def construct_new_line(path_seuil, interval, line, choice, each, norm):
begin, end = interval
line_data = line.split(';')
seuil = line_data[0]
features = line_data[begin+1:end+1]
# keep only if modulo result is 0 (keep only each wanted values)
features = [float(m) for id, m in enumerate(features) if id % each == 0]
# TODO : check if it's always necessary to do that (loss of information for svd)
if norm:
if choice == 'svdne':
features = utils.normalize_arr_with_range(features, min_value_interval, max_value_interval)
if choice == 'svdn':
features = utils.normalize_arr_with_range(features)
with open(path_seuil, "r") as seuil_file:
seuil_learned = int(seuil_file.readline().strip())
if seuil_learned > int(seuil):
line = '1'
else:
line = '0'
for val in features:
line += ';'
line += str(val)
line += '\n'
return line
def get_svd_mean_and_image_rotations(img_path):
print("Extract features from... " + img_path)
img = np.asarray(Image.open(img_path))
width, height, dim = img.shape
img_mean = np.empty([width, height, 3])
rotations = []
svd_data_rotation = []
for i in range(4):
rotations.append(processing.rotate_image(img, (i + 1) * 90, pil=False))
svd_data_rotation.append(processing.get_LAB_L_SVD_s(rotations[i]))
Image.fromarray(rotations[i]).show()
mean_image = processing.fusion_images(rotations, pil=False)
mean_data = processing.get_LAB_L_SVD_s(mean_image)
# getting max and min information from min_max_filename
with open(data_folder + "/lab_min_max_values", 'r') as f:
min_val = float(f.readline())
max_val = float(f.readline())
mean_data = utils.normalize_arr_with_range(mean_data, min_val, max_val)
return [
utils.normalize_arr_with_range(data, min_val, max_val)
for data in svd_data_rotation
], mean_data
def construct_new_line(path_seuil, interval, line, choice, each, norm):
begin, end = interval
line_data = line.split(';')
seuil = line_data[0]
features = line_data[begin + 1:end + 1]
features = [float(m) for id, m in enumerate(features) if id % each == 0]
if norm:
if choice == 'svdne':
features = utils.normalize_arr_with_range(features,
min_value_interval,
max_value_interval)
if choice == 'svdn':
features = utils.normalize_arr(features)
with open(path_seuil, "r") as seuil_file:
seuil_learned = int(seuil_file.readline().strip())
if seuil_learned > int(seuil):
line = '1'
else:
line = '0'
for val in features:
line += ';'
line += str(val)
line += '\n'
return line
def construct_new_line(interval, line_data, choice, each, norm):
begin, end = interval
label = line_data[2]
features = line_data[begin+3:end+3]
# keep only if modulo result is 0 (keep only each wanted values)
features = [float(m) for id, m in enumerate(features) if id % each == 0]
# TODO : check if it's always necessary to do that (loss of information for svd)
if norm:
if choice == 'svdne':
features = utils.normalize_arr_with_range(features, min_value_interval, max_value_interval)
if choice == 'svdn':
features = utils.normalize_arr(features)
line = label
for val in features:
line += ';'
line += str(val)
line += '\n'
return line
def construct_new_line(threshold, interval, line, choice, each, norm):
begin, end = interval
line_data = line.split(';')
seuil = line_data[0]
features = line_data[begin + 1:end + 1]
features = [float(m) for id, m in enumerate(features) if id % each == 0]
if norm:
if choice == 'svdne':
features = utils.normalize_arr_with_range(features,
min_value_interval,
max_value_interval)
if choice == 'svdn':
features = utils.normalize_arr(features)
if threshold > int(seuil):
line = '1'
else:
line = '0'
for val in features:
line += ';'
line += str(val)
line += '\n'
return line
def construct_new_line(path_seuil, indices, line, choice, norm):
# increase indices values by one to avoid label
f = lambda x: x + 1
indices = f(indices)
line_data = np.array(line.split(';'))
seuil = line_data[0]
features = line_data[indices]
features = features.astype('float32')
# TODO : check if it's always necessary to do that (loss of information for svd)
if norm:
if choice == 'svdne':
features = utils.normalize_arr_with_range(features,
min_value_interval,
max_value_interval)
if choice == 'svdn':
features = utils.normalize_arr(features)
with open(path_seuil, "r") as seuil_file:
seuil_learned = int(seuil_file.readline().strip())
if seuil_learned > int(seuil):
line = '1'
else:
line = '0'
for val in features:
line += ';'
line += str(val)
line += '\n'
return line
def display_svd_values(p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim):
"""
@brief Method which gives information about svd curves from zone of picture
@param p_interval, interval [begin, end] of svd data to display
@param p_indices, indices to display
@param p_feature, feature computed to show
@param p_mode, normalization's mode
@param p_norm, normalization or not of selected svd data
@param p_area, area method name to compute area under curve
@param p_ylim, ylim choice to better display of data
@return nothing
"""
image_indices = []
scenes = os.listdir(path)
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
begin_data, end_data = p_interval
begin_index, end_index = p_indices
# Store all informations about scenes
scenes_area_data = []
scenes_images_indices = []
scenes_threshold_mean = []
# go ahead each scenes
for folder_scene in scenes:
max_value_svd = 0
min_value_svd = sys.maxsize
scene_path = os.path.join(path, folder_scene)
# construct each zones folder name
zones_folder = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone"+index_str
zones_folder.append(current_zone)
# store data information for current scene
images_data = []
images_indices = []
threshold_learned_zones = []
# get all images of folder
scene_images = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img])
number_scene_image = len(scene_images)
for id, zone_folder in enumerate(zones_folder):
# get threshold information
zone_path = os.path.join(scene_path, zone_folder)
path_seuil = os.path.join(zone_path, seuil_expe_filename)
# open treshold path and get this information
with open(path_seuil, "r") as seuil_file:
threshold_learned = int(seuil_file.readline().strip())
threshold_learned_zones.append(threshold_learned)
threshold_mean = np.mean(np.asarray(threshold_learned_zones))
threshold_image_found = False
scenes_threshold_mean.append(int(threshold_mean / p_step))
svd_data = []
# for each images
for id_img, img_path in enumerate(scene_images):
current_quality_image = dt.get_scene_image_quality(img_path)
img = Image.open(img_path)
svd_values = get_image_features(p_metric, img)
if p_norm:
svd_values = svd_values[begin_data:end_data]
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
if min_value < min_value_svd:
min_value_svd = min_value
if max_value > min_value_svd:
max_value_svd = max_value
# keep in memory used data
if current_quality_image % p_step == 0:
if current_quality_image >= begin_index and current_quality_image <= end_index:
images_indices.append(dt.get_scene_image_postfix(img_path))
svd_data.append(svd_values)
if threshold_mean < current_quality_image and not threshold_image_found:
threshold_image_found = True
print('%.2f%%' % ((id_img + 1) / number_scene_image * 100))
sys.stdout.write("\033[F")
# all indices of picture to plot
print("Scene %s : %s" % (folder_scene, images_indices))
scenes_images_indices.append(image_indices)
area_data = []
for id, data in enumerate(svd_data):
current_data = data
if not p_norm:
current_data = current_data[begin_data:end_data]
