def profile_face_clf(img_lst, cascade_file, scale_factor, min_neighbors, min_size):
"""Classifies face based on profile image
:param img_lst: list of image objects with profile image
:param cascade_file, scale_factor, min_neighbors, min_size: tuning parameters
:return pred_lst: list of predictions (face or not)
"""
pred_lst = []
i = 0
for img_obj in img_lst:
image = img_obj["face_img"]
face_detector = FaceDetector(cascade_file, scale_factor, min_neighbors, min_size)
face_detector_processor = FaceDetectorProcessor()
face_detector_processor.detector = face_detector
cropped_face, face_in_square = face_detector_processor.process_image(image)
i += 1
print "Inserting prediction result ", i
pred_lst.append(face_in_square)
return pred_lst
def profile_face_clf(img_lst, cascade_file, scale_factor, min_neighbors,
min_size):
"""Classifies face based on profile image
:param img_lst: list of image objects with profile image
:param cascade_file, scale_factor, min_neighbors, min_size: tuning parameters
:return pred_lst: list of predictions (face or not)
"""
pred_lst = []
i = 0
for img_obj in img_lst:
image = img_obj['face_img']
face_detector = FaceDetector(cascade_file, scale_factor, min_neighbors,
min_size)
face_detector_processor = FaceDetectorProcessor()
face_detector_processor.detector = face_detector
cropped_face, face_in_square = face_detector_processor.process_image(
image)
i += 1
print "Inserting prediction result ", i
pred_lst.append(face_in_square)
return pred_lst
def tagged_face_clf(img_lst, cascade_file, scale_factor, min_neighbors,
min_size, crop_radius):
"""Classifies face based on full image and crop position
:param img_lst: list of image objects with full image and crop
position information
:param cascade_file, scale_factor, min_neighbors, min_size,
crop_radius: tuning parameters
:return pred_lst: list of predictions (face or not)
"""
pred_lst = []
i = 0
for img_obj in img_lst:
image = img_obj['full_img']
if image == None:
face_in_square = None
else:
pos_x = img_obj['crop_pos'][0]
pos_y = img_obj['crop_pos'][1]
height, width, channels = image.shape
x_coord = float(pos_x) * width
y_coord = float(pos_y) * height
crop_processor = CropImageProcessor((x_coord, y_coord),
tagged_radius=crop_radius)
cropped_square = crop_processor.process_image(image)
face_detector = FaceDetector(cascade_file, scale_factor,
min_neighbors, min_size)
face_detector_processor = FaceDetectorProcessor()
face_detector_processor.detector = face_detector
cropped_face, face_in_square = face_detector_processor.process_image(
cropped_square)
i += 1
print "Inserting prediction result", i
pred_lst.append(face_in_square)
return pred_lst
def tagged_face_clf(img_lst, cascade_file, scale_factor, min_neighbors, min_size, crop_radius):
"""Classifies face based on full image and crop position
:param img_lst: list of image objects with full image and crop
position information
:param cascade_file, scale_factor, min_neighbors, min_size,
crop_radius: tuning parameters
:return pred_lst: list of predictions (face or not)
"""
pred_lst = []
i = 0
for img_obj in img_lst:
image = img_obj["full_img"]
if image == None:
face_in_square = None
else:
pos_x = img_obj["crop_pos"][0]
pos_y = img_obj["crop_pos"][1]
height, width, channels = image.shape
x_coord = float(pos_x) * width
y_coord = float(pos_y) * height
crop_processor = CropImageProcessor((x_coord, y_coord), tagged_radius=crop_radius)
cropped_square = crop_processor.process_image(image)
face_detector = FaceDetector(cascade_file, scale_factor, min_neighbors, min_size)
face_detector_processor = FaceDetectorProcessor()
face_detector_processor.detector = face_detector
cropped_face, face_in_square = face_detector_processor.process_image(cropped_square)
i += 1
print "Inserting prediction result", i
pred_lst.append(face_in_square)
return pred_lst
