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jfxmap_python
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script/generate/N2_1/2-1.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 2-1
CAMERA_LAYOUT: [0, -1, -1]
POLICY_MASK: [1, -1, -1]
CAMERA_SERIAL_NO: [21135335, -1, -1]
DRIVE_DIRECTION: [4, -1, -1] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: [-1, -1] # -1 means no blind area is used
POINTPILLARS:
MAX_NUM_PILLARS: 40000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [9.194107300534933, -5.626122637755718, 150.0, 20.0, 11.849103226522763,
15.0, -9.0, 80.0, -80.0, -3.487847099057734]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/PillarVFE_JF_ep200_40000_4.0_N19_v2.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/RPNWithHead_JF_ep200_4.0_N19_v2.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[892, 1076], [2232, 1177], [2027, 912], [1289, 857]], [[0, 0], [
0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [1000.052144, -1000.052144], [1000.052144, -1000.052144]]
PC_POLICY_AREA: [[[1519, 874], [1902, 904], [1941, 1019], [1984, 1158], [1304, 1106],
[1443, 963], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0,
0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0,
0], [0, 0], [0, 0], [0, 0]]]
ORIGIN2KITTI: [[0.05045908306549516, -0.997741728436212, 0.04433197799906369, 0.0],
[0.9959209468126726, 0.05359234819149573, 0.07259013648610471, 0.0], [-0.0748020630460102,
0.04048831377621809, 0.9963761076077744, 0.2859260540868016], [0.0, 0.0, 0.0,
1.0]]
KITTI2ORIGIN: [[0.05045908306549516, 0.9959209468126726, -0.0748020630460102, 0.021387858724297854],
[-0.9977417284362121, 0.053592348191495734, 0.04048831377621808, -0.011576663794662325],
[0.04433197799906368, 0.0725901364861047, 0.9963761076077743, -0.2848898888346573],
[0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.992241, -0.124323, 0.00128333, -0.124081], [-0.00340261, 0.0168357,
-0.999852, -0.276797], [0.124283, -0.992099, -0.0171281, 0.736849], [0, 0, 0,
1]]
LIDAR_CAM_RS_1: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
LIDAR_CAM_RS_2: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
CAM_INTRINSICS_0: [[7509.058839767741, 0.0, 1220.4602532816716], [0.0, 7523.874930002482,
1052.3337431444797], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
CAM_INTRINSICS_2: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: false
SAVE_LOG: false^M
TRANS: [[-0.055129300803, -0.997837603092, 0.03578780219, 40610743.054429375], [
0.997480928898, -0.053436487913, 0.046649895608, 3462873.475322144], [-0.044636640698,
0.038269422948, 0.998269975185, 19.47205303], [0.0, 0.0, 0.0, 1.0]]
BLIND_IN: []
BLIND_OUT: []
script/generate/N2_3/2-3.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 2-3
CAMERA_LAYOUT: [0, -1, -1] #-1代表没有此方向相机，非-1的值按照0，1，2排序
X_ROT_ANGLE: 1 # 0代表x轴不旋转，仅微调；1代表x轴顺时针旋转90度；2代表顺时针旋转180度
POLICY_MASK: [1, -1, -1] # 1代表这个方向加远处策略，-1代表不加策略
CAMERA_SERIAL_NO: [21177826, -1, -1]
DRIVE_DIRECTION: [4, -1, -1] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: -1
# -1 indicates no blind area in this loc is considered;
# 1 means normal blind area (one blind in, one blind out);
# 2 means two-way lane (one blind in, two blind out: each for one direction);
POINTPILLARS:
MAX_NUM_PILLARS: 40000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [6.1251366922756505, -6.994626040527446, 150.0, 10.0, 2.0330234900343815,
14.0, -10.0, 80.0, -80.0, -17.61428020647701]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/PillarVFE_JF_ep100_40000_4.0_N19.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/RPNWithHead_JF_ep100_4.0_N19.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[832, 1405], [2031, 1417], [1743, 1140], [1084, 1134]], [[0, 0],
