def worker(i, sample_nums, grasp_amount, good_grasp):
object_name = file_list_all[i][len(home_dir) + 35:]
print('a worker of task {} start'.format(object_name))
yaml_config = YamlConfig(home_dir +
"/code/grasp-pointnet/dex-net/test/config.yaml")
gripper_name = 'robotiq_85'
gripper = RobotGripper.load(
gripper_name, home_dir + "/code/grasp-pointnet/dex-net/data/grippers")
grasp_sample_method = "antipodal"
if grasp_sample_method == "uniform":
ags = UniformGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gaussian":
ags = GaussianGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "antipodal":
ags = AntipodalGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gpg":
ags = GpgGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "point":
ags = PointGraspSampler(gripper, yaml_config)
else:
raise NameError("Can't support this sampler")
print("Log: do job", i)
if os.path.exists(str(file_list_all[i]) + "/google_512k/nontextured.obj"):
of = ObjFile(str(file_list_all[i]) + "/google_512k/nontextured.obj")
sf = SdfFile(str(file_list_all[i]) + "/google_512k/nontextured.sdf")
else:
print("can't find any obj or sdf file!")
raise NameError("can't find any obj or sdf file!")
mesh = of.read()
sdf = sf.read()
obj = GraspableObject3D(sdf, mesh)
print("Log: opened object", i + 1, object_name)
force_closure_quality_config = {}
canny_quality_config = {}
fc_list_sub1 = np.arange(2.0, 0.75, -0.4)
fc_list_sub2 = np.arange(0.5, 0.36, -0.05)
fc_list = np.concatenate([fc_list_sub1, fc_list_sub2])
for value_fc in fc_list:
value_fc = round(value_fc, 2)
yaml_config['metrics']['force_closure']['friction_coef'] = value_fc
yaml_config['metrics']['robust_ferrari_canny'][
'friction_coef'] = value_fc
force_closure_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['force_closure'])
canny_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['robust_ferrari_canny'])
good_count_perfect = np.zeros(len(fc_list))
count = 0
minimum_grasp_per_fc = grasp_amount
while np.sum(good_count_perfect < minimum_grasp_per_fc) != 0:
grasps = ags.generate_grasps(obj,
target_num_grasps=sample_nums,
grasp_gen_mult=10,
vis=False,
random_approach_angle=True)
count += len(grasps)
for j in grasps:
tmp, is_force_closure = False, False
for ind_, value_fc in enumerate(fc_list):
value_fc = round(value_fc, 2)
tmp = is_force_closure
is_force_closure = PointGraspMetrics3D.grasp_quality(
j, obj, force_closure_quality_config[value_fc], vis=False)
if tmp and not is_force_closure:
if good_count_perfect[ind_ - 1] < minimum_grasp_per_fc:
canny_quality = PointGraspMetrics3D.grasp_quality(
j,
obj,
canny_quality_config[round(fc_list[ind_ - 1], 2)],
vis=False)
good_grasp.append((j, round(fc_list[ind_ - 1],
2), canny_quality))
good_count_perfect[ind_ - 1] += 1
break
elif is_force_closure and value_fc == fc_list[-1]:
if good_count_perfect[ind_] < minimum_grasp_per_fc:
canny_quality = PointGraspMetrics3D.grasp_quality(
j, obj, canny_quality_config[value_fc], vis=False)
good_grasp.append((j, value_fc, canny_quality))
good_count_perfect[ind_] += 1
break
print('Object:{} GoodGrasp:{}'.format(object_name, good_count_perfect))
object_name_len = len(object_name)
object_name_ = str(object_name) + " " * (25 - object_name_len)
if count == 0:
good_grasp_rate = 0
else:
good_grasp_rate = len(good_grasp) / count
print('Gripper:{} Object:{} Rate:{:.4f} {}/{}'.format(
gripper_name, object_name_, good_grasp_rate, len(good_grasp), count))
from meshpy.sdf_file import SdfFile
from dexnet.grasping import GraspableObject3D
import numpy as np
