elevenjiang1
commented
2 years ago Here is my code to generate h5py from obj file. tools/generate_gqcnn_dataset.py need:
- graspable_object
- stable_poses
- grasps
- grasp_metrics
The code is reference test/database_test.py and test/grasping_test.py.The data generate have tested to generate gqcnn data and train dex-net 2.0.. If there is mistake in this code,welcome to contact me~
Best
"""
Specify a path which contains .obj files
Generate h5py file,including:
1. create graspable objects with MeshProcesser
3. calculate grasps
4. calculate grasp metrics
"""
from tqdm import tqdm
import copy
import IPython
import logging
import numpy as np
import os
import sys
import time
from unittest import TestCase, TestSuite, TextTestRunner
from autolab_core import RigidTransform, YamlConfig
from perception import CameraIntrinsics, RenderMode
from meshpy.obj_file import ObjFile
from meshpy.mesh_renderer import ViewsphereDiscretizer, VirtualCamera
from dexnet.constants import READ_WRITE_ACCESS,READ_ONLY_ACCESS,WRITE_ACCESS
from dexnet.database import Hdf5Database, MeshProcessor, RescalingType
from dexnet.grasping import ParallelJawPtGrasp3D,GraspableObject3D, AntipodalGraspSampler, GraspQualityConfigFactory, GraspQualityFunctionFactory, RobotGripper
def create_database(config_path,generate_path,objs_path):
#1: load config file
config_data=YamlConfig(config_path)
config_data['obj_rescaling_type'] = RescalingType.RELATIVE
#2: create database and dataset
if not os.path.exists(generate_path):
print("generate_path not exist,generate a new database")
database=Hdf5Database(generate_path,access_level=READ_WRITE_ACCESS)
else:
print("generate_path exist,read this database")
database=Hdf5Database(generate_path,access_level=READ_WRITE_ACCESS)
dataset=database.create_dataset("mini_test")
database.close()
database=Hdf5Database(generate_path,access_level=READ_WRITE_ACCESS)
dataset=database.dataset("mini_test")
#3: load graspable objects
for obj_file in os.listdir(objs_path):
obj_path=os.path.join(objs_path,obj_file)
mesh_processor=MeshProcessor(obj_path,config_data['cache_dir'])
mesh_processor.generate_graspable(config_data)
dataset.create_graspable(mesh_processor.key,mesh_processor.mesh,mesh_processor.sdf,mesh_processor.stable_poses,mass=1.0)
#4: add dataset.metric
for metric_name in config_data['metrics'].keys():
print("******Add metric_name:{}******".format(metric_name))
metric_config=config_data['metrics'][metric_name]
dataset.create_metric(metric_name,metric_config)
#5: add object's grasp and grasp metirc
#init antipodal grasp sampler
GRIPPER_NAME = 'yumi_metal_spline'
gripper=RobotGripper.load(GRIPPER_NAME)
ags = AntipodalGraspSampler(gripper, config_data)
NUM_TEST_CASES=100
rfc_quality_config = GraspQualityConfigFactory.create_config(config_data['metrics']['robust_ferrari_canny'])
setattr(rfc_quality_config, 'force_limits', gripper.force_limit)
setattr(rfc_quality_config, 'finger_radius', gripper.finger_radius)
for key in dataset.object_keys:
print("*****Generating {} grasps and grasp metrics*****".format(key))
graspable_object_3d=dataset.graspable(key)
#init 'force_closure grasp quality' and 'robust_ferrari_canny quality' function
rfc_quality_fn = GraspQualityFunctionFactory.create_quality_function(graspable_object_3d, rfc_quality_config)
#5.1: generate and save grasps
grasps=ags.generate_grasps(graspable_object_3d,target_num_grasps=NUM_TEST_CASES)
dataset.store_grasps(key,grasps,gripper=GRIPPER_NAME)
#5.2: generate and save grasp_metrics
loaded_grasps=dataset.grasps(key)#must use loaded_grasps,otherwise grasp will not have grasp.id
grasp_metrics = {}
for grasp in tqdm(loaded_grasps,total=len(loaded_grasps)):
#get metric
rfc_metric=rfc_quality_fn(grasp).quality
#save metirc
grasp_metrics[grasp.id]={}
grasp_metrics[grasp.id]['robust_ferrari_canny']=rfc_metric
dataset.store_grasp_metrics(key,grasp_metrics,gripper=GRIPPER_NAME)
#N: close database
print("!!!!!!Generate Finish,close data!!!!!!")
database.close()
def check_database(database_path):
"""
Use for show database_path file info
"""
#1: load datasetbase and see datasets name
database=Hdf5Database(database_path,access_level=READ_ONLY_ACCESS)
print("load database,it has {} dataset,their names are:".format(len(database.datasets)))
for dataset in database.datasets:
print(dataset.name)
#select first dataset
dataset=database.dataset(database.datasets[0].name)
#2: see graspable in dataset
print("dataset has {} objects,their names are:".format(len(dataset.objects)))
for key in dataset.object_keys:
print(key)
#3: see stable poses
stable_poses=dataset.stable_poses(key)
print("{} has {} stable poses,first is:".format(key,len(stable_poses)))
print(stable_poses[0].T_obj_table)
#4: see grasps
grasps=dataset.grasps(key)
print(len(grasps))
#5: see grasps metrics
grasp_metrics=dataset.grasp_metrics(key,grasps)
print(len(grasp_metrics))
if __name__ == '__main__':
print("********begin function***********")
config_path="path/to/test/config.yaml"
generate_path="path/to/new_database.hdf5"
objs_path="path/to/models"#models is a Folder which only contain .obj files
create_database(config_path,generate_path,objs_path)
check_database(generate_path)
print("********end function***********")
dong329
Hello,I am now trying to make dataset from my own 3D models. I find that there are code for generation gqcnn dataset,but there are not code for generation database code. I know that there are some demo code in grasping_test.py,database_test.py and dexnet-cli.py,but are there any whole code for generating database code?
Thank you~