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新版mmdetection3d将3D bbox绘制到图像_mmdet3d将3d框投影到图像
作者：花生_TL007 | 2024-04-21 03:12:58
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mmdet3d将3d框投影到图像
环境信息
使用 python mmdet3d/utils/collect_env.py收集环境信息
sys.platform: linux Python: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:21) [GCC 9.4.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0,1: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.8.1+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.9.1+cu111 OpenCV: 4.6.0 MMEngine: 0.9.1 MMDetection: 3.2.0 MMDetection3D: 1.3.0+9d3e162 spconv2.0: True
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以前写过mmdetection3d中的可视化，但mmdetection3d更新后代码已经不适用了，正好我把我的工作全转移到新版mmdetection3d上来了，因此重新写了一下推理结果可视化。整体思路还是构建模型、构建数据、推理、绘制，下面分步讲解
1、构建模型
我用jupyter实现，首先需要确保jupyter的工作路径在mmdetection3d的工作路径下，不然会存在找不到mmdet3d的问题
import sys import os import torch import cv2 import numpy as np # 添加工作路径，不然找不到mmdet3d os.chdir('/home/wistful/work/open_mmlab_mmdetection3d') sys.path.append('/home/wistful/work/open_mmlab_mmdetection3d') # load config config_file = 'configs/point_cls_voxel/pointpillars_hv_secfpn_8x2-160e_kitti-3d-3class.py' checkpoint_file = '/home/wistful/work/open_mmlab_mmdetection3d/work_dirs/pointpillars_hv_secfpn_8x2-160e_kitti-3d-3class/epoch_80.pth' # 构建模型 from mmdet3d.apis import init_model, inference_detector device = 'cuda:0' model = init_model(config_file, checkpoint=checkpoint_file, device=device)
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至此模型已经构建，下一步是构建数据，送入模型以获取推理结果
2、构建数据
新版mmdet3d的模型输入分为两个部分batch_inputs_dict, batch_data_samples。batch_inputs_dict包含了模型推理所需的数据（点云、图像），batch_data_samples包含了训练时需要的bbox等信息。因此，需要构建batch_inputs_dict，我写了一个简单的函数，可以调用
build_dataloader.py文件：
from mmdet3d.registry import DATASETS from tools.misc.browse_dataset import build_data_cfg from mmengine.registry import init_default_scope def load_datasets(config_file, aug=False, set='train'): """ Args: config_file: 配置文件路径 aug:是否数据增强（待测试） set:要读取的数据集，'train','test','val' Returns: """ cfg = build_data_cfg(config_file, aug=aug, cfg_options=None) init_default_scope(cfg.get('default_scope', 'mmdet3d')) # 选择需要读取的数据集 if set == 'train': dataloader = cfg.train_dataloader.dataset elif set == 'val': dataloader = cfg.val_dataloader.dataset elif set == 'test': dataloader = cfg.test_dataloader.dataset return DATASETS.build(dataloader) def build_batch_dict(datasets, batch_size, device, images=False): """ Args: device: 指定设备 datasets: 传入数据集 batch_size: 批次大小 images: 加入图像 Returns: """ # TODO: 编写加入图像的代码 points = [] images = [] batch_data_samples = [] for i in range(batch_size): # 确保在同一个device上 points.append(datasets[i]['inputs']['points'].to(device)) data_samples = datasets[i]['data_samples'] # if data_samples.gt_instances_3d if len(data_samples.gt_instances_3d.keys()) != 0: data_samples.gt_instances_3d.bboxes_3d = data_samples.gt_instances_3d.bboxes_3d.to(device) data_samples.gt_instances_3d.labels_3d = data_samples.gt_instances_3d.labels_3d.to(device) batch_inputs_dict = dict() batch_inputs_dict['points'] = points # batch_data_samples = data_samples return batch_inputs_dict, batch_data_samples def cyclic_load_data_item(datasets, index, device, images=False): """ Args: device: 指定设备 datasets: 传入数据集 index: 索引 images: 加入图像 Returns: 单条数据，适用于循环遍历整个数据集 """ # TODO: 编写加入图像的代码 points = [] images = [] points.append(datasets[index]['inputs']['points'].to(device)) batch_inputs_dict = dict() batch_inputs_dict['points'] = points data_samples = datasets[index]['data_samples'] if len(data_samples.gt_instances_3d.keys()) !