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dataReader.py
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dataReader.py
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import os
import glob
import tensorflow as tf
from random import shuffle
import helpers
import math
import renderer
import multiprocessing
import numpy as np
class dataset:
inputPath = ""
trainFolder = "train"
testFolder = "test"
pathList = []
imageFormat = "png"
which_direction = "AtoB"
nbTargetsToRead = 4
logInput = False
fixCrop = False
mixMaterials = True
useAmbientLight = False
useAugmentationInRenderings = True
tileSize = 256
inputImageSize = 288
batchSize = 1
iterator = None
inputBatch = None
targetBatch = None
pathBatch = None
gammaCorrectedInputsBatch = None
stepsPerEpoch = 0
#Some default constructor with most important parameters
def __init__(self, inputPath, trainFolder = "train", testFolder = "test", nbTargetsToRead = 4, tileSize=256, inputImageSize=288, batchSize=1, imageFormat = "png", which_direction = "AtoB", fixCrop = False, mixMaterials = True, logInput = False, useAmbientLight = False, useAugmentationInRenderings = True):
self.inputPath = inputPath
self.trainFolder = trainFolder
self.testFolder = testFolder
self.nbTargetsToRead = nbTargetsToRead
self.tileSize = tileSize
self.inputImageSize = inputImageSize
self.batchSize = batchSize
self.imageFormat = imageFormat
self.fixCrop = fixCrop
self.mixMaterials = mixMaterials
self.logInput = logInput
self.useAmbientLight = useAmbientLight
self.useAugmentationInRenderings = useAugmentationInRenderings
#Public function to populate the list of path for this dataset
def loadPathList(self, inputMode, runMode, randomizeOrder, inputpythonList):
if self.inputPath is None or not os.path.exists(self.inputPath):
raise ValueError("The input path doesn't exist :(!")
if inputMode == "folder":
self.__loadFromDirectory(runMode, randomizeOrder)
if inputMode == "image":
self.pathList = [self.inputPath]
if inputMode == "pythonList":
self.pathList = inputpythonList
#Handles the reading of files from a directory
def __loadFromDirectory(self, runMode, randomizeOrder = True):
modeFolder = ""
if runMode == "train" or runMode == "finetune":
modeFolder = self.trainFolder
elif runMode == "test":
modeFolder = self.testFolder
path = os.path.join(self.inputPath, modeFolder)
fileList = []
for rootDir, dirs, filenames in os.walk(path):
for filename in filenames:
if filename.endswith(self.imageFormat):
fileList.append(os.path.join(rootDir, filename))
fileList = sorted(fileList)
if randomizeOrder:
shuffle(fileList)
if not fileList:
raise ValueError("The list of filepaths is empty :( : " + path)
self.pathList = fileList
#Handles the reading of a material + examples
def __readImagesGT(self, filename):
material_string = tf.read_file(filename) #Gets a string tensor from a file
decodedInput = tf.image.decode_image(material_string) #Decode a string tensor as image
floatMaterial = tf.image.convert_image_dtype(decodedInput, dtype=tf.float32) #Transform image to float32
assertion = tf.assert_equal(tf.shape(floatMaterial)[-1], 3, message="image does not have 3 channels")
with tf.control_dependencies([assertion]):
#floatInput = tf.identity(floatInput)#Not sure why the use of a identity node is required here (related to the control dependency apparently). Maybe will need to readdit
floatMaterial.set_shape([None, None, 3])
targets = tf.stack(tf.split(floatMaterial, self.nbTargetsToRead, axis=1, name="Split_input_data1"), axis = 0)
return filename, targets
#Handles the reading of a single image
def __readImages(self,filename):
image_string = tf.read_file(filename) #Gets a string tensor from a file
decodedInput = tf.image.decode_image(image_string) #Decode a string tensor as image
floatInput = tf.image.convert_image_dtype(decodedInput, dtype=tf.float32) #Transform image to float32
assertion = tf.assert_equal(tf.shape(floatInput)[-1], 3, message="image does not have 3 channels")
with tf.control_dependencies([assertion]):
floatInput.set_shape([None, None, 3])
gammadInput = floatInput
#print("CAREFUL THE GAMMA IS NOT CORRECTED AUTOMATICALLY")
#input = floatInput
input = tf.pow(floatInput, 2.2) #correct for the gamma
#If we want to log the inputs, we do it here
if self.logInput:
input = helpers.logTensor(input)
#The preprocess function puts the vectors value between [-1; 1] from [0;1]
input = helpers.preprocess(input)
targets = tf.zeros(tf.shape(input)) # is here (None, None, 3)
targets = tf.expand_dims(targets, axis = 0)
targets = tf.tile(targets, (self.nbTargetsToRead, 1,1,1))
return filename, input, targets, gammadInput
def __readMaterial(self, material):
image_string = tf.read_file(material) #Gets a string tensor from a file
