!_TAG_FILE_FORMAT	2	/extended format; --format=1 will not append ;" to lines/
!_TAG_FILE_SORTED	1	/0=unsorted, 1=sorted, 2=foldcase/
!_TAG_PROGRAM_AUTHOR	Darren Hiebert	/dhiebert@users.sourceforge.net/
!_TAG_PROGRAM_NAME	Exuberant Ctags	//
!_TAG_PROGRAM_URL	http://ctags.sourceforge.net	/official site/
!_TAG_PROGRAM_VERSION	5.9~svn20110310	//
C	measure_map.py	/^	C = pickle.load(f_in)$/;"	v
C	test_frcnn.py	/^	C = pickle.load(f_in)$/;"	v
C	train_frcnn.py	/^C = config.Config()$/;"	v
Config	keras_frcnn/config.py	/^class Config:$/;"	c
FixedBatchNormalization	keras_frcnn/FixedBatchNormalization.py	/^class FixedBatchNormalization(Layer):$/;"	c
P	measure_map.py	/^P = {}$/;"	v
P_rpn	train_frcnn.py	/^			P_rpn = model_rpn.predict_on_batch(X)$/;"	v
R	measure_map.py	/^	R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7)$/;"	v
R	test_frcnn.py	/^	R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7)$/;"	v
R	train_frcnn.py	/^			R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)$/;"	v
ROIs	measure_map.py	/^			ROIs = ROIs_padded$/;"	v
ROIs	measure_map.py	/^		ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)$/;"	v
ROIs	test_frcnn.py	/^			ROIs = ROIs_padded$/;"	v
ROIs	test_frcnn.py	/^		ROIs = np.expand_dims(R[C.num_rois*jk:C.num_rois*(jk+1), :], axis=0)$/;"	v
ROIs_padded	measure_map.py	/^			ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)$/;"	v
ROIs_padded	test_frcnn.py	/^			ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)$/;"	v
RoiPoolingConv	keras_frcnn/RoiPoolingConv.py	/^class RoiPoolingConv(Layer):$/;"	c
SampleSelector	keras_frcnn/data_generators.py	/^class SampleSelector:$/;"	c
T	measure_map.py	/^T = {}$/;"	v
X	measure_map.py	/^		X = np.transpose(X, (0, 2, 3, 1))$/;"	v
X	test_frcnn.py	/^		X = np.transpose(X, (0, 2, 3, 1))$/;"	v
__init__	keras_frcnn/FixedBatchNormalization.py	/^    def __init__(self, epsilon=1e-3, axis=-1,$/;"	m	class:FixedBatchNormalization
__init__	keras_frcnn/RoiPoolingConv.py	/^    def __init__(self, pool_size, num_rois, **kwargs):$/;"	m	class:RoiPoolingConv
__init__	keras_frcnn/config.py	/^	def __init__(self):$/;"	m	class:Config
__init__	keras_frcnn/data_generators.py	/^	def __init__(self, class_count):$/;"	m	class:SampleSelector
__init__	keras_frcnn/data_generators.py	/^	def __init__(self, it):$/;"	m	class:threadsafe_iter
__iter__	keras_frcnn/data_generators.py	/^	def __iter__(self):$/;"	m	class:threadsafe_iter	file:
all_aps	measure_map.py	/^	all_aps = []$/;"	v
all_dets	measure_map.py	/^	all_dets = []$/;"	v
all_dets	test_frcnn.py	/^	all_dets = []$/;"	v
all_imgs	test_frcnn.py	/^all_imgs = []$/;"	v
ap	measure_map.py	/^		ap = average_precision_score(T[key], P[key])$/;"	v
apply_regr	keras_frcnn/roi_helpers.py	/^def apply_regr(x, y, w, h, tx, ty, tw, th):$/;"	f
apply_regr_np	keras_frcnn/roi_helpers.py	/^def apply_regr_np(X, T):$/;"	f
augment	keras_frcnn/data_augment.py	/^def augment(img_data, config, augment=True):$/;"	f
bbox	measure_map.py	/^		bbox = np.array(bboxes[key])$/;"	v
bbox	test_frcnn.py	/^		bbox = np.array(bboxes[key])$/;"	v
bbox_threshold	test_frcnn.py	/^bbox_threshold = 0.8$/;"	v
bboxes	measure_map.py	/^	bboxes = {}$/;"	v
