SYMBOL INDEX (604 symbols across 69 files)

FILE: axelerate/evaluate.py
  function save_report (line 23) | def save_report(config, report, report_file):
  function show_image (line 30) | def show_image(filename):
  function prepare_image (line 39) | def prepare_image(img_path, network):
  function setup_evaluation (line 47) | def setup_evaluation(config, weights, threshold = None):

FILE: axelerate/infer.py
  function show_image (line 22) | def show_image(filename):
  function prepare_image (line 31) | def prepare_image(img_path, network, input_size):
  function find_imgs (line 39) | def find_imgs(folder):
  function setup_inference (line 46) | def setup_inference(config, weights, threshold = None, folder = None):

FILE: axelerate/networks/classifier/batch_gen.py
  function create_datagen (line 12) | def create_datagen(img_folder, batch_size, input_size, project_folder, a...
  class ImageDataAugmentor (line 35) | class ImageDataAugmentor(Sequence):
    method __init__ (line 56) | def __init__(self,
    method flow_from_directory (line 82) | def flow_from_directory(self,
    method transform_image (line 189) | def transform_image(self, image, desired_w, desired_h):

FILE: axelerate/networks/classifier/directory_iterator.py
  class DirectoryIterator (line 18) | class DirectoryIterator(BatchFromFilesMixin, Iterator):
    method __init__ (line 68) | def __init__(self,
    method filepaths (line 149) | def filepaths(self):
    method labels (line 153) | def labels(self):
    method sample_weight (line 157) | def sample_weight(self):

FILE: axelerate/networks/classifier/frontend_classifier.py
  function get_labels (line 15) | def get_labels(directory):
  function create_classifier (line 19) | def create_classifier(architecture, labels, input_size, layers, dropout,...
  class Classifier (line 38) | class Classifier(object):
    method __init__ (line 39) | def __init__(self,
    method load_weights (line 51) | def load_weights(self, weight_path, by_name=False):
    method save_bottleneck (line 58) | def save_bottleneck(self, model_path, bottleneck_layer):
    method predict (line 67) | def predict(self, img):
    method evaluate (line 80) | def evaluate(self, img_folder, batch_size):
    method train (line 100) | def train(self,

FILE: axelerate/networks/classifier/iterator.py
  class Iterator (line 23) | class Iterator(IteratorType):
    method __init__ (line 37) | def __init__(self, n, batch_size, shuffle, seed):
    method _set_index_array (line 48) | def _set_index_array(self):
    method __getitem__ (line 53) | def __getitem__(self, idx):
    method __len__ (line 68) | def __len__(self):
    method on_epoch_end (line 71) | def on_epoch_end(self):
    method reset (line 74) | def reset(self):
    method _flow_index (line 77) | def _flow_index(self):
    method __iter__ (line 99) | def __iter__(self):
    method __next__ (line 104) | def __next__(self, *args, **kwargs):
    method next (line 107) | def next(self):
    method _get_batches_of_transformed_samples (line 119) | def _get_batches_of_transformed_samples(self, index_array):
  class BatchFromFilesMixin (line 131) | class BatchFromFilesMixin():
    method set_processing_attrs (line 137) | def set_processing_attrs(self,
    method _get_batch_of_samples (line 214) | def _get_batch_of_samples(self, index_array, apply_standardization=True):
    method _get_batches_of_transformed_samples (line 274) | def _get_batches_of_transformed_samples(self, index_array):
    method show_batch (line 278) | def show_batch(self, rows:int=5, apply_standardization:bool=False, **p...
    method filepaths (line 312) | def filepaths(self):
    method labels (line 320) | def labels(self):
    method sample_weight (line 328) | def sample_weight(self):

FILE: axelerate/networks/classifier/utils.py
  function validate_filename (line 36) | def validate_filename(filename, white_list_formats):
  function save_img (line 50) | def save_img(path,
  function load_img (line 78) | def load_img(fname, color_mode='rgb', target_size=None, interpolation=cv...
  function list_pictures (line 106) | def list_pictures(directory, ext=('jpg', 'jpeg', 'bmp', 'png', 'ppm', 't...
  function _iter_valid_files (line 123) | def _iter_valid_files(directory, white_list_formats, follow_links):
  function _list_valid_filenames_in_directory (line 149) | def _list_valid_filenames_in_directory(directory, white_list_formats, sp...
  function array_to_img (line 196) | def array_to_img(x, data_format='channels_last', scale=True, dtype='floa...
  function img_to_array (line 249) | def img_to_array(img, data_format='channels_last', dtype='float32'):

FILE: axelerate/networks/common_utils/augment.py
  class ImgAugment (line 13) | class ImgAugment(object):
    method __init__ (line 14) | def __init__(self, w, h, jitter):
    method imread (line 25) | def imread(self, img_file, boxes, labels):
  function _to_bbs (line 53) | def _to_bbs(boxes, labels, shape):
  function _to_array (line 62) | def _to_array(bbs):
  function process_image_detection (line 76) | def process_image_detection(image, boxes, labels, desired_w, desired_h, ...
  function process_image_classification (line 102) | def process_image_classification(image, desired_w, desired_h, augment):
  function process_image_segmentation (line 117) | def process_image_segmentation(image, segmap, input_w, input_h, output_w...
  function _create_augment_pipeline (line 134) | def _create_augment_pipeline():
  function visualize_detection_dataset (line 168) | def visualize_detection_dataset(img_folder, ann_folder, num_imgs = None,...
  function visualize_segmentation_dataset (line 202) | def visualize_segmentation_dataset(images_path, segs_path, num_imgs = No...
  function visualize_classification_dataset (line 252) | def visualize_classification_dataset(img_folder, num_imgs = None, img_si...