if p_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if p_mode == 'svdne':
current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
images_data.append(current_data)
# not use this script for 'sub_blocks_stats'
current_area = get_area_under_curve(p_area, current_data)
area_data.append(current_area)
scenes_area_data.append(area_data)
# display all data using matplotlib (configure plt)
plt.title('Scenes area interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + ']' + p_metric + ' metric, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=20)
plt.ylabel('Image samples or time (minutes) generation', fontsize=14)
plt.xlabel('Vector features', fontsize=16)
plt.legend(bbox_to_anchor=(0.7, 1), loc=2, borderaxespad=0.2, fontsize=14)
for id, area_data in enumerate(scenes_area_data):
threshold_id = 0
scene_name = scenes[id]
image_indices = scenes_images_indices[id]
p_label = scene_name + '_' + str(images_indices[id])
threshold_id = scenes_threshold_mean[id]
print(p_label)
plt.plot(area_data, label=p_label)
#ax2.set_xticks(range(len(images_indices)))
#ax2.set_xticklabels(list(map(int, images_indices)))
if threshold_id != 0:
print("Plot threshold ", threshold_id)
plt.plot([threshold_id, threshold_id], [np.min(area_data), np.max(area_data)], 'k-', lw=2, color='red')
start_ylim, end_ylim = p_ylim
plt.ylim(start_ylim, end_ylim)
plt.show()
def extracts_linear_indices(images_path,
n_expected=50,
indices_step=20,
start_at=20,
smooth_arr=False):
# TODO : check this part
default_add = start_at - indices_step
# extract variance for each image path
var_arr = []
n_counter = 0
n_images = len(images_path)
for p in sorted(images_path):
img = Image.open(p)
var_arr.append(np.var(img))
n_counter += 1
write_progress((n_counter + 1) / n_images)
# normalize variance values
norm_arr = np.array(utils.normalize_arr_with_range(var_arr))
if smooth_arr:
norm_arr = utils.normalize_arr_with_range(
savgol_filter(norm_arr, 201,
3)) # window size 7, polynomial order 3
# get expected linear step (using n_expectec output images)
linear_steps = utils.normalize_arr_with_range(
(1 - (np.arange(n_expected) / n_expected)))
# get image indices from variance convergence and linear
# => when linear step is reached we store the index found from variance values
indices_found = []
for i in linear_steps:
find_index = 0
for index, y in enumerate(norm_arr):
if i <= y:
find_index = index
indices_found.append(find_index + 1)
indices = np.array(indices_found) * indices_step
# add tricks to avoid same indice
# => when index is same as previous, then add number of samples expected by step
# Example with step of 20 : [20, 20, 20, 100, 200] => [20, 40, 60, 100, 200]
final_indices = []
for index, i in enumerate(indices):
value = indices[index]
if index > 0:
if i <= indices[index - 1]:
value = indices[index - 1] + indices_step
indices[index] = value
final_indices.append(value)
return np.array(final_indices) + default_add
def display_data_scenes(data_type, p_scene, p_kind):
"""
@brief Method which displays data from scene
@param data_type, feature choice
@param scene, scene choice
@param mode, normalization choice
@return nothing
"""
scenes = os.listdir(path)
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
# go ahead each scenes
for folder_scene in scenes:
if p_scene == folder_scene:
print(folder_scene)
scene_path = os.path.join(path, folder_scene)
# construct each zones folder name
zones_folder = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone" + index_str
zones_folder.append(current_zone)
zones_images_data = []
threshold_info = []
# get all images of folder
scene_images = sorted([
os.path.join(scene_path, img) for img in os.listdir(scene_path)
if cfg.scene_image_extension in img
])
start_image_path = scene_images[0]
end_image_path = scene_images[-1]
start_quality_image = dt.get_scene_image_quality(scene_images[0])
end_quality_image = dt.get_scene_image_quality(scene_images[-1])
for id_zone, zone_folder in enumerate(zones_folder):
zone_path = os.path.join(scene_path, zone_folder)
# get threshold information
path_seuil = os.path.join(zone_path, seuil_expe_filename)
# open treshold path and get this information
with open(path_seuil, "r") as seuil_file:
threshold_learned = int(seuil_file.readline().strip())
threshold_image_found = False
for img_path in scene_images:
current_quality_image = dt.get_scene_image_quality(
img_path)
if threshold_learned < int(current_quality_image
) and not threshold_image_found:
threshold_image_found = True
threshold_image_path = img_path
threshold_image = dt.get_scene_image_postfix(img_path)
threshold_info.append(threshold_image)
# all indexes of picture to plot
images_path = [
start_image_path, threshold_image_path, end_image_path
]
images_data = []
for img_path in images_path:
current_img = Image.open(img_path)
img_blocks = segmentation.divide_in_blocks(
current_img, (200, 200))
# getting expected block id
block = img_blocks[id_zone]
data = get_image_features(data_type, block)
##################
# Data mode part #
##################
# modify data depending mode
if p_kind == 'svdn':
data = utils.normalize_arr(data)
if p_kind == 'svdne':
path_min_max = os.path.join(
path, data_type + min_max_filename)
with open(path_min_max, 'r') as f:
min_val = float(f.readline())
max_val = float(f.readline())
data = utils.normalize_arr_with_range(
data, min_val, max_val)
# append of data
images_data.append(data)
zones_images_data.append(images_data)
fig = plt.figure(figsize=(8, 8))
fig.suptitle(data_type + " values for " + p_scene +
" scene (normalization : " + p_kind + ")",
fontsize=20)
for id, data in enumerate(zones_images_data):
fig.add_subplot(4, 4, (id + 1))
plt.plot(data[0], label='Noisy_' + start_quality_image)
plt.plot(data[1], label='Threshold_' + threshold_info[id])
plt.plot(data[2], label='Reference_' + end_quality_image)
plt.ylabel(data_type + ' SVD, ZONE_' + str(id + 1),
fontsize=18)
plt.xlabel('Vector features', fontsize=18)
plt.legend(bbox_to_anchor=(0.5, 1),
loc=2,
borderaxespad=0.2,
fontsize=18)
plt.ylim(0, 0.1)
plt.show()
def extracts_linear_indices_rawls(images_path,
n_expected=50,
i_indices_step=20,
o_indices_step=20,
start_at=20,
smooth_arr=False,
gamma=False):
default_add = start_at - 20
# by default
if i_indices_step == 1:
default_add = 0
n_start_images = int(start_at / i_indices_step)
n_counter = 0
# extract variance for each image path
var_arr = []
prev_rawls = None
n_images = len(images_path)
for p in sorted(images_path):
if prev_rawls is None:
temp = Rawls.load(p)
if gamma:
temp.gammaConvert()
prev_rawls = temp
else:
temp = Rawls.load(p)
if gamma:
temp.gammaConvert()
prev_rawls = Rawls.fusion(prev_rawls, temp)
write_progress((n_counter + 1) / n_images)
n_counter += 1
if n_counter >= n_start_images:
# only get center variance of image (800 per 800)
width, heigth, _ = prev_rawls.shape
n_w, n_h = (800, 800) # new expected size
# get center of image
middle_w = int(width / 2)
middle_h = int(heigth / 2)
# start coordinates
s_w = middle_w - int(n_w / 2)
s_h = middle_h - int(n_h / 2)
# end coordinates
e_w = middle_w + int(n_w / 2)
e_h = middle_h + int(n_h / 2)
var_arr.append(np.var(prev_rawls.data[s_w:e_w, s_h:e_h]))
# normalize variance values
norm_arr = np.array(utils.normalize_arr_with_range(var_arr))
if smooth_arr:
norm_arr = utils.normalize_arr_with_range(
savgol_filter(norm_arr, 201,
3)) # window size 7, polynomial order 3
# get expected linear step (using n_expectec output images)
linear_steps = utils.normalize_arr_with_range(