bboxes	test_frcnn.py	/^	bboxes = {}$/;"	v
best_loss	train_frcnn.py	/^					best_loss = curr_loss$/;"	v
best_loss	train_frcnn.py	/^best_loss = np.Inf$/;"	v
build	keras_frcnn/FixedBatchNormalization.py	/^    def build(self, input_shape):$/;"	m	class:FixedBatchNormalization
build	keras_frcnn/RoiPoolingConv.py	/^    def build(self, input_shape):$/;"	m	class:RoiPoolingConv
calc_iou	keras_frcnn/roi_helpers.py	/^def calc_iou(R, img_data, C, class_mapping):$/;"	f
calc_rpn	keras_frcnn/data_generators.py	/^def calc_rpn(C, img_data, width, height, resized_width, resized_height, img_length_calc_function):$/;"	f
call	keras_frcnn/FixedBatchNormalization.py	/^    def call(self, x, mask=None):$/;"	m	class:FixedBatchNormalization
call	keras_frcnn/RoiPoolingConv.py	/^    def call(self, x, mask=None):$/;"	m	class:RoiPoolingConv
class_acc	train_frcnn.py	/^				class_acc = np.mean(losses[:, 4])$/;"	v
class_loss_cls	keras_frcnn/losses.py	/^def class_loss_cls(y_true, y_pred):$/;"	f
class_loss_regr	keras_frcnn/losses.py	/^def class_loss_regr(num_classes):$/;"	f
class_loss_regr_fixed_num	keras_frcnn/losses.py	/^	def class_loss_regr_fixed_num(y_true, y_pred):$/;"	f	function:class_loss_regr
class_mapping	measure_map.py	/^class_mapping = C.class_mapping$/;"	v
class_mapping	measure_map.py	/^class_mapping = {v: k for k, v in class_mapping.iteritems()}$/;"	v
class_mapping	test_frcnn.py	/^class_mapping = C.class_mapping$/;"	v
class_mapping	test_frcnn.py	/^class_mapping = {v: k for k, v in class_mapping.items()}$/;"	v
class_mapping_inv	train_frcnn.py	/^class_mapping_inv = {v: k for k, v in class_mapping.items()}$/;"	v
class_to_color	measure_map.py	/^class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}$/;"	v
class_to_color	test_frcnn.py	/^class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}$/;"	v
classes	test_frcnn.py	/^classes = {}$/;"	v
classifier	keras_frcnn/resnet.py	/^def classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False):$/;"	f
classifier	keras_frcnn/vgg.py	/^def classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False):$/;"	f
classifier	measure_map.py	/^classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True)$/;"	v
classifier	test_frcnn.py	/^classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True)$/;"	v
classifier	train_frcnn.py	/^classifier = nn.classifier(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True)$/;"	v
classifier_layers	keras_frcnn/resnet.py	/^def classifier_layers(x, input_shape, trainable=False):$/;"	f
cls_name	measure_map.py	/^			cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]$/;"	v
cls_name	test_frcnn.py	/^			cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]$/;"	v
cls_num	measure_map.py	/^			cls_num = np.argmax(P_cls[0, ii, :])$/;"	v
cls_num	test_frcnn.py	/^			cls_num = np.argmax(P_cls[0, ii, :])$/;"	v
compute_output_shape	keras_frcnn/RoiPoolingConv.py	/^    def compute_output_shape(self, input_shape):$/;"	m	class:RoiPoolingConv
config_output_filename	measure_map.py	/^config_output_filename = options.config_filename$/;"	v
config_output_filename	test_frcnn.py	/^config_output_filename = options.config_filename$/;"	v
