RETURN_NAMES = (
-        "Mean",
-        "Standard deviation",
-    )
-
-    FUNCTION = "test"
-    CATEGORY = "TRI3D"
-
-    def test(self, input_array, input_mask):
-
-        input_array = input_array.cpu().numpy()
-        input_mask = input_mask.cpu().numpy()
-
-        mean, sigma = get_mu_sigma(array_input=input_array[0],
-                                   mask_input=input_mask[0])
-
-        return (
-            mean,
-            sigma,
-        )
-
-
-class renormalize_array:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_array": ("MASK", ),
-                "input_mask": ("MASK", ),
-                "input_mean": (
-                    "FLOAT",
-                    {
-                        "default": 1.0,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.001,
-                        "round":
-                        0.000001,  #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                        "display": "number"
-                    }),
-                "input_standard_deviation": (
-                    "FLOAT",
-                    {
-                        "default": 1.0,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.001,
-                        "round":
-                        0.000001,  #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                        "display": "number"
-                    }),
-            },
-        }
-
-    RETURN_TYPES = ("MASK", )
-    RETURN_NAMES = ("Output array as mask", )
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "TRI3D"
-
-    def test(
-        self,
-        input_array,
-        input_mask,
-        input_mean,
-        input_standard_deviation,
-    ):
-
-        batch_size = input_array.shape[0]
-
-        ret = []
-
-        if input_mask.shape[0] == batch_size:
-
-            for i in range(batch_size):
-
-                tmp = renormalize_array_main(
-                    array_input=input_array[i].cpu().numpy(),
-                    mask_input=input_mask[i].cpu().numpy(),
-                    mu=input_mean,
-                    sigma=input_standard_deviation)
-
-                tmp = torch.from_numpy(tmp)
-                tmp = tmp.unsqueeze(0)
-
-                ret.append(tmp)
-
-        else:
-
-            print('batch size of different layers donot match')
-
-        ret = torch.cat(ret, dim=0)
-
-        return (ret, )

smart_box.py

@@ -1,820 +0,0 @@
-import numpy as np
-import torch
-import json
-
-import cv2
-
-#  {0,  "Nose"},
-# //     {1,  "Neck"},
-# //     {2,  "RShoulder"},
-# //     {3,  "RElbow"},
-# //     {4,  "RWrist"},
-# //     {5,  "LShoulder"},
-# //     {6,  "LElbow"},
-# //     {7,  "LWrist"},
-# //     {8,  "MidHip"},
-# //     {9,  "RHip"},
-# //     {10, "RKnee"},
-# //     {11, "RAnkle"},
-# //     {12, "LHip"},
-# //     {13, "LKnee"},
-# //     {14, "LAnkle"},
-# //     {15, "REye"},
-# //     {16, "LEye"},
-# //     {17, "REar"},
-# //     {18, "LEar"},
-# //     {19, "LBigToe"},
-# //     {20, "LSmallToe"},
-# //     {21, "LHeel"},
-# //     {22, "RBigToe"},
-# //     {23, "RSmallToe"},
-# //     {24, "RHeel"},
-# //     {25, "Background"}
-
-
-class TRI3D_SmartBox:
-    
-    
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "keypoints_json": ("STRING", {"multiline": True}),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def extract_torso_keypoints(self, keypoints):
-        # Indices for torso-related keypoints
-        torso_indices = [8, 9, 10, 11, 12, 13]
-        return [keypoints[i] for i in torso_indices]
-
-
-    def run(self, image, keypoints_json):
-        
-        kp_data = json.loads(open(keypoints_json, 'r').read())
-        original_height, original_width = kp_data['height'], kp_data['width']
-        torso_keypoints = self.extract_torso_keypoints(kp_data['keypoints'])
-
-        # Convert Torch image to OpenCV format
-        cv_image = self.from_torch_image(image)
-        
-        # Remove the batch dimension if present
-        if len(cv_image.shape) == 4:
-            cv_image = cv_image[0]
-
-        # Adjust keypoints to match the image dimensions
-        adjusted_keypoints = self.adjust_keypoints(torso_keypoints, cv_image.shape, original_height, original_width)
-        
-
-        # Fill the area below the hip line
-        filled_image = self.fill_below_hip(cv_image, adjusted_keypoints)
-        
-
-        # Convert back to Torch format
-        torch_image = self.to_torch_image(filled_image)
-
-        # Add the batch dimension back
-        torch_image = torch_image.unsqueeze(0)
-        
-
-        return (torch_image,)
-
-    def adjust_keypoints(self, keypoints, image_shape, original_height, original_width):
-        image_height, image_width = image_shape[:2]
-        scale_x = image_width / original_width
-        scale_y = image_height / original_height
-
-        adjusted_keypoints = [
-            (int(x * scale_x), int(y * scale_y)) for x, y in keypoints
-        ]
-        return adjusted_keypoints
-
-    def fill_below_hip(self, image, keypoints):
-        # Correct the indices for hip keypoints
-        # Assuming indices 8 and 11 are for left and right hips
