def applyAugmentation(self, passes=np.inf):
epochs = 0
aap = AspectAwarePreprocessor(self.width, self.height)
while epochs < passes:
for i in np.arange(0, self.numImages, self.batchSize):
imagPaths = self.imagePaths[i:i + self.batchSize]
labPaths = self.labelPaths[i:i + self.batchSize]
images = [
aap.preprocess(cv2.imread(imagePath))
for imagePath in imagPaths
]
labels = [
aap.preprocess(cv2.imread(labelPath))
for labelPath in labPaths
]
images_labels = [
readAndGenerateImageSegmentation(image, label,
self.generators)
for (image, label) in zip(images, labels)
]
images = [i[0] for i in images_labels]
labels = [i[1] for i in images_labels]
yield (images, labels)
epochs += 1
class HDF5LinearClassificationAugmentor:
# All images must have same width and height
def __init__(self,inputPath,parameters):
IAugmentor.__init__(self)
self.inputPath = inputPath
# output path represents the h5py file where dataset will be stored
if parameters["outputPath"]:
self.outputPath = parameters["outputPath"]
else:
raise ValueError("You should provide an output path in the parameters")
self.generators = []
if parameters["width"]:
self.width = parameters["width"]
else:
raise ValueError("You should provide a width in the parameters")
if parameters["height"]:
self.width = parameters["height"]
else:
raise ValueError("You should provide a height in the parameters")
self.aw = AspectAwarePreprocessor(self.width,self.height)
def addGenerator(self, generator):
self.generators.append(generator)
def readImagesAndAnnotations(self):
self.imagePaths = list(paths.list_images(self.inputPath))
def applyAugmentation(self):
self.readImagesAndAnnotations()
le = LabelEncoder()
labels = [p.split(os.path.sep)[-2] for p in self.imagePaths]
labels = le.fit_transform(labels)
writer = HDF5DatasetWriterClassification((len(self.imagePaths)*len(self.generators),self.width,self.height,3),
self.outputPath)
# We need to define this function outside to work in parallel.
writer.storeClassLabels(le.classes_)
widgets = ["Processing images: ", progressbar.Percentage(), " ",
progressbar.Bar(), " ", progressbar.ETA()]
pbar = progressbar.ProgressBar(maxval=len(self.imagePaths),
widgets=widgets).start()
for i_and_imagePath in enumerate(zip(self.imagePaths,labels)):
(i, (imagePath,label)) = i_and_imagePath
image = cv2.imread(imagePath)
image = self.aw.preprocess(image)
for (j, generator) in enumerate(self.generators):
newimage = generator.applyForClassification(image)
newimage = self.aw.preprocess(newimage)
writer.add([newimage],[label])
pbar.update(i)
writer.close()
pbar.finish()
def applyAugmentation(self,passes=np.inf):
epochs = 0
aap = AspectAwarePreprocessor(self.width,self.height)
batch_features = np.zeros((self.batchSize, self.width, self.height, 3))
batch_labels = np.zeros((self.batchSize, self.classes))
while epochs < passes:
for i in np.arange(0, self.numImages, self.batchSize):
imagPaths = self.imagePaths[i:i+self.batchSize]
labels = self.labels[i:i+self.batchSize]
images = [cv2.imread(imagePath) for imagePath in imagPaths]
images = [aap.preprocess(readAndGenerateImage(image,self.generators)) for image in images]
for j in range(self.batchSize):
index = random.randint(0,len(images)-1)
batch_features[j] = images[index]
batch_labels[j] = labels[index]
yield (batch_features,batch_labels)
epochs += 1
class HDF5PowerSegmentationAugmentor:
# All images must have same width and height
def __init__(self, inputPath, parameters):
IAugmentor.__init__(self)
self.inputPath = inputPath
self.imagesPath = inputPath + "images/"
self.labelsPath = inputPath + "labels/"
# output path represents the folder where the images will be stored
if parameters["outputPath"]:
self.outputPath = parameters["outputPath"]
else:
raise ValueError(
"You should provide an output path in the parameters")
self.generators = []
if parameters["width"]:
self.width = parameters["width"]
else:
raise ValueError("You should provide a width in the parameters")
if parameters["height"]:
self.width = parameters["height"]
else:
raise ValueError("You should provide a height in the parameters")
if parameters["labelsExtension"]:
self.labelsExtension = parameters["labelsExtension"]
else:
self.labelsExtension = ".tiff"
self.aw = AspectAwarePreprocessor(self.width, self.height)
def addGenerator(self, generator):
self.generators.append(generator)
def readImagesAndAnnotations(self):
self.imagePaths = list(
paths.list_files(self.imagesPath,
validExts=(".jpg", ".jpeg", ".png", ".bmp",
".tiff", ".tif")))
self.labelPaths = list(
paths.list_files(self.labelsPath,
validExts=(".jpg", ".jpeg", ".png", ".bmp",
".tiff", ".tif")))
if (len(self.imagePaths) != len(self.labelPaths)):
raise Exception(
"The number of files is different in the folder of images and in the folder of labels"
)
def applyAugmentation(self):
self.readImagesAndAnnotations()
widgets = [
"Processing images: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=len(self.imagePaths),
widgets=widgets).start()
writer = HDF5DatasetWriterSegmentation(
(len(self.imagePaths) *
(2**(len(self.generators) - 1)), self.width, self.height, 3),
self.outputPath)
for i_and_imagePath in enumerate(self.imagePaths):
(i, imagePath) = i_and_imagePath
image = cv2.imread(imagePath)
image = self.aw.preprocess(image)
name = imagePath.split(os.path.sep)[-1]
labelPath = '/'.join(
imagePath.split(os.path.sep)[:-2]
) + "/labels/" + name[0:name.rfind(".")] + self.labelsExtension
label = cv2.imread(labelPath)
label = self.aw.preprocess(label)
images = [image]
labels = [label]
for (j, generator) in enumerate(self.generators):
newimages = []
newlabels = []
for (k, (im, la)) in enumerate(zip(images, labels)):
(newimage,
newlabel) = generator.applyForSegmentation(im, la)
newimage = self.aw.preprocess(newimage)
newlabel = self.aw.preprocess(newlabel)
writer.add([newimage], [newlabel])
newimages.append(newimage)
newlabels.append(newlabel)
images = newimages
labels = newlabels
pbar.update(i)
writer.close()
pbar.finish()