config_output_filename	train_frcnn.py	/^config_output_filename = options.config_filename$/;"	v
conv_block	keras_frcnn/resnet.py	/^def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):$/;"	f
conv_block_td	keras_frcnn/resnet.py	/^def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True):$/;"	f
curr_loss	train_frcnn.py	/^				curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr$/;"	v
curr_shape	measure_map.py	/^			curr_shape = ROIs.shape$/;"	v
curr_shape	test_frcnn.py	/^			curr_shape = ROIs.shape$/;"	v
data_gen_train	train_frcnn.py	/^data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train')$/;"	v
data_gen_val	train_frcnn.py	/^data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length,K.image_dim_ordering(), mode='val')$/;"	v
default	measure_map.py	/^				default="config.pickle")$/;"	v
default	measure_map.py	/^				default="pascal_voc"),$/;"	v
default	test_frcnn.py	/^				default="config.pickle")$/;"	v
default	train_frcnn.py	/^				default="config.pickle")$/;"	v
default	train_frcnn.py	/^				default="pascal_voc")$/;"	v
det	measure_map.py	/^			det = {'x1': x1, 'x2': x2, 'y1': y1, 'y2': y2, 'class': key, 'prob': new_probs[jk]}$/;"	v
epoch_length	train_frcnn.py	/^epoch_length = 1000$/;"	v
epsilon	keras_frcnn/losses.py	/^epsilon = 1e-4$/;"	v
feature_map_input	measure_map.py	/^feature_map_input = Input(shape=input_shape_features)$/;"	v
feature_map_input	test_frcnn.py	/^feature_map_input = Input(shape=input_shape_features)$/;"	v
filepath	measure_map.py	/^	filepath = img_data['filepath']$/;"	v
filepath	test_frcnn.py	/^	filepath = os.path.join(img_path,img_name)$/;"	v
format_img	measure_map.py	/^def format_img(img, C):$/;"	f
format_img	test_frcnn.py	/^def format_img(img, C):$/;"	f
format_img_channels	test_frcnn.py	/^def format_img_channels(img, C):$/;"	f
format_img_size	test_frcnn.py	/^def format_img_size(img, C):$/;"	f
g	keras_frcnn/data_generators.py	/^	def g(*a, **kw):$/;"	f	function:threadsafe_generator
get_anchor_gt	keras_frcnn/data_generators.py	/^def get_anchor_gt(all_img_data, class_count, C, img_length_calc_function, backend, mode='train'):$/;"	f
get_config	keras_frcnn/FixedBatchNormalization.py	/^    def get_config(self):$/;"	m	class:FixedBatchNormalization
get_config	keras_frcnn/RoiPoolingConv.py	/^    def get_config(self):$/;"	m	class:RoiPoolingConv
get_data	keras_frcnn/pascal_voc_parser.py	/^def get_data(input_path):$/;"	f
get_data	keras_frcnn/simple_parser.py	/^def get_data(input_path):$/;"	f
get_img_output_length	keras_frcnn/resnet.py	/^def get_img_output_length(width, height):$/;"	f
get_img_output_length	keras_frcnn/vgg.py	/^def get_img_output_length(width, height):$/;"	f
get_map	measure_map.py	/^def get_map(pred, gt, f):$/;"	f
get_new_img_size	keras_frcnn/data_generators.py	/^def get_new_img_size(width, height, img_min_side=600):$/;"	f
get_output_length	keras_frcnn/resnet.py	/^    def get_output_length(input_length):$/;"	f	function:get_img_output_length
get_output_length	keras_frcnn/vgg.py	/^    def get_output_length(input_length):$/;"	f	function:get_img_output_length
get_real_coordinates	test_frcnn.py	/^def get_real_coordinates(ratio, x1, y1, x2, y2):$/;"	f