(1 - (np.arange(n_expected) / n_expected)))
# get image indices from variance convergence and linear
# => when linear step is reached we store the index found from variance values
indices_found = []
for i in linear_steps:
find_index = len(linear_steps) - 1
for index, y in enumerate(norm_arr):
if i <= y:
find_index = index
indices_found.append(find_index + 1)
indices = np.array(indices_found) * i_indices_step
# add tricks to avoid same indice
# => when index is same as previous, then add number of samples expected by step
# Example with step of 20 : [20, 20, 20, 100, 200] => [20, 40, 60, 100, 200]
final_indices = []
for index, i in enumerate(indices):
value = indices[index]
if index > 0:
if i <= indices[index - 1]:
value = indices[index - 1] + o_indices_step
indices[index] = value
final_indices.append(value)
return np.array(final_indices) + default_add
def main():
parser = argparse.ArgumentParser(description="Read and compute entropy data file")
parser.add_argument('--model', type=str, help='model file')
parser.add_argument('--method', type=str, help='method name to used', choices=cfg.features_choices_labels, default=cfg.features_choices_labels[0])
parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
parser.add_argument('--kind', type=str, help='Kind of normalization level wished', choices=cfg.normalization_choices)
parser.add_argument('--imnorm', type=int, help="specify if image is normalized before computing something", default=0, choices=[0, 1])
parser.add_argument('--scene', type=str, help='Scene index to use', choices=cfg.scenes_indices)
parser.add_argument('--save', type=str, help='filename where to save input data')
parser.add_argument('--label', type=str, help='label to use when saving thresholds')
args = parser.parse_args()
p_model = args.model
p_method = args.method
p_interval = list(map(int, args.interval.split(',')))
#p_n_stop = args.n_stop
p_imnorm = args.imnorm
p_scene = args.scene
p_mode = args.kind
p_save = args.save
p_label = args.label
p_n_stop = 1
begin, end = p_interval
# 1. get scene name
scenes_list = cfg.scenes_names
scenes_indices = cfg.scenes_indices
scene_index = scenes_indices.index(p_scene.strip())
scene = scenes_list[scene_index]
scene_path = os.path.join(cfg.dataset_path, scene)
# 2. load model and compile it
# TODO : check kind of model
model = joblib.load(p_model)
# model.compile(loss='binary_crossentropy',
# optimizer='rmsprop',
# metrics=['accuracy'])
estimated_thresholds = []
n_estimated_thresholds = []
human_thresholds = []
# 3. retrieve human_thresholds
# construct zones folder
zones_list = []
for index in zones_indices:
index_str = str(index)
while len(index_str) < 2:
index_str = "0" + index_str
zones_list.append(cfg.zone_folder + index_str)
for zone in zones_list:
zone_path = os.path.join(scene_path, zone)
with open(os.path.join(zone_path, cfg.seuil_expe_filename), 'r') as f:
human_thresholds.append(int(f.readline()))
# 4. get estimated thresholds using model and specific method
images_path = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img])
number_of_images = len(images_path)
image_indices = [ dt.get_scene_image_quality(img_path) for img_path in images_path ]
image_counter = 0
print(human_thresholds)
# append empty list
for zone in zones_list:
estimated_thresholds.append(None)
n_estimated_thresholds.append(0)
for img_i, img_path in enumerate(images_path):
blocks = segmentation.divide_in_blocks(Image.open(img_path), (200, 200))
for index, block in enumerate(blocks):
if estimated_thresholds[index] is None:
# normalize if necessary
if p_imnorm:
block = np.array(block) / 255.
# check if prediction is possible
data = np.array(get_image_features(p_method, np.array(block)))
if p_mode == 'svdn':
data = utils.normalize_arr_with_range(data)
data = data[begin:end]
#data = np.expand_dims(data, axis=0)
#print(data.shape)
prob = model.predict(np.array(data).reshape(1, -1))[0]
#print(index, ':', image_indices[img_i], '=>', prob)
if prob < 0.5:
n_estimated_thresholds[index] += 1
# if same number of detection is attempted
if n_estimated_thresholds[index] >= p_n_stop:
estimated_thresholds[index] = image_indices[img_i]
else:
n_estimated_thresholds[index] = 0
# write progress bar
write_progress((image_counter + 1) / number_of_images)
image_counter = image_counter + 1
# default label
for i, _ in enumerate(zones_list):
if estimated_thresholds[i] == None:
estimated_thresholds[i] = image_indices[-1]
# 6. save estimated thresholds into specific file
print(estimated_thresholds)
print(p_save)
if p_save is not None:
with open(p_save, 'a') as f:
f.write(p_label + ';')
for t in estimated_thresholds:
f.write(str(t) + ';')
f.write('\n')
def display_svd_values(p_scene, p_interval, p_indices, p_feature, p_mode,
p_step, p_norm, p_error, p_ylim):
"""
@brief Method which gives information about svd curves from zone of picture
@param p_scene, scene expected to show svd values
@param p_interval, interval [begin, end] of svd data to display
@param p_interval, interval [begin, end] of samples or minutes from render generation engine
@param p_feature, feature computed to show
@param p_mode, normalization's mode
@param p_norm, normalization or not of selected svd data
@param p_error, error feature used to display
@param p_ylim, ylim choice to better display of data
@return nothing
"""
max_value_svd = 0
min_value_svd = sys.maxsize
scenes = os.listdir(path)
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
begin_data, end_data = p_interval
begin_index, end_index = p_indices
# go ahead each scenes
for folder_scene in scenes:
if p_scene == folder_scene:
scene_path = os.path.join(path, folder_scene)
# construct each zones folder name
zones_folder = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone" + index_str
zones_folder.append(current_zone)
images_data = []
images_path = []
threshold_learned_zones = []
# get all images of folder
scene_images = sorted([
os.path.join(scene_path, img) for img in os.listdir(scene_path)
if cfg.scene_image_extension in img
])
number_scene_image = len(scene_images)
for id, zone_folder in enumerate(zones_folder):
# get threshold information
zone_path = os.path.join(scene_path, zone_folder)
path_seuil = os.path.join(zone_path, seuil_expe_filename)
# open treshold path and get this information
with open(path_seuil, "r") as seuil_file:
threshold_learned = int(seuil_file.readline().strip())
threshold_learned_zones.append(threshold_learned)
threshold_mean = np.mean(np.asarray(threshold_learned_zones))
threshold_image_found = False
svd_data = []
# for each images
for id_img, img_path in enumerate(scene_images):
current_quality_image = dt.get_scene_image_quality(img_path)
img = Image.open(img_path)
svd_values = get_image_features(p_feature, img)
if p_norm:
svd_values = svd_values[begin_data:end_data]
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
if min_value < min_value_svd:
min_value_svd = min_value
if max_value > min_value_svd:
max_value_svd = max_value
# keep in memory used data
if current_quality_image % p_step == 0:
if current_quality_image >= begin_index and current_quality_image <= end_index:
images_path.append(img_path)
svd_data.append(svd_values)
if threshold_mean < current_quality_image and not threshold_image_found:
threshold_image_found = True
threshold_image_zone = dt.get_scene_image_postfix(
img_path)
print('%.2f%%' % ((id_img + 1) / number_scene_image * 100))
sys.stdout.write("\033[F")
previous_data = []
error_data = [0.]
