(self, img, depth, depth_pred)
| 93 | |
| 94 | class ImageDepthVisualizer: |
| 95 | def __call__(self, img, depth, depth_pred) -> Image: |
| 96 | # min-max normalize depth per sample |
| 97 | depth = per_sample_min_max_normalization(depth) |
| 98 | depth_pred = per_sample_min_max_normalization(depth_pred) |
| 99 | depth = depth.mean(dim=1, keepdim=True).repeat(1, 3, 1, 1) |
| 100 | depth_pred = depth_pred.mean(dim=1, keepdim=True).repeat(1, 3, 1, 1) |
| 101 | img = img / 2 + 0.5 |
| 102 | # => all three should be in shape of [b 3 h w] and [0, 1] |
| 103 | |
| 104 | # concatenate along width |
| 105 | out = torch.cat([img, depth, depth_pred], dim=3) |
| 106 | out = einops.rearrange(out, "b c h w -> (b h) w c") |
| 107 | out = (out * 255).clip(0, 255).cpu().numpy().astype('uint8') |
| 108 | out = Image.fromarray(out) |
| 109 | return out |
nothing calls this directly
no test coverage detected