easy_vision.python.model.text_krcnn

easy_vision.python.model.text_krcnn.text_keypoint_head

class easy_vision.python.model.text_krcnn.text_keypoint_head.TextKeypointHead(feature_dict, head_config, label_dict=None, fpn_config=None, mode='predict')

Bases: easy_vision.python.model.cv_head.CVHead

Text keypoint prediction head for text end2end

__init__(feature_dict, head_config, label_dict=None, fpn_config=None, mode='predict')

Parameters:

• feature_dict – a dict of feature tensors
• head_config – protos.text_head_pb2.TextKeypointHead
• label_dict – a dict of labels tensors
• is_training – train or not(eval/predict)

build_loss_graph()

build_postprocess_graph()

convert predicted_key_points to image shape

build_predict_graph()

correct_texts_keypoints_order(texts_keypoints, texts_direction)

correct texts keypoints order according to texts directions

easy_vision.python.model.text_krcnn.text_krcnn_model

class easy_vision.python.model.text_krcnn.text_krcnn_model.TextKRCNNModel(model_config, feature_dict, label_dict=None, mode='predict', categories=None)

Bases: easy_vision.python.model.cv_model.CVModel

__init__(model_config, feature_dict, label_dict=None, mode='predict', categories=None)

x.__init__(…) initializes x; see help(type(x)) for signature

build_loss_graph()

build_metric_graph(eval_config)

add metrics ops to graph :param eval_config: protobufer object, see python/protos/eval.proto.

Returns:a dict of metric_op, each metric_op is a tuple of (update_op, value_op) Return type:metric_dict

build_predict_graph()

classmethod create_class(name)

get_outputs()

return a list of output key, which can be used to index output tensor result in prediction_dict