FILE: axelerate/networks/common_utils/callbacks.py
  function cosine_decay_with_warmup (line 5) | def cosine_decay_with_warmup(global_step,
  class WarmUpCosineDecayScheduler (line 55) | class WarmUpCosineDecayScheduler(keras.callbacks.Callback):
    method __init__ (line 59) | def __init__(self,
    method on_epoch_end (line 91) | def on_epoch_end(self, epoch, logs={}):
    method on_batch_end (line 95) | def on_batch_end(self, batch, logs=None):
    method on_batch_begin (line 100) | def on_batch_begin(self, batch, logs=None):

FILE: axelerate/networks/common_utils/convert.py
  function run_command (line 18) | def run_command(cmd, cwd=None):
  class Converter (line 28) | class Converter(object):
    method __init__ (line 29) | def __init__(self, converter_type, backend=None, dataset_path=None):
    method edgetpu_dataset_gen (line 77) | def edgetpu_dataset_gen(self):
    method k210_dataset_gen (line 93) | def k210_dataset_gen(self):
    method convert_edgetpu (line 114) | def convert_edgetpu(self, model_path):
    method convert_k210 (line 122) | def convert_k210(self, model_path):
    method convert_ir (line 133) | def convert_ir(self, model_path, model_layers):
    method convert_oak (line 142) | def convert_oak(self, model_path):
    method convert_onnx (line 149) | def convert_onnx(self, model):
    method convert_tflite (line 154) | def convert_tflite(self, model, model_layers, target=None):
    method convert_model (line 194) | def convert_model(self, model_path):

FILE: axelerate/networks/common_utils/feature.py
  function create_feature_extractor (line 12) | def create_feature_extractor(architecture, input_size, weights = None):
  class BaseFeatureExtractor (line 47) | class BaseFeatureExtractor(object):
    method __init__ (line 51) | def __init__(self, input_size):
    method normalize (line 55) | def normalize(self, image):
    method get_input_size (line 58) | def get_input_size(self):
    method get_output_size (line 63) | def get_output_size(self, layer = None):
    method get_output_tensor (line 69) | def get_output_tensor(self, layer):
    method extract (line 72) | def extract(self, input_image):
  class FullYoloFeature (line 75) | class FullYoloFeature(BaseFeatureExtractor):
    method __init__ (line 77) | def __init__(self, input_size, weights=None):
    method normalize (line 215) | def normalize(self, image):
  class TinyYoloFeature (line 218) | class TinyYoloFeature(BaseFeatureExtractor):
    method __init__ (line 220) | def __init__(self, input_size, weights):
    method normalize (line 259) | def normalize(self, image):
  class MobileNetFeature (line 262) | class MobileNetFeature(BaseFeatureExtractor):
    method __init__ (line 264) | def __init__(self, input_size, weights, alpha):
    method normalize (line 284) | def normalize(self, image):
  class SqueezeNetFeature (line 291) | class SqueezeNetFeature(BaseFeatureExtractor):
    method __init__ (line 293) | def __init__(self, input_size, weights):
    method normalize (line 347) | def normalize(self, image):
  class DenseNet121Feature (line 357) | class DenseNet121Feature(BaseFeatureExtractor):
    method __init__ (line 359) | def __init__(self, input_size, weights):
    method normalize (line 373) | def normalize(self, image):
  class NASNetMobileFeature (line 377) | class NASNetMobileFeature(BaseFeatureExtractor):
    method __init__ (line 379) | def __init__(self, input_size, weights):
    method normalize (line 392) | def normalize(self, image):
  class ResNet50Feature (line 396) | class ResNet50Feature(BaseFeatureExtractor):
    method __init__ (line 398) | def __init__(self, input_size, weights):
    method normalize (line 412) | def normalize(self, image):

FILE: axelerate/networks/common_utils/fit.py
  function train (line 14) | def train(model,
  function _print_time (line 141) | def _print_time(process_time):

FILE: axelerate/networks/common_utils/mobilenet_sipeed/__init__.py
  function set_keras_submodules (line 13) | def set_keras_submodules(backend=None,
  function get_keras_submodule (line 29) | def get_keras_submodule(name):
  function get_submodules_from_kwargs (line 58) | def get_submodules_from_kwargs(kwargs):
  function correct_pad (line 69) | def correct_pad(backend, inputs, kernel_size):

FILE: axelerate/networks/common_utils/mobilenet_sipeed/imagenet_utils.py
  function _preprocess_numpy_input (line 21) | def _preprocess_numpy_input(x, data_format, mode, **kwargs):
  function _preprocess_symbolic_input (line 96) | def _preprocess_symbolic_input(x, data_format, mode, **kwargs):
  function preprocess_input (line 157) | def preprocess_input(x, data_format=None, mode='caffe', **kwargs):
  function decode_predictions (line 198) | def decode_predictions(preds, top=5, **kwargs):
  function _obtain_input_shape (line 240) | def _obtain_input_shape(input_shape,

FILE: axelerate/networks/common_utils/mobilenet_sipeed/mobilenet.py
  function preprocess_input (line 75) | def preprocess_input(x, **kwargs):
  function MobileNet (line 87) | def MobileNet(input_shape=None,
  function _conv_block (line 329) | def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
  function _depthwise_conv_block (line 390) | def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,

FILE: axelerate/networks/segnet/data_utils/data_loader.py
  function tqdm (line 16) | def tqdm(iter):
  class DataLoaderError (line 27) | class DataLoaderError(Exception):
  function get_pairs_from_paths (line 30) | def get_pairs_from_paths(images_path, segs_path, ignore_non_matching=True):
  function get_image_array (line 70) | def get_image_array(image_input, norm, ordering='channels_first'):
  function get_segmentation_array (line 90) | def get_segmentation_array(image_input, nClasses, no_reshape=True):
  function verify_segmentation_dataset (line 116) | def verify_segmentation_dataset(images_path, segs_path, n_classes, show_...
  function create_batch_generator (line 150) | def create_batch_generator(images_path, segs_path,
  class BatchGenerator (line 165) | class BatchGenerator(Sequence):
    method __init__ (line 166) | def __init__(self,
    method __len__ (line 182) | def __len__(self):
    method __getitem__ (line 185) | def __getitem__(self, idx):
    method on_epoch_end (line 207) | def on_epoch_end(self):

