def show_face_and_clothing_bboxes(im_path, params=None):
"""
Show bounding boxes of faces found in given image
and of the relative clothing
:im_path: string
:im_path: path of image
:params: dictionary
:params: dictionary with configuration parameters
"""
# Set parameters
align_path = c.ALIGNED_FACES_PATH
clothing_width_pct = c.CLOTHES_BBOX_WIDTH
clothing_height_pct = c.CLOTHES_BBOX_HEIGHT
if params:
if c.ALIGNED_FACES_PATH_KEY in params:
align_path = params[c.ALIGNED_FACES_PATH_KEY]
if c.CLOTHES_BBOX_WIDTH_KEY in params:
clothing_width_pct = params[c.CLOTHES_BBOX_WIDTH_KEY]
if c.CLOTHES_BBOX_HEIGHT_KEY in params:
clothing_height_pct = params[c.CLOTHES_BBOX_HEIGHT_KEY]
rgb_image = cv2.imread(im_path, cv2.IMREAD_COLOR)
result_dict = detect_faces_in_image(im_path, align_path, params, False)
faces = result_dict[c.FACES_KEY]
for face_dict in faces:
(x, y, w, h) = face_dict[c.BBOX_KEY]
cv2.rectangle(rgb_image, (x, y), (x + w, y + h), (255, 0, 0), 4)
cl_w = int(clothing_width_pct * w)
cl_h = int(clothing_height_pct * h)
cl_x = int(x + w / 2.0 - cl_w / 2.0)
cl_y = int(y + h)
cv2.rectangle(rgb_image, (cl_x, cl_y),
(cl_x + cl_w, cl_y + cl_h), (0, 0, 255), 4)
im_name = os.path.basename(im_path)
cv2.imshow(im_name, rgb_image)
cv2.waitKey(0)
def create_ann_file_for_dataset(dataset_path, ann_file_path, params=None):
"""
Create YAML file with annotations for given dataset.
For each image in the dataset,
posizion and size of the found face is stored.
:type dataset_path: string
:param dataset_path: path of dataset
:type ann_file_path: string
:param ann_file_path: path of file that will contain annotations
:type params: dictionary
:param params: configuration parameters (see table)
============================================ ======================================== =============================
Key (params) Value Default value
============================================ ======================================== =============================
aligned_faces_path Path of directory for aligned faces
check_eye_positions If True, check eye positions True
classifiers_dir_path Path of directory with OpenCV
cascade classifiers
eye_detection_classifier Classifier for eye detection 'haarcascade_mcs_lefteye.xml'
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
max_eye_angle Maximum inclination of the line 0.125
connecting the eyes
(in % of pi radians)
min_eye_distance Minimum distance between eyes 0.25
(in % of the width of the face
bounding box)
nose_detection_classifier Classifier for nose detection 'haarcascade_mcs_nose.xml'
software_test_file Path of image to be used for
software test
use_nose_pos_in_detection If True, detections with no good False
nose position are discarded
============================================ ======================================== =============================
"""
# Set parameters
align_path = c.ALIGNED_FACES_PATH
if params:
if c.ALIGNED_FACES_PATH_KEY in params:
align_path = params[c.ALIGNED_FACES_PATH_KEY]
ann_dict = {}
for subject_dir in os.listdir(dataset_path):
subject_path = os.path.join(dataset_path, subject_dir)
for im_name in os.listdir(subject_path):
# Path of image relative to dataset path
rel_im_path = os.path.join(subject_dir, im_name)
ann_dict[rel_im_path] = {}
print('rel_im_path', rel_im_path)
# Full path of image
im_path = os.path.join(subject_path, im_name)
# Detect faces in image and take first result
result_dict = detect_faces_in_image(
im_path, align_path, params, False)
if c.FACES_KEY in result_dict:
faces = result_dict[c.FACES_KEY]
if len(faces) > 0:
face_dict = faces[0]
bbox = face_dict[c.BBOX_KEY]
ann_dict[rel_im_path][c.BBOX_KEY] = bbox
save_YAML_file(ann_file_path, ann_dict)
def get_clothing_model_from_sequence(frame_seq, params):
"""
Calculate clothing model from frame sequence
:type frame_seq: list
:param frame_seq: list of paths of frame sequence
:type params: dictionary
:param params: configuration parameters (see table)