[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [1000.075752, -1000.075752], [1000.075752, -1000.075752]]
PC_POLICY_AREA: [[[1195, 1138], [1673, 1142], [1761, 1299], [1823, 1418], [1068,
1416], [1141, 1267], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0,
0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]]
ORIGIN2KITTI: [[0.07568437565247627, -0.9968308870840116, -0.02449607800031248,
0.0], [0.9971317454228593, 0.07565174868629727, 0.002257252902847979, 0.0],
[-0.0003969282768393359, -0.024596655789107878, 0.9996973776958381, 0.9000000000000004],
[0.0, 0.0, 0.0, 1.0]]
KITTI2ORIGIN: [[0.07568437565247625, 0.9971317454228593, -0.00039692827683933564,
0.00035723544915540245], [-0.9968308870840116, 0.07565174868629727, -0.02459665578910788,
0.0221369902101971], [-0.024496078000312487, 0.0022572529028479795, 0.9996973776958382,
-0.8997276399262547], [0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.994864, -0.101092, 0.00511658, 0.0528449], [-0.0100038, 0.0478969,
-0.998802, -0.273319], [0.100726, -0.993723, -0.0486622, 0.659704], [0, 0, 0,
1]]
LIDAR_CAM_RS_1: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
LIDAR_CAM_RS_2: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
CAM_INTRINSICS_0: [[7559.7, 0.0, 1201.2], [0.0, 7572.8, 985.6993], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
CAM_INTRINSICS_2: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: true
TRANS: [[0.118877768517, 0.990384340286, -0.070760093629, 40610864.76316899], [
-0.992871820927, 0.119187220931, 0.000152187393, 3462876.197313769], [0.008584422991,
0.070237606764, 0.997493386269, 20.07169652], [0.0, 0.0, 0.0, 1.0]]
BLIND_IN: []
BLIND_OUT: []
BLIND_OUT_2: []
script/generate/N3_1/3-1.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 3-1
CAMERA_LAYOUT: [0, -1, -1]
POLICY_MASK: [-1, -1, -1]
CAMERA_SERIAL_NO: [21179749, -1, -1]
DRIVE_DIRECTION: [4, -1, -1] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: [-1, -1] # -1 means no blind area is used
POINTPILLARS:
MAX_NUM_PILLARS: 40000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [7.005945168920547, -6.716958738970565, 80.0, 24.0, 13.64794108585249,
21.0, -10.0, 80.0, -80.0, -4.948397653689416]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/PillarVFE_JF_ep100_40000_4.0_N19.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/RPNWithHead_JF_ep100_4.0_N19.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [1000.102829, -1000.102829], [1000.102829, -1000.102829]]
PC_POLICY_AREA: [[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0,
0]]]
ORIGIN2KITTI: [[0.10282893175566048, -0.9927983688656276, 0.06146226136206685, 0.0],
[0.9946815801416514, 0.10226437626202323, -0.01226994191701453, 0.0], [0.0058961784994428,
0.06239708427073043, 0.9980339868729993, 0.7844127162859094], [0.0, 0.0, 0.0,
1.0]]
KITTI2ORIGIN: [[0.10282893175566046, 0.9946815801416512, 0.005896178499442798, -0.004625037392454504],
[-0.9927983688656277, 0.10226437626202323, 0.062397084270730444, -0.04894506636112445],
[0.06146226136206687, -0.012269941917014532, 0.9980339868729994, -0.782870550588705],
[0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.996872, 0.078968, -0.00318625, 0.0158725], [0.0130107, 0.124211,
-0.992171, -0.129212], [-0.077954, -0.989109, -0.12485, 0.635463], [0, 0, 0,
1]]
LIDAR_CAM_RS_1: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
LIDAR_CAM_RS_2: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
CAM_INTRINSICS_0: [[7539.908889587756, 0.0, 1234.3767381002895], [0.0, 7554.026627701238,
1018.7128705998498], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
CAM_INTRINSICS_2: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: true