decodedInput = tf.image.decode_image(image_string) #Decode a string tensor as image
floatMaterial = tf.image.convert_image_dtype(decodedInput, dtype=tf.float32) #Transform image to float32
return material, tf.split(floatMaterial, self.nbTargetsToRead, axis=1, name="Split_input_data1")
def __renderInputs(self, materials, renderingScene, jitterLightPos, jitterViewPos, mixMaterials):
fullSizeMixedMaterial = materials
if mixMaterials:
alpha = tf.random_uniform([1], minval=0.1, maxval=0.9, dtype=tf.float32, name="mixAlpha")
materials1 = materials[::2]
materials2 = materials[1::2]
fullSizeMixedMaterial = helpers.mixMaterials(materials1, materials2, alpha)
if self.inputImageSize >= self.tileSize :
if self.fixCrop:
xyCropping = (self.inputImageSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([2], 0, self.inputImageSize - self.tileSize, dtype=tf.int32)
cropped_mixedMaterial = fullSizeMixedMaterial[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[1] : xyCropping[1] + self.tileSize, :]
elif self.inputImageSize < self.tileSize:
raise Exception("Size of the input is inferior to the size of the rendering, please provide higher resolution maps")
cropped_mixedMaterial.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
mixedMaterial = helpers.adaptRougness(cropped_mixedMaterial)
targetstoRender = helpers.target_reshape(mixedMaterial) #reshape it to be compatible with the rendering algorithm [?, size, size, 12]
nbRenderings = 1
rendererInstance = renderer.GGXRenderer(includeDiffuse = True)
## Do renderings of the mixedMaterial
targetstoRender = helpers.preprocess(targetstoRender) #Put targets to -1; 1
surfaceArray = helpers.generateSurfaceArray(self.tileSize)
inputs = helpers.generateInputRenderings(rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, jitterLightPos, jitterViewPos, self.useAmbientLight, useAugmentationInRenderings = self.useAugmentationInRenderings)
self.gammaCorrectedInputsBatch = tf.squeeze(inputs, [1])
inputs = tf.pow(inputs, 2.2) # correct gamma
if self.logInput:
inputs = helpers.logTensor(inputs)
inputs = helpers.preprocess(inputs) #Put inputs to -1; 1
targets = helpers.target_deshape(targetstoRender, self.nbTargetsToRead)
return targets, inputs
def populateInNetworkFeedGraph(self, renderingScene, jitterLightPos, jitterViewPos, shuffle = True):
#Create a tensor out of the list of paths
filenamesTensor = tf.constant(self.pathList)
#Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2]
dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor)
#for each slice apply the __readImages function
dataset = dataset.map(self.__readMaterial, num_parallel_calls= int(multiprocessing.cpu_count() / 4)) #Divided by four as the cluster divides cpu availiability for each GPU
#Authorize repetition of the dataset when one epoch is over.
dataset = dataset.repeat()
if shuffle:
dataset = dataset.shuffle(buffer_size=64, reshuffle_each_iteration=True)
#set batch size
#print(self.batchSize)
nbWithdraw = 2 * self.batchSize
if not self.mixMaterials:
nbWithdraw = self.batchSize
batched_dataset = dataset.batch(nbWithdraw)
batched_dataset = batched_dataset.prefetch(buffer_size=2)
#batched_dataset = batched_dataset.cache()
iterator = batched_dataset.make_initializable_iterator()
#Create the node to retrieve next batch
init_paths_batch, init_targets_batch = iterator.get_next()
paths_batch = init_paths_batch
if self.mixMaterials:
paths_batch = init_paths_batch[::2]
targets_batch, inputs_batch = self.__renderInputs(init_targets_batch, renderingScene, jitterLightPos, jitterViewPos, self.mixMaterials)
inputs_batch = tf.squeeze(inputs_batch, [1])#Before this the input has a useless dimension in 1 as we have only 1 rendering
inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3])
targets_batch.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
print("steps per epoch: " + str(int(math.floor(len(self.pathList) / nbWithdraw))))
#Populate the object
self.stepsPerEpoch = int(math.floor(len(self.pathList) / nbWithdraw ))
self.inputBatch = inputs_batch
self.targetBatch = targets_batch
self.iterator = iterator
self.pathBatch = paths_batch
#This function if used to create the iterator to go over the data and create the tensors of input and output
def populateFeedGraph(self, shuffle = True, cutPaperOut = False):
with tf.name_scope("load_images"):
#Create a tensor out of the list of paths
filenamesTensor = tf.constant(self.pathList)
#Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here)
dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor)
#for each slice apply the __readImages function
dataset = dataset.map(self.__readImages, num_parallel_calls=int(multiprocessing.cpu_count() / 4))
#Authorize repetition of the dataset when one epoch is over.
