| import torch |
|
|
| from src.visualization.color_map import apply_color_map_to_image |
| import torch.distributed as dist |
|
|
| def inverse_normalize(tensor, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)): |
| mean = torch.as_tensor(mean, dtype=tensor.dtype, device=tensor.device).view(-1, 1, 1) |
| std = torch.as_tensor(std, dtype=tensor.dtype, device=tensor.device).view(-1, 1, 1) |
| return tensor.mul(std).add(mean) |
|
|
|
|
| |
| def vis_depth_map(result, near=None, far=None): |
| if near is None and far is None: |
| far = result.view(-1)[:16_000_000].quantile(0.99).log() |
| try: |
| near = result[result > 0][:16_000_000].quantile(0.01).log() |
| except: |
| print("No valid depth values found.") |
| near = torch.zeros_like(far) |
| else: |
| near = near.log() |
| far = far.log() |
| |
| result = result.log() |
| result = 1 - (result - near) / (far - near) |
| return apply_color_map_to_image(result, "turbo") |
|
|
|
|
| def confidence_map(result): |
| |
| |
| |
| |
| |
| |
| |
| |
| result = result / result.view(-1).max() |
| return apply_color_map_to_image(result, "magma") |
|
|
|
|
| def get_overlap_tag(overlap): |
| if 0.05 <= overlap <= 0.3: |
| overlap_tag = "small" |
| elif overlap <= 0.55: |
| overlap_tag = "medium" |
| elif overlap <= 0.8: |
| overlap_tag = "large" |
| else: |
| overlap_tag = "ignore" |
|
|
| return overlap_tag |
|
|
|
|
| def is_dist_avail_and_initialized(): |
| if not dist.is_available(): |
| return False |
| if not dist.is_initialized(): |
| return False |
| return True |
|
|
|
|
| def get_world_size(): |
| if not is_dist_avail_and_initialized(): |
| return 1 |
| return dist.get_world_size() |
|
|
|
|
| def get_rank(): |
| if not is_dist_avail_and_initialized(): |
| return 0 |
| return dist.get_rank() |
