new_experiment_dict[ce.MEAN_F1_KEY] = mean_f1
new_experiment_dict[ce.STD_F1_KEY] = std_f1
new_experiment_dict[ce.MEAN_RECOGNITION_TIME_KEY] = mean_rec_time
new_experiment_dict[ce.MODEL_CREATION_TIME_KEY] = fm.model_creation_time
new_experiment_dict[ce.TEST_SET_PATH_KEY] = test_set_path
rec_dict[ce.GLOBAL_RESULTS_KEY] = new_experiment_dict
rec_dict[ce.IMAGES_KEY] = images_list_for_YAML
rec_dict[ce.PEOPLE_KEY] = people_list_for_YAML
all_results_YAML_file_path = os.path.join(results_path,
ce.FACE_RECOGNITION_EXPERIMENT_RESULTS_FILE_NAME + '.yml')
file_check = os.path.isfile(all_results_YAML_file_path)
experiments = list()
if file_check:
experiments = load_experiment_results(all_results_YAML_file_path)
number_of_already_done_experiments = len(experiments)
new_experiment_dict[ce.EXPERIMENT_NUMBER_KEY] = (
number_of_already_done_experiments + 1)
else:
new_experiment_dict[ce.EXPERIMENT_NUMBER_KEY] = 1
new_experiment_dict_extended = {ce.EXPERIMENT_KEY: new_experiment_dict}
experiments.append(new_experiment_dict_extended)
experiments_dict = {ce.EXPERIMENTS_KEY: experiments}
save_YAML_file(all_results_YAML_file_path, experiments_dict)
# Update csv file with results related to all the experiments
all_results_CSV_file_path = os.path.join(results_path,
ce.FACE_RECOGNITION_EXPERIMENT_RESULTS_FILE_NAME + '.csv')
save_rec_experiments_in_CSV_file(all_results_CSV_file_path, experiments)
def clothing_recognition_experiments(dataset_path, params=None):
"""
Execute clothing recognition experiments
:type dataset_path: string
:param dataset_path: path of directory with first frame sequence
:type params: dictionary
:param params: configuration parameters (see table)
============================================ ======================================== =============================
Key (params) Value Default value
============================================ ======================================== =============================
clothes_bounding_box_height Height of bounding box for clothes
(in % of the face bounding box height) 1.0
clothes_bounding_box_width Width of bounding box for clothes 2.0
(in % of the face bounding box width)
clothing_recognition_K Multiplier for intra distances 1
for calculating local threshold
in clothing recognition
neck_height Height of neck (in % of the 0.0
face bounding box height)
nr_of_HSV_channels_in_clothing_recognition Number of HSV channels used
in clothing recognition (1-3) 3
use_mask_in_clothing_recognition If True, use mask for HSV values True
in clothing recognition
use_motion_mask_in_clothing_recognition If True, calculate histograms only False
on regions where motion is detected
classifiers_dir_path Path of directory with OpenCV
cascade classifiers
face_detection_algorithm Classifier for face detection 'HaarCascadeFrontalFaceAlt2'
('HaarCascadeFrontalFaceAlt',
'HaarCascadeFrontalFaceAltTree',
'HaarCascadeFrontalFaceAlt2',
'HaarCascadeFrontalFaceDefault',
'HaarCascadeProfileFace',
'HaarCascadeFrontalAndProfileFaces',
'HaarCascadeFrontalAndProfileFaces2',
'LBPCascadeFrontalface',
'LBPCascadeProfileFace' or
'LBPCascadeFrontalAndProfileFaces')
flags Flags used in face detection 'DoCannyPruning'
('DoCannyPruning', 'ScaleImage',
'FindBiggestObject', 'DoRoughSearch').
If 'DoCannyPruning' is used, regions
that do not contain lines are discarded.
If 'ScaleImage' is used, image instead
of the detector is scaled
(it can be advantegeous in terms of
memory and cache use).
If 'FindBiggestObject' is used,
only the biggest object is returned
by the detector.