get_weight_path	keras_frcnn/resnet.py	/^def get_weight_path():$/;"	f
get_weight_path	keras_frcnn/vgg.py	/^def get_weight_path():$/;"	f
identity_block	keras_frcnn/resnet.py	/^def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):$/;"	f
identity_block_td	keras_frcnn/resnet.py	/^def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainable=True):$/;"	f
img	measure_map.py	/^	img = cv2.imread(filepath)$/;"	v
img	test_frcnn.py	/^	img = cv2.imread(filepath)$/;"	v
img_input	measure_map.py	/^img_input = Input(shape=input_shape_img)$/;"	v
img_input	test_frcnn.py	/^img_input = Input(shape=input_shape_img)$/;"	v
img_input	train_frcnn.py	/^img_input = Input(shape=input_shape_img)$/;"	v
img_path	measure_map.py	/^img_path = options.test_path$/;"	v
img_path	test_frcnn.py	/^img_path = options.test_path$/;"	v
input_shape_features	measure_map.py	/^	input_shape_features = (1024, None, None)$/;"	v
input_shape_features	measure_map.py	/^	input_shape_features = (None, None, 1024)$/;"	v
input_shape_features	test_frcnn.py	/^	input_shape_features = (None, None, num_features)$/;"	v
input_shape_features	test_frcnn.py	/^	input_shape_features = (num_features, None, None)$/;"	v
input_shape_img	measure_map.py	/^	input_shape_img = (3, None, None)$/;"	v
input_shape_img	measure_map.py	/^	input_shape_img = (None, None, 3)$/;"	v
input_shape_img	test_frcnn.py	/^	input_shape_img = (3, None, None)$/;"	v
input_shape_img	test_frcnn.py	/^	input_shape_img = (None, None, 3)$/;"	v
input_shape_img	train_frcnn.py	/^	input_shape_img = (3, None, None)$/;"	v
input_shape_img	train_frcnn.py	/^	input_shape_img = (None, None, 3)$/;"	v
intersection	keras_frcnn/data_generators.py	/^def intersection(ai, bi):$/;"	f
inv_map	train_frcnn.py	/^inv_map = {v: k for k, v in class_mapping.items()}$/;"	v
iou	keras_frcnn/data_generators.py	/^def iou(a, b):$/;"	f
iter_num	train_frcnn.py	/^				iter_num = 0$/;"	v
iter_num	train_frcnn.py	/^iter_num = 0$/;"	v
lambda_cls_class	keras_frcnn/losses.py	/^lambda_cls_class = 1.0$/;"	v
lambda_cls_regr	keras_frcnn/losses.py	/^lambda_cls_regr = 1.0$/;"	v
lambda_rpn_class	keras_frcnn/losses.py	/^lambda_rpn_class = 1.0$/;"	v
lambda_rpn_regr	keras_frcnn/losses.py	/^lambda_rpn_regr = 1.0$/;"	v
loss_class	train_frcnn.py	/^			loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])$/;"	v
loss_class_cls	train_frcnn.py	/^				loss_class_cls = np.mean(losses[:, 2])$/;"	v
loss_class_regr	train_frcnn.py	/^				loss_class_regr = np.mean(losses[:, 3])$/;"	v
loss_rpn	train_frcnn.py	/^			loss_rpn = model_rpn.train_on_batch(X, Y)$/;"	v
loss_rpn_cls	train_frcnn.py	/^				loss_rpn_cls = np.mean(losses[:, 0])$/;"	v
loss_rpn_regr	train_frcnn.py	/^				loss_rpn_regr = np.mean(losses[:, 1])$/;"	v
losses	train_frcnn.py	/^losses = np.zeros((epoch_length, 5))$/;"	v
mean_overlapping_bboxes	train_frcnn.py	/^				mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) \/ len(rpn_accuracy_for_epoch)$/;"	v
mean_overlapping_bboxes	train_frcnn.py	/^				mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))\/len(rpn_accuracy_rpn_monitor)$/;"	v
model_all	train_frcnn.py	/^model_all = Model([img_input, roi_input], rpn[:2] + classifier)$/;"	v
