def fetch(image_nbs=None, force=False):
ref_path = get_reference_path()
if not os.path.isdir(ref_path):
os.makedirs(ref_path)
if image_nbs is None:
image_nbs = get_image_nbs()
else:
# TODO remove the 2 following lines?
# for image_nb in image_nbs:
# assert image_nb in get_image_nbs()
pass
for image_nb in image_nbs:
path = get_path(image_nb)
if os.path.isfile(path) and not force:
continue
np.random.seed(seed=image_nb)
a = np.array([0.0, 1.0])
shape = (_HEIGHT, _WIDTH)
pattern = np.random.choice(a=a, size=shape)
data = np.array(254.0 * pattern, dtype=np.uint8)
image = create_png_image(data)
image.save(path)
return
def get_pattern_data(pattern_nb):
# Load pattern data.
data = load_checkerboard_data(pattern_nb, with_borders=0.5)
data = 2.0 * (data - 0.5) / 254.0
# Save pattern data.
pattern_data_path = os.path.join(base_path,
patterns_params[pattern_nb]['path'])
np.save(pattern_data_path, data[1:-1, 1:-1]) # without borders
# Project.
# a_x = cb.get_horizontal_angles()
# a_y = cb.get_vertical_angles()
# rbs = sp.interpolate.RectBivariateSpline(a_x, a_y, data, kx=1, ky=1)
a_x = cb.get_horizontal_angles(with_borders=True)
a_y = cb.get_vertical_angles(with_borders=True)
a_x, a_y = np.meshgrid(a_x, a_y)
a = np.stack((np.ravel(a_x), np.ravel(a_y)),
axis=1) # i.e. stack along 2nd axis
ni = sp.interpolate.NearestNDInterpolator(a, np.ravel(data))
angular_resolution = math.atan(
frame_resolution / eye_diameter) * (180.0 / math.pi)
a_x = compute_horizontal_angles(width=frame_width,
angular_resolution=angular_resolution)
a_y = compute_vertical_angles(height=frame_height,
angular_resolution=angular_resolution)
a_x, a_y = np.meshgrid(a_x, a_y)
# data = rbs.ev(a_x, a_y)
# data = data.transpose()
a = np.stack((np.ravel(a_x), np.ravel(a_y)),
axis=1) # i.e. stack along 2nd axis
data = ni(a)
shape = (frame_width, frame_height)
data = np.reshape(data, shape)
data = data.transpose()
# Create pattern image.
image_data = data
image_data = 254.0 * image_data
image_data = np.array(
image_data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
image_data = np.transpose(image_data)
image_data = np.flipud(image_data)
pattern = create_png_image(image_data)
# Save pattern.
pattern_image_filename = "pattern_{nb:01d}_image.png".format(
nb=pattern_nb)
pattern_image_path = os.path.join(patterns_path,
pattern_image_filename)
pattern.save(pattern_image_path)
return data
def generate(image_nbs=None, verbose=False):
ref_path = get_reference_path()
if not os.path.isdir(ref_path):
os.makedirs(ref_path)
if image_nbs is None:
image_nbs = get_image_nbs()
else:
for image_nb in image_nbs:
assert image_nb in get_image_nbs()
for image_nb in image_nbs:
grey_level = image_nb
shape = (_HEIGHT, _WIDTH)
data = grey_level * np.ones(shape, dtype=np.uint8)
image = create_png_image(data)
path = get_path(image_nb)
image.save(path)
return
def generate(args):
config = handle_arguments_and_configurations(name, args)
base_path = config['path']
if not os.path.isdir(base_path):
os.makedirs(base_path)
print("Generation in {}".format(base_path))
# Create directories (if necessary).
images_dirname = 'images'
images_path = os.path.join(base_path, images_dirname)
if not os.path.isdir(images_path):
os.makedirs(images_path)
patterns_dirname = 'patterns'
patterns_path = os.path.join(base_path, patterns_dirname)
if not os.path.isdir(patterns_path):
os.makedirs(patterns_path)
frames_dirname = 'frames'
frames_path = os.path.join(base_path, frames_dirname)
if not os.path.isdir(frames_path):
os.makedirs(frames_path)
# Get configuration parameters.
image_keys = config['images']
pattern_nbs = config['perturbations']['pattern_nbs']
amplitude_value = config['perturbations']['amplitude']
eye_diameter = config['eye_diameter']
mean_luminance = config['mean_luminance']
std_luminance = config['std_luminance']
display_rate = config['display_rate']
adaptation_duration = config['adaptation_duration']
flash_duration = config['flash_duration']
inter_flash_duration = config['inter_flash_duration']
frame_width = config['frame']['width']
frame_height = config['frame']['height']
frame_resolution = config['frame']['resolution']
nb_repetitions = config['nb_repetitions']
seed = config['seed']
