def play_bboxes(img_dir, gt, dt, obj_class):
img_list = list_images(img_dir)
gt_bboxes = read_groundtruth(gt, obj_class)
dt_bboxes = read_detections(dt, obj_class)
for img_full_name in img_list:
img = cv2.imread(img_full_name)
img_name = os.path.basename(img_full_name)
img_name, img_ext = os.path.splitext(img_name)
fid = int(img_name)
img_gt_bboxes = get_gt_img_bboxes(gt_bboxes, fid)
img_dt_bboxes = get_dt_img_bboxes(dt_bboxes, fid)
for gt_bbox in img_gt_bboxes:
cv2.rectangle(img, (gt_bbox.bbox.ltp.x, gt_bbox.bbox.ltp.y),
(gt_bbox.bbox.rbp.x, gt_bbox.bbox.rbp.y),
(0, 255, 0), 2)
for dt_bbox in img_dt_bboxes:
cv2.rectangle(img, (dt_bbox.bbox.ltp.x, dt_bbox.bbox.ltp.y),
(dt_bbox.bbox.rbp.x, dt_bbox.bbox.rbp.y),
(0, 0, 255), 2)
cv2.imshow('Image', img)
key = cv2.waitKey(0)
if key == 27:
break
cv2.destroyAllWindows()
return
def fppf_rate(gt, dt, objclass, percentage, kimages):
gt_bboxes = read_groundtruth(gt, objclass)
dt_bboxes = read_detections(dt, objclass)
[fp, fn, tp, n] = compute_detection_rates(gt_bboxes, dt_bboxes, percentage)
print('FP = ' + str(fp))
print('FN = ' + str(fn))
print('TP = ' + str(tp))
print('N = ' + str(n))
return float(fp) / float(kimages)
def calculate_measurements(groundtruth_directory, detections_directory,
object_class, percentage, track_kimages_file,
output_file):
if not os.path.isdir(groundtruth_directory):
print('Parameter \'{}\' is not a directory.'.format(
groundtruth_directory))
return
if not os.path.isdir(detections_directory):
print('Parameter \'{}\' is not a directory.'.format(
detections_directory))
return
if not os.path.isfile(track_kimages_file):
print('Parameter \'{}\' is not a file.'.format(track_kimages_file))
return
detection_files = [ f for f in os.listdir(detections_directory) \
if os.path.isfile(os.path.join(detections_directory, f)) ]
track_kimages_dict = read_track_frames(track_kimages_file)
ofile = open(output_file, 'w')
ofile.write('Track identifier;Model identifier;AP;TPR;FDR;FPperFrame\n')
for detections_file_name in detection_files:
matcher = re.match(r'([\w\-]+)_imgs_([\w\-]+)\.txt',
detections_file_name)
if matcher:
model_name = matcher.group(1)
track_name = matcher.group(2)
groundtruth_file_name = os.path.join(groundtruth_directory,
'{}.txt'.format(track_name))
if not os.path.isfile(groundtruth_file_name):
print('There is no groundtruth file \'{0}\' corresponding \
to the detection file \'{1}\' in the directory.'.format(
groundtruth_file_name, detections_file_name))
continue
detections_file_name = os.path.join(detections_directory,
detections_file_name)
print(groundtruth_file_name)
gt_bboxes = read_groundtruth(groundtruth_file_name, object_class)
dt_bboxes = read_detections(detections_file_name)
kimages = track_kimages_dict[track_name.lower()]
if kimages == None:
print('There is no line corresponding to the track {}'.format(
track_name))
continue
[ap, tpr, fdr,
fppf] = compute_all_detection_rates(gt_bboxes, dt_bboxes,
object_class, percentage,
kimages)
ofile.write('{0};{1};{2};{3};{4};{5}\n'.format(
track_name, model_name, ap, tpr, fdr, fppf))
else:
print('File \'{}\' does not match a template.'.format(
detections_file_name))
ofile.close()
return
help='silent mode (print only TPR)',
action='store_true')
parser.add_argument('-p', '--percentage', help = 'intersection percentage'\
' (from 0 to 1, by default 0.5)', type = float, default = 0.5)
args = parser.parse_args()
if len(sys.argv) < 3:
parser.print_help()
sys.exit()
# print command line arguments
if args.quiet:
print(
average_precision_curve(args.groundtruth, args.detections,
args.object_class, args.percentage))
else:
print('-----------------------------------')
print('Groundtruth: ' + args.groundtruth)
print('Detections: ' + args.detections)
print('Object class: ' + args.object_class)
print('Intersection percentage: ' + str(args.percentage))
print('-----------------------------------')
gt_bboxes = read_groundtruth(args.groundtruth, args.object_class)
dt_bboxes = read_detections(args.detections, args.object_class)
[ap, recall,
precision] = average_precision_curve(gt_bboxes, dt_bboxes,
args.object_class,
args.percentage)
print('Average precision = ' + str(ap))
show_precision_recall_curve(ap, recall, precision)
print('-----------------------------------')
def read_bboxes(bboxes_file):
bboxes = read_groundtruth(bboxes_file)
if len(bboxes) == 0:
bboxes = read_detections(bboxes_file)
return bboxes