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import numpy as np
import time
def IoU(box, boxes):
"""Compute IoU between detect box and gt boxes
Parameters:
----------
box: numpy array , shape (5, ): x1, y1, x2, y2, score
input box
boxes: numpy array, shape (n, 4): x1, y1, x2, y2
input ground truth boxes
Returns:
-------
ovr: numpy.array, shape (n, )
IoU
"""
box_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
area = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
xx1 = np.maximum(box[0], boxes[:, 0])
yy1 = np.maximum(box[1], boxes[:, 1])
xx2 = np.minimum(box[2], boxes[:, 2])
yy2 = np.minimum(box[3], boxes[:, 3])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
inter = w * h
ovr = np.true_divide(inter,(box_area + area - inter))
#ovr = inter / (box_area + area - inter)
return ovr
def convert_to_square(bbox):
"""Convert bbox to square
Parameters:
----------
bbox: numpy array , shape n x 5
input bbox
Returns:
-------
square bbox
"""
square_bbox = bbox.copy()
h = bbox[:, 3] - bbox[:, 1] + 1
w = bbox[:, 2] - bbox[:, 0] + 1
max_side = np.maximum(h,w)
square_bbox[:, 0] = bbox[:, 0] + w*0.5 - max_side*0.5
square_bbox[:, 1] = bbox[:, 1] + h*0.5 - max_side*0.5
square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1
square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1
return square_bbox
# non-maximum suppression: eleminates the box which have large interception with the box which have the largest score
def nms(dets, thresh, mode="Union"):
"""
greedily select boxes with high confidence
keep boxes overlap <= thresh
rule out overlap > thresh
:param dets: [[x1, y1, x2, y2 score]]
:param thresh: retain overlap <= thresh
:return: indexes to keep
"""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
# shape of x1 = (454,), shape of scores = (454,)
# print("shape of x1 = {0}, shape of scores = {1}".format(x1.shape, scores.shape))
# time.sleep(5)
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1] # argsort: ascending order then [::-1] reverse the order --> descending order
# print("shape of order {0}".format(order.size)) # (454,)
# time.sleep(5)
# eleminates the box which have large interception with the box which have the largest score in order
# matain the box with largest score and boxes don't have large interception with it
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
# cacaulate the IOU between box which have largest score with other boxes
if mode == "Union":
# area[i]: the area of largest score
ovr = inter / (areas[i] + areas[order[1:]] - inter)
elif mode == "Minimum":
ovr = inter / np.minimum(areas[i], areas[order[1:]])
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1] # +1: eliminates the first element in order
# print(inds)
# print("shape of order {0}".format(order.shape)) # (454,)
# time.sleep(2)
return keep
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