from dexnet.visualization.visualizer3d import DexNetVisualizer3D as Vis
from dexnet.grasping import RobotGripper
from autolab_core import YamlConfig
from mayavi import mlab
from dexnet.grasping import GpgGraspSampler # temporary way for show 3D gripper using mayavi
import glob
# global configurations:
home_dir = os.environ["HOME"] + "/code/PointNetGPD"
yaml_config = YamlConfig(home_dir + "/dex-net/test/config.yaml")
gripper_name = "robotiq_85"
gripper = RobotGripper.load(gripper_name, home_dir + "/dex-net/data/grippers")
ags = GpgGraspSampler(gripper, yaml_config)
save_fig = False # save fig as png file
show_fig = True # show the mayavi figure
generate_new_file = False # whether generate new file for collision free grasps
check_pcd_grasp_points = False
def open_npy_and_obj(name_to_open_):
npy_m_ = np.load(name_to_open_)
file_dir = home_dir + "/PointNetGPD/data/ycb-tools/models/ycb/"
object_name_ = name_to_open_.split("/")[-1][:-4]
ply_name_ = file_dir + object_name_ + "/google_512k/nontextured.ply"
if not check_pcd_grasp_points:
of = ObjFile(file_dir + object_name_ + "/google_512k/nontextured.obj")
sf = SdfFile(file_dir + object_name_ + "/google_512k/nontextured.sdf")
mesh = of.read()
def worker(i, sample_nums, grasp_amount, good_grasp): #主要是抓取采样器以及打分 100 20
"""
brief: 对制定的模型，利用随机采样算法，进行抓取姿态的检测和打分
param [in] i 处理第i个mesh模型
param [in] sample_nums 每个对象模型返回的目标抓取数量
param [in] grasp_amount
"""
#截取目标对象名称
object_name = file_list_all[i][len(home_dir) + 35:]
print('a worker of task {} start'.format(object_name))
#读取初始配置文件，读取并在内存中复制了一份
yaml_config = YamlConfig(home_dir + "/code/dex-net/test/config.yaml")
#设置夹名称
gripper_name = 'panda'
#根据设置的夹爪名称加载夹爪配置
gripper = RobotGripper.load(gripper_name,
home_dir + "/code/dex-net/data/grippers")
#设置抓取采样的方法
grasp_sample_method = "antipodal"
if grasp_sample_method == "uniform":
ags = UniformGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gaussian":
ags = GaussianGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "antipodal":
#使用对映点抓取，输入夹爪与配置文件
ags = AntipodalGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gpg":
ags = GpgGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "point":
ags = PointGraspSampler(gripper, yaml_config)
else:
raise NameError("Can't support this sampler")
print("Log: do job", i)
#设置obj模型文件与sdf文件路径
if os.path.exists(str(file_list_all[i]) +
"/google_512k/nontextured.obj") and os.path.exists(
str(file_list_all[i]) +
"/google_512k/nontextured.sdf"):
of = ObjFile(str(file_list_all[i]) + "/google_512k/nontextured.obj")
sf = SdfFile(str(file_list_all[i]) + "/google_512k/nontextured.sdf")
else:
print("can't find any obj or sdf file!")
raise NameError("can't find any obj or sdf file!")
#根据路径读取模型与sdf文件
mesh = of.read()
sdf = sf.read()
#构建抓取模型类
obj = GraspableObject3D(sdf, mesh)
print("Log: opened object", i + 1, object_name)
#########################################
#设置
force_closure_quality_config = {} #设置力闭合 字典
canny_quality_config = {}
#生成一个起点是2.0终点是0.75 步长为-0.4 （递减）的等距数列fc_list_sub1 (2.0, 0.75, -0.4)
fc_list_sub1 = np.arange(2.0, 0.75, -0.3)
#生成一个起点是0.5终点是0.36 步长为-0.05的等距数列fc_list_sub2 (0.5, 0.36, -0.05)
fc_list_sub2 = np.arange(0.5, 0.36, -0.1)
#将上面两个向量接起来，变成一个长条向量，使用不同的步长，目的是为了在更小摩擦力的时候，有更多的分辨率
fc_list = np.concatenate([fc_list_sub1, fc_list_sub2])
print("判断摩擦系数")
print(fc_list)
for value_fc in fc_list:
#对value_fc保留2位小数，四舍五入
value_fc = round(value_fc, 2)
#更改内存中配置中的摩擦系数，而没有修改硬盘中的yaml文件
yaml_config['metrics']['force_closure']['friction_coef'] = value_fc
yaml_config['metrics']['robust_ferrari_canny'][
