def load_data_trainval(self, set_name, image_dir, annotation_path):
"""
Load train+val data
"""
data = {}
# load annotations file
if self.verbose:
print(' > Loading annotation file: ' + annotation_path)
annotations = load_json(annotation_path)
# progressbar
if self.verbose:
prgbar = progressbar.ProgressBar(
max_value=len(annotations['annotations']))
# ---------------------------------------------------------
# parse annotations
# ---------------------------------------------------------
if self.verbose:
print(' > Processing image annotations... ')
# get all image filenames + ids into a list
filename_ids, images_annot_by_fname, images_fname_by_id = self.parse_image_annotations(
image_dir, annotations)
if self.verbose:
print(' > Processing category annotations... ')
parsed_annots = self.parse_category_annotations(annotations)
categories, category_list, supercategory_list, category_id = parsed_annots
if self.verbose:
print(' > Processing object annotations... ')
# group annotations by file name
annotation_id_dict = {}
for i, annot in enumerate(annotations['annotations']):
filename = images_fname_by_id[annot['image_id']]
category_annot = categories[annot['category_id']]
obj_id = annot["id"]
annotation_id_dict[obj_id] = i
if isinstance(annot["segmentation"], list):
segmentation = squeeze_list(annot["segmentation"],
-1) # squeeze list
elif isinstance(annot["segmentation"]['counts'], list):
segmentation = annot["segmentation"]["counts"]
else:
segmentation = annot["segmentation"]
# convert from [x,y,w,h] to [xmin,ymin,xmax,ymax]
bbox = [
annot['bbox'][0], # xmin
annot['bbox'][1], # ymin
annot['bbox'][0] + annot['bbox'][2] - 1, # ymax
annot['bbox'][1] + annot['bbox'][3] - 1
] # ymax
obj = {
"category": category_annot['name'],
"supercategory": category_annot['supercategory'],
"area": annot['area'],
"iscrowd": annot['iscrowd'],
"segmentation": segmentation,
"bbox": bbox,
"image_id": annot['image_id'],
"category_id": annot['category_id'],
"id": annot["id"],
"annotation_id": i
}
# add annotations to the image data
try:
images_annot_by_fname[filename]["object"].update({obj_id: obj})
except KeyError:
images_annot_by_fname[filename]["object"] = {obj_id: obj}
# update progressbar
if self.verbose:
prgbar.update(i)
# reset progressbar
if self.verbose:
prgbar.finish()
return {
set_name: [
OrderedDict(sorted(images_annot_by_fname.items())),
annotations, annotation_id_dict, category_list,
supercategory_list, category_id, filename_ids,
images_fname_by_id
]
}
def test_squeeze_list(sample, output, fill_value):
assert (output == squeeze_list(sample, fill_value))