TRANS: [[-0.043562665582, 0.997077941895, -0.062752954662, 40611565.80853813], [
-0.998564898968, -0.041496656835, 0.033858418465, 3463081.4422527524], [0.031155442819,
0.064137846231, 0.997454583645, 19.81514911], [0.0, 0.0, 0.0, 1.0]]
BLIND_IN: []
BLIND_OUT: []
script/generate/N3_2/3-2.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 3-2
CAMERA_LAYOUT: [0, -1, -1] #-1代表没有此方向相机，非-1的值按照0，1，2排序
X_ROT_ANGLE: 0 # 0代表x轴不旋转，仅微调；1代表 正常 kitti旋转；2代表顺时针旋转180度
POLICY_MASK: [-1, -1, -1] # 1代表这个方向加远处策略，-1代表不加策略
CAMERA_SERIAL_NO: [21122017, -1, -1] #相机序列号，-1代表没有
DRIVE_DIRECTION: [-1, -1, -1] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: 1
# -1 indicates no blind area in this loc is considered;
# 1 means normal blind area (one blind in, one blind out);
# 2 means two-way lane (one blind in, two blind out: each for one direction);
POINTPILLARS:
MAX_NUM_PILLARS: 40000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [8.28605438543586, 8.28605438543586, 80.0, 40.0, 6.459208303881683,
104.0, -127.0, 80.0, -80.0, -13.381680298363811]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/PillarVFE_JF_ep200_40000_4.0_N19_v2.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N19/RPNWithHead_JF_ep200_4.0_N19_v2.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [1000.028316, -1000.028316], [1000.028316, -1000.028316]]
PC_POLICY_AREA: [[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0,
0]]]
ORIGIN2KITTI: [[0.9995180204153945, 0.028485544660163925, -0.012341013341895022,
10.0], [-0.02812481342444661, 0.9991986491976914, 0.02847901528053492, 0.0],
[0.013142364122603362, -0.02811820027887063, 0.9995182064766736, 0.7000000000000002],
[0.0, 0.0, 0.0, 1.0]]
KITTI2ORIGIN: [[0.9995180204153945, -0.028124813424446605, 0.013142364122603362,
-0.009199654885822353], [0.02848554466016391, 0.9991986491976912, -0.02811820027887063,
0.019682740195209443], [-0.01234101334189502, 0.028479015280534915, 0.9995182064766736,
-0.6996627445336716], [0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.994998161, 0.0998929805, 0.00022632723, 0.00244574], [0.0181879421,
0.18339046, -0.982871883, -0.209588], [-0.0982235081, -0.9779516, -0.184290018,
0.615876], [0, 0, 0, 1]]
LIDAR_CAM_RS_1: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
LIDAR_CAM_RS_2: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
CAM_INTRINSICS_0: [[7481.380550495129, 0.0, 1149.9210278736896], [0.0, 7494.475356826924,
1022.9019086640917], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
CAM_INTRINSICS_2: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: false
SAVE_LOG: true
TRANS: [[-0.21047334373, -0.977406859398, -0.019409403205, 40611447.92258323], [
0.977276146412, -0.20985160768, -0.029891142622, 3463078.57738011], [0.025142716244,
-0.025259640068, 0.999364733696, 20.34537223], [0.0, 0.0, 0.0, 1.0]]
BLIND_IN: [4.319010529319466, 15.84543415901052, -16.66174637729601, 14.517728383602746]
BLIND_OUT: [3.7139257823670073, 14.91321390226355, 11.03645420678645, 26.545831007005923]
BLIND_OUT_2: []
script/generate/N5_1/5-1.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 5-1
CAMERA_LAYOUT: [0, -1, -1]
POLICY_MASK: [-1, -1, -1]
CAMERA_SERIAL_NO: [21177822, -1, -1]
DRIVE_DIRECTION: [4, -1, -1] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: [-1, -1] # -1 means no blind area is used
POINTPILLARS:
MAX_NUM_PILLARS: 40000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [0.11561325046748723, -1.092793314581994, 80.0, 152.0, 48.33986211090886,
40.0, -58.0, 80.0, -80.0, -4.799776272749738]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/5/PillarVFE_JF_ep150_40000_4.0_N5_v2.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/5/RPNWithHead_JF_ep150_4.0_N5_v2.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [999.904895, -999.904895], [999.904895, -999.904895]]