-        # print(keypoints,"hip keypoints")
-        try:
-            valid_y_coords = [kp[1] for kp in keypoints if kp[1] >= 0]
-            hip_y = min(valid_y_coords) if valid_y_coords else 0
-        except:
-            hip_y = 0
-        
-        if hip_y == 0:
-            return image
-
-        # Find the bounding box of the mask below the hip line
-        mask = image[:, :, 0]  # Assuming single-channel mask
-        below_hip = mask[hip_y:, :]
-        contours, _ = cv2.findContours(below_hip, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        cnt = 0
-        
-        for contour in contours:
-            x, y, w, h = cv2.boundingRect(contour)
-            # print(cnt, x,y,w,h, cv2.contourArea(contour), "cnt,x,y,w,h,area")
-            cnt+=1
-        #     cv2.rectangle(image, (x, y + hip_y), (x + w, y + h + hip_y), (255, 255, 255), -1)
-        contours = [contour for contour in contours if cv2.contourArea(contour) > 20]
-
-        if len(contours) == 0:
-            return image
-        # Combine all contours into one
-        all_contours = np.vstack(contours)
-
-        # Calculate a single bounding rectangle for all contours
-        x, y, w, h = cv2.boundingRect(all_contours)
-        # print(x,y,w,h, "x,y,w,h")
-        cv2.rectangle(image, (x, y + hip_y), (x + w, y + h + hip_y), (255, 255, 255), -1)
-
-        return image
-
-    
-class TRI3D_Skip_HeadMask:
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "head_mask": ("IMAGE", ),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def run(self, image, head_mask):
-        # Convert Torch images to OpenCV format
-        cv_image = self.from_torch_image(image)
-        cv_head_mask = self.from_torch_image(head_mask)
-
-        # Remove the batch dimension if present
-        if len(cv_image.shape) == 4:
-            cv_image = cv_image[0]
-        if len(cv_head_mask.shape) == 4:
-            cv_head_mask = cv_head_mask[0]
-
-        # Find the lowest point in the head mask
-        mask = cv_head_mask[:, :, 0]  # Assuming single-channel mask
-        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        lowest_y = 0
-        for contour in contours:
-            for point in contour:
-                x, y = point[0]
-                if y > lowest_y:
-                    lowest_y = y
-
-        # Black out everything above the lowest point
-        cv_image[:lowest_y, :] = 0
-
-        # Convert back to Torch format
-        torch_image = self.to_torch_image(cv_image)
-
-        # Add the batch dimension back
-        torch_image = torch_image.unsqueeze(0)
-
-        return (torch_image,)
-
-
-    
-class TRI3D_Skip_HeadMask_AddNeck:
-
-    def adjust_keypoints(self, keypoints, image_shape, original_height, original_width):
-        image_height, image_width = image_shape[:2]
-        scale_x = image_width / original_width
-        scale_y = image_height / original_height
-
-        adjusted_keypoints = [
-            (int(x * scale_x), int(y * scale_y)) for x, y in keypoints
-        ]
-        return adjusted_keypoints
-
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-
-
-    def extract_neck_keypoint(self, keypoints):
-        # Indices for torso-related keypoints
-        neck_indices = [1]
-        return [keypoints[i] for i in neck_indices]
-
-    def extract_ear_keypoints(self, keypoints):
-        # Indices for ear keypoints (17=right ear, 18=left ear)
-        ear_indices = [17, 18]
-        return [keypoints[i] for i in ear_indices]
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "head_mask": ("IMAGE", ),
-                "keypoints_json": ("STRING", {"multiline": True}),
-                "ratio_aggression": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}),
-                "neck_width_factor": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.5, "step": 0.05}),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def run(self, image, head_mask, keypoints_json, ratio_aggression, neck_width_factor):
-        # Convert Torch images to OpenCV format
-        cv_image = self.from_torch_image(image)
-        cv_head_mask = self.from_torch_image(head_mask)
-
-        # Remove the batch dimension if present
-        if len(cv_image.shape) == 4:
-            cv_image = cv_image[0]
-        if len(cv_head_mask.shape) == 4:
-            cv_head_mask = cv_head_mask[0]
-
-        kp_data = json.loads(open(keypoints_json, 'r').read())
-        original_height, original_width = kp_data['height'], kp_data['width']
-        neck_keypoints = self.extract_neck_keypoint(kp_data['keypoints'])
-        
-        # Make a copy of the original image
-        result_image = cv_image.copy()
-        
-        # Adjust keypoints to match the image dimensions
-        adjusted_neck_keypoints = self.adjust_keypoints(neck_keypoints, cv_image.shape, original_height, original_width)
-        
-        # Find the lowest point and face dimensions in the head mask
-        mask = cv_head_mask[:, :, 0]  # Assuming single-channel mask
-        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
-        # Find the chin point (lowest point) and calculate face properties