# Fetch images.
image_nbs = np.array(list(image_keys.keys()), dtype=int)
for image_nb in image_nbs:
image_key = image_keys[str(image_nb)]
fetch_image(*image_key)
nb_images = len(image_nbs)
_ = nb_images
# Fetch patterns.
pattern_nbs = np.array(pattern_nbs)
fetch_patterns(image_nbs=pattern_nbs)
nb_patterns = len(pattern_nbs)
# Prepare image parameters.
images_params = collections.OrderedDict()
for image_nb in image_nbs:
filename = 'image_{nb:01d}_data.npy'.format(nb=image_nb)
path = os.path.join(images_dirname, filename)
images_params[image_nb] = collections.OrderedDict([('path', path)])
# Prepare pattern parameters.
patterns_params = collections.OrderedDict()
for pattern_nb in pattern_nbs:
filename = 'pattern_{nb:01d}_data.npy'.format(nb=pattern_nb)
path = os.path.join(patterns_dirname, filename)
patterns_params[pattern_nb] = collections.OrderedDict([('path', path)])
def get_image_data(image_nb):
# Load image data.
image_key = image_keys[str(image_nb)]
dataset_name = image_key[0]
dataset = get_dataset(dataset_name)
data = dataset.load_data(*image_key[1:])
# Cut out central sub-regions.
a_x = dataset.get_horizontal_angles()
a_y = dataset.get_vertical_angles()
rbs = sp.interpolate.RectBivariateSpline(a_x, a_y, data)
angular_resolution = math.atan(
frame_resolution / eye_diameter) * (180.0 / math.pi)
a_x = compute_horizontal_angles(width=frame_width,
angular_resolution=angular_resolution)
a_y = compute_vertical_angles(height=frame_height,
angular_resolution=angular_resolution)
a_x, a_y = np.meshgrid(a_x, a_y)
data = rbs.ev(a_x, a_y)
data = data.transpose()
# # Prepare image data.
# median = np.median(data)
# mad = np.median(np.abs(data - median))
# # mad = np.std(data)
# centered_data = data - median
# if mad > 1.e-13:
# centered_n_reduced_data = centered_data / mad
# else:
# centered_n_reduced_data = centered_data
# normalized_data = centered_n_reduced_data
# scaled_data = 0.1 * normalized_data
# shifted_n_scaled_data = scaled_data + 0.5
# TODO keep the following normalization?
# # Prepare image data.
# mean = np.mean(data)
# scaled_data = data / mean if mean > 0.0 else data
# shifted_n_scaled_data = 0.2 * scaled_data # TODO correct?
# TODO keep the following normalization?
luminance_data = data
log_luminance_data = np.log(1.0 + luminance_data)
log_mean_luminance = np.mean(log_luminance_data)
log_std_luminance = np.std(log_luminance_data)
normalized_log_luminance_data = log_luminance_data - log_mean_luminance
if log_std_luminance > 1e-13:
normalized_log_luminance_data = normalized_log_luminance_data / log_std_luminance
normalized_log_luminance_data = 0.2 * normalized_log_luminance_data
normalized_luminance_data = np.exp(normalized_log_luminance_data) - 1.0
normalized_luminance_data = normalized_luminance_data - np.mean(
normalized_luminance_data)
if np.std(normalized_luminance_data) > 1e-13:
normalized_luminance_data = normalized_luminance_data / np.std(
normalized_luminance_data)
luminance_data = std_luminance * normalized_luminance_data + mean_luminance
# Save image data.
image_data_path = os.path.join(base_path,
images_params[image_nb]['path'])
np.save(image_data_path, luminance_data)
# Prepare image.
data = np.copy(luminance_data)
data[data < 0.0] = 0.0
data[data > 1.0] = 1.0
data = np.array(
254.0 * data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
data = np.transpose(data)
data = np.flipud(data)
image = create_png_image(data)
# Save image.
image_image_filename = "image_{nb:01d}_image.png".format(nb=image_nb)
image_image_path = os.path.join(images_path, image_image_filename)
image.save(image_image_path)
return luminance_data
def get_pattern_data(pattern_nb):
# Load pattern data.
data = load_checkerboard_data(pattern_nb, with_borders=0.5)
data = 2.0 * (data - 0.5) / 254.0
# Save pattern data.
pattern_data_path = os.path.join(base_path,
patterns_params[pattern_nb]['path'])
