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|
| import os |
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| import cv2 |
| import numpy as np |
|
|
| from lib.datasets.tools.transforms import DeNormalize |
| from lib.utils.tools.logger import Logger as Log |
|
|
| TENSOR_DIR = 'vis/results/tensor' |
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|
| class TensorVisualizer(object): |
|
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| def __init__(self, configer): |
| self.configer = configer |
|
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| def vis_tensor(self, tensor, name='default', sub_dir=''): |
| base_dir = os.path.join(self.configer.get('project_dir'), TENSOR_DIR, sub_dir) |
|
|
| if not isinstance(tensor, np.ndarray): |
| if len(tensor.size()) != 3: |
| Log.error('Tensor size is not valid.') |
| exit(1) |
|
|
| tensor = tensor.data.cpu().numpy().transpose(1, 2, 0) |
|
|
| if not os.path.exists(base_dir): |
| Log.error('Dir:{} not exists!'.format(base_dir)) |
| os.makedirs(base_dir) |
|
|
| tensor_img = cv2.resize(tensor, tuple(self.configer.get('data', 'input_size'))) |
| cv2.imwrite(tensor_img, os.path.join(base_dir, '{}.jpg'.format(name))) |
|
|
| def vis_img(self, image_in, name='default', sub_dir='images'): |
| base_dir = os.path.join(self.configer.get('project_dir'), TENSOR_DIR, sub_dir) |
|
|
| if not isinstance(image_in, np.ndarray): |
| if len(image_in.size()) != 3: |
| Log.error('Image size is not valid.') |
| exit(1) |
|
|
| image = DeNormalize(div_value=self.configer.get('normalize', 'div_value'), |
| mean=self.configer.get('normalize', 'mean'), |
| std=self.configer.get('normalize', 'std'))(image_in.clone()) |
| image = image.data.cpu().numpy().transpose(1, 2, 0) |
| else: |
| image = image_in.copy() |
|
|
| if not os.path.exists(base_dir): |
| Log.error('Dir:{} not exists!'.format(base_dir)) |
| os.makedirs(base_dir) |
|
|
| img = cv2.resize(image, tuple(self.configer.get('data', 'input_size'))) |
| cv2.imwrite(img, os.path.join(base_dir, '{}.jpg'.format(name))) |
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|
|
| if __name__ == "__main__": |
| |
| pass |