=0: data_samples.gt_instances_3d.bboxes_3d = data_samples.gt_instances_3d.bboxes_3d.to(device) data_samples.gt_instances_3d.labels_3d = data_samples.gt_instances_3d.labels_3d.to(device) batch_data_samples = [data_samples] return batch_inputs_dict, batch_data_samples
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下面利用这个函数，实现构建数据集
# 构建数据集
from custom_API.build_dataloader import load_datasets # 我放在了custom_API路径下，如何导入取决于读者如何存放
set = 'test'
dataset_config = '配置文件路径'
# set字段表示构建的数据集
datasets = load_datasets(dataset_config, aug=False, set=set) # aug字段表示不使用数据增强
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至此，datasets为一个列表，长度就是数据集的总样本数。eg:datasets[0]里面就包含了第1个样本的全部信息，下面可以看一下输出
3、推理与绘制
我们已经得到了整个数据集，那么我们就可以使用数据集中的任意一条数据进行推理，根据这个思路，我们也能很方便的推理完整个数据集。绘制部分的代码我使用的是旧版mmdetection3d中的代码，下面是代码：
# draw_box.py import os from custom_API.draw_utils import draw_lidar_bbox3d_on_img, draw_depth_bbox3d_on_img, draw_camera_bbox3d_on_img import mmcv from os import path as osp import numpy as np def show_multi_modality_result(img, gt_bboxes, pred_bboxes, batch_data_samples, out_dir, filename, type='train', box_mode='lidar', img_metas=None, show=False, gt_bbox_color=(61, 102, 255), pred_bbox_color=(241, 101, 72)): """Convert multi-modality detection results into 2D results. 将3D边框投影到2D图像平面并且可视化 Project the predicted 3D bbox to 2D image plane and visualize them. Args: img (np.ndarray): The numpy array of image in cv2 fashion. gt_bboxes (:obj:`BaseInstance3DBoxes`): Ground truth boxes. pred_bboxes (:obj:`BaseInstance3DBoxes`): Predicted boxes. proj_mat (numpy.array, shape=[4, 4]): The projection matrix # 投影矩阵 according to the camera intrinsic parameters. out_dir (str): Path of output directory. filename (str): Filename of the current frame. box_mode (str, optional): Coordinate system the boxes are in. Should be one of 'depth', 'lidar' and 'camera'. Defaults to 'lidar'. img_metas (dict, optional): Used in projecting depth bbox. Defaults to None. show (bool, optional): Visualize the results online. Defaults to False. 颜色为B G R，不是RGB！！！ gt_bbox_color (str or tuple(int), optional): Color of bbox lines. The tuple of color should be in BGR order. Default: (255, 102, 61). pred_bbox_color (str or tuple(int), optional): Color of bbox lines. The tuple of color should be in BGR order. Default: (72, 101, 241). """ # 根据传入3D框所处的坐标系调用对应的投影方法，获取投影框 if box_mode == 'depth': draw_bbox = draw_depth_bbox3d_on_img elif box_mode == 'lidar': draw_bbox = draw_lidar_bbox3d_on_img elif box_mode == 'camera': draw_bbox = draw_camera_bbox3d_on_img else: raise NotImplementedError(f'unsupported box mode {box_mode}') # 在out_dir下创建每个文件名字的文件夹 # result_path = osp.join(out_dir, filename) # mmcv.mkdir_or_exist(result_path) out_dir = out_dir + type + '/' # 判断目录是否存在 if not os.path.exists(out_dir): os.makedirs(out_dir) else: pass # os.makedirs(out_dir) # mmcv.mkdir_or_exist(result_path) # if score_thr > 0: # inds = pred_scores > score_thr # pred_bboxes = pred_bboxes[inds] # 获取投影矩阵 proj_mat = batch_data_samples[0].lidar2img proj_mat = proj_mat[0] proj_mat = np.array(proj_mat) if show: show_img = img.copy() if gt_bboxes is not None: show_img = draw_bbox( gt_bboxes, show_img, proj_mat, img_metas, color=gt_bbox_color) if pred_bboxes is not None: show_img = draw_bbox( pred_bboxes, show_img, proj_mat, img_metas, color=pred_bbox_color) mmcv.imshow(show_img, win_name='project_bbox3d_img', wait_time=0) if img is not None: # print('写入原图像') mmcv.imwrite(img, osp.join(out_dir, f'{filename}.png')) if gt_bboxes is not None: # 写入地面真相 gt_img = draw_bbox( gt_bboxes, img, proj_mat, img_metas, color=gt_bbox_color) mmcv.imwrite(gt_img, osp.join(out_dir, f'{filename}_gt.png')) if pred_bboxes is not None: pred_img = draw_bbox( pred_bboxes, img, proj_mat, img_metas, color=pred_bbox_color) mmcv.imwrite(pred_img, osp.join(out_dir, f'{filename}_pred.png')) if pred_bboxes is not None and gt_bboxes is not None: # print('draw_gt_bbox') gt_img = draw_bbox( gt_bboxes, img, proj_mat, img_metas, color=gt_bbox_color) gt_and_pred_img = draw_bbox( pred_bboxes, gt_img, proj_mat, img_metas, color=pred_bbox_color) mmcv.imwrite(gt_and_pred_img, osp.join(out_dir, f'{filename}_pred_gt.png')) # draw_utils.py # Copyright (c) OpenMMLab. All rights reserved. import copy import cv2 import numpy as np import torch from matplotlib import pyplot as plt def project_pts_on_img(points, raw_img, lidar2img_rt, max_distance=70, thickness=-1): """Project the 3D points cloud on 2D image. Args: points (numpy.array): 3D points cloud (x, y, z) to visualize. raw_img (numpy.array): The numpy array of image. lidar2img_rt (numpy.array, shape=[4, 4]): The projection matrix according to the camera intrinsic parameters. max_distance (float, optional): the max distance of the points cloud. Default: 70. thickness (int, optional): The thickness of 2D points. Default: -1. """ img = raw_img.copy() num_points = points.shape[0] pts_4d = np.concatenate([points[:, :3], np.ones((num_points, 1))], axis=-1) pts_2d = pts_4d @ lidar2img_rt.T # cam_points is Tensor of Nx4 whose last column is 1 # transform camera coordinate to image coordinate pts_2d[:, 2] = np.clip(pts_2d[:, 2], a_min=1e-5, a_max=99999) pts_2d[:, 0] /= pts_2d[:, 2] pts_2d[:, 1] /= pts_2d[:, 2] fov_inds = ((pts_2d[:, 0] < img.shape[1]) & (pts_2d[:, 0] >= 0) & (pts_2d[:, 1] < img.shape[0]) & (pts_2d[:, 1] >= 0)) imgfov_pts_2d = pts_2d[fov_inds, :3] # u, v, d cmap = plt.cm.get_cmap('hsv', 256) cmap = np.array([cmap(i) for i in range(256)])[:, :3] * 255 for i in range(imgfov_pts_2d.shape[0]): depth = imgfov_pts_2d[i, 2] color = cmap[np.clip(int(max_distance * 10 / depth), 0, 255), :] cv2.circle( img, center=(int(np.round(imgfov_pts_2d[i, 0])), int(np.round(imgfov_pts_2d[i, 1]))), radius=1, color=tuple(color), thickness=thickness, ) cv2.imshow('project_pts_img', img.astype(np.uint8)) cv2.waitKey(0) def plot_rect3d_on_img(img, num_rects, rect_corners, color=(0, 255, 0), thickness=1): """Plot the boundary lines of 3D rectangular on 2D images. Args: img (numpy.array): The numpy array of image. num_rects (int): Number of 3D rectangulars. rect_corners (numpy.array): Coordinates of the corners of 