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util_bounding_box.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 20:29:55 2018
@author: yy
"""
from __future__ import division
import numpy as np
def convert_coordinates(tensor, start_index, conversion, border_pixels='half'):
if border_pixels == 'half':
d = 0
elif border_pixels == 'include':
d = 1
elif border_pixels == 'exclude':
d = -1
ind = start_index
tensor1 = np.copy(tensor).astype(np.float)
if conversion == 'minmax2centroids':
tensor1[..., ind] = (tensor[..., ind] + tensor[..., ind+1]) / 2.0 # cx
tensor1[..., ind+1] = (tensor[..., ind+2] + tensor[..., ind+3]) / 2.0 # cy
tensor1[..., ind+2] = tensor[..., ind+1] - tensor[..., ind] + d # w
tensor1[..., ind+3] = tensor[..., ind+3] - tensor[..., ind+2] + d # h
elif conversion == 'centroids2minmax':
tensor1[..., ind] = tensor[..., ind] - tensor[..., ind+2] / 2.0 # xmin
tensor1[..., ind+1] = tensor[..., ind] + tensor[..., ind+2] / 2.0 # xmax
tensor1[..., ind+2] = tensor[..., ind+1] - tensor[..., ind+3] / 2.0 # ymin
tensor1[..., ind+3] = tensor[..., ind+1] + tensor[..., ind+3] / 2.0 # ymax
elif conversion == 'corners2centroids':
tensor1[..., ind] = (tensor[..., ind] + tensor[..., ind+2]) / 2.0 # cx
tensor1[..., ind+1] = (tensor[..., ind+1] + tensor[..., ind+3]) / 2.0 # cy
tensor1[..., ind+2] = tensor[..., ind+2] - tensor[..., ind] + d # w
tensor1[..., ind+3] = tensor[..., ind+3] - tensor[..., ind+1] + d # h
elif conversion == 'centroids2corners':
tensor1[..., ind] = tensor[..., ind] - tensor[..., ind+2] / 2.0 # xmin
tensor1[..., ind+1] = tensor[..., ind+1] - tensor[..., ind+3] / 2.0 # ymin
tensor1[..., ind+2] = tensor[..., ind] + tensor[..., ind+2] / 2.0 # xmax
tensor1[..., ind+3] = tensor[..., ind+1] + tensor[..., ind+3] / 2.0 # ymax
elif (conversion == 'minmax2corners') or (conversion == 'corners2minmax'):
tensor1[..., ind+1] = tensor[..., ind+2]
tensor1[..., ind+2] = tensor[..., ind+1]
else:
raise ValueError("Unexpected conversion value. Supported values are 'minmax2centroids', 'centroids2minmax', 'corners2centroids', 'centroids2corners', 'minmax2corners', and 'corners2minmax'.")
return tensor1
def convert_coordinates2(tensor, start_index, conversion):
ind = start_index
tensor1 = np.copy(tensor).astype(np.float)
if conversion == 'minmax2centroids':
M = np.array([[0.5, 0. , -1., 0.],
[0.5, 0. , 1., 0.],
[0. , 0.5, 0., -1.],
[0. , 0.5, 0., 1.]])
tensor1[..., ind:ind+4] = np.dot(tensor1[..., ind:ind+4], M)
elif conversion == 'centroids2minmax':
M = np.array([[ 1. , 1. , 0. , 0. ],
[ 0. , 0. , 1. , 1. ],
[-0.5, 0.5, 0. , 0. ],
[ 0. , 0. , -0.5, 0.5]])
tensor1[..., ind:ind+4] = np.dot(tensor1[..., ind:ind+4], M)
else:
raise ValueError("Unexpected conversion value. Supported values are 'minmax2centroids' and 'centroids2minmax'.")
return tensor1
def intersection_area(boxes1, boxes2, coords='centroids', mode='outer_product', border_pixels='half'):
if boxes1.ndim > 2: raise ValueError("boxes1 must have rank either 1 or 2, but has rank {}.".format(boxes1.ndim))
if boxes2.ndim > 2: raise ValueError("boxes2 must have rank either 1 or 2, but has rank {}.".format(boxes2.ndim))
if boxes1.ndim == 1: boxes1 = np.expand_dims(boxes1, axis=0)
if boxes2.ndim == 1: boxes2 = np.expand_dims(boxes2, axis=0)
if not (boxes1.shape[1] == boxes2.shape[1] == 4): raise ValueError("All boxes must consist of 4 coordinates, but the boxes in `boxes1` and `boxes2` have {} and {} coordinates, respectively.".format(boxes1.shape[1], boxes2.shape[1]))
if not mode in {'outer_product', 'element-wise'}: raise ValueError("`mode` must be one of 'outer_product' and 'element-wise', but got '{}'.",format(mode))
if coords == 'centroids':
boxes1 = convert_coordinates(boxes1, start_index=0, conversion='centroids2corners')
boxes2 = convert_coordinates(boxes2, start_index=0, conversion='centroids2corners')
coords = 'corners'
elif not (coords in {'minmax', 'corners'}):
raise ValueError("Unexpected value for `coords`. Supported values are 'minmax', 'corners' and 'centroids'.")