FILE: axelerate/networks/segnet/frontend_segnet.py
  function masked_categorical_crossentropy (line 12) | def masked_categorical_crossentropy(gt , pr ):
  function create_segnet (line 17) | def create_segnet(architecture, input_size, n_classes, weights = None):
  class Segnet (line 39) | class Segnet(object):
    method __init__ (line 40) | def __init__(self,
    method load_weights (line 52) | def load_weights(self, weight_path, by_name=False):
    method predict (line 59) | def predict(self, image):
    method evaluate (line 70) | def evaluate(self, img_folder, ann_folder, batch_size):
    method train (line 100) | def train(self,

FILE: axelerate/networks/segnet/metrics.py
  function get_iou (line 5) | def get_iou(gt, pr, n_classes):

FILE: axelerate/networks/segnet/models/_pspnet_2.py
  function BN (line 22) | def BN(name=""):
  class Interp (line 26) | class Interp(layers.Layer):
    method __init__ (line 28) | def __init__(self, new_size, **kwargs):
    method build (line 32) | def build(self, input_shape):
    method call (line 35) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 44) | def compute_output_shape(self, input_shape):
    method get_config (line 50) | def get_config(self):
  function residual_conv (line 63) | def residual_conv(prev, level, pad=1, lvl=1, sub_lvl=1, modify_stride=Fa...
  function short_convolution_branch (line 94) | def short_convolution_branch(prev, level, lvl=1, sub_lvl=1,
  function empty_branch (line 112) | def empty_branch(prev):
  function residual_short (line 116) | def residual_short(prev_layer, level, pad=1, lvl=1, sub_lvl=1,
  function residual_empty (line 130) | def residual_empty(prev_layer, level, pad=1, lvl=1, sub_lvl=1):
  function ResNet (line 140) | def ResNet(inp, layers):
  function interp_block (line 207) | def interp_block(prev_layer, level, feature_map_shape, input_shape):
  function build_pyramid_pooling_module (line 240) | def build_pyramid_pooling_module(res, input_shape):
  function _build_pspnet (line 261) | def _build_pspnet(nb_classes, resnet_layers, input_shape,

FILE: axelerate/networks/segnet/models/basic_models.py
  function vanilla_encoder (line 7) | def vanilla_encoder(input_height=224,  input_width=224):

FILE: axelerate/networks/segnet/models/fcn.py
  function crop (line 13) | def crop(o1, o2, i):
  function fcn_8 (line 51) | def fcn_8(n_classes, encoder=vanilla_encoder, input_height=416,
  function fcn_32 (line 96) | def fcn_32(n_classes, encoder=vanilla_encoder, input_height=416,
  function fcn_8_vgg (line 122) | def fcn_8_vgg(n_classes,  input_height=416, input_width=608):
  function fcn_32_vgg (line 129) | def fcn_32_vgg(n_classes,  input_height=416, input_width=608):
  function fcn_8_resnet50 (line 136) | def fcn_8_resnet50(n_classes,  input_height=416, input_width=608):
  function fcn_32_resnet50 (line 143) | def fcn_32_resnet50(n_classes,  input_height=416, input_width=608):
  function fcn_8_mobilenet (line 150) | def fcn_8_mobilenet(n_classes,  input_height=416, input_width=608):
  function fcn_32_mobilenet (line 157) | def fcn_32_mobilenet(n_classes,  input_height=416, input_width=608):

FILE: axelerate/networks/segnet/models/model.py
  class Model (line 3) | class Model:
    method __init__ (line 4) | def __init__(self, n_classes, input_height=None, input_width=None):

FILE: axelerate/networks/segnet/models/model_utils.py
  function transfer_weights (line 14) | def transfer_weights(m1, m2, verbose=True):
  function resize_image (line 43) | def resize_image(inp,  s, data_format):
  function get_segmentation_model (line 67) | def get_segmentation_model(input, output):

FILE: axelerate/networks/segnet/models/pspnet.py
  function pool_block (line 21) | def pool_block(feats, pool_factor):
  function _pspnet (line 46) | def _pspnet(n_classes, encoder,  input_height=384, input_width=576):
  function pspnet (line 78) | def pspnet(n_classes,  input_height=384, input_width=576):
  function vgg_pspnet (line 86) | def vgg_pspnet(n_classes,  input_height=384, input_width=576):
  function resnet50_pspnet (line 94) | def resnet50_pspnet(n_classes,  input_height=384, input_width=576):
  function pspnet_50 (line 102) | def pspnet_50(n_classes,  input_height=473, input_width=473):
  function pspnet_101 (line 115) | def pspnet_101(n_classes,  input_height=473, input_width=473):

FILE: axelerate/networks/segnet/models/segnet.py
  function chopper (line 18) | def chopper(model, model_name, f):
  function segnet_decoder (line 21) | def segnet_decoder(f, n_classes, n_up=3):
  function _segnet (line 53) | def _segnet(n_classes, encoder_input, encoder_output,  input_height=416,...
  function full_yolo_segnet (line 60) | def full_yolo_segnet(n_classes, input_size, encoder_level, weights):
  function tiny_yolo_segnet (line 72) | def tiny_yolo_segnet(n_classes, input_size, encoder_level, weights):
  function squeezenet_segnet (line 84) | def squeezenet_segnet(n_classes, input_size, encoder_level, weights):
  function densenet121_segnet (line 95) | def densenet121_segnet(n_classes, input_size, encoder_level, weights):
  function nasnetmobile_segnet (line 106) | def nasnetmobile_segnet(n_classes, input_size, encoder_level, weights):
  function resnet50_segnet (line 117) | def resnet50_segnet(n_classes, input_size, encoder_level, weights):
  function mobilenet_segnet (line 129) | def mobilenet_segnet(n_classes, input_size, encoder_level, weights, arch...

