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import numpy as np
from .boxes_utils import assert_and_normalize_shape
def translate_boxes(boxes, x_shift=0, y_shift=0, copy=True):
"""translate boxes coordinates in x and y dimensions.
Args:
boxes: (N, 4+K)
x_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift in x dimension
y_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift in y dimension
copy: bool
References:
`datasets.pipelines.RandomCrop` in mmdetection
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
x_shift = np.asarray(x_shift, np.float32)
y_shift = np.asarray(y_shift, np.float32)
x_shift = assert_and_normalize_shape(x_shift, boxes.shape[0])
y_shift = assert_and_normalize_shape(y_shift, boxes.shape[0])
boxes[:, 0] += x_shift
boxes[:, 1] += y_shift
boxes[:, 2] += x_shift
boxes[:, 3] += y_shift
return boxes
def adjust_boxes(boxes, x_min_shift, y_min_shift, x_max_shift, y_max_shift, copy=True):
"""
Args:
boxes: (N, 4+K)
x_min_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift (x_min, y_min) in x dimension
y_min_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift (x_min, y_min) in y dimension
x_max_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift (x_max, y_max) in x dimension
y_max_shift: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
shift (x_max, y_max) in y dimension
copy: bool
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
x_min_shift = np.asarray(x_min_shift, np.float32)
y_min_shift = np.asarray(y_min_shift, np.float32)
x_max_shift = np.asarray(x_max_shift, np.float32)
y_max_shift = np.asarray(y_max_shift, np.float32)
x_min_shift = assert_and_normalize_shape(x_min_shift, boxes.shape[0])
y_min_shift = assert_and_normalize_shape(y_min_shift, boxes.shape[0])
x_max_shift = assert_and_normalize_shape(x_max_shift, boxes.shape[0])
y_max_shift = assert_and_normalize_shape(y_max_shift, boxes.shape[0])
boxes[:, 0] += x_min_shift
boxes[:, 1] += y_min_shift
boxes[:, 2] += x_max_shift
boxes[:, 3] += y_max_shift
return boxes
def inflate_or_deflate_boxes(boxes, width_delta=0, height_delta=0, copy=True):
"""
Args:
boxes: (N, 4+K)
width_delta: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
height_delta: array-like whose shape is (), (1,), (N,), (1, 1) or (N, 1)
copy: bool
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
width_delta = np.asarray(width_delta, np.float32)
height_delta = np.asarray(height_delta, np.float32)
width_delta = assert_and_normalize_shape(width_delta, boxes.shape[0])
height_delta = assert_and_normalize_shape(height_delta, boxes.shape[0])
half_width_delta = width_delta * 0.5
half_height_delta = height_delta * 0.5
boxes[:, 0] -= half_width_delta
boxes[:, 1] -= half_height_delta
boxes[:, 2] += half_width_delta
boxes[:, 3] += half_height_delta
return boxes
def inflate_boxes_to_square(boxes, copy=True):
"""Inflate boxes to square
Args:
boxes: (N, 4+K)
copy: bool
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
max_side_lengths = np.maximum(widths, heights)
width_deltas = np.subtract(max_side_lengths, widths, widths)
height_deltas = np.subtract(max_side_lengths, heights, heights)
width_deltas *= 0.5
height_deltas *= 0.5
boxes[:, 0] -= width_deltas
boxes[:, 1] -= height_deltas
boxes[:, 2] += width_deltas
boxes[:, 3] += height_deltas
return boxes
def deflate_boxes_to_square(boxes, copy=True):
"""Deflate boxes to square
Args:
boxes: (N, 4+K)
copy: bool
"""
boxes = np.array(boxes, dtype=np.float32, copy=copy)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
min_side_lengths = np.minimum(widths, heights)
width_deltas = np.subtract(min_side_lengths, widths, widths)
height_deltas = np.subtract(min_side_lengths, heights, heights)
width_deltas *= 0.5
height_deltas *= 0.5
boxes[:, 0] -= width_deltas
boxes[:, 1] -= height_deltas
boxes[:, 2] += width_deltas
boxes[:, 3] += height_deltas
return boxes
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