:rtype: list
:returns: clothing model
============================================ ======================================== =============================
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)
If True, use mask for HSV values 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_LBP_in_clothing_recognition If True, use LBP as features False
for clothing recognition
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
============================================ ======================================== =============================
"""
# Set parameters
align_path = c.ALIGNED_FACES_PATH
cl_pct_height = c.CLOTHES_BBOX_HEIGHT
cl_pct_width = c.CLOTHES_BBOX_WIDTH
hsv_channels = c.CLOTHING_REC_HSV_CHANNELS_NR
neck_pct_height = c.NECK_HEIGHT
use_LBP = c.CLOTHING_REC_USE_LBP
use_mask = c.CLOTHING_REC_USE_MASK
use_motion_mask = c.CLOTHING_REC_USE_MOTION_MASK
if params:
if c.ALIGNED_FACES_PATH_KEY in params:
align_path = params[c.ALIGNED_FACES_PATH_KEY]
if c.CLOTHES_BBOX_HEIGHT_KEY in params:
cl_pct_height = params[c.CLOTHES_BBOX_HEIGHT_KEY]
if c.CLOTHES_BBOX_WIDTH_KEY in params:
cl_pct_width = params[c.CLOTHES_BBOX_WIDTH_KEY]
if c.CLOTHING_REC_HSV_CHANNELS_NR_KEY in params:
hsv_channels = params[c.CLOTHING_REC_HSV_CHANNELS_NR_KEY]
if c.NECK_HEIGHT_KEY in params:
neck_pct_height = params[c.NECK_HEIGHT_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]
# No alignment must be carried out
if params is None:
params = {}
params[c.USE_EYES_POSITION_KEY] = False
image_masks = None
if use_motion_mask:
# Get regions with detected moving objects
image_masks = background_subtraction_on_images(frame_seq)
# Get clothing information from sequence
model = []
counter = 0
for im_path in frame_seq:
if use_motion_mask and (counter == 0):
# Moving objects are detected since second frame
counter += 1
continue
# Detect faces in image and take first result
result_dict = detect_faces_in_image(im_path, align_path, params, False)
if c.FACES_KEY in result_dict:
faces = result_dict[c.FACES_KEY]
if len(faces) > 0:
face_dict = faces[0]
face_bbox = face_dict[c.BBOX_KEY]
face_x0 = face_bbox[0]
face_y0 = face_bbox[1]
face_width = face_bbox[2]
face_height = face_bbox[3]
face_x1 = face_x0 + face_width
face_y1 = face_y0 + face_height
# Get region of interest for clothes
clothes_width = int(face_width * cl_pct_width)
clothes_height = int(face_height * cl_pct_height)
clothes_x0 = int(face_x0 + face_width / 2.0 - clothes_width / 2.0)
clothes_y0 = int(
face_y0 + face_height + (face_height * neck_pct_height))
clothes_x1 = clothes_x0 + clothes_width
clothes_y1 = clothes_y0 + clothes_height
im = cv2.imread(im_path)
roi = im[clothes_y0:clothes_y1, clothes_x0:clothes_x1]
# Check if bounding box is entirely contained by frame
im_height, im_width, channels = im.shape
if ((clothes_x0 < 0) or (clothes_x1 > im_width)
or (clothes_y0 < 0) or (clothes_y1 > im_height)):
print('Bounding box not entirely contained by frame')
return None
if use_LBP:
# Use LBP histograms
roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
recognizer = cv2.createLBPHFaceRecognizer(1, 8, 1, 1)
recognizer.train(
np.asarray([np.asarray(roi_gray, dtype=np.uint8)]),
np.asarray([0]))
hist = recognizer.getMatVector("histograms")[0][0]
model.append([hist])
else:
# Use HSV histograms
roi_hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = None
if use_mask:
mask = cv2.inRange(roi_hsv,
np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
if image_masks:
motion_mask = None
im_motion_mask = image_masks[counter]
roi_motion_mask = im_motion_mask[clothes_y0:clothes_y1, clothes_x0:clothes_x1]
if use_mask:
mask = roi_motion_mask & mask
else:
mask = roi_motion_mask
hists = []
for ch in range(0, hsv_channels):
hist = cv2.calcHist(
[roi_hsv], [ch], mask, [256], [0, 255])
cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
hists.append(hist)
model.append(hists)
counter += 1
return model
