140 lines
4.8 KiB
Python
140 lines
4.8 KiB
Python
import cv2
|
|
import torch
|
|
import numpy as np
|
|
import os
|
|
from collections import OrderedDict
|
|
import torchvision.transforms as transforms
|
|
from PIL import Image
|
|
|
|
from .schp import networks
|
|
from .schp.utils.transforms import transform_logits, get_affine_transform
|
|
|
|
dataset_settings = {
|
|
'lip': {
|
|
'input_size': [473, 473],
|
|
'num_classes': 20,
|
|
'label': ['Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'Upper-clothes', 'Dress', 'Coat',
|
|
'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm',
|
|
'Left-leg', 'Right-leg', 'Left-shoe', 'Right-shoe']
|
|
},
|
|
'atr': {
|
|
'input_size': [512, 512],
|
|
'num_classes': 18,
|
|
'label': ['Background', 'Hat', 'Hair', 'Sunglasses', 'Upper-clothes', 'Skirt', 'Pants', 'Dress', 'Belt',
|
|
'Left-shoe', 'Right-shoe', 'Face', 'Left-leg', 'Right-leg', 'Left-arm', 'Right-arm', 'Bag', 'Scarf']
|
|
},
|
|
'pascal': {
|
|
'input_size': [512, 512],
|
|
'num_classes': 7,
|
|
'label': ['Background', 'Head', 'Torso', 'Upper Arms', 'Lower Arms', 'Upper Legs', 'Lower Legs'],
|
|
}
|
|
}
|
|
|
|
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
|
|
|
|
def _box2cs(box, aspect_ratio):
|
|
x, y, w, h = box[:4]
|
|
return _xywh2cs(x, y, w, h, aspect_ratio)
|
|
|
|
def _xywh2cs(x, y, w, h, aspect_ratio):
|
|
center = np.zeros((2), dtype=np.float32)
|
|
center[0] = x + w * 0.5
|
|
center[1] = y + h * 0.5
|
|
if w > aspect_ratio * h:
|
|
h = w * 1.0 / aspect_ratio
|
|
elif w < aspect_ratio * h:
|
|
w = h * aspect_ratio
|
|
scale = np.array([w, h], dtype=np.float32)
|
|
return center, scale
|
|
|
|
def check_model_path(model_path):
|
|
# Checks to see if the model exists, if not try adding ComfyUI/ to the start to fix possible errors on Windows (maybe others too)
|
|
if not os.path.exists(model_path):
|
|
new_model_path = os.path.join("ComfyUI", model_path)
|
|
if os.path.exists(new_model_path):
|
|
return new_model_path
|
|
return model_path
|
|
|
|
def generate(image, type, device):
|
|
num_classes = dataset_settings[type]['num_classes']
|
|
input_size = dataset_settings[type]['input_size']
|
|
aspect_ratio = input_size[1] * 1.0 / input_size[0]
|
|
if type == 'lip':
|
|
model_path = 'models/schp/exp-schp-201908261155-lip.pth'
|
|
elif type == 'atr':
|
|
model_path = 'models/schp/exp-schp-201908301523-atr.pth'
|
|
elif type == 'pascal':
|
|
model_path = 'models/schp/exp-schp-201908270938-pascal-person-part.pth'
|
|
|
|
# Check and adjust the model path if necessary
|
|
model_path = check_model_path(model_path)
|
|
|
|
model = networks.init_model('resnet101', num_classes=num_classes, pretrained=None)
|
|
state_dict = torch.load(model_path)['state_dict']
|
|
new_state_dict = OrderedDict()
|
|
for k, v in state_dict.items():
|
|
name = k[7:]
|
|
new_state_dict[name] = v
|
|
model.load_state_dict(new_state_dict)
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
# Get person center and scale
|
|
input = 255. * image.cpu().numpy()
|
|
input = np.clip(input, 0, 255).astype(np.uint8)
|
|
input = cv2.cvtColor(input, cv2.COLOR_RGB2BGR)
|
|
h, w, _ = input.shape
|
|
|
|
person_center, s = _box2cs([0, 0, w - 1, h - 1], aspect_ratio)
|
|
trans = get_affine_transform(person_center, s, 0, input_size)
|
|
input = cv2.warpAffine(
|
|
input,
|
|
trans,
|
|
(int(input_size[1]), int(input_size[0])),
|
|
flags=cv2.INTER_LINEAR,
|
|
borderMode=cv2.BORDER_CONSTANT,
|
|
borderValue=(0, 0, 0))
|
|
|
|
transform = transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
|
|
])
|
|
input = transform(input)
|
|
|
|
palette = get_palette(num_classes)
|
|
with torch.no_grad():
|
|
input = input[None, :, :, :]
|
|
output = model(input.to(device))
|
|
upsample = torch.nn.Upsample(size=input_size, mode='bilinear', align_corners=True)
|
|
upsample_output = upsample(output[0][-1][0].unsqueeze(0))
|
|
upsample_output = upsample_output.squeeze()
|
|
upsample_output = upsample_output.permute(1, 2, 0) # CHW -> HWC
|
|
|
|
logits_result = transform_logits(upsample_output.data.cpu().numpy(), person_center, s, w, h, input_size=input_size)
|
|
parsing_result = np.argmax(logits_result, axis=2)
|
|
|
|
output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
|
|
output_img.putpalette(palette)
|
|
return output_img
|