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Runtime error
Runtime error
| import numpy as np | |
| import torch | |
| def blend_image_segmentation(img, seg, mode, image_size=224): | |
| if mode in {'blur_highlight', 'blur3_highlight', 'blur3_highlight01', 'blur_highlight_random', 'crop'}: | |
| if isinstance(img, np.ndarray): | |
| img = torch.from_numpy(img) | |
| if isinstance(seg, np.ndarray): | |
| seg = torch.from_numpy(seg) | |
| if mode == 'overlay': | |
| out = img * seg | |
| out = [out.astype('float32')] | |
| elif mode == 'highlight': | |
| out = img * seg[None, :, :] * 0.85 + 0.15 * img | |
| out = [out.astype('float32')] | |
| elif mode == 'highlight2': | |
| img = img / 2 | |
| out = (img+0.1) * seg[None, :, :] + 0.3 * img | |
| out = [out.astype('float32')] | |
| elif mode == 'blur_highlight': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=1, bg_fac=0.5).numpy()[0] - 0.01] | |
| elif mode == 'blur3_highlight': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=3, bg_fac=0.5).numpy()[0] - 0.01] | |
| elif mode == 'blur3_highlight01': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=3, bg_fac=0.1).numpy()[0] - 0.01] | |
| elif mode == 'blur_highlight_random': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=0 + torch.randint(0, 3, (1,)).item(), bg_fac=0.1 + 0.8*torch.rand(1).item()).numpy()[0] - 0.01] | |
| elif mode == 'crop': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=1, center_context=0.1, image_size=image_size)[0].numpy()] | |
| elif mode == 'crop_blur_highlight': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=3, center_context=0.1, bg_fac=0.1, image_size=image_size)[0].numpy()] | |
| elif mode == 'crop_blur_highlight352': | |
| from evaluation_utils import img_preprocess | |
| out = [img_preprocess((None, [img], [seg]), blur=3, center_context=0.1, bg_fac=0.1, image_size=352)[0].numpy()] | |
| elif mode == 'shape': | |
| out = [np.stack([seg[:, :]]*3).astype('float32')] | |
| elif mode == 'concat': | |
| out = [np.concatenate([img, seg[None, :, :]]).astype('float32')] | |
| elif mode == 'image_only': | |
| out = [img.astype('float32')] | |
| elif mode == 'image_black': | |
| out = [img.astype('float32')*0] | |
| elif mode is None: | |
| out = [img.astype('float32')] | |
| elif mode == 'separate': | |
| out = [img.astype('float32'), seg.astype('int64')] | |
| elif mode == 'separate_img_black': | |
| out = [img.astype('float32')*0, seg.astype('int64')] | |
| elif mode == 'separate_seg_ones': | |
| out = [img.astype('float32'), np.ones_like(seg).astype('int64')] | |
| elif mode == 'separate_both_black': | |
| out = [img.astype('float32')*0, seg.astype('int64')*0] | |
| else: | |
| raise ValueError(f'invalid mode: {mode}') | |
| return out |