for id, data in enumerate(svd_data):
current_data = data
if not p_norm:
current_data = current_data[begin_data:end_data]
if p_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if p_mode == 'svdne':
current_data = utils.normalize_arr_with_range(
current_data, min_value_svd, max_value_svd)
images_data.append(current_data)
# use of whole image data for computation of ssim or psnr
if p_error == 'ssim' or p_error == 'psnr':
current_data = np.asarray(Image.open(images_path[id]))
if len(previous_data) > 0:
current_error = get_error_distance(p_error, previous_data,
current_data)
error_data.append(current_error)
if len(previous_data) == 0:
previous_data = current_data
# display all data using matplotlib (configure plt)
gridsize = (3, 2)
# fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
fig = plt.figure(figsize=(30, 22))
ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
ax1.set_title(p_scene + ' scene interval information SVD[' +
str(begin_data) + ', ' + str(end_data) +
'], from scenes indices [' + str(begin_index) +
', ' + str(end_index) + '], ' + p_feature +
' feature, ' + p_mode + ', with step of ' +
str(p_step) + ', svd norm ' + str(p_norm),
fontsize=20)
ax1.set_ylabel('Image samples or time (minutes) generation',
fontsize=14)
ax1.set_xlabel('Vector features', fontsize=16)
for id, data in enumerate(images_data):
current_quality_image = dt.get_scene_image_quality(
images_path[id])
current_quality_postfix = dt.get_scene_image_postfix(
images_path[id])
if display_error:
p_label = p_scene + '_' + current_quality_postfix + " | " + p_error + ": " + str(
error_data[id])
else:
p_label = p_scene + '_' + current_quality_postfix
if current_quality_image == threshold_image_zone:
ax1.plot(data,
label=p_label + " (threshold mean)",
lw=4,
color='red')
else:
ax1.plot(data, label=p_label)
ax1.legend(bbox_to_anchor=(0.7, 1),
loc=2,
borderaxespad=0.2,
fontsize=14)
start_ylim, end_ylim = p_ylim
ax1.set_ylim(start_ylim, end_ylim)
ax2.set_title(p_error + " information for whole step images")
ax2.set_ylabel(p_error + ' error')
ax2.set_xlabel('Number of samples per pixels or times')
ax2.set_xticks(range(len(current_quality_image)))
ax2.set_xticklabels(
list(map(dt.get_scene_image_quality, current_quality_image)))
ax2.plot(error_data)
plot_name = p_scene + '_' + p_feature + '_' + str(
p_step) + '_' + p_mode + '_' + str(p_norm) + '.png'
plt.savefig(plot_name)
def generate_data_svd(data_type, color, mode):
"""
@brief Method which generates all .csv files from scenes
@param data_type, feature choice
@param mode, normalization choice
@return nothing
"""
scenes = os.listdir(path)
# filter scene
scenes = [s for s in scenes if calibration_folder not in s]
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
# keep in memory min and max data found from data_type
min_val_found = sys.maxsize
max_val_found = 0
data_min_max_filename = os.path.join(path, data_type + min_max_filename)
# go ahead each scenes
for id_scene, folder_scene in enumerate(scenes):
print(folder_scene)
scene_path = os.path.join(path, folder_scene)
for noise in noise_choices:
noise_path = os.path.join(scene_path, noise)
# getting output filename
if color:
output_svd_filename = data_type + "_color_" + mode + generic_output_file_svd
else:
output_svd_filename = data_type + "_" + mode + generic_output_file_svd
# construct each zones folder name
zones_folder = []
svd_output_files = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone"+index_str
zones_folder.append(current_zone)
zone_path = os.path.join(noise_path, current_zone)
if not os.path.exists(zone_path):
os.makedirs(zone_path)
svd_file_path = os.path.join(zone_path, output_svd_filename)
# add writer into list
svd_output_files.append(open(svd_file_path, 'w'))
counter_index = 1
while(counter_index < end_counter_index):
if counter_index % picture_step == 0:
counter_index_str = str(counter_index)
if color:
img_path = os.path.join(noise_path, folder_scene + "_" + noise + "_color_" + counter_index_str + ".png")
else:
img_path = os.path.join(noise_path, folder_scene + "_" + noise + "_" + counter_index_str + ".png")
current_img = Image.open(img_path)
img_blocks = divide_in_blocks(current_img, (200, 200))
for id_block, block in enumerate(img_blocks):
###########################
# feature computation part #
###########################
data = get_image_features(data_type, block)
##################
# Data mode part #
##################
# modify data depending mode
if mode == 'svdne':
# getting max and min information from min_max_filename
with open(data_min_max_filename, 'r') as f:
min_val = float(f.readline())
max_val = float(f.readline())
data = utils.normalize_arr_with_range(data, min_val, max_val)
if mode == 'svdn':
data = utils.normalize_arr(data)
# save min and max found from dataset in order to normalize data using whole data known
if mode == 'svd':
current_min = data.min()
current_max = data.max()
if current_min < min_val_found:
min_val_found = current_min
if current_max > max_val_found:
max_val_found = current_max
# now write data into current writer
current_file = svd_output_files[id_block]
# add of index
current_file.write(counter_index_str + ';')
for val in data:
current_file.write(str(val) + ";")
current_file.write('\n')
if color:
print(data_type + "_" + noise + "_color_" + mode + "_" + folder_scene + " - " + "{0:.2f}".format((counter_index) / (end_counter_index)* 100.) + "%")
else:
print(data_type + "_" + noise + "_"+ mode + "_" + folder_scene + " - " + "{0:.2f}".format((counter_index) / (end_counter_index)* 100.) + "%")
sys.stdout.write("\033[F")
counter_index += 1
for f in svd_output_files:
f.close()
if color:
print(data_type + "_" + noise + "_color_" + mode + "_" + folder_scene + " - " + "Done...")
else:
print(data_type + "_" + noise + "_"+ mode + "_" + folder_scene + " - " + "Done...")