-        lowest_y = 0
-        face_center_x = cv_image.shape[1] // 2  # Default to center of image
-        face_width = cv_image.shape[1] // 3     # Default face width
-        
-        if contours:
-            # Find the lowest point (chin)
-            for contour in contours:
-                for point in contour:
-                    x, y = point[0]
-                    if y > lowest_y:
-                        lowest_y = y
-            
-            # Calculate face bounding box and center of gravity
-            x, y, w, h = cv2.boundingRect(contours[0])
-            face_width = w
-            
-            # Calculate center of gravity of the face mask
-            M = cv2.moments(contours[0])
-            if M["m00"] != 0:
-                face_center_x = int(M["m10"] / M["m00"])
-            else:
-                face_center_x = x + w // 2
-        
-        # Calculate weighted average point between neck and chin
-        neck_y = adjusted_neck_keypoints[0][1]
-        if neck_y <= 0:
-            neck_y = lowest_y
-        average_y = int((neck_y * ratio_aggression + lowest_y * (1 - ratio_aggression)))
-        
-        print(neck_y, lowest_y, "neck_y, lowest_y")
-        print(average_y, "average_y")
-        
-        # ZONE 1: Black out everything above the chin point
-        result_image[:lowest_y, :] = 0
-        
-        # ZONE 2: Create a triangle for the neck area
-        if lowest_y < average_y:  # Only process if there's a gap between chin and average_y
-            # Create a mask for Zone 2
-            zone2_mask = np.zeros_like(cv_image[:,:,0])
-            
-            # Create a triangle with apex at weighted average point and base at chin level
-            # Apply the neck width factor to the face width
-            neck_width = int(face_width * neck_width_factor)
-            triangle_half_width = neck_width // 2
-            
-            # Create polygon points for the triangle
-            triangle_points = np.array([
-                [face_center_x, average_y],  # Apex at weighted average point
-                [face_center_x - triangle_half_width, lowest_y],  # Left base point at chin level
-                [face_center_x + triangle_half_width, lowest_y]   # Right base point at chin level
-            ], dtype=np.int32)
-            
-            # Fill the triangle in the mask
-            cv2.fillPoly(zone2_mask, [triangle_points], 255)
-            
-            # Apply the mask only to the region between chin and weighted average
-            for y in range(lowest_y, average_y):
-                for x in range(cv_image.shape[1]):
-                    if zone2_mask[y, x] > 0:
-                        result_image[y, x] = 0
-        
-        # ZONE 3: Area below weighted average point is left as is
-        # No action needed for this zone
-        
-        # Convert back to Torch format
-        torch_image = self.to_torch_image(result_image)
-
-        # Add the batch dimension back
-        torch_image = torch_image.unsqueeze(0)
-
-        return (torch_image,)
-
-
-class TRI3D_Image_extend:
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-    
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "face_mask": ("IMAGE", ),
-                "image": ("IMAGE", ),
-                "ratio": ("FLOAT", {"default": 1.5, "min": 1.2, "max": 2, "step": 0.01}),
-            },
-        }
-    
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", "IMAGE", )
-    RETURN_NAMES = ("image", "mask_image", )
-    CATEGORY = "TRI3D"
-
-    def run(self, face_mask, image, ratio):
-        cv_face_mask = self.from_torch_image(face_mask)
-        cv_image = self.from_torch_image(image)
-
-        # Remove the batch dimension if present
-        if len(cv_image.shape) == 4:
-            cv_image = cv_image[0]
-        if len(cv_face_mask.shape) == 4:
-            cv_face_mask = cv_face_mask[0]
-        mask = cv_face_mask[:, :, 0]  # Assuming single-channel mask
-        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        lowest_y = 0
-        highest_y = cv_image.shape[0]
-        for contour in contours:
-            for point in contour:
-                x, y = point[0]
-                if y > lowest_y:
-                    lowest_y = y
-                if y < highest_y:
-                    highest_y = y
-
-        y_below_face = cv_image.shape[0] - lowest_y
-        y_face = lowest_y-highest_y
-
-        # Only extend if the space below face is less than 1.5 times face height
-        target_below_face = int(y_face * ratio)
-        # print("y_face", y_face)
-        # print("lowest_y", lowest_y)
-        # print("highest_y", highest_y)
-        # print("target_below_face", target_below_face)
-        # print("y_below_face", y_below_face)
-
-        original_height = cv_image.shape[0]
-        original_width = cv_image.shape[1]
-        
-        if y_below_face < target_below_face:
-            y_extend = target_below_face - y_below_face
-            
-            # Calculate how much to extend horizontally to maintain aspect ratio
-            new_height = original_height + y_extend
