def expected_records():
source = Path(mantisshrimp.__file__).parent.parent / "samples"
dataset = torchvision.datasets.CocoDetection(source,
source / "annotations.json")
return dataset.coco.dataset
def test_open_img():
fn = Path(__file__).parent.parent.parent
fn = str(fn / "samples/images/000000000089.jpg")
assert open_img(fn).shape == (480, 640, 3)
assert open_img(fn, gray=True).shape == (480, 640)
import pytest
from mantisshrimp.imports import Path, json, plt
from mantisshrimp import *
source = Path(__file__).absolute().parent.parent.parent / "samples"
def test_info_parser():
parser = test_utils.sample_image_info_parser()
infos = parser.parse()[0]
assert len(infos) == 6
expected = {
"imageid": 0,
"filepath": source / "images/000000128372.jpg",
"height": 427,
"width": 640,
}
assert infos[0] == expected
def test_coco_annotation_parser():
parser = test_utils.sample_annotation_parser()
annotations = parser.parse()[0]
annotation = annotations[0]
assert len(annotations) == 5
assert annotation["imageid"] == 0
assert annotation["labels"] == [4]
assert pytest.approx(annotation["bboxes"][0].xyxy) == [
175.14,
175.68,
496.2199,
from mantisshrimp import * from mantisshrimp.imports import Path, json from mantisshrimp.imports import ABC import mantisshrimp source = Path(mantisshrimp.__file__).parent.parent / "samples" annots_dict = json.loads((source / "annotations.json").read()) image_info_parser = COCOImageInfoParser(annots_dict["images"], source) records = image_info_parser.parse_dicted() records coco_annotation_parser = COCOAnnotationParser2(annots_dict["annotations"]) annotations = coco_annotation_parser.parse_dicted() annotations