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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")
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