-            new_width = int(original_width * (new_height / original_height))
-            x_extend = new_width - original_width
-            x_extend_left = x_extend // 2
-            x_extend_right = x_extend - x_extend_left
-            
-            # Extend the image in all necessary directions
-            cv_image = cv2.copyMakeBorder(
-                cv_image, 
-                0, y_extend,                    # top, bottom
-                x_extend_left, x_extend_right,  # left, right
-                cv2.BORDER_CONSTANT, 
-                value=[0, 0, 0]
-            )
-            
-            # Create extension mask
-            extension_mask = np.zeros_like(cv_image)
-            # Make extended portions white
-            extension_mask[original_height:, :] = 255  # bottom extension
-            extension_mask[:, :x_extend_left] = 255    # left extension
-            extension_mask[:, -x_extend_right:] = 255  # right extension
-
-        else:
-            extension_mask = np.zeros_like(cv_image)
-
-        # Convert both images back to torch format
-        torch_image = self.to_torch_image(cv_image)
-        torch_mask = self.to_torch_image(extension_mask)
-        
-        # Add batch dimension to both
-        torch_image = torch_image.unsqueeze(0)
-        torch_mask = torch_mask.unsqueeze(0)
-        
-        return (torch_image, torch_mask)
-
-
-class TRI3D_Smart_Depth:
-    
-    
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "keypoints_json": ("STRING", {"multiline": True}),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    CATEGORY = "TRI3D"
-
-    def extract_torso_keypoints(self, keypoints):
-        # Indices for torso-related keypoints
-        torso_indices = [8, 9, 10, 11, 12, 13]
-        return [keypoints[i] for i in torso_indices]
-
-
-    def run(self, image, keypoints_json):
-        
-        kp_data = json.loads(open(keypoints_json, 'r').read())
-        original_height, original_width = kp_data['height'], kp_data['width']
-        torso_keypoints = self.extract_torso_keypoints(kp_data['keypoints'])
-
-        # Convert Torch image to OpenCV format
-        cv_image = self.from_torch_image(image)
-        
-        # Remove the batch dimension if present
-        if len(cv_image.shape) == 4:
-            cv_image = cv_image[0]
-
-        # Adjust keypoints to match the image dimensions
-        adjusted_keypoints = self.adjust_keypoints(torso_keypoints, cv_image.shape, original_height, original_width)
-        
-
-        # Fill the area below the hip line
-        filled_image = self.fill_below_hip(cv_image, adjusted_keypoints)
-        
-
-        # Convert back to Torch format
-        torch_image = self.to_torch_image(filled_image)
-
-        # Add the batch dimension back
-        torch_image = torch_image.unsqueeze(0)
-        
-
-        return (torch_image,)
-
-    def adjust_keypoints(self, keypoints, image_shape, original_height, original_width):
-        image_height, image_width = image_shape[:2]
-        scale_x = image_width / original_width
-        scale_y = image_height / original_height
-
-        adjusted_keypoints = [
-            (int(x * scale_x), int(y * scale_y)) for x, y in keypoints
-        ]
-        return adjusted_keypoints
-
-    def fill_below_hip(self, image, keypoints):
-        # Correct the indices for hip keypoints
-        # Assuming indices 8 and 11 are for left and right hips
-        # print(keypoints,"hip keypoints")
-        try:
-            valid_y_coords = [kp[1] for kp in keypoints if kp[1] >= 0]
-            hip_y = min(valid_y_coords) if valid_y_coords else 0
-        except:
-            hip_y = 0
-        
-        if hip_y == 0:
-            return image
-
-        # Find the bounding box of the mask below the hip line
-        mask = image[:, :, 0]  # Assuming single-channel mask
-        below_hip = mask[hip_y:, :]
-        contours, _ = cv2.findContours(below_hip, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        cnt = 0
-        
-        for contour in contours:
-            x, y, w, h = cv2.boundingRect(contour)
-            # print(cnt, x,y,w,h, cv2.contourArea(contour), "cnt,x,y,w,h,area")
-            cnt+=1
-        #     cv2.rectangle(image, (x, y + hip_y), (x + w, y + h + hip_y), (255, 255, 255), -1)
-        contours = [contour for contour in contours if cv2.contourArea(contour) > 0]
-
-        if len(contours) == 0:
-            return image
-        # Combine all contours into one
-        all_contours = np.vstack(contours)
-
-        # Calculate a single bounding rectangle for all contours
-        x, y, w, h = cv2.boundingRect(all_contours)
-        # print(x,y,w,h, "x,y,w,h")
-        cv2.rectangle(image, (x, y + hip_y), (x + w, y + h + hip_y), (0, 0, 0), -1)
-
-        return image
-
-
-class TRI3D_NarrowfyImage:
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-    
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "mask": ("IMAGE", ),
-                "aspect_ratio": ("FLOAT", {"default": 0.33, "min": 0.25, "max": 1, "step": 0.01}),
-                "border_margin": ("INT", {"default": 15, "min": 10, "max": 100, "step": 1}),
