def testExportGroundtruthToCOCO(self):
image_ids = ['first', 'second']
groundtruth_boxes = [
np.array([[100, 100, 200, 200]], np.float),
np.array([[50, 50, 100, 100]], np.float)
]
groundtruth_classes = [
np.array([1], np.int32),
np.array([1], np.int32)
]
categories = [{
'id': 0,
'name': 'person'
}, {
'id': 1,
'name': 'cat'
}, {
'id': 2,
'name': 'dog'
}]
output_path = os.path.join(tf.test.get_temp_dir(), 'groundtruth.json')
result = coco_tools.ExportGroundtruthToCOCO(image_ids,
groundtruth_boxes,
groundtruth_classes,
categories,
output_path=output_path)
self.assertDictEqual(result, self._groundtruth_dict)
with tf.gfile.GFile(output_path, 'r') as f:
written_result = f.read()
# The json output should have floats written to 4 digits of precision.
matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,',
re.MULTILINE)
self.assertTrue(matcher.findall(written_result))
written_result = json.loads(written_result)
self.assertAlmostEqual(result, written_result)
def main(_):
model_path = FLAGS.model_path
voc_path = FLAGS.voc_path
labelmap_file = FLAGS.labelmap_file
set_file = FLAGS.set_file
scaler_file = FLAGS.scaler_file
side_input = FLAGS.side_input
# Load Model and read label_map.pbtxt
model = file_util.load_model(model_path)
categories, labelmap_dict, category_index = file_util.load_labelmap(
voc_path, labelmap_file)
# Get information from groundtruth and detection
gt_ids, gt_boxes, gt_classes = _read_annotations_for_groundtruth(
voc_path, set_file, labelmap_dict)
dt_ids, dt_boxes, dt_classes, dt_scores, time_per_image = inference(
model, voc_path, model_path, set_file, category_index, scaler_file,
side_input)
# COCO Evaluation
groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
gt_ids, gt_boxes, gt_classes, categories)
detections_list = coco_tools.ExportDetectionsToCOCO(
dt_ids, dt_boxes, dt_scores, dt_classes, categories)
groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
detections = groundtruth.LoadAnnotations(detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth,
detections,
agnostic_mode=False)
summary_metrics, per_category_ap = evaluator.ComputeMetrics(
include_metrics_per_category=True, all_metrics_per_category=True)
# Convert to Percent format
for k, v in summary_metrics.items():
summary_metrics[k] = v * 100
for k, v in per_category_ap.items():
per_category_ap[k] = v * 100
print(summary_metrics)
print(per_category_ap)
# Prevention for Tensorflow Bug: Cant calculate Flops with custom Inputs
if not side_input:
flops = get_flops(model)
else:
flops = 0
metrics_dict = {
'flops': flops / 1e9,
'time_per_image': time_per_image * 1000
}
# Read Trainable Params and Name Dictionary from Pickle file
name_params_dict = pickle.load(
open(os.path.join(model_path, 'metrics', 'name_params.pkl'), 'rb'))
metrics_dict.update(name_params_dict)
metrics_dict.update(summary_metrics)
metrics_dict.update(per_category_ap)
# Save Metrics to CSV
metrics_df = pd.DataFrame.from_records([metrics_dict])
metrics_df.to_csv(os.path.join(model_path, 'metrics', 'metrics.csv'))
ground_truth_box = []
ground_truth_class = []
if len(data) == 0:
ground_truth_box.append([])
for i in range(len(data)):
ground_truth_box.append(data[i][1:])
ground_truth_class.append(label_map_dict[data[i][0]])
ground_truth_boxes.append(np.array(ground_truth_box, np.float))
ground_truth_classes.append(np.array(ground_truth_class, np.int32))
return image_ids1, ground_truth_boxes, ground_truth_classes
pred_image_ids, detection_boxes, detection_scores, detection_classes = convert_OBJ_DETE_dt(
pred_path)
truth_image_ids, ground_truth_boxes, ground_truth_classes = convert_OBJ_DETE_gt(
truth_path)
detections_list = coco_tools.ExportDetectionsToCOCO(pred_image_ids,
detection_boxes,
detection_scores,
detection_classes,
label_map_dict)
groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(truth_image_ids,
ground_truth_boxes,
ground_truth_classes,
label_map_dict)
groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
detections = groundtruth.LoadAnnotations(detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections)
metrics, ap = evaluator.ComputeMetrics()
for fn in pred_bbox_fn:
if 'not_a_dam' not in fn:
pred_img_ids.append(fn)
coords, conf = parse_txt(os.path.join(args.predicted_bboxes, fn),
args.predicted_format, 'predicted')
pred_bboxes_list.append(coords)
pred_conf_list.append(conf)
pred_classes_list.append(arr0)
# add category input
categories = [{'id': 0, 'name': 'dam'}]
# prepare ground_truth input for COCOWrapper
groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
gt_img_ids,
gt_bboxes_list,
gt_classes_list,
categories,
)
# prepare detections input for COCOWrapper
detections_list = coco_tools.ExportDetectionsToCOCO(pred_img_ids,
pred_bboxes_list,
pred_conf_list,
pred_classes_list,
categories)
# calculate
groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
detections = groundtruth.LoadAnnotations(detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth,
detections,