'friction_coef'] = value_fc
#把每个摩擦力值当成键，
force_closure_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['force_closure'])
canny_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['robust_ferrari_canny'])
#####################准备开始采样############################
#填充一个与摩擦数量相同的数组，每个对应的元素都是0
good_count_perfect = np.zeros(len(fc_list))
count = 0
#设置每个摩擦值需要计算的最少抓取数量 （根据指定输入值20）
minimum_grasp_per_fc = grasp_amount
#如果每个摩擦系数下，有效的抓取(满足力闭合或者其他判断标准)小于要求值，就一直循环查找，直到所有摩擦系数条件下至少都存在20个有效抓取
while np.sum(good_count_perfect < minimum_grasp_per_fc) != 0:
#开始使用antipodes sample获得对映随机抓取，此时并不判断是否满足力闭合，只是先采集满足夹爪条件的抓取
#如果一轮多次随机采样之后，发现无法获得指定数量的随机抓取，就会重复迭代计算3次，之后放弃，并把已经找到的抓取返回来
grasps = ags.generate_grasps(obj,
target_num_grasps=sample_nums,
grasp_gen_mult=10,
max_iter=10,
vis=False,
random_approach_angle=True)
count += len(grasps)
#循环对每个采样抓取进行判断
for j in grasps:
tmp, is_force_closure = False, False
#循环对某个采样抓取应用不同的抓取摩擦系数，判断是否是力闭合
for ind_, value_fc in enumerate(fc_list):
value_fc = round(value_fc, 2)
tmp = is_force_closure
#判断在当前给定的摩擦系数下，抓取是否是力闭合的
is_force_closure = PointGraspMetrics3D.grasp_quality(
j, obj, force_closure_quality_config[value_fc], vis=False)
#假设当前,1号摩擦力为1.6 抓取不是力闭合的，但是上一个0号摩擦系数2.0 条件下抓取是力闭合的
if tmp and not is_force_closure:
#当0号2.0摩擦系数条件下采样的good抓取数量还不足指定的最低数量20
if good_count_perfect[ind_ - 1] < minimum_grasp_per_fc:
#以0号摩擦系数作为边界
canny_quality = PointGraspMetrics3D.grasp_quality(
j,
obj,
canny_quality_config[round(fc_list[ind_ - 1], 2)],
vis=False)
good_grasp.append((j, round(fc_list[ind_ - 1],
2), canny_quality))
#在0号系数的good抓取下计数加1
good_count_perfect[ind_ - 1] += 1
#当前抓取j的边界摩擦系数找到了，退出摩擦循环，判断下一个抓取
break
#如果当前1号摩擦系数1.6条件下，该抓取j本身就是力闭合的，且摩擦系数是列表中的最后一个（所有的摩擦系数都判断完了）
elif is_force_closure and value_fc == fc_list[-1]:
if good_count_perfect[ind_] < minimum_grasp_per_fc:
#以当前摩擦系数作为边界
canny_quality = PointGraspMetrics3D.grasp_quality(
j, obj, canny_quality_config[value_fc], vis=False)
good_grasp.append((j, value_fc, canny_quality))
good_count_perfect[ind_] += 1
#当前抓取j关于当前摩擦系数1.6判断完毕，而且满足所有的摩擦系数，就换到下一个摩擦系数
break
print('Object:{} GoodGrasp:{}'.format(object_name,
good_count_perfect)) #判断
object_name_len = len(object_name)
object_name_ = str(object_name) + " " * (25 - object_name_len)
if count == 0:
good_grasp_rate = 0
else:
good_grasp_rate = len(good_grasp) / count
print('Gripper:{} Object:{} Rate:{:.4f} {}/{}'.format(
gripper_name, object_name_, good_grasp_rate, len(good_grasp), count))
def worker(i, sample_nums, grasp_amount, good_grasp):
object_name = file_list_all[i][len(home_dir) + 48:-12]
print('a worker of task {} start'.format(object_name))
yaml_config = YamlConfig(home_dir +
"/Projects/PointNetGPD/dex-net/test/config.yaml")
gripper_name = 'robotiq_85'
gripper = RobotGripper.load(
gripper_name, home_dir + "/Projects/PointNetGPD/dex-net/data/grippers")
grasp_sample_method = "antipodal"
if grasp_sample_method == "uniform":
ags = UniformGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gaussian":
ags = GaussianGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "antipodal":
ags = AntipodalGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "gpg":
ags = GpgGraspSampler(gripper, yaml_config)
elif grasp_sample_method == "point":
ags = PointGraspSampler(gripper, yaml_config)
else:
raise NameError("Can't support this sampler")
print("Log: do job", i)
if os.path.exists(str(file_list_all[i]) + "/nontextured.obj"):
of = ObjFile(str(file_list_all[i]) + "/nontextured.obj")
sf = SdfFile(str(file_list_all[i]) + "/nontextured.sdf")
else:
print("can't find any obj or sdf file!")