-            },
-        }
-    
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", "IMAGE", "INT", "INT",)
-    RETURN_NAMES = ("cropped_image", "cropped_mask", "cropped_width", "cropped_height",)
-    CATEGORY = "TRI3D"
-
-    def run(self, image, mask, aspect_ratio, border_margin):
-        # Convert to CV format and remove batch dimension
-        cv_image = self.from_torch_image(image)[0]
-        cv_mask = self.from_torch_image(mask)[0]
-        
-        # Find bounding box of the mask
-        mask_channel = cv_mask[:, :, 0]
-        contours, _ = cv2.findContours(mask_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
-        if not contours:
-            return image, mask, aspect_ratio
-            
-        # Filter contours by area
-        significant_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 100]
-        
-        if not significant_contours:
-            return image, mask, aspect_ratio
-            
-        # Get combined bounding box for all significant contours
-        x_min = float('inf')
-        y_min = float('inf')
-        x_max = 0
-        y_max = 0
-        
-        for contour in significant_contours:
-            x, y, w, h = cv2.boundingRect(contour)
-            x_min = min(x_min, x)
-            y_min = min(y_min, y)
-            x_max = max(x_max, x + w)
-            y_max = max(y_max, y + h)
-        
-        # Calculate final width and height with margin
-        margin = border_margin
-        x = max(0, x_min - margin)  # Ensure we don't go below 0
-        y = max(0, y_min - margin)
-        w = min(cv_image.shape[1] - x, (x_max - x_min) + 2 * margin)  # Ensure we don't exceed image width
-        h = min(cv_image.shape[0] - y, (y_max - y_min) + 2 * margin)  # Ensure we don't exceed image height
-        
-        # Crop both image and mask to bounding box
-        cropped_image = cv_image[y:y+h, x:x+w]
-        cropped_mask = cv_mask[y:y+h, x:x+w]
-        
-        # Calculate required height for aspect ratio 1/3
-        min_height = w * 1/aspect_ratio
-        if h < min_height:
-            height_extend = min_height - h
-            
-            # Extend image with black pixels
-            extended_image = cv2.copyMakeBorder(
-                cropped_image,
-                0, int(height_extend),  # top, bottom
-                0, 0,                   # left, right
-                cv2.BORDER_CONSTANT,
-                value=[0, 0, 0]
-            )
-            
-            # Create mask with white pixels only in extended region
-            extended_mask = cv2.copyMakeBorder(
-                np.zeros_like(cropped_mask),  # Start with black base
-                0, int(height_extend),  # top, bottom
-                0, 0,                   # left, right
-                cv2.BORDER_CONSTANT,
-                value=[255, 255, 255]   # White extension
-            )
-            
-            cropped_image = extended_image
-            cropped_mask = extended_mask
-        
-        # Convert back to torch format and add batch dimension
-        torch_image = self.to_torch_image(cropped_image).unsqueeze(0)
-        torch_mask = self.to_torch_image(cropped_mask).unsqueeze(0)
-        
-        return (torch_image, torch_mask,w,h)
-
-
-
-
-class TRI3D_CropAndExtend:
-    def from_torch_image(self, image):
-        image = image.cpu().numpy() * 255.0
-        image = np.clip(image, 0, 255).astype(np.uint8)
-        return image
-
-    def to_torch_image(self, image):
-        image = image.astype(dtype=np.float32)
-        image /= 255.0
-        image = torch.from_numpy(image)
-        return image
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "garment_image": ("IMAGE",),
-                "garment_mask": ("IMAGE",),
-                "human_image": ("IMAGE",),
-                "human_mask": ("IMAGE",),
-                "margin": ("INT", {"default": 10, "min": 0, "max": 50}),
-            },
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "INT", "INT",)
-    RETURN_NAMES = ("cropped_garment", "cropped_garment_mask", "cropped_human", "cropped_human_mask", "cropped_width", "cropped_height",)
-    
-    def run(self, garment_image, garment_mask, human_image, human_mask, margin):
-        # Convert to CV format and remove batch dimension
-        cv_garment = self.from_torch_image(garment_image)[0]
-        cv_garment_mask = self.from_torch_image(garment_mask)[0]
-        cv_human = self.from_torch_image(human_image)[0]
-        cv_human_mask = self.from_torch_image(human_mask)[0]
-        
-        # Process garment
-        mask_channel = cv_garment_mask[:, :, 0]
-        contours, _ = cv2.findContours(mask_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
-        if not contours:
-            return garment_image, garment_mask, human_image, human_mask, cv_garment.shape[1], cv_garment.shape[0]
-            
-        # Get bounding box with margin
-        x, y, w, h = cv2.boundingRect(contours[0])
-        x = max(0, x - margin)
-        y = max(0, y - margin)
-        w = min(cv_garment.shape[1] - x, w + 2 * margin)
-        h = min(cv_garment.shape[0] - y, h + 2 * margin)
