def load_annotations_gt(self):
ret = []
if self.rad_gt_format_coco_json.isChecked():
ret = converter.coco2bb(self.dir_annotations_gt)
elif self.rad_gt_format_cvat_xml.isChecked():
ret = converter.cvat2bb(self.dir_annotations_gt)
elif self.rad_gt_format_openimages_csv.isChecked():
ret = converter.openimage2bb(self.dir_annotations_gt,
self.dir_images_gt,
BBType.GROUND_TRUTH)
elif self.rad_gt_format_labelme_xml.isChecked():
ret = converter.labelme2bb(self.dir_annotations_gt)
elif self.rad_gt_format_pascalvoc_xml.isChecked():
ret = converter.vocpascal2bb(self.dir_annotations_gt)
elif self.rad_gt_format_imagenet_xml.isChecked():
ret = converter.imagenet2bb(self.dir_annotations_gt)
elif self.rad_gt_format_abs_values_text.isChecked():
ret = converter.text2bb(self.dir_annotations_gt,
bb_type=BBType.GROUND_TRUTH)
elif self.rad_gt_format_yolo_text.isChecked():
ret = converter.yolo2bb(self.dir_annotations_gt,
self.dir_images_gt,
self.filepath_classes_gt,
bb_type=BBType.GROUND_TRUTH)
# Make all types as GT
[bb.set_bb_type(BBType.GROUND_TRUTH) for bb in ret]
return ret
def test_toy_example_gts():
############################################################################
# Verify if all files in the toy example follow their expected format
############################################################################
# PASCAL VOC
dir_annots_gts_pascal = 'toyexample/gts_vocpascal_format'
files = general_utils.get_files_recursively(dir_annots_gts_pascal)
assert len(files) > 0
for f in files:
assert validations.is_pascal_format(
f), 'File {f} does not follow the expected format (PASCAL VOC)'
# COCO
dir_annots_gts_coco = 'toyexample/gts_coco_format'
files = general_utils.get_files_recursively(dir_annots_gts_coco)
assert len(files) > 0
for f in files:
assert validations.is_coco_format(f), 'File {f} does not follow the expected format (COCO)'
############################################################################
# Compare if all bounding boxes are the same
############################################################################
pascal_bbs = converter.vocpascal2bb(dir_annots_gts_pascal)
coco_bbs = converter.coco2bb(dir_annots_gts_coco)
coco_bbs.sort(key=lambda x: str(x), reverse=True)
pascal_bbs.sort(key=lambda x: str(x), reverse=True)
assert len(coco_bbs) == len(pascal_bbs)
for coco_bb, pascal_bb in zip(coco_bbs, pascal_bbs):
assert coco_bb == pascal_bb
def test_converters_gts():
# Defining paths with images and annotations
images_dir = 'data/database/images'
gts_dir = 'data/database/gts'
assert os.path.isdir(images_dir)
assert os.path.isdir(gts_dir)
# COCO
coco_dir = os.path.join(gts_dir, 'coco_format_v1')
coco_bbs_v1 = converter.coco2bb(coco_dir)
coco_bbs_v1.sort(key=lambda x: str(x), reverse=True)
# COCO
coco_dir = os.path.join(gts_dir, 'coco_format_v2')
coco_bbs_v2 = converter.coco2bb(coco_dir)
coco_bbs_v2.sort(key=lambda x: str(x), reverse=True)
# CVAT
cvat_dir = os.path.join(gts_dir, 'cvat_format')
cvat_bbs = converter.cvat2bb(cvat_dir)
cvat_bbs.sort(key=lambda x: str(x), reverse=True)
# IMAGENET
imagenet_dir = os.path.join(gts_dir, 'imagenet_format/Annotations')
imagenet_bbs = converter.imagenet2bb(imagenet_dir)
imagenet_bbs.sort(key=lambda x: str(x), reverse=True)
# LABEL ME
labelme_dir = os.path.join(gts_dir, 'labelme_format')
labelme_bbs = converter.labelme2bb(labelme_dir)
labelme_bbs.sort(key=lambda x: str(x), reverse=True)
# OPEN IMAGE
openimage_dir = os.path.join(gts_dir, 'openimages_format')
