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library.py
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library.py
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import os
import math
import copy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from tqdm import tqdm
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
import time
from torchvision.io import read_image
import pandas
import cv2
from torchvision.transforms import ToTensor
import numpy as np
from torchvision import transforms
from mpl_toolkits.axes_grid1 import make_axes_locatable
import scipy.signal as signal
def my_readimage(image_path):
'''
读入图片:[0-255]array(256, 256, 3) ->[0,1]tensor torch.Size([1, 256, 256])
'''
imgcv = cv2.imread(image_path)
transform = transforms.Compose([
transforms.ToTensor()
])
imgcv = cv2.cvtColor(imgcv, cv2.COLOR_BGR2GRAY) #三通道转换为单通道
imgcvb = transform(imgcv) #将一个取值范围在[0,255]的numpy.ndarray图像转换为[0,1.0]的torch.FloadTensor图像，同时各维度的顺序也将自动调整。
return imgcvb
def my_saveimage(matrix,image_path,cmap='viridis',dpi=200):
'''
matrix:float32 [H,W]
'''
plt.clf() # 清图。
plt.cla() # 清坐标轴
plt.figure(figsize=(6, 6))
imgplot = plt.imshow(matrix,cmap=cmap)
plt.colorbar()
plt.savefig(image_path,dpi=dpi)
def my_save2image(matrix1, matrix2, image_path, cmap='viridis'):
'''
matrix1, matrix2: float32 [H,W] - 分别代表两个要显示的图像矩阵
image_path: 保存图像的路径
cmap: 颜色映射
'''
plt.clf() # 清图。
plt.cla() # 清坐标轴
plt.figure(figsize=(12, 6)) # 设定图像大小
# 显示第一个图像
plt.subplot(1, 2, 1)
imgplot1 = plt.imshow(matrix1, cmap=cmap)
plt.colorbar() # 为第一个图像添加颜色条
# 显示第二个图像
plt.subplot(1, 2, 2)
imgplot2 = plt.imshow(matrix2, cmap=cmap)
plt.colorbar() # 为第二个图像添加颜色条
plt.savefig(image_path) # 保存图像
def my_saveimage_plus(matrix,image_path):
'''
matrix:float32 [H,W]
'''
plt.figure(dpi=1000) # 清图。
ax = plt.subplot()
im = ax.imshow(matrix,resample=True)
# create an Axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(image_path)
def my_savetxt(matrix,txt_path):
'''
matrix:float32 [H,W]
'''
np.savetxt(txt_path,matrix,fmt='%.10e',delimiter=",") #frame: 相位图 array:存入文件的数组
def my_readtxt(txt_path):
matrix = np.loadtxt(txt_path,dtype=np.float32,delimiter=",") # frame:文件
return matrix
def mkdir(path):
folder = os.path.exists(path)
if not folder: #判断是否存在文件夹如果不存在则创建为文件夹
os.makedirs(path) #makedirs 创建文件时如果路径不存在会创建这个路径
print("--- new folder... ---")
print("--- OK ---")
else:
print("--- There is this folder! ---")
def visual_data(dataloader,root_path):
for x,y in dataloader:
print(f'shape of input [N,C,H,W]:{x.shape},{x.dtype} {x.max()}')
print(f'shape of output:{y.shape},{x.dtype}')
# print(f'all attribute of x:{dir(x)}')
'''
torchvision.utils.save_image(tensor, fp)
# 参数
# tensor(Tensor or list)：待保存的tensor数据（可以是上述处理好的grid）。如果给以一个四维的batch的tensor，将调用网格方法，然后再保存到本地。最后保存的图片是不带batch的。
# fp：图片保存路径
'''
# torchvision.utils.save_image(x/x.max(),f'{root_path}/input.jpg')
# torchvision.utils.save_image(y/y.max(),f'{root_path}/label.jpg')
my_saveimage(x.reshape(x.shape[2],x.shape[3]),f'{root_path}/input.png')
my_saveimage(y.reshape(x.shape[2],x.shape[3]),f'{root_path}/label.png')
my_savetxt(x.reshape(x.shape[2],x.shape[3]),f'{root_path}/input.txt')
my_savetxt(y.reshape(x.shape[2],x.shape[3]),f'{root_path}/label.txt')
# print(f'label:{y}')
break
def result_visual(pred_ref_path,pred_sam_path,gt_ref_path,gt_sam_path,save_path,cmap='viridis'):
pred_ref = my_readtxt(pred_ref_path)
gt_ref = my_readtxt(gt_ref_path)
pred_sam = my_readtxt(pred_sam_path)
gt_sam = my_readtxt(gt_sam_path)
pred_sam_pred_ref = sam_ref(pred_sam,pred_ref)
pred_sam_gt_ref = sam_ref(pred_sam,gt_ref)
gt_sam_pred_ref = sam_ref(gt_sam,pred_ref)
gt_sam_gt_ref = sam_ref(gt_sam,gt_ref)
my_saveimage(pred_sam_pred_ref,f'{save_path}/pred_sam_pred_ref.png',cmap)
my_saveimage(pred_sam_gt_ref,f'{save_path}/pred_sam_gt_ref.png',cmap)
my_saveimage(gt_sam_pred_ref,f'{save_path}/gt_sam_pred_ref.png',cmap)
my_saveimage(gt_sam_gt_ref,f'{save_path}/gt_sam_gt_ref.png',cmap)
# my_saveimage(gt_sam_gt_ref,f'{save_path}/{cmap}_gt_sam_gt_ref.png',cmap)
def sam_ref(sam,ref,scale='pi'):
"""样品畸变相位减光纤畸变相位得到样品的相位
Args:
sam (numpy array): 样品畸变相位
ref (numpy array): 光纤畸变相位
scale (str, optional): 输入的是pi还是2pi. Defaults to 'pi'.