-        
-        # Store the cropped dimensions before extension
-        cropped_width = w
-        cropped_height = h
-        
-        # Crop garment and its mask
-        cropped_garment = cv_garment[y:y+h, x:x+w]
-        cropped_garment_mask = cv_garment_mask[y:y+h, x:x+w]
-        
-        # Calculate required height for aspect ratio 1/3
-        min_height = w * 3
-        if h < min_height:
-            height_extend = min_height - h
-            
-            # Extend garment image and mask
-            extended_garment = cv2.copyMakeBorder(
-                cropped_garment,
-                0, int(height_extend),
-                0, 0,
-                cv2.BORDER_CONSTANT,
-                value=[0, 0, 0]
-            )
-            
-            extended_garment_mask = cv2.copyMakeBorder(
-                cropped_garment_mask,
-                0, int(height_extend),
-                0, 0,
-                cv2.BORDER_CONSTANT,
-                value=[255, 255, 255]
-            )
-            
-            cropped_garment = extended_garment
-            cropped_garment_mask = extended_garment_mask
-        
-        # Process human image similarly
-        mask_channel = cv_human_mask[:, :, 0]
-        contours, _ = cv2.findContours(mask_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
-        if contours:
-            x, y, w, h = cv2.boundingRect(contours[0])
-            x = max(0, x - margin)
-            y = max(0, y - margin)
-            w = min(cv_human.shape[1] - x, w + 2 * margin)
-            h = min(cv_human.shape[0] - y, h + 2 * margin)
-            
-            cropped_human = cv_human[y:y+h, x:x+w]
-            cropped_human_mask = cv_human_mask[y:y+h, x:x+w]
-            
-            min_height = w * 3
-            if h < min_height:
-                height_extend = min_height - h
-                
-                extended_human = cv2.copyMakeBorder(
-                    cropped_human,
-                    0, int(height_extend),
-                    0, 0,
-                    cv2.BORDER_CONSTANT,
-                    value=[0, 0, 0]
-                )
-                
-                extended_human_mask = cv2.copyMakeBorder(
-                    cropped_human_mask,
-                    0, int(height_extend),
-                    0, 0,
-                    cv2.BORDER_CONSTANT,
-                    value=[255, 255, 255]
-                )
-                
-                cropped_human = extended_human
-                cropped_human_mask = extended_human_mask
-        
-        # Convert back to torch format and add batch dimension
-        torch_garment = self.to_torch_image(cropped_garment).unsqueeze(0)
-        torch_garment_mask = self.to_torch_image(cropped_garment_mask).unsqueeze(0)
-        torch_human = self.to_torch_image(cropped_human).unsqueeze(0)
-        torch_human_mask = self.to_torch_image(cropped_human_mask).unsqueeze(0)
-        
-        return (torch_garment, torch_garment_mask, torch_human, torch_human_mask, cropped_width, cropped_height)

utility_nodes.py

@@ -1,409 +0,0 @@
-import torch, cv2, json
-import numpy as np
-
-
-def from_torch_image(image):
-    image = image.cpu().numpy() * 255.0
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    return image
-
-
-def to_torch_image(image):
-    image = image.astype(dtype=np.float32)
-    image /= 255.0
-    image = torch.from_numpy(image)
-    return image
-
-class TRI3D_clean_mask():
-
-    """For the given mask and threshold area, remove all patches in the mask with area smaller than threshold"""
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "masks": ("MASK", ),
-                "threshold":("FLOAT",{"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01})
-            }
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("MASK", "BOOL")
-    RETURN_NAMES = ("mask", "cleaned")
-    CATEGORY = "TRI3D"
-
-    def run(self, masks, threshold):
-        batch_results = []
-        for mask in masks:
-            mask = from_torch_image(mask)
-            mask = np.where(mask < 127, 0, 255).astype(np.uint8)
-            h,w = mask.shape[:2]
-
-            total_area = h*w
-            # num_labels, labels = cv2.connectedComponents(mask)
-            region_mask = np.zeros_like(mask)
-
-            # for label in range(1, num_labels):  
-            #     area_percent = (np.sum(labels == label)/ total_area) * 100
-            #     if area_percent < threshold:
-            #         continue
-            #     region_mask[labels == label] = 255
-            less_than_threshold = True
-            area_percent = (np.sum(mask == 255)/ total_area) * 100
-            if area_percent > threshold:
-                region_mask[mask == 255] = 255
-                less_than_threshold = False
-            region_mask = to_torch_image(region_mask)
-            batch_results.append(region_mask.squeeze(0))
-
-        batch_results = torch.stack(batch_results)
-        return (batch_results, less_than_threshold)
-        
-
-class TRI3D_extract_pose_part():
-    """
-        For the given pose, extract region around body parts, region can be defined by % of image size  
