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| import numpy as np | |
| from label_colors import colorMap | |
| from PIL import Image | |
| from spade.model import Pix2PixModel | |
| from spade.dataset import get_transform | |
| from torchvision.transforms import ToPILImage | |
| '''colors = np.array([[56, 79, 131], [239, 239, 239], | |
| [93, 110, 50], [183, 210, 78], | |
| [60, 59, 75], [250, 250, 250]])''' | |
| colors = [key['color'] for key in colorMap] | |
| id_list = [key['id'] for key in colorMap] | |
| def semantic(img): | |
| print("semantic", type(img)) | |
| h, w = img.size | |
| imrgb = img.convert("RGB") | |
| pix = list(imrgb.getdata()) | |
| mask = [id_list[colors.index(i)] if i in colors else 156 for i in pix] | |
| return np.array(mask).reshape(h, w) | |
| def evaluate(labelmap): | |
| opt = { | |
| 'label_nc': 182, # num classes in coco model | |
| 'crop_size': 512, | |
| 'load_size': 512, | |
| 'aspect_ratio': 1.0, | |
| 'isTrain': False, | |
| 'checkpoints_dir': 'app', | |
| 'which_epoch': 'latest', | |
| 'use_gpu': False | |
| } | |
| model = Pix2PixModel(opt) | |
| model.eval() | |
| image = Image.fromarray(np.array(labelmap).astype(np.uint8)) | |
| transform_label = get_transform(opt, method=Image.NEAREST, normalize=False) | |
| # transforms.ToTensor in transform_label rescales image from [0,255] to [0.0,1.0] | |
| # lets rescale it back to [0,255] to match our label ids | |
| label_tensor = transform_label(image) * 255.0 | |
| label_tensor[label_tensor == 255] = opt['label_nc'] # 'unknown' is opt.label_nc | |
| print("label_tensor:", label_tensor.shape) | |
| # not using encoder, so creating a blank image... | |
| transform_image = get_transform(opt) | |
| image_tensor = transform_image(Image.new('RGB', (500, 500))) | |
| data = { | |
| 'label': label_tensor.unsqueeze(0), | |
| 'instance': label_tensor.unsqueeze(0), | |
| 'image': image_tensor.unsqueeze(0) | |
| } | |
| generated = model(data, mode='inference') | |
| print("generated_image:", generated.shape) | |
| return generated | |
| def to_image(generated): | |
| to_img = ToPILImage() | |
| normalized_img = ((generated.reshape([3, 512, 512]) + 1) / 2.0) * 255.0 | |
| return to_img(normalized_img.byte().cpu()) | |