复现graspnet并使用自己的数据实现（pycharm）

参考文章：Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020).[paper] [dataset] [API] [doc]

一、下载graspnet

1、安装

获取代码

git clone https://github.com/graspnet/graspnet-baseline.git
cd graspnet-baseline

通过 Pip 安装软件包

pip install -r requirements.txt

编译并安装 pointnet2

cd pointnet2
python setup.py install

编译并安装 knn

cd knn
python setup.py install

安装graspnetAPI,将它的包文件放置在graspnet的环境中

git clone https://github.com/graspnet/graspnetAPI.git
cd graspnetAPI
pip install .

手动构建文档

cd docs
pip install -r requirements.txt
bash build_doc.sh

2、环境构建

安装需要的软件包

• Python 3
• PyTorch 1.6
• Open3d >=0.8
• TensorBoard 2.3
• NumPy
• SciPy
• Pillow
• tqdm

3、生成标签

从 Google Drive/Baidu Pan 下载

运行程序生成：

mv tolerance.tar dataset/
cd dataset
tar -xvf tolerance.tar

4、下载预权重

预训练权重可以从以下位置下载：

• checkpoint-rs.tar [谷歌云端硬盘] [百度盘]
• checkpoint-kn.tar [谷歌云端硬盘] [百度盘]

checkpoint-rs.tar和checkpoint-kn.tar是分别使用 RealSense 数据和 Kinect 数据进行训练。

二、demo复现

1、编辑配置

打开demo.py，在pycharm右上方的位置，展开，选择编辑配置

 在形参那里输入预训练权重

 根据下载的权重输入形参，注意后面的路径要修改为自己存储文件的位置

--checkpoint_path logs/log_kn/checkpoint.tar
--dump_dir logs/dump_rs --checkpoint_path logs/log_rs/checkpoint.tar --camera realsense --dataset_root /data/Benchmark/graspnet
--log_dir logs/log_rs --batch_size 2 

形参输入格式

--形参1 路径1 --形参2 路径2

 2、复现演示

在graspnet-baseline/doc/example_data里可以查看输入图片

 运行demo.py可以得到3D展示图，生成了6D抓取位姿

 结束展示

三、采用自己的数据集实现抓取预测

1、数据介绍

使用realsensel515实感相机，使用数据线连接电脑

realsensel515相机参数

factor_depth          4000                                             深度转换值

intrinsic_matrix                                                          相机内部参数

1351.72	0	979.26
0	1352.93	556.038
0	0	1

2、数据输入

软件包配置

pyrealsense2

cv2

实现实际场景的输入，成功转化为图片形式， 用于抓取输入

将depth_image与color_image对齐

修改相机内部参数（焦距、光学中心） 及深度转化值

 完整代码

 
""" Demo to show prediction results.
    Author: chenxi-wang
"""
 
import os
import sys
import numpy as np
import open3d as o3d
import argparse
import importlib
import scipy.io as scio
from PIL import Image
 
import torch
from graspnetAPI import GraspGroup
 
import pyrealsense2 as rs
import cv2
from matplotlib import pyplot as plt
 
 
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(ROOT_DIR, 'models'))
sys.path.append(os.path.join(ROOT_DIR, 'dataset'))
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
 
from models.graspnet import GraspNet, pred_decode
from graspnet_dataset import GraspNetDataset
from collision_detector import ModelFreeCollisionDetector
from data_utils import CameraInfo, create_point_cloud_from_depth_image
 
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, help='Model checkpoint path')
parser.add_argument('--num_point', type=int, default=20000, help='Point Number [default: 20000]')
parser.add_argument('--num_view', type=int, default=300, help='View Number [default: 300]')
parser.add_argument('--collision_thresh', type=float, default=0.01, help='Collision Threshold in collision detection [default: 0.01]')
parser.add_argument('--voxel_size', type=float, default=0.01, help='Voxel Size to process point clouds before collision detection [default: 0.01]')
cfgs = parser.parse_args()
 
 
def get_net():
    # Init the model
    net = GraspNet(input_feature_dim=0, num_view=cfgs.num_view, num_angle=12, num_depth=4,
            cylinder_radius=0.05, hmin=-0.02, hmax_list=[0.01,0.02,0.03,0.04], is_training=False)
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net.to(device)
    # Load checkpoint
    checkpoint = torch.load(cfgs.checkpoint_path)
    net.load_state_dict(checkpoint['model_state_dict'])
    start_epoch = checkpoint['epoch']
    print("-> loaded checkpoint %s (epoch: %d)"%(cfgs.checkpoint_path, start_epoch))
    # set model to eval mode
    net.eval()
    return net
 
def get_and_process_data(data_dir):
    # load data
    color = np.array(Image.open(os.path.join(data_dir, 'color.png')), dtype=np.float32) / 255.0
    depth = np.array(Image.open(os.path.join(data_dir, 'depth.png')))
    workspace_mask = np.array(Image.open(os.path.join(data_dir, 'workspace_mask.png')))
 
 
 
    meta = scio.loadmat(os.path.join(data_dir, 'meta.mat'))# Resize depth to match color image resolution while preserving spatial alignment
    color_height, color_width = color.shape[:2]
    depth = cv2.resize(depth, (color_width, color_height), interpolation=cv2.INTER_NEAREST)
    intrinsic = meta['intrinsic_matrix']
    factor_depth =meta['factor_depth']
 
