目录
实时图像增强，基于“间距自适应查找表”的方法（CVPR 2022）
https://blog.csdn.net/jacke121/article/details/124777484
https://github.com/HuiZeng/Image-Adaptive-3DLUT
该文是香港理工大学张磊老师及其学生在图像增强领域的又一颠覆性成果。它将深度学习技术与传统3DLUT图像增强技术结合，得到了一种更灵活、更高效的图像增强技术。所提方法能够以1.66ms的速度对4K分辨率图像进行增强(硬件平台：Titan RTX GPU)。
paper: https://www4.comp.polyu.edu.hk/~cslzhang/paper/PAMI_LUT.pdf
code: https://github.com/HuiZeng/Image-Adaptive-3DLUT
需要编译：
trilinear_cpp
from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
if torch.cuda.is_available():
print('Including CUDA code.')
setup(
name='trilinear',
ext_modules=[
CUDAExtension('trilinear', [
'src/trilinear_cuda.cpp',
'src/trilinear_kernel.cu',
])
],
cmdclass={
'build_ext': BuildExtension
})
else:
print('NO CUDA is found. Fall back to CPU.')
setup(name='trilinear',
ext_modules=[CppExtension('trilinear', ['src/trilinear.cpp'])],
cmdclass={'build_ext': BuildExtension})
set DISTUTILS_USE_SDK=1
set MSSdk=1
编译成功，调用 dll找不到，解决方法：
把目录：Lib\site-packages\torch\lib
下面的dll拷贝到pyd目录下面，可以调用了。
https://github.com/MACILLAS/UWCNN
测试代码：
import glob
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, SpatialDropout2D, ReLU, Input, Concatenate, Add
from tensorflow.keras.losses import MeanAbsoluteError, MeanSquaredError
from tensorflow.keras.optimizers import Adam
import os
import pandas as pd
import cv2
class UWCNN(tf.keras.Model):
def __init__(self):
super(UWCNN, self).__init__()
self.conv1 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze1")
self.relu1 = ReLU()
self.conv2 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze2")
self.relu2 = ReLU()
self.conv3 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze3")
self.relu3 = ReLU()
self.concat1 = Concatenate(axis=3)
self.conv4 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze4")
self.relu4 = ReLU()
self.conv5 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze5")
self.relu5 = ReLU()
self.conv6 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze6")
self.relu6 = ReLU()
self.concat2 = Concatenate(axis=3)
self.conv7 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze7")
self.relu7 = ReLU()
self.conv8 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze8")
self.relu8 = ReLU()
self.conv9 = Conv2D(16, 3, (1, 1), 'same', name="conv2d_dehaze9")
self.relu9 = ReLU()
self.concat3 = Concatenate(axis=3)
self.conv10 = Conv2D(3, 3, (1, 1), 'same', name="conv2d_dehaze10")
self.add1 = Add()
def call(self, inputs):
image_conv1 = self.relu1(self.conv1(inputs))
image_conv2 = self.relu2(self.conv2(image_conv1))
image_conv3 = self.relu3(self.conv3(image_conv2))
dehaze_concat1 = self.concat1([image_conv1, image_conv2, image_conv3, inputs])
image_conv4 = self.relu4(self.conv4(dehaze_concat1))
image_conv5 = self.relu5(self.conv5(image_conv4))
image_conv6 = self.relu6(self.conv6(image_conv5))
dehaze_concat2 = self.concat2([dehaze_concat1, image_conv4, image_conv5, image_conv6])
image_conv7 = self.relu7(self.conv7(dehaze_concat2))
image_conv8 = self.relu8(self.conv8(image_conv7))
image_conv9 = self.relu9(self.conv9(image_conv8))
dehaze_concat3 = self.concat3([dehaze_concat2, image_conv7, image_conv8, image_conv9])
image_conv10 = self.conv10(dehaze_concat3)
out = self.add1([inputs, image_conv10])
return out
def parse_function(filename, label):
filename_image_string = tf.io.read_file(filename)
label_image_string = tf.io.read_file(label)
# Decode the filename_image_string
filename_image = tf.image.decode_bmp(filename_image_string, channels=3)
filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)
# Decode the label_image_string
label_image = tf.image.decode_bmp(label_image_string, channels=3)
label_image = tf.image.convert_image_dtype(label_image, tf.float32)
return filename_image, label_image
def combloss (y_actual, y_predicted):
'''
This is the custom loss function for keras model
:param y_actual:
:param y_predicted:
