def test_is_rect_similar(self):
rect1 = (0, 0, 100, 100)
rect2 = (25, 25, 50, 50)
(similar, int_area, int_area_pct) = utils.is_rect_similar(
rect1, rect2, 0)
self.assertTrue(similar)
self.assertEqual(int_area, 50 * 50)
self.assertEqual(int_area_pct, 0.25)
rect1 = (25, 25, 50, 50)
rect2 = (0, 0, 100, 100)
(similar, int_area, int_area_pct) = utils.is_rect_similar(
rect1, rect2, 0)
self.assertTrue(similar)
self.assertEqual(int_area, 50 * 50)
def person_tracking_image_experiments(params=None):
"""
Execute person tracking experiments on images
:type params: dictionary
:param params: configuration parameters
to be used for the experiment (see table)
============================================ ======================================== ==============
Key Value Default value
============================================ ======================================== ==============
person_tracking_clothes_bbox_height Height of bounding box for clothes
(in % of the face bounding box height) 1.0
person_tracking_clothes_bbox_width Width of bounding box for clothes 2.0
(in % of the face bounding box width)
person_tracking_neck_height Height of neck (in % of the 0.0
face bounding box height)
nr_of_hsv_channels_nr_in_person_tracking Number of HSV channels used 2
in person tracking (1-2)
use_mask_in_person_tracking If True, use a mask for HSV values False
min_size_height Minimum height of face detection 20
bounding box (in pixels)
min_size_width Minimum width of face detection 20
bounding box (in pixels)
annotations_path Path of directory containing the
manual annotations for the images
dataset_path Path of dataset
============================================ ======================================== ==============
"""
ann_path = ce.PERSON_TRACKING_IMAGE_ANN_PATH
dataset_path = ce.PERSON_TRACKING_IMAGE_DATASET_PATH
if params is not None:
if ce.PERSON_TRACKING_IMAGE_ANN_PATH_KEY in params:
ann_path = params[ce.PERSON_TRACKING_IMAGE_ANN_PATH_KEY]
if ce.PERSON_TRACKING_IMAGE_DATASET_PATH_KEY in params:
dataset_path = params[ce.PERSON_TRACKING_IMAGE_DATASET_PATH_KEY]
# Load file with annotations
ann_dict = utils.load_YAML_file(ann_path)
tot_discrete_score = 0
tot_cont_score = 0
tot_tracking_time = 0
matches_counter = 0
for subject_dir in os.listdir(dataset_path):
subject_path = os.path.join(dataset_path, subject_dir)
# Every image in directory is used as a reference image
for ref_im_name in os.listdir(subject_path):
print('ref_im_name', ref_im_name)
# Full path of reference image
ref_im_path = os.path.join(subject_path, ref_im_name)
# Path of image relative to dataset path
ref_rel_im_path = os.path.join(subject_dir, ref_im_name)
ref_bbox = ann_dict[ref_rel_im_path][c.BBOX_KEY]
# Other images in same directory are used as test images
for test_im_name in os.listdir(subject_path):
if test_im_name != ref_im_name:
# Full path of test image
test_im_path = os.path.join(subject_path, test_im_name)
# Path of image relative to dataset path
test_rel_im_path = os.path.join(subject_dir, ref_im_name)
test_true_bbox = ann_dict[test_rel_im_path][c.BBOX_KEY]
true_face_width = test_true_bbox[2]
true_face_height = test_true_bbox[3]
show_results = False
# Saving processing time
start_time = cv2.getTickCount()
test_found_bbox = pt.find_person_by_clothes(
test_im_path, ref_im_path, ref_bbox, params, show_results)
clocks = cv2.getTickCount() - start_time
seconds = clocks / cv2.getTickFrequency()
tot_tracking_time += seconds
if test_found_bbox:
(sim, int_area, int_area_pct) = utils.is_rect_similar(
test_true_bbox, test_found_bbox, 0)
if sim:
tot_discrete_score += 1
tot_cont_score += int_area_pct
if int_area_pct > 1.8:
print('int_area_pct', int_area_pct)
pt.find_person_by_clothes(
test_im_path, ref_im_path, ref_bbox,
params, True)
matches_counter += 1
discrete_score = float(tot_discrete_score) / matches_counter
cont_score = tot_cont_score / matches_counter
mean_tracking_time = tot_tracking_time / matches_counter
print('matches_counter', matches_counter)
print("\n ### RESULTS ###\n")
print('Mean of discrete score: ' + str(discrete_score * 100) + '%')
print('Mean of continuous score: ' + str(cont_score * 100) + '%')
print('Mean tracking time: ' + str(mean_tracking_time) + ' s\n\n')