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128 lines
3.6 KiB
Python
128 lines
3.6 KiB
Python
import torch
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# import torch.nn as nn
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# import torch.nn.functional as F
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import numpy as np
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HUGE_NUMBER = 1e10
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TINY_NUMBER = 1e-6 # float32 only has 7 decimal digits precision
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# misc utils
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def img2mse(x, y, mask=None):
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if mask is None:
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return torch.mean((x - y) * (x - y))
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else:
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return torch.sum((x - y) * (x - y) * mask.unsqueeze(-1)) / (torch.sum(mask) * x.shape[-1] + TINY_NUMBER)
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img_HWC2CHW = lambda x: x.permute(2, 0, 1)
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gray2rgb = lambda x: x.unsqueeze(2).repeat(1, 1, 3)
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def normalize(x):
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min = x.min()
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max = x.max()
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return (x - min) / ((max - min) + TINY_NUMBER)
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to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
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# gray2rgb = lambda x: np.tile(x[:,:,np.newaxis], (1, 1, 3))
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mse2psnr = lambda x: -10. * np.log(x+TINY_NUMBER) / np.log(10.)
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########################################################################################################################
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#
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########################################################################################################################
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from matplotlib.backends.backend_agg import FigureCanvasAgg
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from matplotlib.figure import Figure
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import matplotlib as mpl
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from matplotlib import cm
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import cv2
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def get_vertical_colorbar(h, vmin, vmax, cmap_name='jet', label=None):
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fig = Figure(figsize=(1.2, 8), dpi=100)
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fig.subplots_adjust(right=1.5)
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canvas = FigureCanvasAgg(fig)
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# Do some plotting.
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ax = fig.add_subplot(111)
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cmap = cm.get_cmap(cmap_name)
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norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
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tick_cnt = 6
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tick_loc = np.linspace(vmin, vmax, tick_cnt)
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cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
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norm=norm,
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ticks=tick_loc,
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orientation='vertical')
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tick_label = ['{:3.2f}'.format(x) for x in tick_loc]
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cb1.set_ticklabels(tick_label)
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cb1.ax.tick_params(labelsize=18, rotation=0)
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if label is not None:
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cb1.set_label(label)
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fig.tight_layout()
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canvas.draw()
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s, (width, height) = canvas.print_to_buffer()
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im = np.frombuffer(s, np.uint8).reshape((height, width, 4))
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im = im[:, :, :3].astype(np.float32) / 255.
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if h != im.shape[0]:
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w = int(im.shape[1] / im.shape[0] * h)
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im = cv2.resize(im, (w, h), interpolation=cv2.INTER_AREA)
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return im
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def colorize_np(x, cmap_name='jet', mask=None, append_cbar=False):
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if mask is not None:
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# vmin, vmax = np.percentile(x[mask], (1, 99))
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vmin = np.min(x[mask])
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vmax = np.max(x[mask])
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vmin = vmin - np.abs(vmin) * 0.01
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x[np.logical_not(mask)] = vmin
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x = np.clip(x, vmin, vmax)
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# print(vmin, vmax)
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else:
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vmin = x.min()
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vmax = x.max() + TINY_NUMBER
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x = (x - vmin) / (vmax - vmin)
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# x = np.clip(x, 0., 1.)
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cmap = cm.get_cmap(cmap_name)
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x_new = cmap(x)[:, :, :3]
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if mask is not None:
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mask = np.float32(mask[:, :, np.newaxis])
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x_new = x_new * mask + np.zeros_like(x_new) * (1. - mask)
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cbar = get_vertical_colorbar(h=x.shape[0], vmin=vmin, vmax=vmax, cmap_name=cmap_name)
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if append_cbar:
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x_new = np.concatenate((x_new, np.zeros_like(x_new[:, :5, :]), cbar), axis=1)
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return x_new
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else:
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return x_new, cbar
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# tensor
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def colorize(x, cmap_name='jet', append_cbar=False, mask=None):
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x = x.numpy()
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if mask is not None:
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mask = mask.numpy().astype(dtype=np.bool)
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x, cbar = colorize_np(x, cmap_name, mask)
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if append_cbar:
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x = np.concatenate((x, np.zeros_like(x[:, :5, :]), cbar), axis=1)
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x = torch.from_numpy(x)
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return x
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