:return:
'''
# this is just l2 + lssim
lssim = tf.constant(1, dtype=tf.float32) - tf.reduce_mean(tf.image.ssim(y_actual, y_predicted, max_val=1, filter_size=13)) #remove max_val=1.0
lmse = MeanSquaredError(reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)(y_actual, y_predicted)
lmse = tf.math.multiply(lmse, 4)
return tf.math.add(lmse, lssim)
def train(datafile="data.csv", ckptpath="./train_type1/cp.ckpt", type='type1'):
df = pd.read_csv(datafile)
augfiles = list(df["AUGFILE"])
gtfiles = list(df["GTFILE"])
augImages = tf.constant(augfiles)
gtImages = tf.constant(gtfiles)
dataset = tf.data.Dataset.from_tensor_slices((augImages, gtImages))
dataset = dataset.shuffle(len(augImages))
#dataset = dataset.repeat()
dataset = dataset.map(parse_function).batch(10)
# Call backs
#checkpoint_path = "./train_type1/cp.ckpt"
checkpoint_path = ckptpath
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)
model = UWCNN()
model.compile(optimizer=Adam(), loss=combloss)
model.fit(dataset, epochs=40, callbacks=[cp_callback])
os.listdir(checkpoint_dir)
#model.save('saved_model/my_model')
model.save('save_model/'+type)
def model_test(imgfile="12433.png", ckdir="./train_type1/cp.ckpt", outdir="./results/", type='type1'):
# model = tf.keras.models.load_model('save_model/'+type, custom_objects={'loss': combloss}, compile=False)
model = UWCNN()
# model.summary()
model.compile(optimizer=Adam(), loss=combloss)
model.load_weights(ckdir)
filename_image_string = tf.io.read_file(imgfile)
filename_image = tf.image.decode_png(filename_image_string, channels=3)
filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)
filename_image = tf.image.resize(filename_image, (460, 620))
l, w, c = filename_image.shape
filename_image = tf.reshape(filename_image, [1, l, w, c])
output = model.predict(filename_image)
output = output.reshape((l, w, c)) * 255
cv2.imwrite(outdir+type+"_"+os.path.basename(imgfile), output)
def eval_dir():
dir_a=r'F:\project\zengqiang_shuixia\raw-890-s\raw_img'
out_dir=r'F:\project\zengqiang_shuixia\raw-890-s\result/'
files=glob.glob(dir_a+'/*.png')
model = UWCNN()
# model.summary()
model.compile(optimizer=Adam(), loss=combloss)
model.load_weights(ckdir)
for imgfile in files:
filename_image_string = tf.io.read_file(imgfile)
filename_image = tf.image.decode_png(filename_image_string, channels=3)
filename_image = tf.image.convert_image_dtype(filename_image, tf.float32)
filename_image = tf.image.resize(filename_image, (256, 256))
l, w, c = filename_image.shape
filename_image = tf.reshape(filename_image, [1, l, w, c])
output = model.predict(filename_image)
output = output.reshape((l, w, c)) * 255
cv2.imwrite(out_dir+ os.path.basename(imgfile), output)
if __name__ == "__main__":
# train(datafile="data_type1.csv", ckptpath="./train_type1/cp.ckpt", type='type1')
type = "type1"
ckdir = "./train_type1/cp.ckpt"
# model_test(imgfile="./test_images/532_img_.png", ckdir=ckdir, outdir="./results/", type=type)
eval_dir()
# model_test(imgdir="./test_images/", imgfile="602_img_.png", ckdir=ckdir, outdir="./results/", type=type)
# model_test(imgdir="./test_images/", imgfile="617_img_.png", ckdir=ckdir, outdir="./results/", type=type)
# model_test(imgdir="./test_images/", imgfile="12422.png", ckdir=ckdir, outdir="./results/", type=type)
# model_test(imgdir="./test_images/", imgfile="12433.png", ckdir=ckdir, outdir="./results/", type=type)
# This code normalizes the output images in HSI space
# Code Inspired By: Li C, Anwar S, Porikli F. Underwater scene prior inspired deep learning image and video enhancement[J]. Pattern Recognition, 2020, 98: 107038
# Implementation By: Max Midwinter
#HSI and RGB conversion code by: DaiPuWei
import os
import cv2
import numpy as np
import math
def RGB2HSI(rgb_img):
"""
This is the function to convert RGB color image to HSI image
