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import cv2
import sys
import numpy as np
import torch
from torch.utils import data
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from PIL import Image
from collections import OrderedDict
sys.path.insert(1, './schp')
from utils.transforms import get_affine_transform
import networks
from utils.transforms import transform_logits
class PILImageDataset(data.Dataset):
def __init__(self, img_lst=[], input_size=[512, 512], transform=None):
self.img_lst = img_lst
self.input_size = input_size
self.transform = transform
self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
self.input_size = np.asarray(input_size)
def __len__(self):
return len(self.img_lst)
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 __getitem__(self, index):
img = np.array(self.img_lst[index])[:,:,::-1]
h, w, _ = img.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(
img,
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
PALLETE_DICT = {
'Background': [],
'Face': [],
'Upper-clothes':[],
'Dress':[],
'Coat':[],
'Soaks':[],
'Pants':[],
'Jumpsuits':[],
'Scarf':[],
'Skirt':[],
'Arm':[],
'Leg':[],
'Shoe':[]
}
val_list = [[0],[1,4,13],[5],[6],[7],[8],[9],[10],[11],[12],[14,15],[16,17],[18,19]]
for c,j in enumerate(PALLETE_DICT.keys()):
val = val_list[c]
pallete = []
for i in range(60):
if len(val) == 1:
if (i >= (val[0]*3)) & (i < ((val[0]+1)*3)):
pallete.append(255)
else:
pallete.append(0)
if len(val) == 2:
if (i >= (val[0]*3)) & (i < ((val[0]+1)*3)) or (i >= (val[1]*3)) & (i < ((val[1]+1)*3)):
pallete.append(255)
else:
pallete.append(0)
if len(val) == 3:
if (i >= (val[0]*3)) & (i < ((val[0]+1)*3)) or (i >= (val[1]*3)) & (i < ((val[1]+1)*3)) or (i >= (val[2]*3)) & (i < ((val[2]+1)*3)):
pallete.append(255)
else:
pallete.append(0)
PALLETE_DICT[j] = pallete
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'],
}
}
class SCHPParser:
def __init__(self, checkpoint_path, dataset_settings):
self.cp_path = checkpoint_path
self.ops = []
self.num_classes = dataset_settings['lip']['num_classes']
self.input_size = dataset_settings['lip']['input_size']
self.label = dataset_settings['lip']['label']
self.pallete_dict = PALLETE_DICT
self.img_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
])
self.model = self.load_model()
def load_model(self):
model = networks.init_model('resnet101', num_classes=self.num_classes, pretrained=None)
state_dict = torch.load(self.cp_path)['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.cuda()
model.eval()
return model
def create_dataloader(self, img_lst):
dataset = PILImageDataset(img_lst, input_size=self.input_size, transform=self.img_transforms)
# dataset = SimpleFolderDataset('inputs',input_size, transform)
dataloader = DataLoader(dataset)
return dataloader
def get_image_masks(self, img_lst):
print("Evaluating total class number {} with {}".format(self.num_classes, self.label))
dataloader = self.create_dataloader(img_lst)
with torch.no_grad():
for batch in dataloader:
op_dict = {}
image, meta = batch
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
w = meta['width'].numpy()[0]
h = meta['height'].numpy()[0]
output = self.model(image.cuda())
upsample = torch.nn.Upsample(size=self.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=self.input_size)
parsing_result = np.argmax(logits_result, axis=2)
output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
for loc, key in enumerate(self.pallete_dict.keys()):
output_img.putpalette(self.pallete_dict[key])
op_dict.update({
key: output_img.convert('L')
})
self.ops.append(op_dict)
return self.ops
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