np.save(pattern_data_path, data[1:-1, 1:-1]) # without borders
# Project.
# a_x = cb.get_horizontal_angles()
# a_y = cb.get_vertical_angles()
# rbs = sp.interpolate.RectBivariateSpline(a_x, a_y, data, kx=1, ky=1)
a_x = cb.get_horizontal_angles(with_borders=True)
a_y = cb.get_vertical_angles(with_borders=True)
a_x, a_y = np.meshgrid(a_x, a_y)
a = np.stack((np.ravel(a_x), np.ravel(a_y)),
axis=1) # i.e. stack along 2nd axis
ni = sp.interpolate.NearestNDInterpolator(a, np.ravel(data))
angular_resolution = math.atan(
frame_resolution / eye_diameter) * (180.0 / math.pi)
a_x = compute_horizontal_angles(width=frame_width,
angular_resolution=angular_resolution)
a_y = compute_vertical_angles(height=frame_height,
angular_resolution=angular_resolution)
a_x, a_y = np.meshgrid(a_x, a_y)
# data = rbs.ev(a_x, a_y)
# data = data.transpose()
a = np.stack((np.ravel(a_x), np.ravel(a_y)),
axis=1) # i.e. stack along 2nd axis
data = ni(a)
shape = (frame_width, frame_height)
data = np.reshape(data, shape)
data = data.transpose()
# Create pattern image.
image_data = data
image_data = 254.0 * image_data
image_data = np.array(
image_data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
image_data = np.transpose(image_data)
image_data = np.flipud(image_data)
pattern = create_png_image(image_data)
# Save pattern.
pattern_image_filename = "pattern_{nb:01d}_image.png".format(
nb=pattern_nb)
pattern_image_path = os.path.join(patterns_path,
pattern_image_filename)
pattern.save(pattern_image_path)
return data
def get_frame_path(image_nb, pattern_nb):
image_index = np.where(image_nbs == image_nb)[0][0]
pattern_index = np.where(pattern_nbs == pattern_nb)[0][0]
frame_nb = (image_index * nb_patterns) + pattern_index
filename = "frame_{nb:04d}.png".format(nb=frame_nb)
path = os.path.join(frames_path, filename)
return path
# Set condition parameters.
condition_nb = 0
conditions_params = collections.OrderedDict()
frame_paths = collections.OrderedDict()
for image_nb in image_nbs:
for pattern_nb in pattern_nbs:
assert condition_nb not in conditions_params
conditions_params[condition_nb] = collections.OrderedDict([
('image_nb', image_nb),
('pattern_nb', pattern_nb),
])
frame_paths[condition_nb] = get_frame_path(image_nb, pattern_nb)
condition_nb += 1
condition_nbs = np.array(list(conditions_params.keys()))
nb_conditions = len(condition_nbs)
# Create frames.
# # Preload images data.
images_data = {}
for image_nb in image_nbs:
# Get image data.
image_data = get_image_data(image_nb)
# Store image data.
images_data[image_nb] = image_data
# # Create frames.
pattern_data = None
for pattern_nb in tqdm.tqdm(pattern_nbs):
for image_nb in image_nbs:
frame_path = get_frame_path(image_nb, pattern_nb)
if os.path.isfile(frame_path):
continue
# Get image data.
image_data = images_data[image_nb]
# Get pattern data.
pattern_data = get_pattern_data(
pattern_nb) if pattern_data is None else pattern_data
# Create frame data.
data = image_data + amplitude_value * pattern_data
# Create frame image.
data[data < 0.0] = 0.0
data[data > 1.0] = 1.0
data = np.array(
254.0 * data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
data = np.transpose(data)
data = np.flipud(data)
image = create_png_image(data)
# Save frame image.
image.save(frame_path)
pattern_data = None
# Set image ordering for each repetitions.
repetition_orderings = collections.OrderedDict()
np.random.seed(seed)
for repetition_nb in range(0, nb_repetitions):
ordering = np.copy(condition_nbs)
np.random.shuffle(ordering)
repetition_orderings[repetition_nb] = ordering
# Create conditions .csv file.
conditions_csv_filename = '{}_conditions.csv'.format(name)
conditions_csv_path = os.path.join(base_path, conditions_csv_filename)
print("Start creating conditions .csv file...")
conditions_csv_file = open_csv_file(conditions_csv_path,
columns=['image_nb', 'pattern_nb'])
for condition_nb in condition_nbs:
condition_params = conditions_params[condition_nb]
conditions_csv_file.append(**condition_params)
conditions_csv_file.close()
print("End of conditions .csv file creation.")
