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
