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| from pathlib import Path | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from mpl_toolkits.axes_grid1 import ImageGrid | |
| from matplotlib.colors import Normalize, LogNorm | |
| import torch | |
| from torchvision.utils import make_grid | |
| from einops import rearrange | |
| from .data import blend_rgba | |
| import imageio | |
| from my.utils.plot import mpl_fig_to_buffer | |
| from my.utils.event import read_stats | |
| def vis(ref_img, pred_img, pred_depth, *, msg="", return_buffer=False): | |
| # plt the 2 images side by side and compare | |
| fig = plt.figure(figsize=(15, 6)) | |
| grid = ImageGrid( | |
| fig, 111, nrows_ncols=(1, 3), | |
| cbar_location="right", cbar_mode="single", | |
| ) | |
| grid[0].imshow(ref_img) | |
| grid[0].set_title("gt") | |
| grid[1].imshow(pred_img) | |
| grid[1].set_title(f"rendering {msg}") | |
| h = grid[2].imshow(pred_depth, norm=LogNorm(vmin=2, vmax=10), cmap="Spectral") | |
| grid[2].set_title("expected depth") | |
| plt.colorbar(h, cax=grid.cbar_axes[0]) | |
| plt.tight_layout() | |
| if return_buffer: | |
| plot = mpl_fig_to_buffer(fig) | |
| return plot | |
| else: | |
| plt.show() | |
| def _bad_vis(pred_img, pred_depth, *, return_buffer=False): | |
| """emergency function for one-off use""" | |
| fig, grid = plt.subplots(1, 2, squeeze=True, figsize=(10, 6)) | |
| grid[0].imshow(pred_img) | |
| grid[0].set_title("rendering") | |
| h = grid[1].imshow(pred_depth, norm=LogNorm(vmin=0.5, vmax=10), cmap="Spectral") | |
| grid[1].set_title("expected depth") | |
| # plt.colorbar(h, cax=grid.cbar_axes[0]) | |
| plt.tight_layout() | |
| if return_buffer: | |
| plot = mpl_fig_to_buffer(fig) | |
| return plot | |
| else: | |
| plt.show() | |
| colormap = plt.get_cmap('Spectral') | |
| def bad_vis(pred_img, pred_depth, final_H=512): | |
| # pred_img = pred_img.cpu() | |
| depth = pred_depth.cpu().numpy() | |
| del pred_depth | |
| depth = np.log(1. + depth + 1e-12) | |
| depth = depth / np.log(1+10.) | |
| # depth = 1 - depth | |
| depth = colormap(depth) | |
| depth = blend_rgba(depth) | |
| depth = rearrange(depth, "h w c -> 1 c h w", c=3) | |
| depth = torch.from_numpy(depth) | |
| depth = torch.nn.functional.interpolate( | |
| depth, (final_H, final_H), mode='bilinear', antialias=True | |
| ) | |
| pred_img = torch.nn.functional.interpolate( | |
| pred_img, (final_H, final_H), mode='bilinear', antialias=True | |
| ) | |
| pred_img = (pred_img + 1) / 2 | |
| pred_img = pred_img.clamp(0, 1).cpu() | |
| stacked = torch.cat([pred_img, depth], dim=0) | |
| pane = make_grid(stacked, nrow=2) | |
| pane = rearrange(pane, "c h w -> h w c") | |
| pane = (pane * 255.).clamp(0, 255) | |
| pane = pane.to(torch.uint8) | |
| pane = pane.numpy() | |
| # plt.imshow(pane) | |
| # plt.show() | |
| return pane | |
| def export_movie(seqs, fname, fps=30): | |
| fname = Path(fname) | |
| if fname.suffix == "": | |
| fname = fname.with_suffix(".mp4") | |
| writer = imageio.get_writer(fname, fps=fps) | |
| for img in seqs: | |
| writer.append_data(img) | |
| writer.close() | |
| def stitch_vis(save_fn, img_fnames, fps=10): | |
| figs = [imageio.imread(fn) for fn in img_fnames] | |
| export_movie(figs, save_fn, fps) | |