# save current information about min file found
if mode == 'svd':
with open(data_min_max_filename, 'w') as f:
f.write(str(min_val_found) + '\n')
f.write(str(max_val_found) + '\n')
print("%s : end of data generation\n" % mode)
def main():
parser = argparse.ArgumentParser(
description="Display svd of images with noise level")
parser.add_argument(
'--prefix',
type=str,
help='Generated noise folder prefix (ex: `generated/prefix/noise`)')
parser.add_argument('--mode',
type=str,
help='Kind of normalization',
default=normalization_choices)
parser.add_argument('--feature',
type=str,
help='feature choice',
default=feature_choices)
parser.add_argument('--n', type=int, help='Number of images')
parser.add_argument('--color',
type=int,
help='Use of color or grey level',
default=0)
parser.add_argument(
'--norm',
type=int,
help='Use of normalization from interval or whole data vector',
default=0)
parser.add_argument('--interval',
type=str,
help='Interval data choice (ex: `0, 200`)',
default="0, 200")
parser.add_argument('--step',
type=int,
help='Step of image indices to keep',
default=1)
parser.add_argument('--ylim',
type=str,
help='Limite to display data (ex: `0, 1`)',
default="0, 1")
args = parser.parse_args()
param_prefix = args.prefix
param_mode = args.mode
param_feature = args.feature
param_n = args.n
param_color = args.color
param_norm = args.norm
param_interval = list(map(int, args.interval.split(',')))
param_step = args.step
param_ylim = list(map(float, args.ylim.split(',')))
param_prefix = param_prefix.split('/')[1].replace('_', '')
noise_name = param_prefix.split('/')[2]
if param_color:
file_path = param_prefix + "/" + param_prefix + "_" + noise_name + "_color_{}." + filename_ext
else:
file_path = param_prefix + "/" + param_prefix + "_" + noise_name + "_{}." + filename_ext
begin, end = param_interval
all_svd_data = []
svd_data = []
image_indices = []
# get all data from images
for i in range(1, param_n):
if i % steparam_picture == 0:
image_path = file_path.format(str(i))
img = Image.open(image_path)
svd_values = get_image_features(param_feature, img)
if param_norm:
svd_values = svd_values[begin:end]
all_svd_data.append(svd_values)
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
if min_value < min_value_svd:
min_value_svd = min_value
if max_value > max_value_svd:
max_value_svd = max_value
print('%.2f%%' % ((i + 1) / param_n * 100))
sys.stdout.write("\033[F")
for id, data in enumerate(all_svd_data):
if (id * steparam_picture) % param_step == 0:
current_data = data
if param_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if param_mode == 'svdne':
current_data = utils.normalize_arr_with_range(
current_data, min_value_svd, max_value_svd)
svd_data.append(current_data)
image_indices.append(str(id * steparam_picture))
# display all data using matplotlib (configure plt)
plt.rcParams['figure.figsize'] = (25, 18)
plt.title(param_prefix + ' noise, interval information [' + str(begin) +
', ' + str(end) + '], ' + param_feature + ' feature, step ' +
str(param_step) + ' normalization ' + param_mode,
fontsize=20)
plt.ylabel('Importance of noise [1, 999]', fontsize=14)
plt.xlabel('Vector features', fontsize=16)
for id, data in enumerate(svd_data):
param_label = param_prefix + str(image_indices[id])
plt.plot(data, label=param_label)
plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14)
if not param_norm:
plt.xlim(begin, end)
# adapt ylim
y_begin, y_end = param_ylim
plt.ylim(y_begin, y_end)
output_filename = param_prefix + "_" + noise_name + "_1_to_" + str(
param_n) + "_B" + str(begin) + "_E" + str(
end) + "_" + param_feature + "_S" + str(
param_step) + "_norm" + str(param_norm) + "_" + param_mode
if param_color:
output_filename = output_filename + '_color'
print("Generation of output figure... %s" % output_filename)
output_path = os.path.join(pictures_folder, output_filename)
if not os.path.exists(pictures_folder):
os.makedirs(pictures_folder)
plt.savefig(output_path, dpi=(200))
def display_svd_values(p_scene, p_interval, p_indices, p_zone, p_feature,
p_mode, p_step, p_norm, p_ylim):
"""
@brief Method which gives information about svd curves from zone of picture
@param p_scene, scene expected to show svd values
@param p_interval, interval [begin, end] of svd data to display
@param p_interval, interval [begin, end] of samples or minutes from render generation engine
@param p_zone, zone's identifier of picture
@param p_feature, feature computed to show
@param p_mode, normalization's mode
@param p_step, step of images indices
@param p_norm, normalization or not of selected svd data
@param p_ylim, ylim choice to better display of data
@return nothing
"""
scenes = os.listdir(path)
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
begin_data, end_data = p_interval
begin_index, end_index = p_indices
data_min_max_filename = os.path.join(path, p_feature + min_max_filename)
# go ahead each scenes
for folder_scene in scenes:
if p_scene == folder_scene:
scene_path = os.path.join(path, folder_scene)
# construct each zones folder name
zones_folder = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone" + index_str
zones_folder.append(current_zone)
zones_images_data = []
images_path = []
zone_folder = zones_folder[p_zone]
zone_path = os.path.join(scene_path, zone_folder)
# get threshold information
path_seuil = os.path.join(zone_path, seuil_expe_filename)
# open treshold path and get this information
with open(path_seuil, "r") as seuil_file:
seuil_learned = int(seuil_file.readline().strip())
threshold_image_found = False
# get all images of folder
scene_images = sorted([
os.path.join(scene_path, img) for img in os.listdir(scene_path)
if cfg.scene_image_extension in img
])
# for each images
for img_path in scene_images:
current_quality_image = dt.get_scene_image_quality(img_path)
if current_quality_image % p_step == 0:
if current_quality_image >= begin_index and current_quality_image <= end_index:
images_path.append(img_path)
if seuil_learned < current_quality_image and not threshold_image_found:
threshold_image_found = True
threshold_image_zone = dt.get_scene_image_postfix(
img_path)
if img_path not in images_path:
images_path.append(img_path)
for img_path in images_path:
current_img = Image.open(img_path)
img_blocks = segmentation.divide_in_blocks(
current_img, (200, 200))
# getting expected block id
block = img_blocks[p_zone]
# get data from mode
# Here you can add the way you compute data
data = get_image_features(p_feature, block)
# TODO : improve part of this code to get correct min / max values
if p_norm:
data = data[begin_data:end_data]
##################
# Data mode part #
##################
if p_mode == 'svdne':
# getting max and min information from min_max_filename
if not p_norm:
with open(data_min_max_filename, 'r') as f:
min_val = float(f.readline())
max_val = float(f.readline())
else:
min_val = min_value_interval
max_val = max_value_interval
data = utils.normalize_arr_with_range(
data, min_val, max_val)
if p_mode == 'svdn':
data = utils.normalize_arr(data)
if not p_norm:
zones_images_data.append(data[begin_data:end_data])
else:
zones_images_data.append(data)
fig, ax = plt.subplots(figsize=(30, 22))
ax.set_facecolor('#FFFFFF')