# Create images .csv file.
images_csv_filename = '{}_images.csv'.format(name)
images_csv_path = os.path.join(base_path, images_csv_filename)
print("Start creating images .csv file...")
images_csv_file = open_csv_file(images_csv_path, columns=['path'])
for image_nb in image_nbs:
image_params = images_params[image_nb]
images_csv_file.append(**image_params)
images_csv_file.close()
print("End of images .csv file creation.")
# Create patterns .csv file.
patterns_csv_filename = '{}_patterns.csv'.format(name)
patterns_csv_path = os.path.join(base_path, patterns_csv_filename)
print("Start creating patterns .csv file...")
patterns_csv_file = open_csv_file(patterns_csv_path, columns=['path'])
for pattern_nb in pattern_nbs:
pattern_params = patterns_params[pattern_nb]
patterns_csv_file.append(**pattern_params)
patterns_csv_file.close()
print("End of patterns .csv file creation.")
# Create .bin file.
print("Start creating .bin file...")
bin_filename = '{}.bin'.format(name)
bin_path = os.path.join(base_path, bin_filename)
nb_bin_images = 1 + nb_conditions # i.e. grey image and other conditions
bin_frame_nbs = {}
# Open .bin file.
bin_file = open_bin_file(bin_path,
nb_bin_images,
frame_width=frame_width,
frame_height=frame_height,
reverse=False,
mode='w')
# Add grey frame.
grey_frame = get_grey_frame(frame_width,
frame_height,
luminance=mean_luminance)
grey_frame = float_frame_to_uint8_frame(grey_frame)
bin_file.append(grey_frame)
bin_frame_nbs[None] = bin_file.get_frame_nb()
# Add frames.
for condition_nb in tqdm.tqdm(condition_nbs):
frame_path = frame_paths[condition_nb]
frame = load_png_image(frame_path)
bin_file.append(frame.data)
bin_frame_nbs[condition_nb] = bin_file.get_frame_nb()
# Close .bin file.
bin_file.close()
# ...
print("End of .bin file creation.")
# Create .vec file.
print("Start creating .vec file...")
vec_filename = "{}.vec".format(name)
vec_path = os.path.join(base_path, vec_filename)
csv_filename = "{}_trials.csv".format(name)
csv_path = os.path.join(base_path, csv_filename)
# ...
nb_displays_during_adaptation = int(
np.ceil(adaptation_duration * display_rate))
nb_displays_per_flash = int(np.ceil(flash_duration * display_rate))
nb_displays_per_inter_flash = int(
np.ceil(inter_flash_duration * display_rate))
nb_displays_per_repetition = nb_conditions * (nb_displays_per_flash +
nb_displays_per_inter_flash)
nb_displays = nb_displays_during_adaptation + nb_repetitions * nb_displays_per_repetition
# Open .vec file.
vec_file = open_vec_file(vec_path, nb_displays=nb_displays)
# Open .csv file.
csv_file = open_csv_file(
csv_path,
columns=['condition_nb', 'start_display_nb', 'end_display_nb'])
# Add adaptation.
bin_frame_nb = bin_frame_nbs[None] # i.e. default frame (grey)
for _ in range(0, nb_displays_during_adaptation):
vec_file.append(bin_frame_nb)
# Add repetitions.
for repetition_nb in tqdm.tqdm(range(0, nb_repetitions)):
condition_nbs = repetition_orderings[repetition_nb]
for condition_nb in condition_nbs:
# Add flash.
start_display_nb = vec_file.get_display_nb() + 1
bin_frame_nb = bin_frame_nbs[condition_nb]
for _ in range(0, nb_displays_per_flash):
vec_file.append(bin_frame_nb)
end_display_nb = vec_file.get_display_nb()
csv_file.append(condition_nb=condition_nb,
start_display_nb=start_display_nb,
end_display_nb=end_display_nb)
# Add inter flash.
bin_frame_nb = bin_frame_nbs[None] # i.e. default frame (grey)
for _ in range(0, nb_displays_per_inter_flash):
vec_file.append(bin_frame_nb)
# Close .csv file.
csv_file.close()
# Close .vec file.
vec_file.close()
# ...
print("End of .vec file creation.")