FILE: axelerate/networks/segnet/models/unet.py
  function unet_mini (line 18) | def unet_mini(n_classes, input_height=360, input_width=480):
  function _unet (line 69) | def _unet(n_classes, encoder, l1_skip_conn=True, input_height=416,
  function unet (line 111) | def unet(n_classes, input_height=416, input_width=608, encoder_level=3):
  function vgg_unet (line 119) | def vgg_unet(n_classes, input_height=416, input_width=608, encoder_level...
  function resnet50_unet (line 127) | def resnet50_unet(n_classes, input_height=416, input_width=608,
  function mobilenet_unet (line 136) | def mobilenet_unet(n_classes, input_height=224, input_width=224,

FILE: axelerate/networks/segnet/predict.py
  function model_from_checkpoint_path (line 20) | def model_from_checkpoint_path(checkpoints_path):
  function get_colored_segmentation_image (line 36) | def get_colored_segmentation_image(seg_arr, n_classes, colors=class_colo...
  function get_legends (line 47) | def get_legends(class_names,  colors=class_colors):
  function overlay_seg_image (line 62) | def overlay_seg_image(inp_img , seg_img):
  function concat_lenends (line 70) | def concat_lenends(  seg_img , legend_img  ):
  function visualize_segmentation (line 82) | def visualize_segmentation(seg_arr, inp_img=None, n_classes=None,
  function predict (line 115) | def predict(model=None, inp=None, out_fname=None, image = None, overlay_...
  function predict_multiple (line 134) | def predict_multiple(model=None, inps=None, inp_dir=None, out_dir=None,
  function evaluate (line 167) | def evaluate(model=None, inp_images=None, annotations=None, inp_images_d...

FILE: axelerate/networks/segnet/train.py
  function find_latest_checkpoint (line 8) | def find_latest_checkpoint(checkpoints_path, fail_safe=True):
  function masked_categorical_crossentropy (line 30) | def masked_categorical_crossentropy(gt , pr ):
  function train (line 38) | def train(model,

FILE: axelerate/networks/yolo/backend/batch_gen.py
  function create_batch_generator (line 12) | def create_batch_generator(annotations,
  class BatchGenerator (line 42) | class BatchGenerator(Sequence):
    method __init__ (line 43) | def __init__(self,
    method __len__ (line 67) | def __len__(self):
    method __getitem__ (line 70) | def __getitem__(self, idx):
    method on_epoch_end (line 116) | def on_epoch_end(self):
  class _YoloBox (line 120) | class _YoloBox(object):
    method __init__ (line 122) | def __init__(self, input_size, grid_size):
    method trans (line 126) | def trans(self, boxes):
  class _NetinGen (line 145) | class _NetinGen(object):
    method __init__ (line 146) | def __init__(self, input_size, norm):
    method run (line 150) | def run(self, image):
    method _set_norm (line 153) | def _set_norm(self, norm):
  class _NetoutGen (line 159) | class _NetoutGen(object):
    method __init__ (line 160) | def __init__(self,
    method run (line 168) | def run(self, norm_boxes, labels):
    method _set_tensor_shape (line 186) | def _set_tensor_shape(self, grid_size, nb_classes):
    method _xy_grid_index (line 190) | def _xy_grid_index(self, box_xy: np.ndarray, layer: int):
    method _fake_iou (line 211) | def _fake_iou(a: np.ndarray, b: np.ndarray) -> float:
    method _get_anchor_index (line 242) | def _get_anchor_index(self, wh: np.ndarray) -> np.ndarray:
    method box_to_label (line 259) | def box_to_label(self, true_box: np.ndarray) -> tuple:

FILE: axelerate/networks/yolo/backend/decoder.py
  class YoloDecoder (line 5) | class YoloDecoder(object):
    method __init__ (line 7) | def __init__(self,
    method run (line 18) | def run(self, netout, obj_threshold):
  function _sigmoid (line 53) | def _sigmoid(x):

FILE: axelerate/networks/yolo/backend/loss.py
  function tf_xywh_to_all (line 10) | def tf_xywh_to_all(grid_pred_xy, grid_pred_wh, layer, params):
  function tf_xywh_to_grid (line 37) | def tf_xywh_to_grid(all_true_xy, all_true_wh, layer, params):
  function tf_reshape_box (line 62) | def tf_reshape_box(true_xy_A: tf.Tensor, true_wh_A: tf.Tensor, p_xy_A: t...
  function tf_iou (line 104) | def tf_iou(pred_xy: tf.Tensor, pred_wh: tf.Tensor, vaild_xy: tf.Tensor, ...
  function calc_ignore_mask (line 149) | def calc_ignore_mask(t_xy_A: tf.Tensor, t_wh_A: tf.Tensor, p_xy: tf.Tens...
  class Params (line 188) | class Params:
    method __init__ (line 190) | def __init__(self, obj_thresh, iou_thresh, obj_weight, noobj_weight, w...
    method _coordinate_offset (line 209) | def _coordinate_offset(anchors: np.ndarray, out_hw: np.ndarray) -> np....
    method _anchor_scale (line 232) | def _anchor_scale(anchors: np.ndarray, grid_wh: np.ndarray) -> np.array:
  function create_loss_fn (line 250) | def create_loss_fn(params, layer, batch_size):

FILE: axelerate/networks/yolo/backend/network.py
  function create_yolo_network (line 9) | def create_yolo_network(architecture,
  class YoloNetwork (line 23) | class YoloNetwork(object):
    method __init__ (line 25) | def __init__(self,
    method _init_layers (line 82) | def _init_layers(self, layers):
    method load_weights (line 93) | def load_weights(self, weight_path, by_name):
    method forward (line 96) | def forward(self, image):
    method get_model (line 100) | def get_model(self, first_trainable_layer=None):
    method get_grid_size (line 103) | def get_grid_size(self):
    method get_normalize_func (line 109) | def get_normalize_func(self):