3D rectangulars. Should be in the shape of [num_rect, 8, 2]. color (tuple[int], optional): The color to draw bboxes. Default: (0, 255, 0). thickness (int, optional): The thickness of bboxes. Default: 1. """ line_indices = ((0, 1), (0, 3), (0, 4), (1, 2), (1, 5), (3, 2), (3, 7), (4, 5), (4, 7), (2, 6), (5, 6), (6, 7)) # thickness = 0.5 # print('rect_corners type:', rect_corners.dtype) # print('img type',type(img)) for i in range(num_rects): corners = rect_corners[i].astype(np.int64) # print("opencv corners type:", corners.dtype) for start, end in line_indices: # cv2.line(img, (corners[start, 0], corners[start, 1]), # (corners[end, 0], corners[end, 1]), color, thickness, # cv2.LINE_AA) # print("change:", type(int(corners[start, 0]))) cv2.line(img, tuple(corners[start]), tuple(corners[end]), color, thickness, cv2.LINE_AA) # cv2.line(img, # (int(corners[start, 0]), int(corners[start, 1])), # (int(corners[end, 0]), int(corners[end, 1])), # color, # thickness, # cv2.LINE_AA) # return img.astype(np.uint8) return img def draw_lidar_bbox3d_on_img(bboxes3d, raw_img, lidar2img_rt, img_metas, color=(0, 255, 0), thickness=1): """Project the 3D bbox on 2D plane and draw on input image. Args: bboxes3d (:obj:`LiDARInstance3DBoxes`): 3d bbox in lidar coordinate system to visualize. raw_img (numpy.array): The numpy array of image. lidar2img_rt (numpy.array, shape=[4, 4]): The projection matrix according to the camera intrinsic parameters. img_metas (dict): Useless here. color (tuple[int], optional): The color to draw bboxes. Default: (0, 255, 0). thickness (int, optional): The thickness of bboxes. Default: 1. """ img = raw_img.copy() corners_3d = bboxes3d.corners.cpu().numpy() num_bbox = corners_3d.shape[0] pts_4d = np.concatenate( [corners_3d.reshape(-1, 3), np.ones((num_bbox * 8, 1))], axis=-1) lidar2img_rt = copy.deepcopy(lidar2img_rt).reshape(4, 4) if isinstance(lidar2img_rt, torch.Tensor): lidar2img_rt = lidar2img_rt.cpu().numpy() pts_2d = pts_4d @ lidar2img_rt.T pts_2d[:, 2] = np.clip(pts_2d[:, 2], a_min=1e-5, a_max=1e5) pts_2d[:, 0] /= pts_2d[:, 2] pts_2d[:, 1] /= pts_2d[:, 2] imgfov_pts_2d = pts_2d[..., :2].reshape(num_bbox, 8, 2) return plot_rect3d_on_img(img, num_bbox, imgfov_pts_2d, color, thickness) # TODO: remove third parameter in all functions here in favour of img_metas def draw_depth_bbox3d_on_img(bboxes3d, raw_img, calibs, img_metas, color=(0, 255, 0), thickness=1): """Project the 3D bbox on 2D plane and draw on input image. Args: bboxes3d (:obj:`DepthInstance3DBoxes`, shape=[M, 7]): 3d bbox in depth coordinate system to visualize. raw_img (numpy.array): The numpy array of image. calibs (dict): Camera calibration information, Rt and K. img_metas (dict): Used in coordinates transformation. color (tuple[int], optional): The color to draw bboxes. Default: (0, 255, 0). thickness (int, optional): The thickness of bboxes. Default: 1. """ from mmdet3d.structures import points_cam2img from mmdet3d.models import apply_3d_transformation img = raw_img.copy() img_metas = copy.deepcopy(img_metas) corners_3d = bboxes3d.corners num_bbox = corners_3d.shape[0] points_3d = corners_3d.reshape(-1, 3) # first reverse the data transformations xyz_depth = apply_3d_transformation( points_3d, 