openimage_bbs = converter.openimage2bb(openimage_dir, images_dir)
openimage_bbs.sort(key=lambda x: str(x), reverse=True)
# VOC PASCAL
vocpascal_dir = os.path.join(gts_dir, 'pascalvoc_format')
vocpascal_bbs = converter.vocpascal2bb(vocpascal_dir)
vocpascal_bbs.sort(key=lambda x: str(x), reverse=True)
# YOLO
yolo_annotations_dir = os.path.join(gts_dir, 'yolo_format/obj_train_data')
yolo_names_file = os.path.join(gts_dir, 'yolo_format/obj.names')
yolo_bbs = converter.yolo2bb(yolo_annotations_dir,
images_dir,
yolo_names_file,
bb_type=BBType.GROUND_TRUTH)
yolo_bbs.sort(key=lambda x: str(x), reverse=True)
assert len(coco_bbs_v1) == len(coco_bbs_v2) == len(cvat_bbs) == len(
imagenet_bbs) == len(labelme_bbs) == len(openimage_bbs) == len(
vocpascal_bbs) == len(yolo_bbs)
for coco_bb_v1, coco_bb_v2, cvat_bb, imagenet_bb, labelme_bb, openimage_bb, vocpascal_bb, yolo_bb in zip(
coco_bbs_v1, coco_bbs_v2, cvat_bbs, imagenet_bbs, labelme_bbs,
openimage_bbs, vocpascal_bbs, yolo_bbs):
assert coco_bb_v1 == coco_bb_v2 == cvat_bb == imagenet_bb == labelme_bb == openimage_bb == vocpascal_bb == yolo_bb
plt.xlabel('amount of bounding boxes')
plt.ylabel('classes')
else:
plt.bar(dict_bbs_per_class.keys(), dict_bbs_per_class.values())
plt.xlabel('classes')
plt.ylabel('amount of bounding boxes')
plt.xticks(rotation=rotation)
title = f'Distribution of bounding boxes per class {extra_title}'
plt.title(title)
if show:
plt.tick_params(axis='x', labelsize=10) # Set the x-axis label size
plt.show(block=True)
return plt
# Get annotations (ground truth and detections)
gt_bbs = converter.vocpascal2bb(dir_gts)
det_bbs = converter.text2bb(dir_dets, bb_type=BBType.DETECTED, bb_format=BBFormat.XYWH,type_coordinates=CoordinatesType.ABSOLUTE, img_dir=dir_imgs)
# Leave only the annotations of cats
gt_bbs = [bb for bb in gt_bbs if bb.get_class_id() == 'cat']
det_bbs = [bb for bb in det_bbs if bb.get_class_id() == 'cat']
# Uncomment to plot the distribution bounding boxes per classes
# dict_gt = BoundingBox.get_amount_bounding_box_all_classes(gt_bbs, reverse=False)
# plot_bb_per_classes(dict_gt, horizontally=True, rotation=0, show=True, extra_title=' (groundtruths)')
# clases_gt = [b.get_class_id() for b in gt_bbs]
# dict_det = BoundingBox.get_amount_bounding_box_all_classes(det_bbs, reverse=True)
# general_utils.plot_bb_per_classes(dict_det, horizontally=False, rotation=80, show=True, extra_title=' (detections)')
#############################################################
# EVALUATE WITH COCO METRICS
cv2.putText(image, label, (xin_bb, yin_bb), font, fontScale, (0, 0, 0),
fontThickness, cv2.LINE_AA)
return image
# Drawing bb into the images
green_color = [62, 235, 59]
red_color = [255, 0, 0]
for img_file in general_utils.get_files_recursively(dir_imgs):
# Get corresponding GT bounding boxes
gt_annotation_file = general_utils.find_file(dir_gts,
os.path.basename(img_file),
match_extension=False)
if gt_annotation_file is None:
continue
gt_bbs = converter.vocpascal2bb(gt_annotation_file)
# Get corresponding detections bounding boxes
det_annotation_file = general_utils.find_file(dir_dets,
os.path.basename(img_file),
match_extension=False)
if det_annotation_file is None:
det_bbs = []
else:
det_bbs = converter.text2bb(det_annotation_file,
bb_type=BBType.DETECTED,
bb_format=BBFormat.XYWH,
type_coordinates=CoordinatesType.ABSOLUTE,
img_dir=dir_imgs)
# Leave only the annotations of cats
gt_bbs = [bb for bb in gt_bbs if bb.get_class_id() == 'cat']
det_bbs = [bb for bb in det_bbs if bb.get_class_id() == 'cat']