FILE: axelerate/networks/yolo/backend/utils/annotation.py
  function get_unique_labels (line 8) | def get_unique_labels(files):
  function get_train_annotations (line 18) | def get_train_annotations(labels,
  class PascalVocXmlParser (line 61) | class PascalVocXmlParser(object):
    method __init__ (line 64) | def __init__(self):
    method get_fname (line 67) | def get_fname(self, annotation_file):
    method get_path (line 80) | def get_path(self, annotation_file):
    method get_width (line 97) | def get_width(self, annotation_file):
    method get_height (line 111) | def get_height(self, annotation_file):
    method get_labels (line 125) | def get_labels(self, annotation_file):
    method get_boxes (line 142) | def get_boxes(self, annotation_file):
    method _root_tag (line 166) | def _root_tag(self, fname):
    method _tree (line 171) | def _tree(self, fname):
  function parse_annotation (line 175) | def parse_annotation(ann_dir, img_dir, labels_naming=[], is_only_detect=...
  class Annotation (line 219) | class Annotation(object):
    method __init__ (line 226) | def __init__(self, filename):
    method add_object (line 231) | def add_object(self, x1, y1, x2, y2, name):
  class Annotations (line 239) | class Annotations(object):
    method __init__ (line 240) | def __init__(self, label_namings):
    method n_classes (line 244) | def n_classes(self):
    method add (line 247) | def add(self, annotation):
    method shuffle (line 250) | def shuffle(self):
    method fname (line 253) | def fname(self, i):
    method boxes (line 257) | def boxes(self, i):
    method labels (line 261) | def labels(self, i):
    method code_labels (line 269) | def code_labels(self, i):
    method _valid_index (line 280) | def _valid_index(self, i):
    method __len__ (line 284) | def __len__(self):
    method __getitem__ (line 287) | def __getitem__(self, idx):

FILE: axelerate/networks/yolo/backend/utils/box.py
  class BoundBox (line 4) | class BoundBox:
    method __init__ (line 5) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 14) | def get_label(self):
    method get_score (line 17) | def get_score(self):
    method iou (line 20) | def iou(self, bound_box):
    method as_centroid (line 25) | def as_centroid(self):
  function boxes_to_array (line 29) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 46) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function draw_scaled_boxes (line 75) | def draw_scaled_boxes(image, boxes, probs, labels, desired_size=400):
  function draw_boxes (line 92) | def draw_boxes(image, boxes, scores, classes, labels):
  function centroid_box_iou (line 124) | def centroid_box_iou(box1, box2):
  function to_centroid (line 153) | def to_centroid(minmax_boxes):
  function to_minmax (line 173) | def to_minmax(centroid_boxes):
  function create_anchor_boxes (line 188) | def create_anchor_boxes(anchors):
  function find_match_box (line 202) | def find_match_box(centroid_box, centroid_boxes):

FILE: axelerate/networks/yolo/backend/utils/custom.py
  class Yolo_Precision (line 15) | class Yolo_Precision(Metric):
    method __init__ (line 16) | def __init__(self, thresholds=None, name=None, dtype=None):
    method update_state (line 30) | def update_state(self, y_true, y_pred, sample_weight=None):
    method result (line 44) | def result(self):
  class Yolo_Recall (line 48) | class Yolo_Recall(Metric):
    method __init__ (line 49) | def __init__(self, thresholds=None, name=None, dtype=None):
    method update_state (line 62) | def update_state(self, y_true, y_pred, sample_weight=None):
    method result (line 76) | def result(self):
  class MergeMetrics (line 79) | class MergeMetrics(tensorflow.keras.callbacks.Callback):
    method __init__ (line 81) | def __init__(self,
    method on_epoch_end (line 112) | def on_epoch_end(self, epoch, logs={}):

FILE: axelerate/networks/yolo/backend/utils/eval/_box_match.py
  class BoxMatcher (line 5) | class BoxMatcher(object):
    method __init__ (line 15) | def __init__(self, boxes1, boxes2, labels1=None, labels2=None):
    method match_idx_of_box1_idx (line 33) | def match_idx_of_box1_idx(self, box1_idx):
    method match_idx_of_box2_idx (line 57) | def match_idx_of_box2_idx(self, box2_idx):
    method _find (line 81) | def _find(self, input_idx, input_idx_list, output_idx_list):
    method _calc_maximun_ious (line 89) | def _calc_maximun_ious(self):
    method _calc (line 94) | def _calc(self, boxes, true_boxes, labels, true_labels):

FILE: axelerate/networks/yolo/backend/utils/eval/fscore.py
  function count_true_positives (line 4) | def count_true_positives(detect_boxes, true_boxes, detect_labels=None, t...
  function calc_score (line 23) | def calc_score(n_true_positives, n_truth, n_pred):

FILE: axelerate/networks/yolo/frontend.py
  function get_object_labels (line 20) | def get_object_labels(ann_directory):
  function create_yolo (line 25) | def create_yolo(architecture,
  class YOLO (line 51) | class YOLO(object):
    method __init__ (line 52) | def __init__(self,
    method load_weights (line 71) | def load_weights(self, weight_path, by_name=True):
    method predict (line 78) | def predict(self, image, height, width, threshold=0.3):
    method evaluate (line 108) | def evaluate(self, img_folder, ann_folder, batch_size):
    method train (line 121) | def train(self,
    method _get_loss_func (line 167) | def _get_loss_func(self, batch_size):
    method _get_batch_generator (line 170) | def _get_batch_generator(self, annotations, batch_size, repeat_times, ...

FILE: axelerate/train.py
  function train_from_config (line 30) | def train_from_config(config,project_folder):
  function setup_training (line 149) | def setup_training(config_file=None, config_dict=None):

FILE: example_scripts/arm_nn/box.py
  class BoundBox (line 6) | class BoundBox:
    method __init__ (line 7) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 16) | def get_label(self):
    method get_score (line 19) | def get_score(self):
    method iou (line 22) | def iou(self, bound_box):
    method as_centroid (line 27) | def as_centroid(self):
  function boxes_to_array (line 31) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 48) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function draw_scaled_boxes (line 77) | def draw_scaled_boxes(image, boxes, probs, labels, desired_size=400):
  function draw_boxes (line 94) | def draw_boxes(image, boxes, probs, labels):
  function centroid_box_iou (line 107) | def centroid_box_iou(box1, box2):
  function to_centroid (line 136) | def to_centroid(minmax_boxes):
  function to_minmax (line 154) | def to_minmax(centroid_boxes):
  function create_anchor_boxes (line 169) | def create_anchor_boxes(anchors):
  function find_match_box (line 183) | def find_match_box(centroid_box, centroid_boxes):