:param rgm_img: RGB color image
:return: HSI image
"""
#Save the number of rows and columns of the original image
row = np.shape(rgb_img)[0]
col = np.shape(rgb_img)[1]
#Copy the original image
hsi_img = rgb_img.copy()
#Channel splitting the image
B,G,R = cv2.split(rgb_img)
#R, G, B = cv2.split(rgb_img)
# Normalize the channel to [0,1]
[B,G,R] = [ i/ 255.0 for i in ([B,G,R])]
H = np.zeros((row, col)) #Define H channel
I = (R + G + B) / 3.0 #Calculate I channel
S = np.zeros((row,col)) #Define S channel
for i in range(row):
den = np.sqrt((R[i]-G[i])**2+(R[i]-B[i])*(G[i]-B[i]))
thetha = np.arccos(0.5*(R[i]-B[i]+R[i]-G[i])/den) #Calculate the included angle
h = np.zeros(col) #Define temporary array
#den>0 and G>=B element h is assigned to thetha
h[B[i]<=G[i]] = thetha[B[i]<=G[i]]
#den>0 and G<=B element h is assigned to thetha
h[G[i]<B[i]] = 2*np.pi-thetha[G[i]<B[i]]
#den<0 element h is assigned a value of 0
h[den == 0] = 0
H[i] = h/(2*np.pi) #Assign to the H channel after radiating
#Calculate S channel
for i in range(row):
min = []
#Find the minimum value of each group of RGB values
for j in range(col):
arr = [B[i][j],G[i][j],R[i][j]]
min.append(np.min(arr))
min = np.array(min)
#Calculate S channel
S[i] = 1 - min*3/(R[i]+B[i]+G[i])
#I is 0 directly assigned to 0
S[i][R[i]+B[i]+G[i] == 0] = 0
#Extend to 255 for easy display, generally H component is between [0,2pi], S and I are between [0,1]
hsi_img[:,:,0] = H*255
hsi_img[:,:,1] = S*255
hsi_img[:,:,2] = I*255
return hsi_img
def HSI2RGB(hsi_img):
"""
This is the function to convert HSI image to RGB image
:param hsi_img: HSI color image
:return: RGB image
"""
# Save the number of rows and columns of the original image
row = np.shape(hsi_img)[0]
col = np.shape(hsi_img)[1]
#Copy the original image
rgb_img = hsi_img.copy()
#Channel splitting the image
H,S,I = cv2.split(hsi_img)
# Normalize the channel to [0,1]
[H,S,I] = [ i/ 255.0 for i in ([H,S,I])]
R,G,B = H,S,I
for i in range(row):
h = H[i]*2*np.pi
#H is greater than or equal to 0 and less than 120 degrees
a1 = h >=0
a2 = h < 2*np.pi/3
a = a1 & a2 #Fancy index of the first case
tmp = np.cos(np.pi / 3 - h)
b = I[i] * (1 - S[i])
r = I[i]*(1+S[i]*np.cos(h)/tmp)
g = 3*I[i]-r-b
B[i][a] = b[a]
R[i][a] = r[a]
G[i][a] = g[a]
#H is greater than or equal to 120 degrees and less than 240 degrees
a1 = h >= 2*np.pi/3
a2 = h < 4*np.pi/3
a = a1 & a2 #Fancy index of the second case
tmp = np.cos(np.pi - h)
r = I[i] * (1 - S[i])
g = I[i]*(1+S[i]*np.cos(h-2*np.pi/3)/tmp)
b = 3 * I[i] - r - g
R[i][a] = r[a]
G[i][a] = g[a]
B[i][a] = b[a]
#H is greater than or equal to 240 degrees and less than 360 degrees
a1 = h >= 4 * np.pi / 3
a2 = h < 2 * np.pi
a = a1 & a2 #Fancy index of the third case
tmp = np.cos(5 * np.pi / 3 - h)
g = I[i] * (1-S[i])
b = I[i]*(1+S[i]*np.cos(h-4*np.pi/3)/tmp)
r = 3 * I[i] - g - b
B[i][a] = b[a]
G[i][a] = g[a]
R[i][a] = r[a]
rgb_img[:,:,0] = B*255
rgb_img[:,:,1] = G*255
rgb_img[:,:,2] = R*255
return rgb_img
def transform (dir = None):
img = cv2.imread(dir)
hsi_img = RGB2HSI(img)/255
h, s, i = cv2.split(hsi_img)
s = cv2.normalize(s, dst=None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
i = cv2.normalize(i, dst=None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
norm_hsi_img = cv2.merge((h, s, i))
norm_hsi_img = norm_hsi_img*255
rgb_img = HSI2RGB(norm_hsi_img)
cv2.imwrite(dir, rgb_img)
def allImgInDir (path = './results'):
fname = []
for root, d_names, f_names in os.walk(path):
for f in f_names:
fname.append(os.path.join(root, f))
print("File: "+str(f))
transform(os.path.join(root, f))
print("fname %s" % fname)
if __name__ == "__main__":
allImgInDir('./results')GitHub - ioannispol/UnderWaterGAN: CycleGAN model to generate images with underwater features
5年前：
GitHub - aitorzip/PyTorch-CycleGAN: A clean and readable Pytorch implementation of CycleGAN