tools/accuracy_checker/accuracy_checker/postprocessor/clip_points.py

   1 """"
   2 Copyright (c) 2019 Intel Corporation
   3
   4 Licensed under the Apache License, Version 2.0 (the "License");
   5 you may not use this file except in compliance with the License.
   6 You may obtain a copy of the License at
   7
   8       http://www.apache.org/licenses/LICENSE-2.0
   9
  10 Unless required by applicable law or agreed to in writing, software
  11 distributed under the License is distributed on an "AS IS" BASIS,
  12 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 See the License for the specific language governing permissions and
  14 limitations under the License.
  15 """
  16
  17 import numpy as np
  18 from ..config import BoolField, NumberField
  19 from ..representation import TextDetectionAnnotation, TextDetectionPrediction
  20 from ..utils import get_size_from_config
  21 from .postprocessor import PostprocessorWithSpecificTargets, PostprocessorWithTargetsConfigValidator
  22
  23
  24 class ClipPointsConfigValidator(PostprocessorWithTargetsConfigValidator):
  25     dst_width = NumberField(floats=False, optional=True, min_value=1)
  26     dst_height = NumberField(floats=False, optional=True, min_value=1)
  27     size = NumberField(floats=False, optional=True, min_value=1)
  28     points_normalized = BoolField(optional=True)
  29
  30
  31 class ClipPoints(PostprocessorWithSpecificTargets):
  32     __provider__ = 'clip_points'
  33
  34     annotation_types = (TextDetectionAnnotation, )
  35     prediction_types = (TextDetectionPrediction, )
  36
  37     def validate_config(self):
  38         clip_points_config_validator = ClipPointsConfigValidator(
  39             self.__provider__, on_extra_argument=ClipPointsConfigValidator.ERROR_ON_EXTRA_ARGUMENT
  40         )
  41         clip_points_config_validator.validate(self.config)
  42
  43     def configure(self):
  44         self.dst_height, self.dst_width = get_size_from_config(self.config, allow_none=True)
  45         self.points_normalized = self.config.get('points_normalized', False)
  46
  47     def process_image(self, annotation, prediction):
  48         target_width = self.dst_width or self.image_size[1] - 1
  49         target_height = self.dst_height or self.image_size[0] - 1
  50
  51         max_width = target_width if not self.points_normalized else 1
  52         max_height = target_height if not self.points_normalized else 1
  53         for target in annotation:
  54             points = []
  55             for polygon in target.points:
  56                 polygon[:, 0] = np.clip(polygon[:, 0], 0, max_width)
  57                 polygon[:, 1] = np.clip(polygon[:, 1], 0, max_height)
  58                 points.append(polygon)
  59             target.points = points
  60         for target in prediction:
  61             points = []
  62             for polygon in target.points:
  63                 polygon[:, 0] = np.clip(polygon[:, 0], 0, max_width)
  64                 polygon[:, 1] = np.clip(polygon[:, 1], 0, max_height)
  65                 points.append(polygon)
  66             target.points = points
  67
  68         return annotation, prediction