| import os |
| import cv2 |
| import gdown |
| import shutil |
| import argparse |
| import numpy as np |
| import torch |
| import torch.backends.cudnn as cudnn |
| import torchvision.transforms as transforms |
| from torchvision.utils import save_image |
|
|
| from inplace_abn import InPlaceABN |
| from dml_csr import dml_csr |
| from dml_csr import transforms as dml_transforms |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser(description="Plot segmentation mask of an image.") |
| parser.add_argument( |
| "--image_path", |
| type=str, |
| default=None, |
| help="Path to the image file." |
| ) |
| parser.add_argument("--size", type=int, default=512) |
| parser.add_argument( |
| "--checkpoint_path", |
| type=str, |
| default='ckpt/DML_CSR/dml_csr_celebA.pth', |
| help="Path to the DML-CSR pretrained model." |
| ) |
| parser.add_argument( |
| "--output_dir", |
| type=str, |
| default="output/masks/", |
| help="Folder to save segmentation mask." |
| ) |
| args = parser.parse_args() |
|
|
| return args |
|
|
| def download_checkpoint(): |
| os.makedirs('ckpt', exist_ok=True) |
| id = "1xttWuAj633-ujp_vcm5DtL98PP0b-sUm" |
| gdown.download(id=id, output='ckpt/DML_CSR.zip') |
| shutil.unpack_archive('ckpt/DML_CSR.zip', 'ckpt') |
| os.remove('ckpt/DML_CSR.zip') |
|
|
| def box2cs(box: list) -> tuple: |
| x, y, w, h = box[:4] |
| return xywh2cs(x, y, w, h) |
|
|
| def xywh2cs(x: float, y: float, w: float, h: float) -> tuple: |
| center = np.zeros((2), dtype=np.float32) |
| center[0] = x + w * 0.5 |
| center[1] = y + h * 0.5 |
| if w > h: |
| h = w |
| elif w < h: |
| w = h |
| scale = np.array([w * 1.0, h * 1.0], dtype=np.float32) |
|
|
| return center, scale |
|
|
| def labelcolormap(N): |
| if N == 19: |
| cmap = np.array([(0, 0, 0), (204, 0, 0), (76, 153, 0), |
| (204, 204, 0), (204, 0, 204), (204, 0, 204), (255, 204, 204), |
| (255, 204, 204), (102, 51, 0), (102, 51, 0), (102, 204, 0), |
| (255, 255, 0), (0, 0, 153), (0, 0, 204), (255, 51, 153), |
| (0, 204, 204), (0, 51, 0), (255, 153, 51), (0, 204, 0)], |
| dtype=np.uint8) |
| else: |
| def uint82bin(n, count=8): |
| """returns the binary of integer n, count refers to amount of bits""" |
| return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) |
| |
| cmap = np.zeros((N, 3), dtype=np.uint8) |
| for i in range(N): |
| r, g, b = 0, 0, 0 |
| id = i |
| for j in range(7): |
| str_id = uint82bin(id) |
| r = r ^ (np.uint8(str_id[-1]) << (7-j)) |
| g = g ^ (np.uint8(str_id[-2]) << (7-j)) |
| b = b ^ (np.uint8(str_id[-3]) << (7-j)) |
| id = id >> 3 |
| cmap[i, 0] = r |
| cmap[i, 1] = g |
| cmap[i, 2] = b |
| return cmap |
|
|
| class Colorize(object): |
| def __init__(self, n=19): |
| self.cmap = labelcolormap(n) |
| self.cmap = torch.from_numpy(self.cmap[:n]) |
|
|
| def __call__(self, gray_image): |
| size = gray_image.size() |
| color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) |
|
|
| for label in range(0, len(self.cmap)): |
| mask = (label == gray_image[0]).cpu() |
| color_image[0][mask] = self.cmap[label][0] |
| color_image[1][mask] = self.cmap[label][1] |
| color_image[2][mask] = self.cmap[label][2] |
|
|
| return color_image |
|
|
| def tensor2label(label_tensor, n_label): |
| label_tensor = label_tensor.cpu().float() |
| if label_tensor.size()[0] > 1: |
| label_tensor = label_tensor.max(0, keepdim=True)[1] |
| label_tensor = Colorize(n_label)(label_tensor) |
| |
| label_numpy = label_tensor.numpy() |
| label_numpy = label_numpy / 255.0 |
|
|
| return label_numpy |
|
|
| def generate_label(inputs, imsize): |
| pred_batch = [] |
| for input in inputs: |
| input = input.view(1, 19, imsize, imsize) |
| pred = np.squeeze(input.data.max(1)[1].cpu().numpy(), axis=0) |
| pred_batch.append(pred) |
|
|
| pred_batch = np.array(pred_batch) |
| pred_batch = torch.from_numpy(pred_batch) |
| |
| label_batch = [] |
| for p in pred_batch: |
| p = p.view(1, imsize, imsize) |
| label_batch.append(tensor2label(p, 19)) |
| |
| label_batch = np.array(label_batch) |
| label_batch = torch.from_numpy(label_batch) |
|
|
| return label_batch |
|
|
| def get_mask(model, image, input_size): |
| interp = torch.nn.Upsample(size=input_size, mode='bilinear', align_corners=True) |
|
|
| image = image.unsqueeze(0) |
| with torch.no_grad(): |
| outputs = model(image.cuda()) |
| labels = generate_label(interp(outputs), input_size[0]) |
| return labels[0] |
|
|
| def save_mask(args): |
| os.makedirs(args.output_dir, exist_ok=True) |
|
|
| cudnn.benchmark = True |
| cudnn.enabled = True |
|
|
| model = dml_csr.DML_CSR(19, InPlaceABN, False) |
|
|
| normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], |
| std=[0.229, 0.224, 0.225]) |
| transform = transforms.Compose([transforms.ToTensor(), normalize]) |
|
|
| input_size = (args.size, args.size) |
| image = cv2.imread(args.image_path, cv2.IMREAD_COLOR) |
| h, w, _ = image.shape |
| center, s = box2cs([0, 0, w - 1, h - 1]) |
| r = 0 |
| crop_size = np.asarray(input_size) |
| trans = dml_transforms.get_affine_transform(center, s, r, crop_size) |
| image = cv2.warpAffine(image, trans, (int(crop_size[1]), int(crop_size[0])), |
| flags=cv2.INTER_LINEAR, |
| borderMode=cv2.BORDER_CONSTANT, |
| borderValue=(0, 0, 0)) |
| image = transform(image) |
|
|
| if not os.path.exists(args.checkpoint_path): |
| download_checkpoint() |
| state_dict = torch.load(args.checkpoint_path, map_location='cuda:0') |
| model.load_state_dict(state_dict) |
| |
| model.cuda() |
| model.eval() |
|
|
| mask = get_mask(model, image, input_size) |
| filename = os.path.join(args.output_dir, os.path.basename(args.image_path).split('.')[0] + '.png') |
| save_image(mask, filename) |
| print(f'Mask saved in {filename}') |
|
|
|
|
| if __name__ == '__main__': |
| args = parse_args() |
| save_mask(args) |