model_classifier	measure_map.py	/^model_classifier = Model([feature_map_input, roi_input], classifier)$/;"	v
model_classifier	test_frcnn.py	/^model_classifier = Model([feature_map_input, roi_input], classifier)$/;"	v
model_classifier	train_frcnn.py	/^model_classifier = Model([img_input, roi_input], classifier)$/;"	v
model_classifier_only	measure_map.py	/^model_classifier_only = Model([feature_map_input, roi_input], classifier)$/;"	v
model_classifier_only	test_frcnn.py	/^model_classifier_only = Model([feature_map_input, roi_input], classifier)$/;"	v
model_rpn	measure_map.py	/^model_rpn = Model(img_input, rpn_layers)$/;"	v
model_rpn	test_frcnn.py	/^model_rpn = Model(img_input, rpn_layers)$/;"	v
model_rpn	train_frcnn.py	/^model_rpn = Model(img_input, rpn[:2])$/;"	v
neg_samples	train_frcnn.py	/^				neg_samples = []$/;"	v
neg_samples	train_frcnn.py	/^				neg_samples = neg_samples[0]$/;"	v
neg_samples	train_frcnn.py	/^			neg_samples = np.where(Y1[0, :, -1] == 1)$/;"	v
next	keras_frcnn/data_generators.py	/^	def next(self):$/;"	m	class:threadsafe_iter
nn_base	keras_frcnn/resnet.py	/^def nn_base(input_tensor=None, trainable=False):$/;"	f
nn_base	keras_frcnn/vgg.py	/^def nn_base(input_tensor=None, trainable=False):$/;"	f
non_max_suppression_fast	keras_frcnn/roi_helpers.py	/^def non_max_suppression_fast(boxes, probs, overlap_thresh=0.9, max_boxes=300):$/;"	f
num_anchors	measure_map.py	/^num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)$/;"	v
num_anchors	test_frcnn.py	/^num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)$/;"	v
num_anchors	train_frcnn.py	/^num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)$/;"	v
num_epochs	train_frcnn.py	/^num_epochs = int(options.num_epochs)$/;"	v
num_features	test_frcnn.py	/^	num_features = 1024$/;"	v
num_features	test_frcnn.py	/^	num_features = 512$/;"	v
num_imgs	train_frcnn.py	/^num_imgs = len(all_imgs)$/;"	v
optimizer	train_frcnn.py	/^optimizer = Adam(lr=1e-5)$/;"	v
optimizer_classifier	train_frcnn.py	/^optimizer_classifier = Adam(lr=1e-5)$/;"	v
parser	measure_map.py	/^parser = OptionParser()$/;"	v
parser	test_frcnn.py	/^parser = OptionParser()$/;"	v
parser	train_frcnn.py	/^parser = OptionParser()$/;"	v
pos_samples	train_frcnn.py	/^				pos_samples = []$/;"	v
pos_samples	train_frcnn.py	/^				pos_samples = pos_samples[0]$/;"	v
pos_samples	train_frcnn.py	/^			pos_samples = np.where(Y1[0, :, -1] == 0)$/;"	v
probs	measure_map.py	/^	probs = {}$/;"	v
probs	test_frcnn.py	/^	probs = {}$/;"	v
progbar	train_frcnn.py	/^	progbar = generic_utils.Progbar(epoch_length)$/;"	v
roi_input	measure_map.py	/^roi_input = Input(shape=(C.num_rois, 4))$/;"	v
roi_input	test_frcnn.py	/^roi_input = Input(shape=(C.num_rois, 4))$/;"	v
roi_input	train_frcnn.py	/^roi_input = Input(shape=(None, 4))$/;"	v
rpn	keras_frcnn/resnet.py	/^def rpn(base_layers,num_anchors):$/;"	f
rpn	keras_frcnn/vgg.py	/^def rpn(base_layers, num_anchors):$/;"	f
rpn	train_frcnn.py	/^rpn = nn.rpn(shared_layers, num_anchors)$/;"	v
rpn_accuracy_for_epoch	train_frcnn.py	/^				rpn_accuracy_for_epoch = []$/;"	v
