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import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import matplotlib as mpl
from matplotlib import cm
import cv2
import os
from datetime import datetime
import shutil
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp
import lpips
lpips_alex = lpips.LPIPS(net="alex") # best forward scores
lpips_vgg = lpips.LPIPS(
net="vgg"
) # closer to "traditional" perceptual loss, when used for optimization
HUGE_NUMBER = 1e10
TINY_NUMBER = 1e-6 # float32 only has 7 decimal digits precision
img_HWC2CHW = lambda x: x.permute(2, 0, 1)
gray2rgb = lambda x: x.unsqueeze(2).repeat(1, 1, 3)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
mse2psnr = lambda x: -10.0 * np.log(x + TINY_NUMBER) / np.log(10.0)
def save_current_code(outdir):
now = datetime.now() # current date and time
date_time = now.strftime("%m_%d-%H:%M:%S")
src_dir = "."
dst_dir = os.path.join(outdir, "code_{}".format(date_time))
shutil.copytree(
src_dir,
dst_dir,
ignore=shutil.ignore_patterns(
"data*",
"pretrained*",
"logs*",
"out*",
"*.png",
"*.mp4",
"*__pycache__*",
"*.git*",
"*.idea*",
"*.zip",
"*.jpg",
),
)
def img2mse(x, y, mask=None):
"""
:param x: img 1, [(...), 3]
:param y: img 2, [(...), 3]
:param mask: optional, [(...)]
:return: mse score
"""
if mask is None:
return torch.mean((x - y) * (x - y))
else:
return torch.sum((x - y) * (x - y) * mask.unsqueeze(-1)) / (
torch.sum(mask) * x.shape[-1] + TINY_NUMBER
)
def img2psnr(x, y, mask=None):
return mse2psnr(img2mse(x, y, mask).item())
def cycle(iterable):
while True:
for x in iterable:
yield x
def get_vertical_colorbar(h, vmin, vmax, cmap_name="jet", label=None, cbar_precision=2):
"""
:param w: pixels
:param h: pixels
:param vmin: min value
:param vmax: max value
:param cmap_name:
:param label
:return:
"""
fig = Figure(figsize=(2, 8), dpi=100)
fig.subplots_adjust(right=1.5)
canvas = FigureCanvasAgg(fig)
# Do some plotting.
ax = fig.add_subplot(111)
cmap = cm.get_cmap(cmap_name)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
tick_cnt = 6
tick_loc = np.linspace(vmin, vmax, tick_cnt)
cb1 = mpl.colorbar.ColorbarBase(
ax, cmap=cmap, norm=norm, ticks=tick_loc, orientation="vertical"
)
tick_label = [str(np.round(x, cbar_precision)) for x in tick_loc]
if cbar_precision == 0:
tick_label = [x[:-2] for x in tick_label]
cb1.set_ticklabels(tick_label)
cb1.ax.tick_params(labelsize=18, rotation=0)
if label is not None:
cb1.set_label(label)
fig.tight_layout()
canvas.draw()
s, (width, height) = canvas.print_to_buffer()
im = np.frombuffer(s, np.uint8).reshape((height, width, 4))
im = im[:, :, :3].astype(np.float32) / 255.0
if h != im.shape[0]:
w = int(im.shape[1] / im.shape[0] * h)
im = cv2.resize(im, (w, h), interpolation=cv2.INTER_AREA)
return im
def colorize_np(
x,
cmap_name="jet",
mask=None,
range=None,
append_cbar=False,
cbar_in_image=False,
cbar_precision=2,
):
"""
turn a grayscale image into a color image
:param x: input grayscale, [H, W]
:param cmap_name: the colorization method
:param mask: the mask image, [H, W]
:param range: the range for scaling, automatic if None, [min, max]
:param append_cbar: if append the color bar
:param cbar_in_image: put the color bar inside the image to keep the output image the same size as the input image
:return: colorized image, [H, W]
"""
if range is not None:
vmin, vmax = range
elif mask is not None:
# vmin, vmax = np.percentile(x[mask], (2, 100))
vmin = np.min(x[mask][np.nonzero(x[mask])])
vmax = np.max(x[mask])
# vmin = vmin - np.abs(vmin) * 0.01
x[np.logical_not(mask)] = vmin
# print(vmin, vmax)
else:
vmin, vmax = np.percentile(x, (1, 100))
vmax += TINY_NUMBER
x = np.clip(x, vmin, vmax)
x = (x - vmin) / (vmax - vmin)