# plt.title(p_scene + ' scene (zone ' + str(p_zone) + ') interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + '], ' + p_feature + ' feature, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=24)
ax.set_ylabel('Component values', fontsize=28)
ax.set_xlabel('Vector features', fontsize=28)
ax.tick_params(labelsize=22)
for id, data in enumerate(zones_images_data):
p_label = p_scene + "_" + dt.get_scene_image_postfix(
images_path[id])
if int(dt.get_scene_image_postfix(
images_path[id])) == int(threshold_image_zone):
ax.plot(data,
label=p_label + ' (zone ' + str(p_zone) +
' threshold)',
lw=4,
color='red')
else:
ax.plot(data, label=p_label)
plt.legend(bbox_to_anchor=(0.60, 0.98),
loc=2,
borderaxespad=0.2,
fontsize=24)
start_ylim, end_ylim = p_ylim
plt.ylim(start_ylim, end_ylim)
plot_name = p_scene + '_zone_' + str(
p_zone) + '_' + p_feature + '_' + str(
p_step) + '_' + p_mode + '_' + str(p_norm) + '.png'
plt.savefig(plot_name, facecolor=ax.get_facecolor())
def generate_data_feature(path, output, human_thresholds, data_type, mode):
"""
@brief Method which generates all .csv files from scenes
@param data_type, feature choice
@param mode, normalization choice
@return nothing
"""
scenes = os.listdir(path)
# remove min max file from scenes folder
scenes = [s for s in scenes if min_max_filename not in s]
# keep in memory min and max data found from data_type
min_val_found = sys.maxsize
max_val_found = 0
output_path = os.path.join(cfg.output_data_generated, output)
if not os.path.exists(output_path):
os.makedirs(output_path)
data_min_max_filename = os.path.join(output_path, data_type + min_max_filename)
# go ahead each scenes
for folder_scene in human_thresholds:
print(folder_scene)
scene_path = os.path.join(path, folder_scene)
output_scene_path = os.path.join(output_path, folder_scene)
if not os.path.exists(output_scene_path):
os.makedirs(output_scene_path)
# getting output filename
output_svd_filename = data_type + "_" + mode + generic_output_file_svd
# construct each zones folder name
zones_folder = []
svd_output_files = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone"+index_str
zones_folder.append(current_zone)
zone_path = os.path.join(scene_path, current_zone)
output_zone_path = os.path.join(output_scene_path, current_zone)
if not os.path.exists(output_zone_path):
os.makedirs(output_zone_path)
svd_file_path = os.path.join(output_zone_path, output_svd_filename)
# add writer into list
svd_output_files.append(open(svd_file_path, 'w'))
# get all images of folder
scene_images = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img])
number_scene_image = len(scene_images)
for id_img, img_path in enumerate(scene_images):
current_image_postfix = dt.get_scene_image_postfix(img_path)
current_img = Image.open(img_path)
img_blocks = segmentation.divide_in_blocks(current_img, (200, 200))
for id_block, block in enumerate(img_blocks):
###########################
# feature computation part #
###########################
data = get_image_features(data_type, block)
##################
# Data mode part #
##################
# modify data depending mode
if mode == 'svdne':
# getting max and min information from min_max_filename
with open(data_min_max_filename, 'r') as f:
min_val = float(f.readline())
max_val = float(f.readline())
data = utils.normalize_arr_with_range(data, min_val, max_val)
if mode == 'svdn':
data = utils.normalize_arr(data)
# save min and max found from dataset in order to normalize data using whole data known
if mode == 'svd':
current_min = data.min()
current_max = data.max()
if current_min < min_val_found:
min_val_found = current_min
if current_max > max_val_found:
max_val_found = current_max
# now write data into current writer
current_file = svd_output_files[id_block]
# add of index
current_file.write(current_image_postfix + ';')
for val in data:
current_file.write(str(val) + ";")
current_file.write('\n')
print(data_type + "_" + mode + "_" + folder_scene + " - " + "{0:.2f}".format((id_img + 1) / number_scene_image * 100.) + "%")
sys.stdout.write("\033[F")
for f in svd_output_files:
f.close()
print('\n')
# save current information about min file found
if mode == 'svd':
with open(data_min_max_filename, 'w') as f:
f.write(str(min_val_found) + '\n')
f.write(str(max_val_found) + '\n')
print("%s_%s : end of data generation\n" % (data_type, mode))
def display_svd_values(p_scene, p_thresholds, p_interval, p_indices, p_feature,
p_mode, p_step, p_norm, p_ylim, p_label):
"""
@brief Method which gives information about svd curves from zone of picture
@param p_scene, scene expected to show svd values
@param p_interval, interval [begin, end] of svd data to display
@param p_interval, interval [begin, end] of samples or minutes from render generation engine
@param p_feature, feature computed to show
@param p_mode, normalization's mode
@param p_norm, normalization or not of selected svd data
@param p_ylim, ylim choice to better display of data
@return nothing
"""
max_value_svd = 0
min_value_svd = sys.maxsize
begin_data, end_data = p_interval
begin_index, end_index = p_indices
# go ahead selected scene
scene_path = p_scene
# construct each zones folder name
zones_folder = []
# get zones list info
for index in zones:
index_str = str(index)
if len(index_str) < 2:
index_str = "0" + index_str
current_zone = "zone" + index_str
zones_folder.append(current_zone)
images_data = []
images_indices = []
threshold_learned_zones = []
# get all images of folder
scene_images = sorted([
os.path.join(scene_path, img) for img in os.listdir(scene_path)
if cfg.scene_image_extension in img
])
number_scene_image = len(scene_images)
_, scene_name = os.path.split(p_scene)
threshold_learned_zones = p_thresholds[scene_name]
threshold_mean = np.mean(np.asarray(threshold_learned_zones))
threshold_image_found = False
svd_data = []
# for each images
for id_img, img_path in enumerate(scene_images):
current_quality_image = dt.get_scene_image_quality(img_path)
img = Image.open(img_path)
svd_values = get_image_features(p_feature, img)
if p_norm:
svd_values = svd_values[begin_data:end_data]
#svd_values = np.asarray([math.log(x) for x in svd_values])
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
if min_value < min_value_svd:
min_value_svd = min_value
if max_value > min_value_svd:
max_value_svd = max_value
# keep in memory used data
if current_quality_image % p_step == 0:
if current_quality_image >= begin_index and current_quality_image <= end_index:
images_indices.append(dt.get_scene_image_postfix(img_path))
svd_data.append(svd_values)
if threshold_mean < current_quality_image and not threshold_image_found:
threshold_image_found = True
threshold_image_zone = current_quality_image
print("Quality mean : ", current_quality_image, "\n")
if dt.get_scene_image_postfix(img_path) not in images_indices:
images_indices.append(dt.get_scene_image_postfix(img_path))
print('%.2f%%' % ((id_img + 1) / number_scene_image * 100))
sys.stdout.write("\033[F")
# all indices of picture to plot
print(images_indices)
for id, data in enumerate(svd_data):
# current_data = [ math.log10(d + 1.) for d in data ]
# print(current_data)
current_data = data
if not p_norm:
current_data = current_data[begin_data:end_data]
if p_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if p_mode == 'svdne':