# TODO add unperturbed flashed images?
return
def get_image_data(image_nb):
# Load image data.
image_key = image_keys[str(image_nb)]
dataset_name = image_key[0]
dataset = get_dataset(dataset_name)
data = dataset.load_data(*image_key[1:])
# Cut out central sub-regions.
a_x = dataset.get_horizontal_angles()
a_y = dataset.get_vertical_angles()
rbs = sp.interpolate.RectBivariateSpline(a_x, a_y, data)
angular_resolution = math.atan(
frame_resolution / eye_diameter) * (180.0 / math.pi)
a_x = compute_horizontal_angles(width=frame_width,
angular_resolution=angular_resolution)
a_y = compute_vertical_angles(height=frame_height,
angular_resolution=angular_resolution)
a_x, a_y = np.meshgrid(a_x, a_y)
data = rbs.ev(a_x, a_y)
data = data.transpose()
# # Prepare image data.
# median = np.median(data)
# mad = np.median(np.abs(data - median))
# # mad = np.std(data)
# centered_data = data - median
# if mad > 1.e-13:
# centered_n_reduced_data = centered_data / mad
# else:
# centered_n_reduced_data = centered_data
# normalized_data = centered_n_reduced_data
# scaled_data = 0.1 * normalized_data
# shifted_n_scaled_data = scaled_data + 0.5
# TODO keep the following normalization?
# # Prepare image data.
# mean = np.mean(data)
# scaled_data = data / mean if mean > 0.0 else data
# shifted_n_scaled_data = 0.2 * scaled_data # TODO correct?
# TODO keep the following normalization?
luminance_data = data
log_luminance_data = np.log(1.0 + luminance_data)
log_mean_luminance = np.mean(log_luminance_data)
log_std_luminance = np.std(log_luminance_data)
normalized_log_luminance_data = log_luminance_data - log_mean_luminance
if log_std_luminance > 1e-13:
normalized_log_luminance_data = normalized_log_luminance_data / log_std_luminance
normalized_log_luminance_data = 0.2 * normalized_log_luminance_data
normalized_luminance_data = np.exp(normalized_log_luminance_data) - 1.0
normalized_luminance_data = normalized_luminance_data - np.mean(
normalized_luminance_data)
if np.std(normalized_luminance_data) > 1e-13:
normalized_luminance_data = normalized_luminance_data / np.std(
normalized_luminance_data)
luminance_data = std_luminance * normalized_luminance_data + mean_luminance
# Save image data.
image_data_path = os.path.join(base_path,
images_params[image_nb]['path'])
np.save(image_data_path, luminance_data)
# Prepare image.
data = np.copy(luminance_data)
data[data < 0.0] = 0.0
data[data > 1.0] = 1.0
data = np.array(
254.0 * data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
data = np.transpose(data)
data = np.flipud(data)
image = create_png_image(data)
# Save image.
image_image_filename = "image_{nb:01d}_image.png".format(nb=image_nb)
image_image_path = os.path.join(images_path, image_image_filename)
image.save(image_image_path)
return luminance_data
def generate(args):
config = handle_arguments_and_configurations(name, args)
base_path = config['path']
if not os.path.isdir(base_path):
os.makedirs(base_path)
print("Generation in: {}".format(base_path))
images_path = os.path.join(base_path, "images")
if not os.path.isdir(images_path):
os.makedirs(images_path)
# Get configuration parameters.
vh_image_nbs = config['vh_image_nbs']
eye_diameter = config['eye_diameter']
mean_luminance = config['mean_luminance']
std_luminance = config['std_luminance']
normalized_value_median = config['normalized_value_median']
normalized_value_mad = config['normalized_value_mad']
display_rate = config['display_rate']
adaptation_duration = config['adaptation_duration']
flash_duration = config['flash_duration']
inter_flash_duration = config['inter_flash_duration']
frame_resolution = config['frame']['resolution']
frame_width = config['frame']['width']
frame_height = config['frame']['height']
nb_repetitions = config['nb_repetitions']
stuttering_vh_image_nbs = config['stuttering_vh_image_nbs']
nb_stuttering_vh_images = config['nb_stuttering_vh_images']
nb_stutters = config['nb_stutters']
seed = config['seed']
verbose = config['verbose']
# Fetch van Hateren images.
vh.fetch(image_nbs=vh_image_nbs,
download_if_missing=False,
verbose=verbose)
# Select unsaturated van Hateren image numbers.
if vh_image_nbs is None:
vh_image_nbs = vh.get_image_nbs()
else:
vh_image_nbs = np.array(vh_image_nbs)
are_saturated = vh.get_are_saturated(image_nbs=vh_image_nbs,
verbose=verbose)
are_unsaturated = np.logical_not(are_saturated)
assert vh_image_nbs.size == are_unsaturated.size, "{} != {}".format(
vh_image_nbs.size, are_unsaturated.size)
unsaturated_vh_image_nbs = vh_image_nbs[are_unsaturated]
