def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
"""Evaluate bounding box detection."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
if _use_json_dataset_evaluator(dataset):
coco_eval = json_dataset_evaluator.evaluate_boxes(dataset,
all_boxes,
output_dir,
use_salt=not_comp,
cleanup=not_comp)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_cityscapes_evaluator(dataset):
logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
coco_eval = json_dataset_evaluator.evaluate_boxes(dataset,
all_boxes,
output_dir,
use_salt=not_comp,
cleanup=not_comp)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_voc_evaluator(dataset):
# For VOC, always use salt and always cleanup because results are
# written to the shared VOCdevkit results directory
voc_eval = voc_dataset_evaluator.evaluate_boxes(dataset,
all_boxes,
output_dir,
use_matlab=use_matlab)
box_results = _voc_eval_to_box_results(voc_eval)
else:
raise NotImplementedError('No evaluator for dataset: {}'.format(
dataset.name))
return OrderedDict([(dataset.name, box_results)])
def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
"""Evaluate bounding box detection."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
if _use_json_dataset_evaluator(dataset):
coco_eval = json_dataset_evaluator.evaluate_boxes(
dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_cityscapes_evaluator(dataset):
logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
coco_eval = json_dataset_evaluator.evaluate_boxes(
dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_voc_evaluator(dataset):
# For VOC, always use salt and always cleanup because results are
# written to the shared VOCdevkit results directory
voc_eval = voc_dataset_evaluator.evaluate_boxes(
dataset, all_boxes, output_dir, use_matlab=use_matlab
)
box_results = _voc_eval_to_box_results(voc_eval)
else:
raise NotImplementedError(
'No evaluator for dataset: {}'.format(dataset.name)
)
return OrderedDict([(dataset.name, box_results)])
def evaluate_boxes(dataset, all_boxes, output_dir, args=None):
"""Evaluate bounding box detection."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
if _use_json_dataset_evaluator(dataset):
coco_eval = json_dataset_evaluator.evaluate_boxes(
dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_cityscapes_evaluator(dataset):
logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
coco_eval = json_dataset_evaluator.evaluate_boxes(
dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
)
box_results = _coco_eval_to_box_results(coco_eval)
elif _use_wad_evaluator(dataset):
wad_eval = json_dataset_evaluator.evaluate_boxes_wad(dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp, args=args)
box_results = _coco_eval_to_box_results(wad_eval)
else:
raise NotImplementedError('No evaluator for dataset: {}'.format(dataset.name))
return OrderedDict([(dataset.name, box_results)])
def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
"""Evaluate bounding box detection."""
logger.info('Evaluating detections')
not_comp = not cfg.TEST.COMPETITION_MODE
if _use_json_dataset_evaluator(dataset):
coco_eval = json_dataset_evaluator.evaluate_boxes(dataset,
all_boxes,
output_dir,
use_salt=not_comp,
cleanup=not_comp)
box_results = _coco_eval_to_box_results(coco_eval)
else:
raise NotImplementedError('No evaluator for dataset: {}'.format(
dataset.name))
return OrderedDict([(dataset.name, box_results)])
def eval_json(det_json, gt_json):
json_dataset = JsonDataset(gt_dataset_name)
gt_json = dataset_catalog.DATASETS[gt_dataset_name]['annotation_file']
with open(det_json, 'rb') as f:
det = json.load(f)
f.close()
with open(gt_json, 'rb') as f:
gt = json.load(f)
f.close()
# convert det to the all_boxes list
num_images = len(gt['images'])
num_classes = 2
print('Total number of images:', len(det['images']))
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
for cls in range(num_classes):
for image in range(num_images):
filename = gt['images'][image]['file_name']
fid = gt['images'][image]['id']
img_prop = get_by_filename(det, filename)
if not (img_prop is None):
img_id, det_prop = img_prop
boxes = get_boxes_by_img_id(det, img_id)
if image % 100 == 0:
print('Reading detections for:', filename, '--',
det_prop['file_name'])
print('Det json:', det_json)
if 'score' in boxes[0]:
boxes = np.array([b['bbox'] + [b['score']] for b in boxes])
else:
boxes = np.array([b['bbox'] for b in boxes])
if len(boxes) > 0:
# add w, h to get (x2,y2)
boxes[:, 2] += boxes[:, 0]
boxes[:, 3] += boxes[:, 1]
all_boxes[cls][image] = boxes
else:
all_boxes[cls][image] = []
# save detections
with open(os.path.join(output_dir, 'detections.pkl'), 'wb') as f:
pickle.dump(
dict(all_boxes=all_boxes, all_segms=all_segms,
all_keyps=all_keyps), f)
f.close()
#input(len(all_boxes[0]))
coco_eval = evaluate_boxes(json_dataset, all_boxes, output_dir)
#coco_eval = task_evaluation.evaluate_all(json_dataset,all_boxes,all_segms,all_keyps,output_dir)
disp_detection_eval_metrics(json_dataset,
coco_eval,
iou_low=0.5,
iou_high=0.5,
output_dir=output_dir)
disp_detection_eval_metrics(json_dataset,
coco_eval,
iou_low=0.75,
iou_high=0.75,
output_dir=output_dir)
disp_detection_eval_metrics(json_dataset,
coco_eval,
iou_low=0.5,
iou_high=0.95,
output_dir=output_dir)
