easy_vision.python.model.text_rectification

easy_vision.python.model.text_rectification.text_recification_head

class easy_vision.python.model.text_rectification.text_recification_head.TextRectificationHead(feature_dict, head_config, label_dict=None, mode='predict')

Bases: easy_vision.python.model.cv_head.CVHead

Text keypoint prediction head for text rectification

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

Parameters:

• feature_dict – a dict of feature tensors
• head_config – protos.text_head_pb2.TextRectificationHead
• 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()

decode_keypoints(text_keypoints_encoding)

decode keypoint encoding to coords on original image

easy_vision.python.model.text_rectification.text_rectification_model

class easy_vision.python.model.text_rectification.text_rectification_model.TextRectificationModel(model_config, feature_dict, label_dict=None, mode='predict')

Bases: easy_vision.python.model.cv_model.CVModel

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

Init text rectification model

Parameters:

• model_config – a protobuf config object
• feature_dict – a dict of tensor, used as features
• label_dict – a dict of tensor, used as labels
• mode – indicats to build a model used in train, evaluate, predict

build_loss_graph()

build_metric_graph(eval_config)

Add metrics ops to graph

Parameters: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