# Select ...
# mean_luminances = vh.get_mean_luminances(image_nbs=unsaturated_vh_image_nbs, verbose=verbose)
# std_luminances = vh.get_std_luminances(image_nbs=unsaturated_vh_image_nbs, verbose=verbose)
max_luminances = vh.get_max_luminances(image_nbs=unsaturated_vh_image_nbs,
verbose=verbose)
# TODO remove the following lines?
# max_centered_luminances = max_luminances / mean_luminances
# # print(np.min(max_centered_luminances))
# # print(np.median(max_centered_luminances))
# # print(np.max(max_centered_luminances))
# are_good = max_centered_luminances <= 8.3581 # TODO correct?
# TODO remove the following lines?
# max_normalized_luminances = (max_luminances / mean_luminances - 1.0) / std_luminances + 1.0
# are_good = max_normalized_luminances <= 1.02
# TODO remove the following lines?
log_mean_luminances = vh.get_log_mean_luminances(
image_nbs=unsaturated_vh_image_nbs, verbose=verbose)
log_std_luminances = vh.get_log_mean_luminances(
image_nbs=unsaturated_vh_image_nbs, verbose=verbose)
log_max_luminances = np.log(1.0 + max_luminances)
log_max_normalized_luminances = (log_max_luminances -
log_mean_luminances) / log_std_luminances
are_good = log_max_normalized_luminances <= 5.0
# ...
good_vh_image_nbs = unsaturated_vh_image_nbs[are_good]
# ...
# selected_vh_image_nbs = unsaturated_vh_image_nbs
selected_vh_image_nbs = good_vh_image_nbs
# Check stuttering van Hateren image numbers.
np.random.seed(seed)
if stuttering_vh_image_nbs is None:
stuttering_vh_image_nbs = np.array([])
else:
assert len(stuttering_vh_image_nbs) <= nb_stuttering_vh_images
for stuttering_vh_image_nb in stuttering_vh_image_nbs:
assert stuttering_vh_image_nb in selected_vh_image_nbs, stuttering_vh_image_nb
potential_stuttering_vh_image_nbs = np.setdiff1d(selected_vh_image_nbs,
stuttering_vh_image_nbs,
assume_unique=True)
nb_missing_stuttering_vh_image_nbs = nb_stuttering_vh_images - len(
stuttering_vh_image_nbs)
stuttering_vh_image_nbs = np.concatenate(
(stuttering_vh_image_nbs,
np.random.choice(potential_stuttering_vh_image_nbs,
nb_missing_stuttering_vh_image_nbs,
replace=False)))
stuttering_vh_image_nbs.sort()
# Generate grey image.
image_filename = "image_{i:04d}.png".format(i=0)
image_path = os.path.join(images_path, image_filename)
# if not os.path.isfile(image_path): # TODO uncomment this line.
frame = get_grey_frame(frame_width, frame_height, luminance=mean_luminance)
frame = float_frame_to_uint8_frame(frame)
image = create_png_image(frame)
image.save(image_path)
# Extract images from the van Hateren images.
for vh_image_nb in tqdm.tqdm(selected_vh_image_nbs):
# Check if image already exists.
image_filename = "image_{i:04d}.png".format(i=vh_image_nb)
image_path = os.path.join(images_path, image_filename)
if os.path.isfile(image_path):
continue
# Cut out central sub-region.
a_x = vh.get_horizontal_angles()
a_y = vh.get_vertical_angles()
luminance_data = vh.load_luminance_data(vh_image_nb)
rbs = sp.interpolate.RectBivariateSpline(a_x,
a_y,
luminance_data,
kx=1,
ky=1)
angular_resolution = math.atan(
frame_resolution / eye_diameter) * (180.0 / math.pi)
a_x = compute_horizontal_angles(width=frame_width,
angular_resolution=angular_resolution)
a_y = compute_vertical_angles(height=frame_height,
angular_resolution=angular_resolution)
a_x, a_y = np.meshgrid(a_x, a_y)
luminance_data = rbs.ev(a_x, a_y)
luminance_data = luminance_data.transpose()