FILE: example_scripts/arm_nn/cv_utils.py
  function preprocess (line 17) | def preprocess(frame: np.ndarray, input_binding_info: tuple):
  function resize_with_aspect_ratio (line 46) | def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
  function create_video_writer (line 74) | def create_video_writer(video: cv2.VideoCapture, video_path: str, output...
  function init_video_file_capture (line 104) | def init_video_file_capture(video_path: str, output_path: str):
  function init_video_stream_capture (line 127) | def init_video_stream_capture(video_source: int):
  function draw_bounding_boxes (line 144) | def draw_bounding_boxes(frame: np.ndarray, detections: list, resize_fact...
  function get_source_encoding_int (line 186) | def get_source_encoding_int(video_capture):

FILE: example_scripts/arm_nn/network_executor.py
  function create_network (line 11) | def create_network(model_file: str, backends: list, input_names: Tuple[s...
  function execute_network (line 68) | def execute_network(input_tensors: list, output_tensors: list, runtime, ...
  class ArmnnNetworkExecutor (line 86) | class ArmnnNetworkExecutor:
    method __init__ (line 88) | def __init__(self, model_file: str, backends: list):
    method run (line 100) | def run(self, input_tensors: list) -> List[np.ndarray]:

FILE: example_scripts/arm_nn/run_video_file.py
  function preprocess (line 30) | def preprocess(frame: np.ndarray, input_binding_info: tuple):
  function process_faces (line 58) | def process_faces(frame, detections, executor_kp, resize_factor):
  function draw_bounding_boxes (line 92) | def draw_bounding_boxes(frame: np.ndarray, detections: list, resize_fact...
  function main (line 138) | def main(args):

FILE: example_scripts/arm_nn/run_video_stream.py
  function preprocess (line 24) | def preprocess(frame: np.ndarray, input_binding_info: tuple):
  function process_faces (line 52) | def process_faces(frame, detections, executor_kp, resize_factor):
  function draw_bounding_boxes (line 86) | def draw_bounding_boxes(frame: np.ndarray, detections: list, resize_fact...
  function main (line 131) | def main(args):

FILE: example_scripts/arm_nn/yolov2.py
  function yolo_processing (line 13) | def yolo_processing(netout):
  function _sigmoid (line 74) | def _sigmoid(x):
  function _softmax (line 77) | def _softmax(x, axis=-1, t=-100.):
  function yolo_resize_factor (line 84) | def yolo_resize_factor(video: cv2.VideoCapture, input_binding_info: tuple):

FILE: example_scripts/edge_tpu/detector/box.py
  class BoundBox (line 7) | class BoundBox:
    method __init__ (line 8) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 17) | def get_label(self):
    method get_score (line 20) | def get_score(self):
    method iou (line 23) | def iou(self, bound_box):
    method as_centroid (line 28) | def as_centroid(self):
  function boxes_to_array (line 32) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 49) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function draw_scaled_boxes (line 78) | def draw_scaled_boxes(image, boxes, probs, labels, desired_size=400):
  function draw_boxes (line 95) | def draw_boxes(image, boxes, probs, labels):
  function centroid_box_iou (line 108) | def centroid_box_iou(box1, box2):
  function to_centroid (line 137) | def to_centroid(minmax_boxes):
  function to_minmax (line 155) | def to_minmax(centroid_boxes):
  function create_anchor_boxes (line 170) | def create_anchor_boxes(anchors):
  function find_match_box (line 184) | def find_match_box(centroid_box, centroid_boxes):

FILE: example_scripts/edge_tpu/detector/detector_video.py
  class Detector (line 11) | class Detector(object):
    method __init__ (line 13) | def __init__(self, label_file, model_file, threshold):
    method load_labels (line 21) | def load_labels(self, path):
    method preprocess (line 25) | def preprocess(self, img):
    method get_output_tensor (line 35) | def get_output_tensor(self, index):
    method detect_objects (line 41) | def detect_objects(self, image):
    method detect (line 52) | def detect(self, original_image):
    method run (line 76) | def run(self, netout):
  function _sigmoid (line 120) | def _sigmoid(x):
  function _softmax (line 123) | def _softmax(x, axis=-1, t=-100.):

FILE: example_scripts/oak/yolov2/box.py
  class BoundBox (line 7) | class BoundBox:
    method __init__ (line 8) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 17) | def get_label(self):
    method get_score (line 20) | def get_score(self):
    method iou (line 23) | def iou(self, bound_box):
    method as_centroid (line 28) | def as_centroid(self):
  function boxes_to_array (line 32) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 49) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function draw_scaled_boxes (line 78) | def draw_scaled_boxes(image, boxes, probs, labels, desired_size=400):
  function draw_boxes (line 95) | def draw_boxes(image, boxes, probs, labels):
  function centroid_box_iou (line 108) | def centroid_box_iou(box1, box2):
  function to_centroid (line 137) | def to_centroid(minmax_boxes):
  function to_minmax (line 155) | def to_minmax(centroid_boxes):
  function create_anchor_boxes (line 170) | def create_anchor_boxes(anchors):
  function find_match_box (line 184) | def find_match_box(centroid_box, centroid_boxes):

FILE: example_scripts/oak/yolov2/yolo.py
  function sigmoid (line 16) | def sigmoid(x):
  function calculate_overlap (line 20) | def calculate_overlap(x1, w1, x2, w2):
  function calculate_iou (line 27) | def calculate_iou(box, truth):
  function apply_nms (line 41) | def apply_nms(boxes):
  function post_processing (line 66) | def post_processing(output, label_list, threshold):
  function show_tiny_yolo (line 147) | def show_tiny_yolo(results, original_img, is_depth=0):

FILE: example_scripts/oak/yolov2/yolo_alt.py
  class Detector (line 9) | class Detector(object):
    method __init__ (line 11) | def __init__(self, label_file, model_file, threshold):
    method load_labels (line 16) | def load_labels(self, path):
    method parse (line 20) | def parse(self, original_image, tensor):
    method run (line 43) | def run(self, netout):
  function _sigmoid (line 87) | def _sigmoid(x):
  function _softmax (line 90) | def _softmax(x, axis=-1, t=-100.):

