Commit bb21007b authored by Jiangxin Dong's avatar Jiangxin Dong
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parent dcbcdded
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
# import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def imshow(x, title=None, cbar=False, figsize=None):
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show()
def surf(Z, cmap='rainbow', figsize=None):
plt.figure(figsize=figsize)
ax3 = plt.axes(projection='3d')
w, h = Z.shape[:2]
xx = np.arange(0,w,1)
yy = np.arange(0,h,1)
X, Y = np.meshgrid(xx, yy)
ax3.plot_surface(X,Y,Z,cmap=cmap)
#ax3.contour(X,Y,Z, zdim='z',offset=-2,cmap=cmap)
plt.show()
'''
# --------------------------------------------
# get image pathes
# --------------------------------------------
'''
def get_image_paths(dataroot):
paths = None # return None if dataroot is None
if dataroot is not None:
paths = sorted(_get_paths_from_images(dataroot))
return paths
def _get_paths_from_images(path):
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
if is_image_file(fname):
img_path = os.path.join(dirpath, fname)
images.append(img_path)
assert images, '{:s} has no valid image file'.format(path)
return images
'''
# --------------------------------------------
# split large images into small images
# --------------------------------------------
'''
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
w1.append(w-p_size)
h1.append(h-p_size)
# print(w1)
# print(h1)
for i in w1:
for j in h1:
patches.append(img[i:i+p_size, j:j+p_size,:])
else:
patches.append(img)
return patches
def imssave(imgs, img_path):
"""
imgs: list, N images of size WxHxC
"""
img_name, ext = os.path.splitext(os.path.basename(img_path))
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
new_path = os.path.join(os.path.dirname(img_path), img_name+str('_{:04d}'.format(i))+'.png')
cv2.imwrite(new_path, img)
def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
will be splitted.
Args:
original_dataroot:
taget_dataroot:
p_size: size of small images
p_overlap: patch size in training is a good choice
p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
"""
paths = get_image_paths(original_dataroot)
for img_path in paths:
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path)))
#if original_dataroot == taget_dataroot:
#del img_path
'''
# --------------------------------------------
# makedir
# --------------------------------------------
'''
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path)
'''
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
'''
# --------------------------------------------
# get uint8 image of size HxWxn_channles (RGB)
# --------------------------------------------
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
def imsave(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
img = img.astype(np.float32) / 255.
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
'''
# --------------------------------------------
# image format conversion
# --------------------------------------------
# numpy(single) <---> numpy(uint)
# numpy(single) <---> tensor
# numpy(uint) <---> tensor
# --------------------------------------------
'''
# --------------------------------------------
# numpy(single) [0, 1] <---> numpy(uint)
# --------------------------------------------
def uint2single(img):
return np.float32(img/255.)
def single2uint(img):
return np.uint8((img.clip(0, 1)*255.).round())
def uint162single(img):
return np.float32(img/65535.)
def single2uint16(img):
return np.uint16((img.clip(0, 1)*65535.).round())
# --------------------------------------------
# numpy(uint) (HxWxC or HxW) <---> tensor
# --------------------------------------------
# convert uint to 4-dimensional torch tensor
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
# convert 2/3/4-dimensional torch tensor to uint
def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return np.uint8((img*255.0).round())
# --------------------------------------------
# numpy(single) (HxWxC) <---> tensor
# --------------------------------------------
# convert single (HxWxC) to 3-dimensional torch tensor
def single2tensor3(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
# convert torch tensor to single
def tensor2single(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return img
# convert torch tensor to single
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
elif img.ndim == 2:
img = np.expand_dims(img, axis=2)
return img
def single2tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
def single32tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
def single42tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.uint8() WILL NOT round by default.
return img_np.astype(out_type)
'''
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
'''
def augment_img(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return np.flipud(np.rot90(img))
elif mode == 2:
return np.flipud(img)
elif mode == 3:
return np.rot90(img, k=3)
elif mode == 4:
return np.flipud(np.rot90(img, k=2))
elif mode == 5:
return np.rot90(img)
elif mode == 6:
return np.rot90(img, k=2)
elif mode == 7:
return np.flipud(np.rot90(img, k=3))
def augment_img_tensor4(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return img.rot90(1, [2, 3]).flip([2])
elif mode == 2:
return img.flip([2])
elif mode == 3:
return img.rot90(3, [2, 3])
elif mode == 4:
return img.rot90(2, [2, 3]).flip([2])
elif mode == 5:
return img.rot90(1, [2, 3])
elif mode == 6:
return img.rot90(2, [2, 3])
elif mode == 7:
return img.rot90(3, [2, 3]).flip([2])
def augment_img_tensor(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
img_np = np.transpose(img_np, (1, 2, 0))
elif len(img_size) == 4:
img_np = np.transpose(img_np, (2, 3, 1, 0))
img_np = augment_img(img_np, mode=mode)
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
if len(img_size) == 3:
img_tensor = img_tensor.permute(2, 0, 1)
elif len(img_size) == 4:
img_tensor = img_tensor.permute(3, 2, 0, 1)
return img_tensor.type_as(img)
def augment_img_np3(img, mode=0):
if mode == 0:
return img
elif mode == 1:
return img.transpose(1, 0, 2)
elif mode == 2:
return img[::-1, :, :]
elif mode == 3:
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 4:
return img[:, ::-1, :]
elif mode == 5:
img = img[:, ::-1, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 6:
img = img[:, ::-1, :]
img = img[::-1, :, :]
return img
elif mode == 7:
img = img[:, ::-1, :]
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
def augment_imgs(img_list, hflip=True, rot=True):
# horizontal flip OR rotate
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip:
img = img[:, ::-1, :]
if vflip:
img = img[::-1, :, :]
if rot90:
img = img.transpose(1, 0, 2)
return img
return [_augment(img) for img in img_list]
'''
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
'''
def modcrop(img_in, scale):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r, :]
else:
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
return img
def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
img = img[border:h-border, border:w-border]
return img
'''
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
# rgb2ycbcr(img, only_y=True):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
'''
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def ycbcr2rgb(img):
'''same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def bgr2ycbcr(img, only_y=True):
'''bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def channel_convert(in_c, tar_type, img_list):
# conversion among BGR, gray and y
if in_c == 3 and tar_type == 'gray': # BGR to gray
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
return [np.expand_dims(img, axis=2) for img in gray_list]
elif in_c == 3 and tar_type == 'y': # BGR to y
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
return [np.expand_dims(img, axis=2) for img in y_list]
elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
else:
return img_list
'''
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
'''