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| """Numpy BoxList classes and functions."""
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|
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| import numpy as np
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| class BoxList:
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| """Box collection.
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|
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| BoxList represents a list of bounding boxes as numpy array, where each
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| bounding box is represented as a row of 4 numbers,
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| [y_min, x_min, y_max, x_max]. It is assumed that all bounding boxes within
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| a given list correspond to a single image.
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|
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| Optionally, users can add additional related fields (such as
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| objectness/classification scores).
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| """
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|
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| def __init__(self, data):
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| """Constructs box collection.
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|
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| Args:
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| data: a numpy array of shape [N, 4] representing box coordinates
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|
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| Raises:
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| ValueError: if bbox data is not a numpy array
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| ValueError: if invalid dimensions for bbox data
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| """
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| if not isinstance(data, np.ndarray):
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| raise ValueError('data must be a numpy array.')
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| if len(data.shape) != 2 or data.shape[1] != 4:
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| raise ValueError('Invalid dimensions for box data.')
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| if data.dtype != np.float32 and data.dtype != np.float64:
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| raise ValueError(
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| 'Invalid data type for box data: float is required.')
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| if not self._is_valid_boxes(data):
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| raise ValueError('Invalid box data. data must be a numpy array of '
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| 'N*[y_min, x_min, y_max, x_max]')
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| self.data = {'boxes': data}
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|
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| def num_boxes(self):
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| """Return number of boxes held in collections."""
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| return self.data['boxes'].shape[0]
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|
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| def get_extra_fields(self):
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| """Return all non-box fields."""
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| return [k for k in self.data if k != 'boxes']
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|
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| def has_field(self, field):
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| return field in self.data
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|
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| def add_field(self, field, field_data):
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| """Add data to a specified field.
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|
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| Args:
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| field: a string parameter used to specify a related field to be
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| accessed.
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| field_data: a numpy array of [N, ...] representing the data
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| associated with the field.
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| Raises:
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| ValueError: if the field is already exist or the dimension of the
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| field data does not matches the number of boxes.
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| """
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| if self.has_field(field):
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| raise ValueError('Field ' + field + 'already exists')
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| if len(field_data.shape) < 1 or field_data.shape[0] != self.num_boxes(
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| ):
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| raise ValueError('Invalid dimensions for field data')
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| self.data[field] = field_data
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|
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| def get(self):
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| """Convenience function for accesssing box coordinates.
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|
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| Returns:
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| a numpy array of shape [N, 4] representing box corners
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| """
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| return self.get_field('boxes')
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|
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| def get_field(self, field):
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| """Accesses data associated with the specified field in the box
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| collection.
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| Args:
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| field: a string parameter used to specify a related field to be
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| accessed.
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|
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| Returns:
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| a numpy 1-d array representing data of an associated field
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|
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| Raises:
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| ValueError: if invalid field
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| """
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| if not self.has_field(field):
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| raise ValueError(f'field {field} does not exist')
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| return self.data[field]
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|
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| def get_coordinates(self):
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| """Get corner coordinates of boxes.
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|
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| Returns:
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| a list of 4 1-d numpy arrays [y_min, x_min, y_max, x_max]
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| """
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| box_coordinates = self.get()
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| y_min = box_coordinates[:, 0]
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| x_min = box_coordinates[:, 1]
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| y_max = box_coordinates[:, 2]
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| x_max = box_coordinates[:, 3]
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| return [y_min, x_min, y_max, x_max]
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|
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| @staticmethod
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| def _is_valid_boxes(data):
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| """Check whether data fulfills the format of N*[ymin, xmin, ymax,
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| xmin].
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|
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| Args:
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| data: a numpy array of shape [N, 4] representing box coordinates
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|
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| Returns:
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| a boolean indicating whether all ymax of boxes are equal or greater
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| than ymin, and all xmax of boxes are equal or greater than xmin.
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| """
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| if len(data) != 0:
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| for v in data:
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| if v[0] > v[2] or v[1] > v[3]:
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| return False
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| return True
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|
|
