def readAndGenerateImage(outputPath, transformers, i_and_imagePath):
(i, imagePath) = i_and_imagePath
image = cv2.imread(imagePath)
name = imagePath.split(os.path.sep)[-1]
labelPath = '/'.join(imagePath.split(
os.path.sep)[:-1]) + "/" + name[0:name.rfind(".")] + ".xml"
tree = ET.parse(labelPath)
root = tree.getroot()
objects = root.findall('object')
#if(len(objects)<1):
# raise Exception("The xml should contain at least one object")
boxes = []
for object in objects:
category = object.find('name').text
confidence = object.find('confidence')
if confidence is None:
confidence = 1.0
else:
confidence = float(confidence.text)
bndbox = object.find('bndbox')
x = int(bndbox.find('xmin').text)
y = int(bndbox.find('ymin').text)
h = int(bndbox.find('ymax').text) - y
w = int(bndbox.find('xmax').text) - x
boxes.append((category, (x, y, w, h), confidence))
for (j, transformer) in enumerate(transformers):
(newimage, newboxes) = transformer.transform(image, boxes)
if newboxes is not None:
cv2.imwrite(outputPath + "/" + str(i) + "_" + str(j) + "_" + name,
newimage)
(hI, wI) = image.shape[:2]
if (len(image.shape) == 3):
d = 3
else:
d = 1
file = open(
outputPath + "/" + str(i) + "_" + str(j) + "_" +
name[0:name.rfind(".")] + ".xml", "w")
file.write(
generateXML(
str(i) + "_" + str(j) + "_" + name, outputPath, wI, hI, d,
newboxes))
file.close()
def readAndGenerateImage(outputPath, transformers, i_and_imagePath):
(i, imagePath) = i_and_imagePath
image = cv2.imread(imagePath)
name = imagePath.split(os.path.sep)[-1]
labelPath = '/'.join(imagePath.split(
os.path.sep)[:-1]) + "/" + name[0:name.rfind(".")] + ".xml"
tree = ET.parse(labelPath)
root = tree.getroot()
objects = root.findall('object')
if (len(objects) != 1):
raise Exception(
"Since this is a localization problem, the xml should only contain one object"
)
object = objects[0]
category = object.find('name').text
bndbox = object.find('bndbox')
x = int(bndbox.find('xmin').text)
y = int(bndbox.find('ymin').text)
h = int(bndbox.find('ymax').text) - y
w = int(bndbox.find('xmax').text) - x
for (j, transformer) in enumerate(transformers):
(newimage, box) = transformer.transform(image,
(category, (x, y, w, h)))
if box is not None:
cv2.imwrite(outputPath + "/" + str(i) + "_" + str(j) + "_" + name,
newimage)
(hI, wI) = image.shape[:2]
if (len(image.shape) == 3):
d = 3
else:
d = 1
file = open(
outputPath + "/" + str(i) + "_" + str(j) + "_" +
name[0:name.rfind(".")] + ".xml", "w")
file.write(
generateXML(
str(i) + "_" + str(j) + "_" + name, outputPath, wI, hI, d,
[box]))
file.close()
def applyAugmentation(self):
self.readImagesAndAnnotations()
le = LabelEncoder()
writer = HDF5DatasetWriterClassification((len(self.imagePaths)*len(self.transformers),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(self.imagePaths):
(i, imagePath) = i_and_imagePath
image = cv2.imread(imagePath)
name = imagePath.split(os.path.sep)[-1]
labelPath = '/'.join(imagePath.split(os.path.sep)[:-1]) + "/" + name[0:name.rfind(".")] + ".xml"
tree = ET.parse(labelPath)
root = tree.getroot()
objects = root.findall('object')
if (len(objects) != 1):
raise Exception("Since this is a localization problem, the xml should only contain one object")
object = objects[0]
category = object.find('name').text
bndbox = object.find('bndbox')
x = int(bndbox.find('xmin').text)
y = int(bndbox.find('ymin').text)
w = int(bndbox.find('ymax').text) - y
h = int(bndbox.find('xmax').text) - x
for (j, transformer) in enumerate(self.transformers):
(newimage, box) = transformer.transform(image, (category, (x, y, w, h)))
writer.add([newimage],[0,box[1][0],box[1][1],box[1][2],box[1][3]])
pbar.update(i)
pbar.finish()
writer.close()