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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# +
# #!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : peike.li@yahoo.com
@File : simple_extractor.py
@Time : 8/30/19 8:59 PM
@Desc : Simple Extractor (modified for single image input)
"""
import os
import torch
import argparse
import numpy as np
from PIL import Image
from tqdm import tqdm
import cv2
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import networks
from preprocess.utils.transforms import transform_logits, get_affine_transform
class SimpleFileDataset(Dataset):
def __init__(self, image_path, input_size=[512, 512], transform=None):
self.image_path = image_path
self.input_size = np.asarray(input_size)
self.transform = transform
self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
self.img_name = os.path.basename(image_path)
def __len__(self):
return 1
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 = cv2.imread(self.image_path, cv2.IMREAD_COLOR)
h, w, _ = img.shape
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 = {
'name': self.img_name,
'center': person_center,
'height': h,
'width': w,
'scale': s,
'rotation': r
}
return input, meta
dataset_settings = {
'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']
}
}
def get_palette(num_cls):
n = 18
palette = [0] * (n * 3)
j = num_cls
lab = num_cls
palette[j * 3 + 0] = 0
palette[j * 3 + 1] = 0
palette[j * 3 + 2] = 0
i = 0
while lab:
palette[j * 3 + 0] = 255
palette[j * 3 + 1] = 255
palette[j * 3 + 2] = 255
i += 1
lab >>= 3
return palette
def masking(image_path, class_num=0):
num_classes = dataset_settings['atr']['num_classes']
input_size = dataset_settings['atr']['input_size']
label = dataset_settings['atr']['label']
print("Evaluating total class number {} with {}".format(num_classes, label))
model = networks.init_model('resnet101', num_classes=num_classes, pretrained=None)
state_dict = torch.load('./ckpts/exp-schp-201908301523-atr.pth')['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
model.load_state_dict(new_state_dict)
model.cuda()
model.eval()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
])
dataset = SimpleFileDataset(image_path=image_path, input_size=input_size, transform=transform)
dataloader = DataLoader(dataset)
if not os.path.exists('./outputs'):
os.makedirs('./outputs')
palette = get_palette(class_num)
with torch.no_grad():
for idx, batch in enumerate(tqdm(dataloader)):
image, meta = batch
img_name = meta['name'][0]
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
w = meta['width'].numpy()[0]
h = meta['height'].numpy()[0]
output = model(image.cuda())
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)
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)
parsing_result_path = os.path.join('./outputs', img_name[:-4] + '.png')
output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
output_img.putpalette(palette)
output_img.save(parsing_result_path)
gray_img = output_img.convert('L')
return gray_img
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