'DEPTH', img_metas, reverse=True) # project to 2d to get image coords (uv) uv_origin = points_cam2img(xyz_depth, xyz_depth.new_tensor(img_metas['depth2img'])) uv_origin = (uv_origin - 1).round() imgfov_pts_2d = uv_origin[..., :2].reshape(num_bbox, 8, 2).numpy() return plot_rect3d_on_img(img, num_bbox, imgfov_pts_2d, color, thickness) def draw_camera_bbox3d_on_img(bboxes3d, raw_img, cam2img, img_metas, color=(0, 255, 0), thickness=1): """Project the 3D bbox on 2D plane and draw on input image. Args: bboxes3d (:obj:`CameraInstance3DBoxes`, shape=[M, 7]): 3d bbox in camera coordinate system to visualize. raw_img (numpy.array): The numpy array of image. cam2img (dict): Camera intrinsic matrix, denoted as `K` in depth bbox coordinate system. img_metas (dict): Useless here. color (tuple[int], optional): The color to draw bboxes. Default: (0, 255, 0). thickness (int, optional): The thickness of bboxes. Default: 1. """ from mmdet3d.structures import points_cam2img img = raw_img.copy() cam2img = copy.deepcopy(cam2img) corners_3d = bboxes3d.corners num_bbox = corners_3d.shape[0] points_3d = corners_3d.reshape(-1, 3) if not isinstance(cam2img, torch.Tensor): cam2img = torch.from_numpy(np.array(cam2img)) assert (cam2img.shape == torch.Size([3, 3]) or cam2img.shape == torch.Size([4, 4])) cam2img = cam2img.float().cpu() # project to 2d to get image coords (uv) uv_origin = points_cam2img(points_3d, cam2img) uv_origin = (uv_origin - 1).round() imgfov_pts_2d = uv_origin[..., :2].reshape(num_bbox, 8, 2).numpy() return plot_rect3d_on_img(img, num_bbox, imgfov_pts_2d, color, thickness)
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下面是推理和绘制的完整代码，必要的注释已经给出。
from custom_API.draw_box import show_multi_modality_result #如何导入取决于读者如何存放 print(f'datasets length:{len(datasets)}') data_root = 'data/kitti/' # 数据集根路径 save_root = '/home/wistful/work/open_mmlab_mmdetection3d/visual_dir/predict_imgs/' # 保存可视化结果的根路径 data_num = 100 # 最大不能超过数据集长度 # 判断一开始是读取的哪个数据集 if set == 'train' or set == 'val': new_set = 'training' else: new_set = 'testing' # 推理整个数据集的前data_num条数据 for i in tqdm(range(data_num), desc='process situation'): # cyclic_load_data_item代码位于第2步 batch_inputs_dict, batch_data_samples = cyclic_load_data_item(datasets, index=i, device=device) # 读取一条数据，并构建批次 points = batch_inputs_dict['points'][0] # 获取点云，因为是单条数据，所以直接取0 # 获取检测结果 result, data = inference_detector(model, points.cpu()) bboxes_3d = result.pred_instances_3d.bboxes_3d labels_3d = result.pred_instances_3d.labels_3d scores_3d = result.pred_instances_3d.scores_3d # 设置阈值 thr = 0.4 score = (scores_3d > thr) bboxes_3d = bboxes_3d[score] # 根据阈值筛选 # 读取原始图像 img_file_path = data_root + new_set + '/image_2/' + batch_data_samples[0].img_path[0] image = cv2.imread(img_file_path) img_name = batch_data_samples[0].img_path[0].split('.')[0] # 取一下文件名 # 保存多模态结果（调用的旧版mmdet代码接口） show_multi_modality_result(img=image, box_mode='lidar', gt_bboxes=None, pred_bboxes=bboxes_3d, batch_data_samples=batch_data_samples, out_dir=save_root, filename=img_name, type=set, show=False) # result = model(batch_inputs_dict, batch_data_samples) # model的输入与具体模型有关
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运行上述代码后，会在设置的save_root下生成可视化图片
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