    # generate cloud
    camera = CameraInfo(1280.0, 720.0, intrinsic[0][0], intrinsic[1][1], intrinsic[0][2], intrinsic[1][2], factor_depth)
    cloud = create_point_cloud_from_depth_image(depth, camera, organized=True)
 
    # get valid points
    mask = (workspace_mask & (depth > 0))
    cloud_masked = cloud[mask]
    color_masked = color[mask]
 
 
    # sample points
 
    if len(cloud_masked) >= cfgs.num_point:
        idxs = np.random.choice(len(cloud_masked), cfgs.num_point, replace=False)
    else:
        idxs1 = np.arange(len(cloud_masked))
        idxs2 = np.random.choice(len(cloud_masked), cfgs.num_point-len(cloud_masked), replace=True)
        idxs = np.concatenate([idxs1, idxs2], axis=0)
    cloud_sampled = cloud_masked[idxs]
    color_sampled = color_masked[idxs]
 
    # convert data
    cloud = o3d.geometry.PointCloud()
    cloud.points = o3d.utility.Vector3dVector(cloud_masked.astype(np.float32))
    cloud.colors = o3d.utility.Vector3dVector(color_masked.astype(np.float32))
    end_points = dict()
    cloud_sampled = torch.from_numpy(cloud_sampled[np.newaxis].astype(np.float32))
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    cloud_sampled = cloud_sampled.to(device)
    end_points['point_clouds'] = cloud_sampled
    end_points['cloud_colors'] = color_sampled
 
    return end_points, cloud
 
def get_grasps(net, end_points):
    # Forward pass
    with torch.no_grad():
        end_points = net(end_points)
        grasp_preds = pred_decode(end_points)
    gg_array = grasp_preds[0].detach().cpu().numpy()
    gg = GraspGroup(gg_array)
    return gg
 
def collision_detection(gg, cloud):
    mfcdetector = ModelFreeCollisionDetector(cloud, voxel_size=cfgs.voxel_size)
    collision_mask = mfcdetector.detect(gg, approach_dist=0.05, collision_thresh=cfgs.collision_thresh)
    gg = gg[~collision_mask]
    return gg
 
def vis_grasps(gg, cloud):
    gg.nms()
    gg.sort_by_score()
    gg = gg[:50]
    grippers = gg.to_open3d_geometry_list()
    o3d.visualization.draw_geometries([cloud, *grippers])
 
def demo(data_dir):
    net = get_net()
    end_points, cloud = get_and_process_data(data_dir)
    gg = get_grasps(net, end_points)
    if cfgs.collision_thresh > 0:
        gg = collision_detection(gg, np.array(cloud.points))
    vis_grasps(gg, cloud)
 
 
def input1():
    # Create a pipeline
    pipeline = rs.pipeline()
 
    # Create a config object to configure the pipeline
    config = rs.config()
    config.enable_stream(rs.stream.depth, 1024, 768, rs.format.z16, 30)
    config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
 
    # Start the pipeline
    pipeline.start(config)
    align = rs.align(rs.stream.color)  # Create align object for depth-color alignment
 
    try:
        while True:
            # Wait for a coherent pair of frames: color and depth
            frames = pipeline.wait_for_frames()
            aligned_frames = align.process(frames)
            if not aligned_frames:
                continue  # If alignment fails, go back to the beginning of the loop
 
            color_frame = aligned_frames.get_color_frame()
            aligned_depth_frame = aligned_frames.get_depth_frame()
 
            if not color_frame or not aligned_depth_frame:
                continue
 
            # Convert aligned_depth_frame and color_frame to numpy arrays
            aligned_depth_image = np.asanyarray(aligned_depth_frame.get_data())
            color_image = np.asanyarray(color_frame.get_data())
 
            # Display the aligned depth image
            aligned_depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(aligned_depth_image, alpha=0.03),
                                                           cv2.COLORMAP_JET)
            cv2.imshow("Aligned Depth colormap", aligned_depth_colormap)
 
            cv2.imshow("Aligned Depth Image", aligned_depth_image)
            cv2.imwrite('./data1/depth.png', aligned_depth_image)
 
            # Display the color image
            cv2.imshow("Color Image", color_image)
            cv2.imwrite('./data1/color.png', color_image)
 
            # Press 'q' to quit
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
 
    finally:
        # Stop the pipeline and close all windows
        pipeline.stop()
        cv2.destroyAllWindows()
 
if __name__=='__main__':
    input1()
    data_dir = 'data1'
    demo(data_dir)

其中dada1为自己的数据文件，里面包含

color.png     自己相机生成的彩色图

depth.png   与彩色图对齐后的深度图

workspace.png  从demo数据文件中直接复制过来

meta.mat   复制demo数据文件的meta.mat，将里面的参数修改为自己相机的参数

记得权重赋予与demo.py一致

3、结果展示

彩色图 RGB

 深度图

生成6d抓取位姿及3d图

4、只保留最优抓取位姿

 修改部分代码

def vis_grasps(gg, cloud, num_top_grasps=10):
    gg.nms()
    gg.sort_by_score(reverse=True)  # Sort the grasps in descending order of scores
    gg = gg[:num_top_grasps]  # Keep only the top num_top_grasps grasps
    grippers = gg.to_open3d_geometry_list()
    o3d.visualization.draw_geometries([cloud, *grippers])

运行程序

实验结束