FILE: example_scripts/tensorflow_lite/classifier/base_camera.py
  class CameraEvent (line 12) | class CameraEvent(object):
    method __init__ (line 16) | def __init__(self):
    method wait (line 19) | def wait(self):
    method set (line 29) | def set(self):
    method clear (line 49) | def clear(self):
  class BaseCamera (line 54) | class BaseCamera(object):
    method __init__ (line 60) | def __init__(self):
    method get_frame (line 73) | def get_frame(self):
    method frames (line 84) | def frames():
    method _thread (line 89) | def _thread(cls):

FILE: example_scripts/tensorflow_lite/classifier/camera_opencv.py
  class Camera (line 5) | class Camera(BaseCamera):
    method set_video_source (line 9) | def set_video_source(source):
    method frames (line 13) | def frames():

FILE: example_scripts/tensorflow_lite/classifier/camera_pi.py
  class Camera (line 9) | class Camera(BaseCamera):
    method set_video_source (line 13) | def set_video_source(source):
    method frames (line 17) | def frames():

FILE: example_scripts/tensorflow_lite/classifier/classifier_file.py
  function load_labels (line 11) | def load_labels(path):
  class NetworkExecutor (line 15) | class NetworkExecutor(object):
    method __init__ (line 17) | def __init__(self, model_file):
    method get_output_tensors (line 24) | def get_output_tensors(self):
    method run (line 36) | def run(self, image):
  function main (line 44) | def main(args):

FILE: example_scripts/tensorflow_lite/classifier/classifier_stream.py
  function load_labels (line 13) | def load_labels(path):
  class NetworkExecutor (line 17) | class NetworkExecutor(object):
    method __init__ (line 19) | def __init__(self, model_file):
    method get_output_tensors (line 26) | def get_output_tensors(self):
    method run (line 38) | def run(self, image):
  class Classifier (line 46) | class Classifier(NetworkExecutor):
    method __init__ (line 48) | def __init__(self, label_file, model_file, top_k):
    method classify (line 57) | def classify(self, frame):
  function index (line 72) | def index():
  function gen (line 75) | def gen(camera):
  function video_feed (line 82) | def video_feed():

FILE: example_scripts/tensorflow_lite/classifier/cv_utils.py
  function preprocess (line 13) | def preprocess(img):
  function decode_yolov2 (line 23) | def decode_yolov2(netout,
  function decode_yolov3 (line 64) | def decode_yolov3(netout,
  function decode_classifier (line 106) | def decode_classifier(netout, top_k=3):
  function decode_segnet (line 112) | def decode_segnet(netout, labels, class_colors):
  function get_legends (line 126) | def get_legends(class_names, colors):
  function overlay_seg_image (line 138) | def overlay_seg_image(inp_img, seg_img):
  function concat_lenends (line 146) | def concat_lenends(seg_img, legend_img):
  function _sigmoid (line 152) | def _sigmoid(x):
  function _softmax (line 155) | def _softmax(x, axis=-1, t=-100.):
  function resize_with_aspect_ratio (line 162) | def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
  function create_video_writer (line 190) | def create_video_writer(video, video_path, output_name):
  function init_video_file_capture (line 217) | def init_video_file_capture(video_path, output_name):
  function draw_bounding_boxes (line 240) | def draw_bounding_boxes(frame, detections, labels=None, processing_funct...
  function draw_classification (line 301) | def draw_classification(frame, classifications, labels):
  function get_source_encoding_int (line 308) | def get_source_encoding_int(video_capture):
  class BoundBox (line 311) | class BoundBox:
    method __init__ (line 312) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 321) | def get_label(self):
    method get_score (line 324) | def get_score(self):
    method iou (line 327) | def iou(self, bound_box):
    method as_centroid (line 332) | def as_centroid(self):
  function boxes_to_array (line 336) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 352) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function centroid_box_iou (line 380) | def centroid_box_iou(box1, box2):
  function to_minmax (line 408) | def to_minmax(centroid_boxes):

FILE: example_scripts/tensorflow_lite/detector/base_camera.py
  class CameraEvent (line 12) | class CameraEvent(object):
    method __init__ (line 16) | def __init__(self):
    method wait (line 19) | def wait(self):
    method set (line 29) | def set(self):
    method clear (line 49) | def clear(self):
  class BaseCamera (line 54) | class BaseCamera(object):
    method __init__ (line 60) | def __init__(self):
    method get_frame (line 73) | def get_frame(self):
    method frames (line 84) | def frames():
    method _thread (line 89) | def _thread(cls):

FILE: example_scripts/tensorflow_lite/detector/camera_opencv.py
  class Camera (line 5) | class Camera(BaseCamera):
    method set_video_source (line 9) | def set_video_source(source):
    method frames (line 13) | def frames():

FILE: example_scripts/tensorflow_lite/detector/camera_pi.py
  class Camera (line 9) | class Camera(BaseCamera):
    method set_video_source (line 13) | def set_video_source(source):
    method frames (line 17) | def frames():

FILE: example_scripts/tensorflow_lite/detector/cv_utils.py
  function preprocess (line 13) | def preprocess(img):
  function decode_yolov2 (line 23) | def decode_yolov2(netout,
  function decode_yolov3 (line 64) | def decode_yolov3(netout,
  function decode_classifier (line 106) | def decode_classifier(netout, top_k=3):
  function decode_segnet (line 112) | def decode_segnet(netout, labels, class_colors):
  function get_legends (line 126) | def get_legends(class_names, colors):
  function overlay_seg_image (line 138) | def overlay_seg_image(inp_img, seg_img):
  function concat_lenends (line 146) | def concat_lenends(seg_img, legend_img):
  function _sigmoid (line 152) | def _sigmoid(x):
  function _softmax (line 155) | def _softmax(x, axis=-1, t=-100.):
  function resize_with_aspect_ratio (line 162) | def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
  function create_video_writer (line 190) | def create_video_writer(video, video_path, output_name):
  function init_video_file_capture (line 217) | def init_video_file_capture(video_path, output_name):
  function draw_bounding_boxes (line 240) | def draw_bounding_boxes(frame, detections, labels=None, processing_funct...
  function draw_classification (line 301) | def draw_classification(frame, classifications, labels):
  function get_source_encoding_int (line 308) | def get_source_encoding_int(video_capture):
  class BoundBox (line 311) | class BoundBox:
    method __init__ (line 312) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 321) | def get_label(self):
    method get_score (line 324) | def get_score(self):
    method iou (line 327) | def iou(self, bound_box):
    method as_centroid (line 332) | def as_centroid(self):
  function boxes_to_array (line 336) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 352) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function centroid_box_iou (line 380) | def centroid_box_iou(box1, box2):
  function to_minmax (line 408) | def to_minmax(centroid_boxes):