rpn_accuracy_for_epoch	train_frcnn.py	/^rpn_accuracy_for_epoch = []$/;"	v
rpn_accuracy_rpn_monitor	train_frcnn.py	/^				rpn_accuracy_rpn_monitor = []$/;"	v
rpn_accuracy_rpn_monitor	train_frcnn.py	/^rpn_accuracy_rpn_monitor = []$/;"	v
rpn_layers	measure_map.py	/^rpn_layers = nn.rpn(shared_layers, num_anchors)$/;"	v
rpn_layers	test_frcnn.py	/^rpn_layers = nn.rpn(shared_layers, num_anchors)$/;"	v
rpn_loss_cls	keras_frcnn/losses.py	/^def rpn_loss_cls(num_anchors):$/;"	f
rpn_loss_cls_fixed_num	keras_frcnn/losses.py	/^	def rpn_loss_cls_fixed_num(y_true, y_pred):$/;"	f	function:rpn_loss_cls
rpn_loss_regr	keras_frcnn/losses.py	/^def rpn_loss_regr(num_anchors):$/;"	f
rpn_loss_regr_fixed_num	keras_frcnn/losses.py	/^	def rpn_loss_regr_fixed_num(y_true, y_pred):$/;"	f	function:rpn_loss_regr
rpn_to_roi	keras_frcnn/roi_helpers.py	/^def rpn_to_roi(rpn_layer, regr_layer, C, dim_ordering, use_regr=True, max_boxes=300,overlap_thresh=0.9):$/;"	f
sel_samples	train_frcnn.py	/^					sel_samples = random.choice(neg_samples)$/;"	v
sel_samples	train_frcnn.py	/^					sel_samples = random.choice(pos_samples)$/;"	v
sel_samples	train_frcnn.py	/^				sel_samples = selected_pos_samples + selected_neg_samples$/;"	v
selected_neg_samples	train_frcnn.py	/^					selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()$/;"	v
selected_neg_samples	train_frcnn.py	/^					selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()$/;"	v
selected_neg_samples	train_frcnn.py	/^				selected_neg_samples = neg_samples.tolist()$/;"	v
selected_pos_samples	train_frcnn.py	/^					selected_pos_samples = np.random.choice(pos_samples, C.num_rois\/\/2, replace=False).tolist()$/;"	v
selected_pos_samples	train_frcnn.py	/^					selected_pos_samples = pos_samples.tolist()$/;"	v
selected_pos_samples	train_frcnn.py	/^				selected_pos_samples = pos_samples.tolist()$/;"	v
shared_layers	measure_map.py	/^shared_layers = nn.nn_base(img_input, trainable=True)$/;"	v
shared_layers	test_frcnn.py	/^shared_layers = nn.nn_base(img_input, trainable=True)$/;"	v
shared_layers	train_frcnn.py	/^shared_layers = nn.nn_base(img_input, trainable=True)$/;"	v
skip_sample_for_balanced_class	keras_frcnn/data_generators.py	/^	def skip_sample_for_balanced_class(self, img_data):$/;"	m	class:SampleSelector
st	measure_map.py	/^	st = time.time()$/;"	v
st	test_frcnn.py	/^	st = time.time()$/;"	v
start_time	train_frcnn.py	/^				start_time = time.time()$/;"	v
start_time	train_frcnn.py	/^start_time = time.time()$/;"	v
target_shape	measure_map.py	/^			target_shape = (curr_shape[0], C.num_rois, curr_shape[2])$/;"	v
target_shape	test_frcnn.py	/^			target_shape = (curr_shape[0],C.num_rois,curr_shape[2])$/;"	v
textLabel	test_frcnn.py	/^			textLabel = '{}: {}'.format(key,int(100*new_probs[jk]))$/;"	v
textOrg	test_frcnn.py	/^			textOrg = (real_x1, real_y1-0)$/;"	v
threadsafe_generator	keras_frcnn/data_generators.py	/^def threadsafe_generator(f):$/;"	f
threadsafe_iter	keras_frcnn/data_generators.py	/^class threadsafe_iter:$/;"	c
union	keras_frcnn/data_generators.py	/^def union(au, bu, area_intersection):$/;"	f
vis	train_frcnn.py	/^vis = True$/;"	v
visualise	test_frcnn.py	/^visualise = True$/;"	v