-    """
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "pose_json": ("STRING",{"default" : "dwpose/keypoints/input.json"}),
-                "width_pad": ("FLOAT",{"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                "height_pad": ("FLOAT",{"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                "shoulders":("BOOLEAN", {
-                    "default": False
-                })
-            }
-        }
-
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", "STRING")
-    RETURN_NAMES = ("image", "coords")
-    CATEGORY = "TRI3D"
-
-    def get_frame_coords(self,point1, point2):
-        x1, y1 = point1
-        x2, y2 = point2
-
-        xmin, xmax, ymin, ymax = min(x1, x2), max(x1, x2), min(y1, y2), max(y1, y2)
-
-        for i in [xmin, xmax, ymin, ymax]:
-            if i < 0: 
-                return None
-
-        return [xmin, xmax, ymin, ymax]
-
-    def run(self, image, pose_json, width_pad, height_pad, shoulders):
-        """
-        image : input image
-        width_pad: % of image width you want to apply on both size of pose body part
-        height_pad: % of image width you want to apply on both size of pose body part
-        rest of them are body parts
-        """
-
-        image = from_torch_image(image[0])
-        batch_result = []
-        input_pose = json.load(open(pose_json))
-        keypoints = input_pose['keypoints']
-
-        og_h, og_w = image.shape[:2]
-        ph, pw = [input_pose['height'], input_pose['width']]
-
-        for i,point in enumerate(keypoints):
-            x,y = point
-            y = int((y/ph)*og_h)
-            x = int((x/pw)*og_w)
-            keypoints[i] = [x, y]
-
-        width_offset = int(og_w * (width_pad) / 100)
-        height_offset = int(og_h * (height_pad) / 100)
-
-        xmin, xmax, ymin, ymax = [0, og_w, 0, og_h]
-
-        part_to_coords = {
-            "shoulders":self.get_frame_coords(keypoints[2], keypoints[5])
-        }
-
-        if shoulders:
-            print(part_to_coords["shoulders"])
-            if part_to_coords["shoulders"] != None:
-                new_xmin, new_xmax, new_ymin, new_ymax = part_to_coords["shoulders"]
-
-                xmin, xmax, ymin, ymax = new_xmin, new_xmax, new_ymin, new_ymax
-        
-        xmin = max(0, xmin - width_offset)
-        xmax = min(og_w, xmax + width_offset)
-        ymin = max(0, ymin - height_offset)
-        ymax = min(og_h, ymax + height_offset)
-
-        image = image[ymin:ymax, xmin:xmax, :].astype(np.uint8)
-        image = to_torch_image(image)
-        batch_result.append(image)
-        batch_result = torch.stack(batch_result)
-        print("final_coords", xmin, xmax, ymin, ymax)
-        coords = ",".join([str(xmin), str(xmax), str(ymin), str(ymax)])
-        
-        return batch_result, coords
-
-class TRI3D_position_pose_part():
-    """
-        put back extracted parts on OG image 
-    """
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "og_image": ("IMAGE", ),
-                "extracted_image": ("IMAGE", ),
-                "coords": ("STRING",{"default" : "xmin, xmax, ymin, ymax"}),
-            }
-        }
-
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE", )
-    RETURN_NAMES = ("image", )
-    CATEGORY = "TRI3D"
-
-    def run(self, og_image, extracted_image, coords):
-
-        batch_result = []
-        og_image = from_torch_image(og_image[0])
-        extracted_image = from_torch_image(extracted_image[0])
-        
-        xmin, xmax, ymin, ymax = [int(i) for i in coords.split(",")]
-
-        og_image[ymin:ymax, xmin:xmax, :] = extracted_image
-
-        og_image = to_torch_image(og_image).unsqueeze(0)
-        batch_result.append(og_image)
-        batch_result = torch.stack(batch_result)
-        return batch_result
-
-
-class TRI3D_fill_mask():
-    """
-        fill mask with the neighbouring pixels
-    """
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "mask": ("MASK", ),
-                "negative_mask": ("MASK", ),
-                "offset":("FLOAT",{"default": 1, "min": 0.0, "max": 100.0, "step": 0.01})
-            }
-        }
-
-    FUNCTION = "run"
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    CATEGORY = "TRI3D"
-
-    def run(self, image, mask, negative_mask, offset):
-        image = from_torch_image(image[0])
-
-        mask = mask[0].cpu().numpy()
-        mask = np.expand_dims(mask, -1)
-        mh, mw, _ = mask.shape
-
-        inverse_mask = np.ones_like(mask) - mask
-        
-        negative_mask = negative_mask[0].cpu().numpy()
-        indices = np.where(mask > 0)
-
-        offset = offset / 100
-        
-        source = image.copy()
-
-        for y,x in zip(indices[0],indices[1]):
-            x_off = min(mw-1, int(x + offset * mw))
-            if negative_mask[y][x_off] == 0:         #check if pixles on right are outside body
-                source[y][x] = image[y][x_off]
-                
-            else:
-                x_off = max(0, int(x - offset * mw))       #check if pixles on left are outside body
-                if negative_mask[y][x_off] == 0:
