目标检测数据增强，旋转方法

# 旋转
	def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.):
	'''
	参考:https://blog.csdn.net/u014540717/article/details/53301195crop_rate
	输入:
	img:图像array,(h,w,c)
	bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值
	angle:旋转角度
	scale:默认1
	输出:
	rot_img:旋转后的图像array
	rot_bboxes:旋转后的boundingbox坐标list
	'''
	#---------------------- 旋转图像 ----------------------
	w = img.shape[1]
	h = img.shape[0]
	# 角度变弧度
	rangle = np.deg2rad(angle) # angle in radians
	# now calculate new image width and height
	nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
	nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
	# ask OpenCV for the rotation matrix
	rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
	# calculate the move from the old center to the new center combined
	# with the rotation
	rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
	# the move only affects the translation, so update the translation
	# part of the transform
	rot_mat[0,2] += rot_move[0]
	rot_mat[1,2] += rot_move[1]
	# 仿射变换
	rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
	#---------------------- 矫正bbox坐标 ----------------------
	# rot_mat是最终的旋转矩阵
	# 获取原始bbox的四个中点，然后将这四个点转换到旋转后的坐标系下
	rot_bboxes = list()
	for bbox in bboxes:
	xmin = bbox[0]
	ymin = bbox[1]
	xmax = bbox[2]
	ymax = bbox[3]
	point1 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymin, 1]))
	point2 = np.dot(rot_mat, np.array([xmax, (ymin+ymax)/2, 1]))
	point3 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymax, 1]))
	point4 = np.dot(rot_mat, np.array([xmin, (ymin+ymax)/2, 1]))
	# 合并np.array
	concat = np.vstack((point1, point2, point3, point4))
	# 改变array类型
	concat = concat.astype(np.int32)
	# 得到旋转后的坐标
	rx, ry, rw, rh = cv2.boundingRect(concat)
	rx_min = rx
	ry_min = ry
	rx_max = rx+rw
	ry_max = ry+rh
	# 加入list中
	rot_bboxes.append([rx_min, ry_min, rx_max, ry_max])
	return rot_img, rot_bboxes

参考链接：https://github.com/maozezhong/CV_ToolBox/blob/master/DataAugForObjectDetection/DataAugmentForObejctDetection.py