Datasets:

hamnaanaa
/

Duckietown-Multiclass-Semantic-Segmentation-Dataset

	import xml.etree.ElementTree as ET


	class CVATPreprocessor():
	"""Helper class to preprocess annotations in `CVAT for images 1.1` XML-encoded format"""
	@staticmethod
	def get_all_image_names(annotation_path):
	"""Returns a list of all image names present in the annotation file"""
	annotations = ET.parse(annotation_path).getroot()
	images = annotations.findall("image")
	return [image.attrib["name"] for image in images]

	@staticmethod
	def get_all_image_polygons(image_name, annotation_path):
	"""
	Returns a dictionary of all polygons for the given image name.
	The key is the label and the value is a list of polygons (= each a list of points) associated with that label.
	"""
	annotations = ET.parse(annotation_path).getroot()
	image = annotations.find(f"image[@name='{image_name}']")
	raw_polygons = image.findall("polygon")

	# Extract the label and the raw points for each polygon,
	# parse the points to (x, y) and store each label-polygon pair in a list
	processed_polygons = {}
	for raw_polygon in raw_polygons:
	label, points = raw_polygon.attrib["label"], raw_polygon.attrib["points"].split(";")
	# Parse the points to (x, y) int pairs
	points = [(int(float(point.split(",")[0])), int(float(point.split(",")[1]))) for point in points]
	processed_polygons[label] = processed_polygons.get(label, []) + [points]

	return processed_polygons


	if __name__ == "__main__":
	# Example usage
	PATH_TO_ANNOTATIONS = "offline learning/semantic segmentation/data/annotations/"
	PATH_TO_IMAGES = "offline learning/semantic segmentation/data/frames/"
	CVAT_XML_FILENAME = "segmentation_annotation.xml"
	imgs = CVATPreprocessor.get_all_image_names(PATH_TO_ANNOTATIONS + CVAT_XML_FILENAME)
	polygons = CVATPreprocessor.get_all_image_polygons(imgs[0], PATH_TO_ANNOTATIONS + CVAT_XML_FILENAME)
	print(f"Loaded {len(imgs)} images from {PATH_TO_ANNOTATIONS + CVAT_XML_FILENAME}")
	print(f"Image '{imgs[0]} has {len(polygons)} polygon categories")