raise NameError("can't find any obj or sdf file!")
mesh = of.read()
sdf = sf.read()
obj = GraspableObject3D(sdf, mesh)
print("Log: opened object", i + 1, object_name)
force_closure_quality_config = {}
canny_quality_config = {}
fc_list_sub1 = np.arange(2.0, 0.75, -0.4)
fc_list_sub2 = np.arange(0.5, 0.36, -0.05)
fc_list = np.concatenate(
[fc_list_sub1,
fc_list_sub2]) # 摩擦系数列表 fc_list [2. 1.6 1.2 0.8 0.5 0.45 0.4 ]
print("fc_list", fc_list, "fc_list[-1]", fc_list[-1])
for value_fc in fc_list:
value_fc = round(value_fc, 2)
yaml_config['metrics']['force_closure']['friction_coef'] = value_fc
yaml_config['metrics']['robust_ferrari_canny'][
'friction_coef'] = value_fc
force_closure_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['force_closure'])
canny_quality_config[
value_fc] = GraspQualityConfigFactory.create_config(
yaml_config['metrics']['robust_ferrari_canny'])
good_count_perfect = np.zeros(len(fc_list))
count = 0
minimum_grasp_per_fc = grasp_amount
# 各个摩擦系数生成一些抓取
while np.sum(good_count_perfect < minimum_grasp_per_fc
) != 0 and good_count_perfect[-1] < minimum_grasp_per_fc:
print("[INFO]:good | mini", good_count_perfect, minimum_grasp_per_fc)
print("[INFO]:good < mini", good_count_perfect < minimum_grasp_per_fc,
np.sum(good_count_perfect < minimum_grasp_per_fc))
grasps = ags.generate_grasps(
obj,
target_num_grasps=sample_nums,
grasp_gen_mult=10, # 生成抓取姿态
vis=False,
random_approach_angle=True) # 随机调整抓取方向
print("\033[0;32m%s\033[0m" %
"[INFO] Worker{} generate {} grasps.".format(i, len(grasps)))
count += len(grasps)
for j in grasps: # 遍历生成的抓取姿态, 判断是否为力闭合, 及其对应的摩擦系数
tmp, is_force_closure = False, False
for ind_, value_fc in enumerate(fc_list): # 为每个摩擦系数分配抓取姿态
value_fc = round(value_fc, 2)
tmp = is_force_closure
is_force_closure = PointGraspMetrics3D.grasp_quality(
j,
obj, # 依据摩擦系数 value_fc 评估抓取姿态
force_closure_quality_config[value_fc],
vis=False)
print("[INFO] is_force_closure:", is_force_closure,
"value_fc:", value_fc, "tmp:", tmp)
if tmp and not is_force_closure: # 前一个摩擦系数下为力闭合, 当前摩擦系数下非力闭合, 即找到此抓取对应的最小摩擦系数
print("[debug] tmp and not is_force_closure")
if good_count_perfect[ind_ - 1] < minimum_grasp_per_fc:
canny_quality = PointGraspMetrics3D.grasp_quality(
j,
obj,
canny_quality_config[round(fc_list[ind_ - 1], 2)],
vis=False)
good_grasp.append(
(j, round(fc_list[ind_ - 1],
2), canny_quality)) # 保存前一个抓取
good_count_perfect[ind_ - 1] += 1
break
elif is_force_closure and value_fc == fc_list[
-1]: # 力闭合并且摩擦系数最小
print(
"[debug] is_force_closure and value_fc == fc_list[-1]")
if good_count_perfect[ind_] < minimum_grasp_per_fc:
canny_quality = PointGraspMetrics3D.grasp_quality(
j, obj, canny_quality_config[value_fc], vis=False)
good_grasp.append((j, value_fc, canny_quality))
good_count_perfect[ind_] += 1
break
print('Worker:', i,
'Object:{} GoodGrasp:{}'.format(object_name, good_count_perfect))
object_name_len = len(object_name)
object_name_ = str(object_name) + " " * (25 - object_name_len)
if count == 0:
good_grasp_rate = 0
else:
good_grasp_rate = len(good_grasp) / count
print(
'Worker:', i, 'Gripper:{} Object:{} Rate:{:.4f} {}/{}'.format(
gripper_name, object_name_, good_grasp_rate, len(good_grasp),
count))