m = boxes1.shape[0]
n = boxes2.shape[0]
if coords == 'corners':
xmin = 0
ymin = 1
xmax = 2
ymax = 3
elif coords == 'minmax':
xmin = 0
xmax = 1
ymin = 2
ymax = 3
if border_pixels == 'half':
d = 0
elif border_pixels == 'include':
d = 1
elif border_pixels == 'exclude':
d = -1
if mode == 'outer_product':
# 输出： (m,n,2)
min_xy = np.maximum(np.tile(np.expand_dims(boxes1[:,[xmin,ymin]], axis=1), reps=(1, n, 1)),
np.tile(np.expand_dims(boxes2[:,[xmin,ymin]], axis=0), reps=(m, 1, 1)))
# 输出： (m,n,2)
max_xy = np.minimum(np.tile(np.expand_dims(boxes1[:,[xmax,ymax]], axis=1), reps=(1, n, 1)),
np.tile(np.expand_dims(boxes2[:,[xmax,ymax]], axis=0), reps=(m, 1, 1)))
side_lengths = np.maximum(0, max_xy - min_xy + d)
return side_lengths[:,:,0] * side_lengths[:,:,1]
elif mode == 'element-wise':
min_xy = np.maximum(boxes1[:,[xmin,ymin]], boxes2[:,[xmin,ymin]])
max_xy = np.minimum(boxes1[:,[xmax,ymax]], boxes2[:,[xmax,ymax]])
side_lengths = np.maximum(0, max_xy - min_xy + d)
return side_lengths[:,0] * side_lengths[:,1]
def intersection_area_(boxes1, boxes2, coords='corners', mode='outer_product', border_pixels='half'):
m = boxes1.shape[0]
n = boxes2.shape[0]
if coords == 'corners':
xmin = 0
ymin = 1
xmax = 2
ymax = 3
elif coords == 'minmax':
xmin = 0
xmax = 1
ymin = 2
ymax = 3
if border_pixels == 'half':
d = 0
elif border_pixels == 'include':
d = 1
elif border_pixels == 'exclude':
d = -1
if mode == 'outer_product':
# 输出： (m,n,2)
min_xy = np.maximum(np.tile(np.expand_dims(boxes1[:,[xmin,ymin]], axis=1), reps=(1, n, 1)),
np.tile(np.expand_dims(boxes2[:,[xmin,ymin]], axis=0), reps=(m, 1, 1)))
# 输出： (m,n,2)
max_xy = np.minimum(np.tile(np.expand_dims(boxes1[:,[xmax,ymax]], axis=1), reps=(1, n, 1)),
np.tile(np.expand_dims(boxes2[:,[xmax,ymax]], axis=0), reps=(m, 1, 1)))
side_lengths = np.maximum(0, max_xy - min_xy + d)
return side_lengths[:,:,0] * side_lengths[:,:,1]
elif mode == 'element-wise':
min_xy = np.maximum(boxes1[:,[xmin,ymin]], boxes2[:,[xmin,ymin]])
max_xy = np.minimum(boxes1[:,[xmax,ymax]], boxes2[:,[xmax,ymax]])
side_lengths = np.maximum(0, max_xy - min_xy + d)
return side_lengths[:,0] * side_lengths[:,1]
def iou(boxes1, boxes2, coords='centroids', mode='outer_product', border_pixels='half'):
if boxes1.ndim > 2: raise ValueError("boxes1 must have rank either 1 or 2, but has rank {}.".format(boxes1.ndim))
if boxes2.ndim > 2: raise ValueError("boxes2 must have rank either 1 or 2, but has rank {}.".format(boxes2.ndim))
if boxes1.ndim == 1: boxes1 = np.expand_dims(boxes1, axis=0)
if boxes2.ndim == 1: boxes2 = np.expand_dims(boxes2, axis=0)
if not (boxes1.shape[1] == boxes2.shape[1] == 4): raise ValueError("All boxes must consist of 4 coordinates, but the boxes in `boxes1` and `boxes2` have {} and {} coordinates, respectively.".format(boxes1.shape[1], boxes2.shape[1]))
if not mode in {'outer_product', 'element-wise'}: raise ValueError("`mode` must be one of 'outer_product' and 'element-wise', but got '{}'.".format(mode))
if coords == 'centroids':
boxes1 = convert_coordinates(boxes1, start_index=0, conversion='centroids2corners')
boxes2 = convert_coordinates(boxes2, start_index=0, conversion='centroids2corners')
coords = 'corners'
elif not (coords in {'minmax', 'corners'}):
raise ValueError("Unexpected value for `coords`. Supported values are 'minmax', 'corners' and 'centroids'.")
## 计算 IoU.
# 计算交集Compute the interesection areas.
intersection_areas = intersection_area_(boxes1, boxes2, coords=coords, mode=mode)
# 计算并集
# A or B = A + B - A and B
m = boxes1.shape[0]
n = boxes2.shape[0]
if coords == 'corners':
xmin = 0
ymin = 1
xmax = 2
ymax = 3
elif coords == 'minmax':
xmin = 0
xmax = 1
ymin = 2
ymax = 3
#边界处理
if border_pixels == 'half':
d = 0
elif border_pixels == 'include':
d = 1
elif border_pixels == 'exclude':
d = -1
if mode == 'outer_product':
boxes1_areas = np.tile(np.expand_dims((boxes1[:,xmax] - boxes1[:,xmin] + d) * (boxes1[:,ymax] - boxes1[:,ymin] + d), axis=1), reps=(1,n))
boxes2_areas = np.tile(np.expand_dims((boxes2[:,xmax] - boxes2[:,xmin] + d) * (boxes2[:,ymax] - boxes2[:,ymin] + d), axis=0), reps=(m,1))
elif mode == 'element-wise':
boxes1_areas = (boxes1[:,xmax] - boxes1[:,xmin] + d) * (boxes1[:,ymax] - boxes1[:,ymin] + d)
boxes2_areas = (boxes2[:,xmax] - boxes2[:,xmin] + d) * (boxes2[:,ymax] - boxes2[:,ymin] + d)
union_areas = boxes1_areas + boxes2_areas - intersection_areas
return intersection_areas / union_areas