'DoRoughSearch', used together with
'FindBiggestObject',
terminates the search as soon as
the first candidate object is found
min_neighbors Mininum number of neighbor bounding 5
boxes for retaining face detection
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)
scale_factor Scale factor between two scans 1.1
in face detection
clothing_recognition_results_path Directory that will contain results
of clothing recognition experiments
video_name Name of video to be tested
============================================ ======================================== =============================
"""
# Set parameters
cloth_models_dir_path = None
clothing_rec_k = c.CLOTHING_REC_K
hsv_channels = c.CLOTHING_REC_HSV_CHANNELS_NR
results_path = ce.CLOTHING_RECOGNITION_RESULTS_PATH
use_LBP = c.CLOTHING_REC_USE_LBP
use_mask = c.CLOTHING_REC_USE_MASK
use_motion_mask = c.CLOTHING_REC_USE_MOTION_MASK
video_name = ce.TEST_VIDEO_NAME
if params is not None:
if ce.CLOTH_MODELS_DIR_PATH_KEY in params:
cloth_models_dir_path = params[ce.CLOTH_MODELS_DIR_PATH_KEY]
if c.CLOTHING_REC_K_KEY in params:
clothing_rec_k = params[c.CLOTHING_REC_K_KEY]
if c.CLOTHING_REC_HSV_CHANNELS_NR_KEY in params:
hsv_channels = params[c.CLOTHING_REC_HSV_CHANNELS_NR_KEY]
if ce.CLOTHING_RECOGNITION_RESULTS_PATH_KEY in params:
results_path = params[ce.CLOTHING_RECOGNITION_RESULTS_PATH_KEY]
if c.CLOTHING_REC_USE_LBP_KEY in params:
use_LBP = params[c.CLOTHING_REC_USE_LBP_KEY]
if c.CLOTHING_REC_USE_MASK_KEY in params:
use_mask = params[c.CLOTHING_REC_USE_MASK_KEY]
if c.CLOTHING_REC_USE_MOTION_MASK_KEY in params:
use_motion_mask = params[c.CLOTHING_REC_USE_MOTION_MASK_KEY]
if ce.VIDEO_NAME_KEY in params:
video_name = params[ce.VIDEO_NAME_KEY]
# Number of correctly matched pairs of frame sequences
rec_pairs_nr = 0
# Number of checked pairs of frame sequences
total_test_pairs_nr = 0
# Iterate through videos
for video in os.listdir(dataset_path):
video_path = os.path.join(dataset_path, video)
if video != video_name:
continue
# If directory for clothing models is provided and
# file for this video exists, load models from file
file_path = None
loaded = False
models = None
if cloth_models_dir_path:
file_path = os.path.join(cloth_models_dir_path, video_name)
if os.path.exists(file_path):
with open(file_path) as f:
models = pickle.load(f)
loaded = True
if not loaded:
# Calculate clothing models
models = []
for subj in os.listdir(video_path):
subj_path = os.path.join(video_path, subj)
frame_seq = []
for im in os.listdir(subj_path):
im_path = os.path.join(subj_path, im)
frame_seq.append(im_path)
model = pt.get_clothing_model_from_sequence(frame_seq, params)
models.append(model)
# If directory for clothing models is provided,
# save models into a file
if cloth_models_dir_path:
with open(file_path, 'w') as f:
pickle.dump(models, f)
# Compare each possible pair of frame sequences in the same video
counter_1 = 0
for subj_1 in os.listdir(video_path):
counter_2 = 0
for subj_2 in os.listdir(video_path):
if subj_2 != subj_1:
model_1 = models[counter_1]
model_2 = models[counter_2]
if model_1 and model_2:
total_test_pairs_nr += 1
# If LBP histograms are used, there is only one channel
if use_LBP:
if params is None:
params = {}
params[c.CLOTHING_REC_HSV_CHANNELS_NR_KEY] = 1
(sim, dist_ratio) = utils.compare_clothes(
model_1, model_2, '', '', 0, None, params,
clothing_rec_k)
# First part of directory name contains person's tag
tag1 = subj_1.split('-')[0]
tag2 = subj_2.split('-')[0]
# Check correctness of result
if ((sim and (tag1 == tag2))
or (not sim and (tag1 != tag2))):
rec_pairs_nr += 1
counter_2 += 1
counter_1 += 1
recognition_rate = 0
if total_test_pairs_nr != 0:
recognition_rate = float(rec_pairs_nr) / float(total_test_pairs_nr)
print("\n ### RESULTS ###\n")
print('Number of checked pairs of image sequences: ' + str(total_test_pairs_nr))
print('Number of correctly matched pairs of image sequences: ' + str(rec_pairs_nr))
print('Recognition rate: ' + str(recognition_rate * 100) + '%')
# Update YAML file with the results of all the experiments
new_experiment_dict = {ce.VIDEO_NAME_KEY: video_name,
c.CLOTHING_REC_HSV_CHANNELS_NR_KEY: hsv_channels,
c.CLOTHING_REC_K_KEY: clothing_rec_k,
c.CLOTHING_REC_USE_LBP_KEY: use_LBP,
c.CLOTHING_REC_USE_MASK_KEY: use_mask,
c.CLOTHING_REC_USE_MOTION_MASK_KEY: use_motion_mask,
ce.TEST_IMAGES_NR_KEY: total_test_pairs_nr,
ce.RECOGNITION_RATE_KEY: recognition_rate}
experiment_results_file_name = ce.EXPERIMENT_RESULTS_FILE_NAME
yaml_results_file_name = experiment_results_file_name + '.yaml'
all_results_YAML_file_path = os.path.join(
results_path, yaml_results_file_name)
file_check = os.path.isfile(all_results_YAML_file_path)
experiments = list()
if file_check:
experiments = load_experiment_results(all_results_YAML_file_path)
number_of_already_done_experiments = len(experiments)
new_experiment_dict[ce.EXPERIMENT_NUMBER_KEY] = (
number_of_already_done_experiments + 1)
else:
new_experiment_dict[ce.EXPERIMENT_NUMBER_KEY] = 1
new_experiment_dict_extended = {ce.EXPERIMENT_KEY: new_experiment_dict}
experiments.append(new_experiment_dict_extended)
experiments_dict = {ce.EXPERIMENTS_KEY: experiments}
utils.save_YAML_file(all_results_YAML_file_path, experiments_dict)
