def reprocess_images(list_folder_data):
print('Init reprocess_images')
for index, item in enumerate(list_folder_data):
folder = item[0]
min_score = item[1]
print('Processing folder: {0}'.format(folder))
list_files = os.listdir(folder)
random.Random(seed).shuffle(list_files)
for num_file, filename in enumerate(list_files):
file = os.path.join(folder, filename)
extension = ClassUtils.get_filename_extension(file)
if extension == '.json':
with open(file, 'r') as f:
data_str = f.read()
data_json = json.loads(data_str)
if 'vectors' in data_json:
print('Processing json file with new format: {0}'.format(file))
person_arr = data_json['vectors']
else:
print('Processing json file: {0}'.format(file))
person_arr = data_json
valid = ClassUtils.check_vector_integrity_pos(person_arr, min_score)
only_pos = ClassUtils.check_vector_only_pos(person_arr, min_score)
if not valid:
raise Exception('Vector integrity not valid for file: {0}'.format(file))
if only_pos:
raise Exception('Invalid vector to perform detection')
descriptors = ClassDescriptors.get_person_descriptors(person_arr, min_score, cam_number=0,
image=None, calib_params=None,
decode_img=False,
instance_nn_pose=None)
with open(file, 'w') as f:
f.write(json.dumps(descriptors))
transformed_points = descriptors['transformedPoints']
# Save pose for debugging purposes
re_scale_factor = 100
new_points = ClassDescriptors.re_scale_pose_factor(transformed_points,
re_scale_factor, min_score)
img_pose = ClassDescriptors.draw_pose_image(new_points, min_score, is_transformed=True)
new_file_name = ClassUtils.get_filename_no_extension(file) + '_1.jpg'
cv2.imwrite(new_file_name, img_pose)
print('Done!')
def load_pose_descriptors(cls, type_desc: EnumDesc):
training_data = list()
training_labels = list()
training_files = list()
eval_data = list()
eval_labels = list()
eval_files = list()
classes_number = 0
if type_desc == EnumDesc.ANGLES or type_desc == EnumDesc.ANGLES_TRANSFORMED:
data_folder = cls.list_folder_data_angles
else:
data_folder = cls.list_folder_data
cont = True
while cont:
cont = False
for folder_data in data_folder:
if folder_data[2] == classes_number:
classes_number += 1
cont = True
break
# Iterate folder
for index, item in enumerate(data_folder):
folder = item[0]
min_score = item[1]
label = item[2]
list_files = os.listdir(folder)
random.Random(cls.seed).shuffle(list_files)
total_train = int(len(list_files)) * 70 / 100
for num_file, file in enumerate(list_files):
full_path = os.path.join(folder, file)
extension = ClassUtils.get_filename_extension(full_path)
if extension != '.json':
print('Ignoring file {0}'.format(full_path))
continue
with open(full_path, 'r') as text_file:
arr_json = text_file.read()
params = json.loads(arr_json)
vectors = params['vectors']
angles = params['angles']
transformed_points = params['transformedPoints']
valid = ClassUtils.check_vector_integrity_pos(
vectors, min_score)
only_pos = ClassUtils.check_vector_only_pos(vectors, min_score)
if not valid:
raise Exception(
'Vector integrity not valid for file: {0}'.format(
full_path))
if only_pos:
raise Exception('Invalid vector to perform detection')
# Fill training and eval list
# Use angles and position information
data_to_add = cls._get_descriptor_list(angles,
transformed_points,
type_desc)
if num_file < total_train:
training_data.append(data_to_add)
training_labels.append(label)
training_files.append(full_path)
else:
eval_data.append(data_to_add)
eval_labels.append(label)
eval_files.append(full_path)
# Convert data to numpy array
training_data_np = np.asanyarray(training_data, dtype=np.float)
training_labels_np = np.asanyarray(training_labels, dtype=int)
print(training_labels_np)
eval_data_np = np.asanyarray(eval_data, dtype=np.float)
eval_labels_np = np.asanyarray(eval_labels, dtype=int)
print(eval_labels_np)
training_files_np = np.asanyarray(training_files, dtype=np.str)
eval_files_np = np.asanyarray(eval_files, dtype=np.str)
# Getting label_names
label_names = []
for folder, _, label in data_folder:
names = folder.split('/')
label_name = names[-1]
# Check if last character is /
if len(label_name) == 0:
label_names = names[-2]
label_names.append((label_name, label))
print('Total training: {0}'.format(len(training_labels)))
print('Total eval: {0}'.format(len(eval_labels)))
print('Shape training: {0}'.format(training_data_np.shape))
print('Shape eval: {0}'.format(eval_data_np.shape))
print('Classes number: {0}'.format(classes_number))
results = {
'trainingData': training_data_np,
'trainingLabels': training_labels_np,
'evalData': eval_data_np,
'evalLabels': eval_labels_np,
'trainingFiles': training_files_np,
'evalFiles': eval_files_np,
'labelNames': label_names,
'classesNumber': classes_number
}
return results