Returns:
numpy array: 样品的相位
"""
if scale=='pi':
diff = (sam - ref + 2.3)%(np.pi)
diff = signal.medfilt(diff,(7,7)) #二维中值滤波
elif scale=='2pi':
diff = (sam - ref + 4.1)%(np.pi*2)
diff = signal.medfilt(diff,(11,11)) #二维中值滤波
return diff
def PaddingImage(img,original_width,original_height,target_width, target_height):
"""Pad the image with zeros to expand to a fixed size.
Args:
img (_type_): input little image
original_width (_type_): width of original image
original_height (_type_): height of original image
target_width (_type_): width of target image
target_height (_type_): height of target image
Returns:
extended_image: Image after pixel padding
"""
x_padding = target_width - original_width
y_padding = target_height - original_height
# 使用copyMakeBorder函数在原始图片的周围添加像素
# blk_constant参数指定添加的像素颜色，这里是0（黑色）
extended_image = cv2.copyMakeBorder(img, y_padding//2, y_padding//2, x_padding//2, x_padding//2, cv2.BORDER_CONSTANT, value=0)
return extended_image
def sam_ref_2pi(sam,ref):
diff = (sam - ref + 4.1)%(np.pi*2)
# diff = (sam - ref)%(np.pi*2)
diff = signal.medfilt(diff,(11,11)) #二维中值滤波
return diff
if __name__=='__main__':
# cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis','Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
# 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
# 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn','binary', 'gist_yarg', 'gist_gray', 'gray', 'bone',
# 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool',
# 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper','PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu',
# 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic','twilight', 'twilight_shifted', 'hsv','flag', 'prism', 'ocean', 'gist_earth', 'terrain',
# 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap',
# 'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet',
# 'turbo', 'nipy_spectral', 'gist_ncar']
ref_path = '/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/result/1536_1536_ref_pi_01_prop_pi/2024-02-01-18-15/img_txt_folder/9000_pred.txt'
sam_path= '/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/result/1536_1536_sam_pi_01_prop_pi/2024-02-01-18-53/img_txt_folder/4500_pred.txt'
gt_ref_path = '/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/1536_1536_ref_pi_01_prop_pi.txt'
gt_sam_path = '/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/1536_1536_sam_pi_01_prop_pi.txt'
# ref = my_readtxt(sam_path)
# sam = my_readtxt(gt_sam_path)
# diff = (sam - ref)
save_path = '/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/result_visual/baseline'
# my_saveimage(diff,save_path)
# for cmap in tqdm(cmaps):
# result_visual(ref_path,sam_path,gt_ref_path,gt_sam_path,save_path,cmap)
result_visual(ref_path,sam_path,gt_ref_path,gt_sam_path,save_path)
# ref = my_readtxt(ref_path)
# sam = my_readtxt(gt_sam_path)
# diff = (sam - ref + 2.3)%(np.pi)
# import scipy.signal as signal
# diff = signal.medfilt(diff,(7,7)) #二维中值滤波
# save_path = './ref_ref.png'
# my_saveimage(diff,save_path)
# dif = my_readtxt('/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/512_512_imagenet_prop_pi.txt')
# dif640 = np.pad(dif,(64,64),'constant')
# noise = (np.random.random((512,512))+np.random.random((512,512))+np.random.random((512,512)))/3
# ref = noise*np.pi
# ref640 = np.pad(ref,(64,64),'constant')
# noise = my_readtxt('/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/1536_1536_phase_ref_prop_pi.txt')
# ref = noise[768-256:768+256,768-256:768+256]
# ref640 = np.pad(ref,(64,64),'constant')
# sam640 = (dif640+ref640)/np.pi
# # # print(ref.shape())
# # # print(ref640.shape())
# # my_saveimage(ref640,'./temp.png')
# my_saveimage(dif640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/dif640.png')
# my_savetxt(dif640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/dif640.txt')
# my_saveimage(ref640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/ref640.png')
# my_savetxt(ref640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/ref640.txt')
# my_saveimage(sam640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/sam640.png')
# my_savetxt(sam640,'/mnt/data/optimal/tangyuhang/workspace/iopen/ai4optical/phynet_git/traindata/gt/sam640.txt')