if shuffle:
dataset = dataset.shuffle(buffer_size=16, reshuffle_each_iteration=True)
#set batch size
dataset = dataset.repeat()
batched_dataset = dataset.batch(self.batchSize)
batched_dataset = batched_dataset.prefetch(buffer_size=4)
#Create an iterator to be initialized
iterator = batched_dataset.make_initializable_iterator()
#Create the node to retrieve next batch
paths_batch, inputs_batch, targets_batch, gammadInputBatch = iterator.get_next()
self.gammaCorrectedInputsBatch = gammadInputBatch
reshaped_targets = helpers.target_reshape(targets_batch)
#inputRealSize = self.tileSize
inputRealSize = self.inputImageSize
#Do the random crop, if the crop if fix, crop in the middle
if inputRealSize > self.tileSize:
if self.fixCrop:
xyCropping = (inputRealSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([1], 0, inputRealSize - self.tileSize, dtype=tf.int32)
inputs_batch = inputs_batch[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
targets_batch = targets_batch[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
#Set shapes
inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3])
targets_batch.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
#Populate the object
self.stepsPerEpoch = int(math.floor(len(self.pathList) / self.batchSize))
self.inputBatch = inputs_batch
self.targetBatch = targets_batch
self.iterator = iterator
self.pathBatch = paths_batch
#This function if used to crate the iterator to go over the data and create the tensors of input and output
def populateInNetworkFeedGraphSpatialMix(self,renderingScene, shuffle = True, imageSize = 512, useSpatialMix = True):
with tf.name_scope("load_images"):
#Create a tensor out of the list of paths
filenamesTensor = tf.constant(self.pathList)
#Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here)
dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor)
#for each slice apply the __readImages function
dataset = dataset.map(self.__readImagesGT, num_parallel_calls=int(multiprocessing.cpu_count() / 4))
#Authorize repetition of the dataset when one epoch is over.
#shuffle = True
if shuffle:
dataset = dataset.shuffle(buffer_size=16, reshuffle_each_iteration=True)
#set batch size
dataset = dataset.repeat()
toPull = self.batchSize
if useSpatialMix:
toPull = self.batchSize * 2
batched_dataset = dataset.batch(toPull)
batched_dataset = batched_dataset.prefetch(buffer_size=4)
#Create an iterator to be initialized
iterator = batched_dataset.make_initializable_iterator()
#Create the node to retrieve next batch
paths_batch, targets_batch = iterator.get_next()
inputRealSize = imageSize#Should be input image size but changed tmp
if useSpatialMix:
threshold = 0.5
perlinNoise = tf.expand_dims(tf.expand_dims(helpers.generate_perlin_noise_2d((inputRealSize, inputRealSize), (1,1)), axis = -1), axis = 0)
perlinNoise = (perlinNoise + 1.0) * 0.5
perlinNoise = perlinNoise >= threshold
perlinNoise = tf.cast(perlinNoise, tf.float32)
inverted = 1.0 - perlinNoise
materialsMixed1 = targets_batch[::2] * perlinNoise
materialsMixed2 = targets_batch[1::2] * inverted
fullSizeMixedMaterial = materialsMixed1 + materialsMixed2
targets_batch = fullSizeMixedMaterial
paths_batch = paths_batch[::2]
targetstoRender = helpers.target_reshape(targets_batch) #reshape it to be compatible with the rendering algorithm [?, size, size, 12]
nbRenderings = 1
rendererInstance = renderer.GGXRenderer(includeDiffuse = True)
## Do renderings of the mixedMaterial
mixedMaterial = helpers.adaptRougness(targetstoRender)
targetstoRender = helpers.preprocess(targetstoRender) #Put targets to -1; 1
surfaceArray = helpers.generateSurfaceArray(inputRealSize)
inputs_batch = helpers.generateInputRenderings(rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, False, False, self.useAmbientLight, useAugmentationInRenderings = self.useAugmentationInRenderings)
targets_batch = helpers.target_deshape(targetstoRender, self.nbTargetsToRead)
self.gammaCorrectedInputsBatch = tf.squeeze(inputs_batch, [1])
#tf.summary.image("GammadInputs", helpers.convert(inputs[0, :]), max_outputs=5)
inputs_batch = tf.pow(inputs_batch, 2.2) # correct gamma
if self.logInput:
inputs_batch = helpers.logTensor(inputs_batch)
#Do the random crop, if the crop if fix, crop in the middle
if inputRealSize > self.tileSize:
if self.fixCrop:
xyCropping = (inputRealSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([1], 0, inputRealSize - self.tileSize, dtype=tf.int32)
inputs_batch = inputs_batch[:, :, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
targets_batch = targets_batch[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
#Set shapes
inputs_batch = tf.squeeze(inputs_batch, [1]) #Before this the input has a useless dimension in 1 as we have only 1 rendering
inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3])
targets_batch.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
#Populate the object
self.stepsPerEpoch = int(math.floor(len(self.pathList) / self.batchSize))
self.inputBatch = inputs_batch
self.targetBatch = targets_batch
self.iterator = iterator
self.pathBatch = paths_batch