(
x, cmap_name="jet", mask=None, value_range=None, append_cbar=False, cbar_in_image=False, cbar_precision=2
)
| 98 | |
| 99 | |
| 100 | def colorize_optimized( |
| 101 | x, cmap_name="jet", mask=None, value_range=None, append_cbar=False, cbar_in_image=False, cbar_precision=2 |
| 102 | ): |
| 103 | device = x.device |
| 104 | original_shape = x.shape |
| 105 | if x.dim() == 2: |
| 106 | x = x.unsqueeze(0) |
| 107 | B, H, W = x.shape |
| 108 | |
| 109 | # deal with vmin/vmax |
| 110 | if value_range is not None: |
| 111 | vmin, vmax = value_range |
| 112 | vmin = torch.full((B,), vmin, device=device) |
| 113 | vmax = torch.full((B,), vmax, device=device) |
| 114 | else: |
| 115 | if mask is not None: |
| 116 | if mask.dim() == 2: |
| 117 | mask = mask.unsqueeze(0) |
| 118 | mask = mask.expand(B, H, W) |
| 119 | non_zero_mask = mask & (x != 0) |
| 120 | has_non_zero = non_zero_mask.reshape(B, -1).any(dim=1) |
| 121 | |
| 122 | # min value of non-zero elements in the mask |
| 123 | non_zero_vmin = x.masked_fill(~non_zero_mask, float('inf')).view(B, -1).min(dim=1)[0] |
| 124 | # min value of all masked elements |
| 125 | mask_vmin = x.masked_fill(~mask, float('inf')).view(B, -1).min(dim=1)[0] |
| 126 | vmin = torch.where(has_non_zero, non_zero_vmin, mask_vmin) |
| 127 | # set unmasked values -> vmin |
| 128 | x = x.masked_fill(~mask, vmin.view(B, 1, 1)) |
| 129 | # calculate vmax |
| 130 | vmax = x.masked_fill(~mask, float('-inf')).view(B, -1).max(dim=1)[0] |
| 131 | else: |
| 132 | # if no mask, use quantiles |
| 133 | x_flatten = x.view(B, -1) |
| 134 | vmin = torch.quantile(x_flatten, 0.01, dim=1) |
| 135 | vmax = torch.quantile(x_flatten, 1.0, dim=1) + 1e-6 |
| 136 | |
| 137 | # clip and normalize the input |
| 138 | x_clipped = torch.clamp(x, min=vmin.view(B,1,1), max=vmax.view(B,1,1)) |
| 139 | x_normalized = (x_clipped - vmin.view(B,1,1)) / (vmax.view(B,1,1) - vmin.view(B,1,1) + 1e-6) |
| 140 | |
| 141 | # generate color map |
| 142 | cmap = mpl.cm.get_cmap(cmap_name) |
| 143 | colormap = cmap(np.linspace(0, 1, 256))[:,:3] # (256,3) |
| 144 | colormap = torch.from_numpy(colormap).float().to(device) # (256,3) |
| 145 | |
| 146 | # vectorized color mapping |
| 147 | x_scaled = (x_normalized * 255).long().clamp(0, 255) # (B,H,W) |
| 148 | colorized = colormap[x_scaled.flatten()].view(B,H,W,3) # (B,H,W,3) |
| 149 | |
| 150 | # erode the mask |
| 151 | if mask is not None: |
| 152 | kernel = torch.ones(3,3, device=device) |
| 153 | mask_eroded = F.conv2d( |
| 154 | mask.float().unsqueeze(1), |
| 155 | kernel.view(1,1,3,3), |
| 156 | padding=1 |
| 157 | ) == kernel.numel() |
no test coverage detected