-                    source[y][x] = image[y][x_off]
-                else:
-                    y_off = max(0, int(y - offset * mh))
-                    if negative_mask[y_off][x] == 0:            #check if pixles on top are outside body
-                        source[y][x] = image[y_off][x]
-
-                    else:
-                        y_off = min(mh-1, int(y + offset * mh))
-                        if negative_mask[y_off][x] == 0:            #check if pixles on bottom are outside body
-                            source[y][x] = image[y_off][x]
-
-        image = mask * source + inverse_mask * image
-        image = to_torch_image(image).unsqueeze(0)
-
-        return (image,)
-
-class TRI3D_is_only_trouser:
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "pose_json_file": ("STRING", {
-                    "default": "dwpose/keypoints"
-                })
-            }
-        }
-
-    RETURN_TYPES = ("BOOLEAN", )
-    FUNCTION = "main"
-    CATEGORY = "TRI3D"
-
-    def main(self, pose_json_file):
-        pose = json.load(open(pose_json_file))
-        height = pose['height']
-        width = pose['width']
-        keypoints = pose['keypoints']
-
-        points = [0,14,15,16,17,2,1,5]
-        point_to_part = {0:'nose',14:"left eye",15:"right eye",16:"left ear",17:"right ear",2:"left shoulder",1:"neck",5:"right shoulder"}
-        all_negative = True          #if all face and shoulder points are negative means it is a bottom shot
-        for point in points:
-            x,y = keypoints[point]
-            if x > 0 and y > 0:
-                all_negative = False    
-                print(f"{point_to_part[point]} exist")
-        return (all_negative,)
-
-class TRI3D_extract_facer_mask:
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "background": ("BOOLEAN", {
-                    "default": False
-                }),
-                'hair':("BOOLEAN", {
-                    "default": False
-                }),
-                'lower_lip':("BOOLEAN", {
-                    "default": False
-                }),
-                'inner_mouth':("BOOLEAN", {
-                    "default": False
-                }),
-                'upper_lip':("BOOLEAN", {
-                    "default": False
-                }),
-                'nose':("BOOLEAN", {
-                    "default": False
-                }),
-                'left_eyebrow':("BOOLEAN", {
-                    "default": False
-                }),
-                'right_eyebrow':("BOOLEAN", {
-                    "default": False
-                }),
-                'left_eye':("BOOLEAN", {
-                    "default": False
-                }),
-                'right_eye':("BOOLEAN", {
-                    "default": False
-                }),
-                'face':("BOOLEAN", {
-                    "default": False
-                })
-            }
-        }
-
-    RETURN_TYPES = ("MASK", )
-    FUNCTION = "main"
-    CATEGORY = "TRI3D"
-
-    def main(self, image, background, hair, lower_lip, inner_mouth, upper_lip, nose, left_eyebrow, right_eyebrow, left_eye, right_eye, face):
-
-        image = from_torch_image(image[0])
-        h,w,_ = image.shape
-        
-        mask = np.zeros_like(image)
-        
-        label_to_rgb = {'background':[0,0,0], 'face':[0,138,255], 'right_eye':[180, 255, 0], 'left_eye':[42, 255, 0], 'right_eyebrow':[0, 255, 96],
-                'left_eyebrow':[0,255,234], 'nose':[255, 192, 0],  'upper_lip':[255, 54, 0], 'inner_mouth':[255, 0, 84], 'lower_lip':[255, 0, 222], 
-                'hair':[150,0,255]}
-
-        if background:
-            temp = np.all(image == label_to_rgb['background'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-        
-        if face:
-            temp = np.all(image == label_to_rgb['face'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if right_eye:
-            temp = np.all(image == label_to_rgb['right_eye'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if left_eye:
-            temp = np.all(image == label_to_rgb['left_eye'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if right_eyebrow:
-            temp = np.all(image == label_to_rgb['right_eyebrow'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if left_eyebrow:
-            temp = np.all(image == label_to_rgb['left_eyebrow'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if nose:
-            temp = np.all(image == label_to_rgb['nose'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if upper_lip:
-            temp = np.all(image == label_to_rgb['upper_lip'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if inner_mouth:
-            temp = np.all(image == label_to_rgb['inner_mouth'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if lower_lip:
-            temp = np.all(image == label_to_rgb['lower_lip'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        if hair:
-            temp = np.all(image == label_to_rgb['hair'], axis=-1)
-            idcs = np.where(temp==True)
-            mask[idcs] = 255
-
-        mask = to_torch_image(mask[:,:,0]).unsqueeze(0)   
-        return (mask,)