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# Self-Correction-Human-Parsing
# Original https://github.com/GoGoDuck912/Self-Correction-Human-Parsing
import os
import torch
import numpy as np
from PIL import Image
import cv2
import torchvision.transforms as T
from .transforms import transform_logits, get_affine_transform
from . import networks
from annotator.util import annotator_ckpts_path
from huggingface_hub import snapshot_download
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
class Segmentator(torch.nn.Module):
def __init__(self, dataset='lip'):
super().__init__()
num_classes = dataset_settings[dataset]['num_classes']
input_size = dataset_settings[dataset]['input_size']
label = dataset_settings[dataset]['label']
if dataset == 'atr':
model_path='exp-schp-201908301523-atr.pth'
elif dataset == 'lip':
model_path='exp-schp-201908261155-lip.pth'
model_path = os.path.join(annotator_ckpts_path, model_path)
snapshot_download(repo_id="soonyau/visconet", allow_patterns="exp-schp-201908301523-atr.pth", local_dir=annotator_ckpts_path)
self.model = networks.init_model('resnet101', num_classes=num_classes, pretrained=None)
state_dict = torch.load(model_path)['state_dict']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
self.model.load_state_dict(new_state_dict)
self.model.eval()
self.palette = get_palette(num_classes)
self.transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
])
self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
self.input_size = np.asarray(input_size)
def _box2cs(self, box):
x, y, w, h = box[:4]
return self._xywh2cs(x, y, w, h)
def _xywh2cs(self, x, y, w, h):
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
if w > self.aspect_ratio * h:
h = w * 1.0 / self.aspect_ratio
elif w < self.aspect_ratio * h:
w = h * self.aspect_ratio
scale = np.array([w, h], dtype=np.float32)
return center, scale
def preprocess(self, image:np.array):
# convert numpy to cv2
image = image[:,:,::-1]
h, w, _ = image.shape
# Get person center and scale
person_center, s = self._box2cs([0, 0, w - 1, h - 1])
r = 0
trans = get_affine_transform(person_center, s, r, self.input_size)
input = cv2.warpAffine(
image,
trans,
(int(self.input_size[1]), int(self.input_size[0])),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0))
input = self.transform(input)
meta = {
'center': person_center,
'height': h,
'width': w,
'scale': s,
'rotation': r
}
return input, meta
@torch.no_grad()
def __call__(self, input_image):
image, meta = self.preprocess(input_image)
c = meta['center']
s = meta['scale']
w = meta['width']
h = meta['height']
input_size = list(self.input_size)
device = next(self.parameters()).device
output = self.model(image.unsqueeze(0).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(), c, 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))
#return output_img
output_img.putpalette(self.palette)
return output_img
#return np.array(output_img)
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