PC_POLICY_AREA: [[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [
0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0,
0]]]
ORIGIN2KITTI: [[-0.09480387225484764, -0.9950266779148981, 0.03056363923239795,
0.0], [0.9954426694786479, -0.09507180857810195, -0.007432563145518051, 0.0],
[0.010301339073630237, 0.02971971485950677, 0.9995051880615526, 0.2999999999999998],
[0.0, 0.0, 0.0, 1.0]]
KITTI2ORIGIN: [[-0.09480387225484765, 0.9954426694786479, 0.010301339073630237,
-0.003090401722089069], [-0.9950266779148981, -0.09507180857810194, 0.029719714859506766,
-0.008915914457852024], [0.03056363923239795, -0.007432563145518051, 0.9995051880615526,
-0.2998515564184656], [0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.995931, 0.0900209, -0.00411634, -0.00573507], [0.0128231, 0.0963568,
-0.995264, -0.25262], [-0.089198, -0.991268, -0.0971191, 0.610466], [0, 0, 0,
1]]
LIDAR_CAM_RS_1: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
LIDAR_CAM_RS_2: [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]
CAM_INTRINSICS_0: [[7622.689563753009, 0.0, 1211.247963539479], [0.0, 7639.9807282917445,
1059.6689492152698], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
CAM_INTRINSICS_2: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: false
SAVE_LOG: true
TRANS: [[0.916303992271, -0.40038600564, 0.008842683397, 40611912.88483711], [0.400390565395,
0.915391683578, -0.041782874614, 3462876.4089042605], [0.008634760976, 0.041826341301,
0.999087572098, 21.27533309], [0.0, 0.0, 0.0, 1.0]]
BLIND_IN: []
BLIND_OUT: []
script/generate/N7_1/7-1.yaml 0 → 100644
View file @ c9f3aab8
NODE_NAME: 7-1
CAMERA_LAYOUT: [0, 1, 2] #-1代表没有此方向相机，非-1的值按照0，1，2排序
X_ROT_ANGLE: 1 # 0代表x轴不旋转，仅微调；1代表x轴顺时针旋转90度；2代表顺时针旋转180度
POLICY_MASK: [1, 1, 1] # 1代表这个方向加远处策略，-1代表不加策略
CAMERA_SERIAL_NO: [21185406, 21185390, 21177824]
DRIVE_DIRECTION: [0, 0, 0] # 0为双向，1为单向朝左，2为单向朝右，3为单向朝上，4为单向朝下，-1代表没有
USE_BLIND: 1
# -1 indicates no blind area in this loc is considered;
# 1 means normal blind area (one blind in, one blind out);
# 2 means two-way lane (one blind in, two blind out: each for one direction);
POINTPILLARS:
MAX_NUM_PILLARS: 20000
MAX_NUM_POINTS_PER_PILLAR: 32
NUM_CLASS: 5
SCORE_THRESHOLD: 0.3
NMS_OVERLAP_THRESHOLD: 0.5
PFE_OUT_SIZE: 64
NUM_THREADS: 64
BATCH_SIZE: 1
PILLAR_SIZES: [0.16, 0.16, 6]
GRID_SIZES: [512, 1024, 1]
PC_T_RANGE: [15.04341374235729, -5.260303364170211, 150.0, 30.0, 19.38420271149741,
28.0, -23.0, 150.0, -150.0, -8.369847269674542]
PC_RANGE: [0, -81.92, -7.0, 81.92, 81.92, -1.0]
PFE_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N7_2/PillarVFE_JF_ep100_20000_4.0_N7_2.onnx
RPN_ONNX_FILE: /home/nvidia/catkin_ws/src/jfxrosperceiver/pointpillars/build/models/4.0_N7_2/RPNWithHead_JF_ep100_4.0_N7_2.onnx
IMAGE_SIZES: [[2048, 2448], [2048, 2448], [2048, 2448]]
IMG_CONTOURS: [[[744, 520], [2447, 561], [1347, 328], [2351, 347]], [[0, 751], [
1979, 790], [0, 548], [1224, 574]], [[737, 729], [2447, 680], [1512, 502],
[2447, 468]]]
PC_POLICY_ROTY: [[3.141593, 0.0], [-1.591032, 1.591032], [1.550686, -1.550686]]
PC_POLICY_AREA: [[[1598, 331], [1797, 339], [1842, 430], [1796, 544], [1084, 528],
[1373, 413], [1798, 333], [2223, 344], [2312, 436], [2403, 563], [1798, 548],
[1840, 425]], [[251, 557], [469, 563], [553, 626], [739, 770], [369, 757], [
294, 641], [471, 559], [752, 565], [960, 661], [1228, 778], [734, 765], [
580, 655]], [[1651, 493], [1953, 489], [1833, 581], [1687, 696], [1142, 716],
[1418, 598], [1951, 483], [2215, 475], [2173, 573], [2136, 673], [1674, 704],
[1835, 573]]]
ORIGIN2KITTI: [[-0.03414222029249569, -0.9993794541775686, 0.008661140874264117,