FILE: example_scripts/tensorflow_lite/detector/detector_file.py
  function load_labels (line 11) | def load_labels(path):
  class NetworkExecutor (line 15) | class NetworkExecutor(object):
    method __init__ (line 17) | def __init__(self, model_file):
    method get_output_tensors (line 24) | def get_output_tensors(self):
    method run (line 36) | def run(self, image):
  function main (line 44) | def main(args, detector):

FILE: example_scripts/tensorflow_lite/detector/detector_stream.py
  function load_labels (line 13) | def load_labels(path):
  class NetworkExecutor (line 17) | class NetworkExecutor(object):
    method __init__ (line 19) | def __init__(self, model_file):
    method get_output_tensors (line 26) | def get_output_tensors(self):
    method run (line 38) | def run(self, image):
  class Detector (line 46) | class Detector(NetworkExecutor):
    method __init__ (line 48) | def __init__(self, label_file, model_file, threshold):
    method detect (line 57) | def detect(self, original_image):
  function index (line 71) | def index():
  function gen (line 74) | def gen(camera):
  function video_feed (line 81) | def video_feed():

FILE: example_scripts/tensorflow_lite/segnet/base_camera.py
  class CameraEvent (line 12) | class CameraEvent(object):
    method __init__ (line 16) | def __init__(self):
    method wait (line 19) | def wait(self):
    method set (line 29) | def set(self):
    method clear (line 49) | def clear(self):
  class BaseCamera (line 54) | class BaseCamera(object):
    method __init__ (line 60) | def __init__(self):
    method get_frame (line 73) | def get_frame(self):
    method frames (line 84) | def frames():
    method _thread (line 89) | def _thread(cls):

FILE: example_scripts/tensorflow_lite/segnet/camera_opencv.py
  class Camera (line 5) | class Camera(BaseCamera):
    method set_video_source (line 9) | def set_video_source(source):
    method frames (line 13) | def frames():

FILE: example_scripts/tensorflow_lite/segnet/camera_pi.py
  class Camera (line 9) | class Camera(BaseCamera):
    method frames (line 11) | def frames():

FILE: example_scripts/tensorflow_lite/segnet/cv_utils.py
  function preprocess (line 13) | def preprocess(img):
  function decode_yolov2 (line 23) | def decode_yolov2(netout,
  function decode_yolov3 (line 64) | def decode_yolov3(netout,
  function decode_classifier (line 106) | def decode_classifier(netout, top_k=3):
  function decode_segnet (line 112) | def decode_segnet(netout, labels, class_colors):
  function get_legends (line 126) | def get_legends(class_names, colors):
  function overlay_seg_image (line 138) | def overlay_seg_image(inp_img, seg_img):
  function concat_lenends (line 146) | def concat_lenends(seg_img, legend_img):
  function _sigmoid (line 152) | def _sigmoid(x):
  function _softmax (line 155) | def _softmax(x, axis=-1, t=-100.):
  function resize_with_aspect_ratio (line 162) | def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
  function create_video_writer (line 190) | def create_video_writer(video, video_path, output_name):
  function init_video_file_capture (line 217) | def init_video_file_capture(video_path, output_name):
  function draw_bounding_boxes (line 240) | def draw_bounding_boxes(frame, detections, labels=None, processing_funct...
  function draw_classification (line 301) | def draw_classification(frame, classifications, labels):
  function get_source_encoding_int (line 308) | def get_source_encoding_int(video_capture):
  class BoundBox (line 311) | class BoundBox:
    method __init__ (line 312) | def __init__(self, x, y, w, h, c = None, classes = None):
    method get_label (line 321) | def get_label(self):
    method get_score (line 324) | def get_score(self):
    method iou (line 327) | def iou(self, bound_box):
    method as_centroid (line 332) | def as_centroid(self):
  function boxes_to_array (line 336) | def boxes_to_array(bound_boxes):
  function nms_boxes (line 352) | def nms_boxes(boxes, n_classes, nms_threshold=0.3, obj_threshold=0.3):
  function centroid_box_iou (line 380) | def centroid_box_iou(box1, box2):
  function to_minmax (line 408) | def to_minmax(centroid_boxes):

FILE: example_scripts/tensorflow_lite/segnet/segnet_file.py
  function load_labels (line 14) | def load_labels(path):
  class NetworkExecutor (line 18) | class NetworkExecutor(object):
    method __init__ (line 20) | def __init__(self, model_file):
    method get_output_tensors (line 27) | def get_output_tensors(self):
    method run (line 39) | def run(self, image):
  function main (line 47) | def main(args):

FILE: example_scripts/tensorflow_lite/segnet/segnet_stream.py
  function load_labels (line 17) | def load_labels(path):
  class NetworkExecutor (line 21) | class NetworkExecutor(object):
    method __init__ (line 23) | def __init__(self, model_file):
    method get_output_tensors (line 30) | def get_output_tensors(self):
    method run (line 42) | def run(self, image):
  class Segnet (line 50) | class Segnet(NetworkExecutor):
    method __init__ (line 52) | def __init__(self, label_file, model_file, overlay):
    method segment (line 64) | def segment(self, frame):
  function index (line 82) | def index():
  function gen (line 85) | def gen(camera):
  function video_feed (line 92) | def video_feed():

FILE: tests_training_and_inference.py
  function configs (line 9) | def configs(network_type):