current_data = utils.normalize_arr_with_range(
current_data, min_value_svd, max_value_svd)
images_data.append(current_data)
# display all data using matplotlib (configure plt)
fig, ax = plt.subplots(figsize=(30, 15))
ax.set_facecolor('#FFFFFF')
#fig.patch.set_facecolor('#F9F9F9')
ax.tick_params(labelsize=26)
#plt.rc('xtick', labelsize=22)
#plt.rc('ytick', labelsize=22)
#plt.title(p_scene + ' scene interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + '], ' + p_feature + ' feature, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=24)
ax.set_ylabel('Component values', fontsize=36)
ax.set_xlabel('Singular value component indices', fontsize=36)
for id, data in enumerate(images_data):
#p_label = p_scene + "_" + images_indices[id]
p_label = images_indices[id] + " samples"
if int(images_indices[id]) == int(threshold_image_zone):
ax.plot(data,
label=p_label + " (threshold mean)",
lw=8,
color='red')
else:
ax.plot(data, label=p_label, lw=4)
plt.legend(bbox_to_anchor=(0.60, 0.98),
loc=2,
borderaxespad=0.2,
fontsize=32)
start_ylim, end_ylim = p_ylim
ax.set_ylim(start_ylim, end_ylim)
plot_name = scene_name + '_' + p_feature + '_' + str(
p_step) + '_' + p_mode + '_' + str(p_norm) + '.png'
# plt.title('Tend of Singular values at different samples of ' + p_label + ' scene', fontsize=40)
plt.savefig(plot_name, transparent=True)
def main():
parser = argparse.ArgumentParser(description="Display threshold svd data")
parser.add_argument(
'--prefix',
type=str,
help='Generated noise folder prefix (ex: `generated/prefix/noise`)')
parser.add_argument('--file', type=str, help='Threshold file to use')
parser.add_argument('--mode',
type=str,
help='Kind of normalization',
default=normalization_choices)
parser.add_argument('--feature',
type=str,
help='feature choice',
default=feature_choices)
parser.add_argument('--color',
type=int,
help='Use of color or grey level',
default=0)
parser.add_argument(
'--norm',
type=int,
help='Use of normalization from interval or whole data vector',
default=0)
parser.add_argument('--interval',
type=str,
help='Interval data choice (ex: `0, 200`)',
default="0, 200")
parser.add_argument('--step',
type=int,
help='Step of image indices to keep',
default=1)
parser.add_argument('--ylim',
type=str,
help='Limite to display data (ex: `0, 1`)',
default="0, 1")
args = parser.parse_args()
param_prefix = args.prefix
param_file = args.file
param_mode = args.mode
param_feature = args.feature
param_n = args.n
param_color = args.color
param_norm = args.norm
param_interval = list(map(int, args.interval.split(',')))
param_step = args.step
param_ylim = list(map(float, args.ylim.split(',')))
param_prefix = param_prefix.split('/')[1].replace('_', '')
if param_color:
file_path = param_prefix + "{}/" + param_prefix + "_{}_color_{}." + filename_ext
else:
file_path = param_prefix + "{}/" + param_prefix + "_{}_{}." + filename_ext
begin, end = param_interval
svd_data = []
final_svd_data = []
image_indices = []
min_max_list = {}
threshold_data = []
# read data threshold file
with open(param_file, 'r') as f:
lines = f.readlines()
for line in lines:
data = line.replace('\n', '').split(';')
print(data)
threshold = ThresholdData(data[0], float(data[1]), int(data[2]))
threshold_data.append(threshold)
# filter data if color or not
threshold_data = [t for t in threshold_data if t.isColor() == param_color]
for id, threshold in enumerate(threshold_data):
current_noise = threshold.get_noise()
current_threshold = threshold.get_threshold()
min_max_list[current_noise] = (sys.maxsize, 0)
threshold_found = False
# get all data from images
for i in range(1, param_n):
if i % steparam_picture == 0:
image_path = file_path.format(current_noise, current_noise,
str(i))
img = Image.open(image_path)
svd_values = get_image_features(param_feature, img)
if param_norm:
svd_values = svd_values[begin:end]
# only append data once
if not threshold_found and current_threshold < i:
svd_data.append(svd_values)
image_indices.append(i)
if current_threshold < i:
threshold_found = True
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
# update of min max values for noise
current_min, current_max = min_max_list[current_noise]
if min_value < current_min:
current_min = min_value
if max_value > current_max:
current_max = max_value
min_max_list[current_noise] = (current_min, current_max)
print('%.2f%%' % (((i + 1) * 100 + (id * param_n * 100)) /
(param_n * len(threshold_data))))
sys.stdout.write("\033[F")
for id, data in enumerate(svd_data):
current_data = data
threshold = threshold_data[id]
min_value_svd, max_value_svd = min_max_list[threshold.get_noise()]
if param_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if param_mode == 'svdne':
current_data = utils.normalize_arr_with_range(
current_data, min_value_svd, max_value_svd)
final_svd_data.append(current_data)
# display all data using matplotlib (configure plt)
plt.rcParams['figure.figsize'] = (25, 18)
plt.title(param_prefix + ' noise, interval information [' + str(begin) +
', ' + str(end) + '], ' + param_feature + ' feature, step ' +
str(param_step) + ' normalization ' + param_mode,
fontsize=20)
plt.ylabel('Importance of noise [1, 999]', fontsize=14)
plt.xlabel('Vector features', fontsize=16)
for id, data in enumerate(final_svd_data):
param_label = param_prefix + '_' + threshold_data[id].get_noise(
) + str(image_indices[id])
plt.plot(data, label=param_label)
plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14)
if not param_norm:
plt.xlim(begin, end)
# adapt ylim
y_begin, y_end = param_ylim
plt.ylim(y_begin, y_end)
output_filename = param_prefix + "_threshold_1_to_" + str(
param_n) + "_B" + str(begin) + "_E" + str(
end) + "_" + param_feature + "_S" + str(
param_step) + "_norm" + str(param_norm) + "_" + param_mode
if param_color:
output_filename = output_filename + '_color'
print("Generation of output figure... %s" % output_filename)
output_path = os.path.join(pictures_folder, output_filename)
if not os.path.exists(pictures_folder):
os.makedirs(pictures_folder)
plt.savefig(output_path, dpi=(200))
def main():
# getting all params
parser = argparse.ArgumentParser(
description=
"Script which detects if an image is noisy or not using specific model"
)
parser.add_argument('--image', type=str, help='Image path')
parser.add_argument('--interval',
type=str,
help='Interval value to keep from svd',
default='"0, 200"')
parser.add_argument('--model',
type=str,
help='.joblib or .json file (sklearn or keras model)')
parser.add_argument('--mode',
type=str,
help='Kind of normalization level wished',
choices=normalization_choices)
parser.add_argument('--feature',
type=str,
help='feature data choice',
choices=features_choices)
parser.add_argument(
'--custom',
type=str,
help='Name of custom min max file if use of renormalization of data',
default=False)
args = parser.parse_args()
p_img_file = args.image
p_model_file = args.model
p_interval = list(map(int, args.interval.split(',')))
p_mode = args.mode
p_feature = args.feature
p_custom = args.custom
if '.joblib' in p_model_file:
kind_model = 'sklearn'
if '.json' in p_model_file:
kind_model = 'keras'
if 'corr' in p_model_file:
corr_model = True
indices_corr_path = os.path.join(
