count_aflw,
duplicate_until=-1)
'''
# Log found ratios
if os.path.isfile(detector_log_path):
file = open(detector_log_path, 'a')
file.write("%.2f,%d,%f,%f,%d,%f,%f\n" % (confidence_threshold, count_aflw, t_ratio_aflw, f_ratio_aflw, count_p04 - count_aflw, t_ratio_p04, f_ratio_p04))
else:
file = open(detector_log_path, 'w')
file.write('threshold,count_aflw,t_ratio_aflw,f_ratio_aflw,count_p04,t_ratio_p04,f_ratio_p04\n')
file.write("%.2f,%d,%f,%f,%d,%f,%f\n" % (confidence_threshold, count_aflw, t_ratio_aflw, f_ratio_aflw, count_p04 - count_aflw, t_ratio_p04, f_ratio_p04))
file.close()
'''
# Assign classes
class_assign(destination_dir, num_splits_tilt, num_splits_pan)
# Split dataset
split_dataset(destination_dir, test_ratio, validation_ratio)
# Get normalization parameters
find_norm_parameters(destination_dir)
# OPTIONAL: Save dataset as numpy arrays (for uploading to Google Colab)
store_dataset_arrays(destination_dir)
def main():
'''
This function acts as a testbench for the function clean_pointing04, using it to perform the basic processing of the
Pointing'04 dataset from a set of default values defined below.
'''
# Source paths.
pointing04_dir = 'original/HeadPoseImageDatabase/'
# Destination path.
destination_dir = 'clean/pointing04/'
# Detector model path.
head_detector_path = 'models/head-detector.h5'
# Detection parameters.
in_size = 512
out_size = 64
confidence_threshold = 0.75
# Output parameters.
grayscale_output = True
downscaling_interpolation = cv2.INTER_LINEAR
# Number of splits for class assignation.
num_splits_tilt = 8
num_splits_pan = 8
# Ratios for train/test and train/validation split.
test_ratio = 0.2
validation_ratio = 0.2
# Detector model.
detector = ssd_512(image_size=(in_size, in_size, 3),
n_classes=1,
min_scale=0.1,
max_scale=1,
mode='inference')
detector.load_weights(head_detector_path)
# Check if output directory exists.
try:
os.mkdir(destination_dir)
print("Directory", destination_dir, "created.")
except FileExistsError:
print("Directory", destination_dir, "already exists.")
shutil.rmtree(destination_dir)
os.mkdir(destination_dir)
# Actual cleaning.
clean_pointing04(pointing04_dir, destination_dir, detector,
confidence_threshold, out_size, grayscale_output,
downscaling_interpolation)
# Assign classes.
class_assign(destination_dir, num_splits_tilt, num_splits_pan)
# Split dataset.
split_dataset(destination_dir, test_ratio, validation_ratio)
# Get normalization parameters.
find_norm_parameters(destination_dir)
# OPTIONAL: Save dataset as numpy arrays (for uploading to Google Colab).
store_dataset_arrays(destination_dir)
def main():
'''
This function acts as a testbench for the function clean_pointing04, using it to perform the basic processing of the
Pointing'04 dataset from a set of default values defined below.
'''
# Source paths.
pointing04_dir = '../original/HeadPoseImageDatabase/'
# Destination path.
destination_dir = 'clean/pointing04_haar_area_color/'
# Detector model path.
frontal_detector_path = 'models/haarcascade_frontalface_alt.xml'
profile_detector_path = 'models/haarcascade_profileface.xml'
# Detection parameters.
out_size = 64
# Output paramenters
grayscale_output = True
downscaling_interpolation = cv2.INTER_AREA
# Number of splits for class assignation.
num_splits_tilt = 8
num_splits_pan = 8
# Ratios for train/test and train/validation split.
test_ratio = 0.2
validation_ratio = 0.2
# Detector model.
detector = HaarFaceDetector(frontal_detector_path, profile_detector_path)
# Check if output directory exists.
try:
os.mkdir(destination_dir)
print("Directory", destination_dir, "created.")
except FileExistsError:
print("Directory", destination_dir, "already exists.")
shutil.rmtree(destination_dir)
os.mkdir(destination_dir)
# Actual cleaning.
clean_pointing04(pointing04_dir, destination_dir, detector, out_size,
grayscale_output, downscaling_interpolation)
# Assign classes.
class_assign(destination_dir, num_splits_tilt, num_splits_pan)
# Split dataset.
split_dataset(destination_dir, test_ratio, validation_ratio)
# Get normalization parameters.
find_norm_parameters(destination_dir)
# OPTIONAL: Save dataset as numpy arrays (for uploading to Google Colab).
store_dataset_arrays(destination_dir)