# TODO uncomment the following lines?
# # Prepare data.
# luminance_median = np.median(luminance_data)
# luminance_mad = np.median(np.abs(luminance_data - luminance_median))
# centered_luminance_data = luminance_data - luminance_median
# if luminance_mad > 0.0:
# centered_n_reduced_luminance_data = centered_luminance_data / luminance_mad
# else:
# centered_n_reduced_luminance_data = centered_luminance_data
# normalized_luminance_data = centered_n_reduced_luminance_data
# scaled_data = normalized_value_mad * normalized_luminance_data
# shifted_n_scaled_data = scaled_data + normalized_value_median
# data = shifted_n_scaled_data
# TODO remove the 2 following lines?
# scaled_luminance_data = luminance_data / np.mean(luminance_data)
# data = scaled_luminance_data / 8.3581
# TODO remove the 2 following lines?
# normalized_luminance_data = (luminance_data / np.mean(luminance_data) - 1.0) / np.std(luminance_data) + 1.0
# data = (normalized_luminance_data - 1.0) / 0.02 * 0.8 + 0.2
# TODO remove the following lines?
log_luminance_data = np.log(1.0 + luminance_data)
log_mean_luminance = np.mean(log_luminance_data)
log_std_luminance = np.std(log_luminance_data)
normalized_log_luminance_data = log_luminance_data - log_mean_luminance
if log_std_luminance > 1e-13:
normalized_log_luminance_data = normalized_log_luminance_data / log_std_luminance
normalized_log_luminance_data = 0.2 * normalized_log_luminance_data
normalized_luminance_data = np.exp(normalized_log_luminance_data) - 1.0
normalized_luminance_data = normalized_luminance_data - np.mean(
normalized_luminance_data)
if np.std(normalized_luminance_data) > 1e-13:
normalized_luminance_data = normalized_luminance_data / np.std(
normalized_luminance_data)
data = std_luminance * normalized_luminance_data + mean_luminance
# Prepare image data
if np.count_nonzero(data < 0.0) > 0:
s = "some pixels are negative in image {} (consider changing the configuration, 'normalized_value_mad': {})"
message = s.format(
vh_image_nb, normalized_value_mad /
((normalized_value_median - np.min(data)) /
(normalized_value_median - 0.0)))
warnings.warn(message)
data[data < 0.0] = 0.0
if np.count_nonzero(data > 1.0) > 0:
s = "some pixels saturate in image {} (consider changing the configuration, 'normalized_value_mad': {})"
message = s.format(
vh_image_nb, normalized_value_mad /
((np.max(data) - normalized_value_median) /
(1.0 - normalized_value_median)))
warnings.warn(message)
data[data > 1.0] = 1.0
data = np.array(
254.0 * data,
dtype=np.uint8) # 0.0 -> 0 and 1.0 -> 254 such that 0.5 -> 127
data = np.transpose(data)
data = np.flipud(data)
image = create_png_image(data)
# Save image.
image.save(image_path)
# Set condition numbers and image paths.
condition_nbs = []
stuttering_condition_nbs = []
image_paths = {}
for k, vh_image_nb in enumerate(selected_vh_image_nbs):
# condition_nb = k + 1
condition_nb = k
image_filename = 'image_{i:04d}.png'.format(i=vh_image_nb)
image_path = os.path.join(images_path, image_filename)
assert condition_nb not in condition_nbs
assert os.path.isfile(image_path)
condition_nbs.append(condition_nb)
if vh_image_nb in stuttering_vh_image_nbs:
stuttering_condition_nbs.append(condition_nb)
image_paths[condition_nb] = image_path
condition_nbs = np.array(condition_nbs)
stuttering_condition_nbs = np.array(stuttering_condition_nbs)
nb_conditions = len(condition_nbs)
nb_stuttering_conditions = len(stuttering_condition_nbs)
# Create conditions .csv file.
conditions_csv_filename = '{}_conditions.csv'.format(name)
conditions_csv_path = os.path.join(base_path, conditions_csv_filename)
print("Start creating conditions .csv file...")
# Open conditions .csv file.
conditions_csv_file = open_csv_file(conditions_csv_path, columns=['path'])
# Add conditions for van Hateren images.
for condition_nb in condition_nbs:
image_path = image_paths[condition_nb]
path = image_path.replace(base_path, '')
path = path[1:] # remove separator
conditions_csv_file.append(path=path)
# Close conditions .csv file.
conditions_csv_file.close()
# ...
print("End of conditions .csv file creation.")