cfg.correlation_indices_folder,
p_model_file.split('/')[1].replace('.json', '').replace(
'.joblib', '') + '.csv')
with open(indices_corr_path, 'r') as f:
data_corr_indices = [
int(x) for x in f.readline().split(';') if x != ''
]
else:
corr_model = False
if kind_model == 'sklearn':
# load of model file
model = joblib.load(p_model_file)
if kind_model == 'keras':
with open(p_model_file, 'r') as f:
json_model = json.load(f)
model = model_from_json(json_model)
model.load_weights(p_model_file.replace('.json', '.h5'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
features=['accuracy'])
# load image
img = Image.open(p_img_file)
data = get_image_features(p_feature, img)
# get interval values
begin, end = p_interval
# check if custom min max file is used
if p_custom:
if corr_model:
test_data = data[data_corr_indices]
else:
test_data = data[begin:end]
if p_mode == 'svdne':
# set min_max_filename if custom use
min_max_file_path = custom_min_max_folder + '/' + p_custom
# need to read min_max_file
file_path = os.path.join(os.path.dirname(__file__),
min_max_file_path)
with open(file_path, 'r') as f:
min_val = float(f.readline().replace('\n', ''))
max_val = float(f.readline().replace('\n', ''))
test_data = utils.normalize_arr_with_range(test_data, min_val,
max_val)
if p_mode == 'svdn':
test_data = utils.normalize_arr(test_data)
else:
# check mode to normalize data
if p_mode == 'svdne':
# set min_max_filename if custom use
min_max_file_path = path + '/' + p_feature + min_max_ext
# need to read min_max_file
file_path = os.path.join(os.path.dirname(__file__),
min_max_file_path)
with open(file_path, 'r') as f:
min_val = float(f.readline().replace('\n', ''))
max_val = float(f.readline().replace('\n', ''))
l_values = utils.normalize_arr_with_range(data, min_val, max_val)
elif p_mode == 'svdn':
l_values = utils.normalize_arr(data)
else:
l_values = data
if corr_model:
test_data = data[data_corr_indices]
else:
test_data = data[begin:end]
# get prediction of model
if kind_model == 'sklearn':
prediction = model.predict([test_data])[0]
if kind_model == 'keras':
test_data = np.asarray(test_data).reshape(1, len(test_data), 1)
prediction = model.predict_classes([test_data])[0][0]
# output expected from others scripts
print(prediction)
def main():
max_value_svd = 0
min_value_svd = sys.maxsize
parser = argparse.ArgumentParser(description="Display svd tend of images with noise level")
parser.add_argument('--prefix', type=str, help='Generated noise folder prefix (ex: `generated/prefix/noise`)')
parser.add_argument('--mode', type=str, help='Kind of normalization', default=normalization_choices)
parser.add_argument('--feature', type=str, help='feature choice', default=feature_choices)
parser.add_argument('--n', type=int, help='Number of images')
parser.add_argument('--color', type=int, help='Use of color or grey level', default=0)
parser.add_argument('--norm', type=int, help='Use of normalization from interval or whole data vector', default=0)
parser.add_argument('--interval', type=str, help='Interval data choice (ex: `0, 200`)', default="0, 200")
parser.add_argument('--step', type=int, help='Step of image indices to keep', default=1)
parser.add_argument('--ylim', type=str, help='Limite to display data (ex: `0, 1`)', default="0, 1")
parser.add_argument('--error', type=str, help='Error used for information data', default=error_data_choices)
args = parser.parse_args()
param_prefix = args.prefix
param_mode = args.mode
param_feature = args.feature
param_n = args.n
param_color = args.color
param_norm = args.norm
param_interval = list(map(int, args.interval.split(',')))
param_step = args.step
param_ylim = list(map(float, args.ylim.split(',')))
param_error = args.error
param_prefix = param_prefix.split('/')[1].replace('_', '')
noise_name = param_prefix.split('/')[2]
if param_color:
file_path = os.path.join(param_prefix, param_prefix + "_" + noise_name + "_color_{}." + filename_ext)
else:
file_path = os.path.join(param_prefix, param_prefix + "_" + noise_name + "_{}." + filename_ext)
begin, end = param_interval
all_svd_data = []
svd_data = []
image_indices = []
noise_indices = range(1, param_n)[::-1]
# get all data from images
for i in noise_indices:
if i % steparam_picture == 0:
image_path = file_path.format(str(i))
img = Image.open(image_path)
svd_values = get_image_features(param_feature, img)
if param_norm:
svd_values = svd_values[begin:end]
all_svd_data.append(svd_values)
# update min max values
min_value = svd_values.min()
max_value = svd_values.max()
if min_value < min_value_svd:
min_value_svd = min_value
if max_value > max_value_svd:
max_value_svd = max_value
print('%.2f%%' % ((param_n - i + 1) / param_n * 100))
sys.stdout.write("\033[F")
previous_data = []
error_data = [0.]
for id, data in enumerate(all_svd_data):
current_id = (param_n - ((id + 1) * 10))
if current_id % param_step == 0:
current_data = data
if param_mode == 'svdn':
current_data = utils.normalize_arr(current_data)
if param_mode == 'svdne':
current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
svd_data.append(current_data)
image_indices.append(current_id)
# use of whole image data for computation of ssim or psnr
if param_error == 'ssim' or param_error == 'psnr':
image_path = file_path.format(str(current_id))
current_data = np.asarray(Image.open(image_path))
if len(previous_data) > 0:
current_error = get_error_distance(param_error, previous_data, current_data)
error_data.append(current_error)
if len(previous_data) == 0:
previous_data = current_data
# display all data using matplotlib (configure plt)
gridsize = (3, 2)
# fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
fig = plt.figure(figsize=(30, 22))
ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
ax1.set_title(param_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + param_feature + ' feature, step ' + str(param_step) + ' normalization ' + param_mode)
ax1.set_label('Importance of noise [1, 999]')
ax1.set_xlabel('Vector features')
for id, data in enumerate(svd_data):
param_label = param_prefix + str(image_indices[id]) + " | " + param_error + ": " + str(error_data[id])
ax1.plot(data, label=param_label)
ax1.legend(bbox_to_anchor=(0.75, 1), loc=2, borderaxespad=0.2, fontsize=12)
if not param_norm:
ax1.set_xlim(begin, end)
# adapt ylim
y_begin, y_end = param_ylim
ax1.set_ylim(y_begin, y_end)
output_filename = param_prefix + "_" + noise_name + "_1_to_" + str(param_n) + "_B" + str(begin) + "_E" + str(end) + "_" + param_feature + "_S" + str(param_step) + "_norm" + str(param_norm )+ "_" + param_mode + "_" + param_error
if param_color:
output_filename = output_filename + '_color'
ax2.set_title(param_error + " information for : " + param_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + param_feature + ' feature, step ' + str(param_step) + ', normalization ' + param_mode)
ax2.set_ylabel(param_error + ' error')
ax2.set_xlabel('Number of samples per pixels')
ax2.set_xticks(range(len(image_indices)))
ax2.set_xticklabels(image_indices)
ax2.plot(error_data)
print("Generation of output figure... %s" % output_filename)
output_path = os.path.join(pictures_folder, output_filename)
if not os.path.exists(pictures_folder):
os.makedirs(pictures_folder)
fig.savefig(output_path, dpi=(200))