# x = np.clip(x, 0., 1.)
cmap = cm.get_cmap(cmap_name)
x_new = cmap(x)[:, :, :3]
if mask is not None:
mask = np.float32(mask[:, :, np.newaxis])
x_new = x_new * mask + np.ones_like(x_new) * (1.0 - mask)
cbar = get_vertical_colorbar(
h=x.shape[0], vmin=vmin, vmax=vmax, cmap_name=cmap_name, cbar_precision=cbar_precision
)
if append_cbar:
if cbar_in_image:
x_new[:, -cbar.shape[1] :, :] = cbar
else:
x_new = np.concatenate((x_new, np.zeros_like(x_new[:, :5, :]), cbar), axis=1)
return x_new
else:
return x_new
# tensor
def colorize(x, cmap_name="jet", mask=None, range=None, append_cbar=False, cbar_in_image=False):
device = x.device
x = x.cpu().numpy()
if mask is not None:
mask = mask.cpu().numpy() > 0.99
kernel = np.ones((3, 3), np.uint8)
mask = cv2.erode(mask.astype(np.uint8), kernel, iterations=1).astype(bool)
x = colorize_np(x, cmap_name, mask, range, append_cbar, cbar_in_image)
x = torch.from_numpy(x).to(device)
return x
def gaussian(window_size, sigma):
gauss = torch.Tensor(
[exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) for x in range(window_size)]
)
return gauss / gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def _ssim(img1, img2, window, window_size, channel, size_average=True):
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
(mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
)
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
class SSIM(torch.nn.Module):
def __init__(self, window_size=11, size_average=True):
super(SSIM, self).__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.window = create_window(window_size, self.channel)
def forward(self, img1, img2):
(_, channel, _, _) = img1.size()
if channel == self.channel and self.window.data.type() == img1.data.type():
window = self.window
else:
window = create_window(self.window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
self.window = window
self.channel = channel
return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
def ssim_utils(img1, img2, window_size=11, size_average=True):
(_, channel, _, _) = img1.size()
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, size_average)
def ssim(img1, img2, window_size=11, size_average=True, format="NCHW"):
if format == "HWC":
img1 = img1.permute([2, 0, 1])[None, ...]
img2 = img2.permute([2, 0, 1])[None, ...]
elif format == "NHWC":
img1 = img1.permute([0, 3, 1, 2])
img2 = img2.permute([0, 3, 1, 2])
return ssim_utils(img1, img2, window_size, size_average)
def lpips(img1, img2, net="vgg", format="NCHW"):
if format == "HWC":
img1 = img1.permute([2, 0, 1])[None, ...]
img2 = img2.permute([2, 0, 1])[None, ...]
elif format == "NHWC":
img1 = img1.permute([0, 3, 1, 2])
img2 = img2.permute([0, 3, 1, 2])
if net == "alex":
return lpips_alex(img1, img2)
elif net == "vgg":
return lpips_vgg(img1, img2)
def concat_images(img0,img1,vert=False):
if not vert:
h0,h1=img0.shape[0],img1.shape[0],
if h0<h1: img0=cv2.copyMakeBorder(img0,0,h1-h0,0,0,borderType=cv2.BORDER_CONSTANT,value=0)
if h1<h0: img1=cv2.copyMakeBorder(img1,0,h0-h1,0,0,borderType=cv2.BORDER_CONSTANT,value=0)
img = np.concatenate([img0, img1], axis=1)
else:
w0,w1=img0.shape[1],img1.shape[1]
if w0<w1: img0=cv2.copyMakeBorder(img0,0,0,0,w1-w0,borderType=cv2.BORDER_CONSTANT,value=0)
if w1<w0: img1=cv2.copyMakeBorder(img1,0,0,0,w0-w1,borderType=cv2.BORDER_CONSTANT,value=0)
img = np.concatenate([img0, img1], axis=0)
return img
def concat_images_list(*args,vert=False):
if len(args)==1: return args[0]
img_out=args[0]
for img in args[1:]:
img_out=concat_images(img_out,img,vert)
return img_out |