# Set sequence of conditions for each repetition.
repetition_sequences = {}
np.random.seed(seed)
normal_condition_nbs = np.setdiff1d(condition_nbs,
stuttering_condition_nbs,
assume_unique=True)
nb_normal_indices = len(normal_condition_nbs)
nb_stuttering_indices = nb_stuttering_conditions * nb_stutters
nb_indices = nb_normal_indices + nb_stuttering_indices
sequence = np.empty(nb_indices, dtype=np.int)
stuttering_indices = np.linspace(0,
nb_indices,
num=nb_stuttering_indices,
endpoint=False)
stuttering_indices = stuttering_indices.astype(np.int)
normal_indices = np.setdiff1d(np.arange(0, nb_indices), stuttering_indices)
sequence[stuttering_indices] = np.concatenate(
tuple([stuttering_condition_nbs for _ in range(0, nb_stutters)]))
for repetition_nb in range(0, nb_repetitions):
repetition_sequence = np.copy(sequence)
# Normal.
repetition_normal_sequence = np.copy(normal_condition_nbs)
np.random.shuffle(repetition_normal_sequence)
repetition_sequence[normal_indices] = repetition_normal_sequence
# Stuttering.
repetition_stuttering_sequence = []
for _ in range(0, nb_stutters):
repetition_stuttering_condition_nbs = np.copy(
stuttering_condition_nbs)
np.random.shuffle(repetition_stuttering_condition_nbs)
repetition_stuttering_sequence.append(
repetition_stuttering_condition_nbs)
repetition_stuttering_sequence = np.concatenate(
tuple(repetition_stuttering_sequence))
repetition_sequence[
stuttering_indices] = repetition_stuttering_sequence
# ...
repetition_sequences[repetition_nb] = repetition_sequence
# Create .bin file.
print("Start creating .bin file...")
bin_filename = '{}.bin'.format(name)
bin_path = os.path.join(base_path, bin_filename)
nb_bin_images = 1 + nb_conditions # i.e. grey image and other conditions
bin_frame_nbs = {}
# Open .bin file.
bin_file = open_bin_file(bin_path,
nb_bin_images,
frame_width=frame_width,
frame_height=frame_height,
reverse=False,
mode='w')
# Add grey frame.
grey_frame = get_grey_frame(frame_width,
frame_height,
luminance=mean_luminance)
grey_frame = float_frame_to_uint8_frame(grey_frame)
bin_file.append(grey_frame)
bin_frame_nbs[None] = bin_file.get_frame_nb()
# Add van Hateren frames.
for condition_nb in tqdm.tqdm(condition_nbs):
image_path = image_paths[condition_nb]
image = load_png_image(image_path)
frame = image.data
bin_file.append(frame)
bin_frame_nbs[condition_nb] = bin_file.get_frame_nb()
# Close .bin file.
bin_file.close()
# ...
print("End of .bin file creation.")
# Create .vec file and stimulation .csv file.
print("Start creating .vec file...")
vec_filename = "{}.vec".format(name)
vec_path = os.path.join(base_path, vec_filename)
csv_filename = "{}_trials.csv".format(name)
csv_path = os.path.join(base_path, csv_filename)
# ...
nb_displays_during_adaptation = int(
np.ceil(adaptation_duration * display_rate))
nb_displays_per_flash = int(np.ceil(flash_duration * display_rate))
nb_displays_per_inter_flash = int(
np.ceil(inter_flash_duration * display_rate))
nb_flashes_per_repetition = nb_conditions + nb_stuttering_vh_images * (
nb_stutters - 1)
nb_displays_per_repetition = nb_flashes_per_repetition * (
nb_displays_per_flash + nb_displays_per_inter_flash)
nb_displays = nb_displays_during_adaptation + nb_repetitions * nb_displays_per_repetition
# Open .vec file.
vec_file = open_vec_file(vec_path, nb_displays=nb_displays)
# Open .csv file.
csv_file = open_csv_file(
csv_path,
columns=['condition_nb', 'start_display_nb', 'end_display_nb'])
# Add adaptation.
bin_frame_nb = bin_frame_nbs[None] # i.e. default frame (grey)
for _ in range(0, nb_displays_during_adaptation):
vec_file.append(bin_frame_nb)
# Add repetitions.
for repetition_nb in tqdm.tqdm(range(0, nb_repetitions)):
repetition_sequence = repetition_sequences[repetition_nb]
for condition_nb in repetition_sequence:
# Add flash.
start_display_nb = vec_file.get_display_nb() + 1
bin_frame_nb = bin_frame_nbs[condition_nb]
for _ in range(0, nb_displays_per_flash):
vec_file.append(bin_frame_nb)
end_display_nb = vec_file.get_display_nb()
csv_file.append(condition_nb=condition_nb,
start_display_nb=start_display_nb,
end_display_nb=end_display_nb)
# Add inter flash.
bin_frame_nb = bin_frame_nbs[None] # i.e. default frame (grey)
for _ in range(0, nb_displays_per_inter_flash):
vec_file.append(bin_frame_nb)
# Close .csv file.
csv_file.close()
# Close .vec file.
vec_file.close()
# ...
print("End of .vec file creation.")
return