0.0], [0.9993821935410669, -0.0340674395723183, 0.008639490380331674, 0.0],
[-0.008339066287306893, 0.00895076134927045, 0.999925169122532, -0.7000000000000002],
[0.0, 0.0, 0.0, 1.0]]
KITTI2ORIGIN: [[-0.03414222029249568, 0.9993821935410669, -0.008339066287306894,
-0.005837346401114827], [-0.9993794541775686, -0.03406743957231831, 0.00895076134927045,
0.0062655329444893165], [0.008661140874264119, 0.008639490380331674, 0.9999251691225322,
0.6999476183857727], [0.0, 0.0, 0.0, 1.0]]
GROUND_Z_VALUE: [-6.0, -6.0, -6.0]
MINMAX_DEPTH: [82.0, 150.0]
LIDAR_CAM_RS_0: [[-0.995936, -0.0897805, 0.0070823, -0.0132271], [-0.0173271, 0.11385,
-0.993347, -0.239199], [0.0883769, -0.989433, -0.114943, 0.131749], [0, 0, 0,
1]]
LIDAR_CAM_RS_1: [[-0.155891, -0.987287, 0.0310081, 0.0516705], [-0.0836811, -0.0180789,
-0.996329, -0.194909], [0.984223, -0.157914, -0.0797989, -0.0143181], [0, 0,
0, 1]]
LIDAR_CAM_RS_2: [[-0.130709, 0.990636, -0.0394256, -0.0604812], [0.11214, -0.0247386,
-0.993384, -0.199602], [-0.985058, -0.134266, -0.107857, 0.530719], [0, 0, 0,
1]]
CAM_INTRINSICS_0: [[7350.0, 0.0, 1173.5], [0.0, 7364.8, 999.1346], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_1: [[7293.442930597376, 0.0, 1182.551725157397], [0.0, 7304.495100903007,
981.5291640436195], [0.0, 0.0, 1.0]]
CAM_INTRINSICS_2: [[7552.764938482027, 0.0, 1228.4353420623622], [0.0, 7566.224849928247,
1019.0673853747635], [0.0, 0.0, 1.0]]
TRACKING:
LOG_FOLDER: /home/nvidia/catkin_ws/src/jfxrosperceiver/tracking_logs/
TIMING: false
TRANS: [[ 6.60447598e-01, -7.50757873e-01, 1.30958920e-02, 4.06130966e+07],
[ 7.50822484e-01, 6.60503983e-01, -2.45426620e-05, 3.46271628e+06],
[-8.63146223e-03, 9.84889828e-03, 9.99914229e-01, 1.87395959e+01],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]
BLIND_IN: [0.8793207584354021, 11.318516057393802, -11.726795120867939, 12.364750077435513]
BLIND_OUT: [2.9499341591370176, 10.918559387668694, -18.931044274997923, -10.873520715931155]
BLIND_OUT_2: []
script/trans_and_export_data_multi_auto.py
View file @ c9f3aab8
...@@ -459,6 +459,19 @@ class NumpyEncoder(json.JSONEncoder): ...@@ -459,6 +459,19 @@ class NumpyEncoder(json.JSONEncoder):
return obj.tolist() return obj.tolist()
return json.JSONEncoder.default(self, obj) return json.JSONEncoder.default(self, obj)
car_yaw_cal_angle = {}
def InitCarYawAngle():
global car_yaw_cal_angle
car_yaw_cal_angle["N5_3"] = 79.89299572540227
car_yaw_cal_angle["N2_1"] = 131.419987
car_yaw_cal_angle["N2_3"] = 263.172408323
car_yaw_cal_angle["N3_1"] = 272.497959364
car_yaw_cal_angle["N3_2"] = 131.419987
car_yaw_cal_angle["N5_1"] = 156.39647170561636
car_yaw_cal_angle["N7_1"] = 131.419987
InitCarYawAngle()
g_saveFileType = 1 #0为pcd文件，1是bin文件 g_saveFileType = 1 #0为pcd文件，1是bin文件
g_showCloud = 0 #0不显示，1显示 g_showCloud = 0 #0不显示，1显示
...@@ -585,7 +598,7 @@ threads = [] ...@@ -585,7 +598,7 @@ threads = []
threadID = 1 threadID = 1
class pcdThread (threading.Thread): class pcdThread (threading.Thread):
def __init__(self, threadID, name,start,idx, _dirs,_dir_pcd_list,save_list,jsn_list, trans,kit2o,box_info_count): def __init__(self, threadID, name,start,idx, _dirs,_dir_pcd_list,save_list,jsn_list, trans,kit2o,box_info_count,_dir_g):
threading.Thread.__init__(self) threading.Thread.__init__(self)
self.threadID = threadID self.threadID = threadID
self.name = name self.name = name
...@@ -598,11 +611,13 @@ class pcdThread (threading.Thread): ...@@ -598,11 +611,13 @@ class pcdThread (threading.Thread):
self.trans = trans self.trans = trans
self.kit2ori = kit2o self.kit2ori = kit2o
self.box_info_count = box_info_count self.box_info_count = box_info_count
self.dir_g = _dir_g
def run(self): def run(self):
global g_save_count global g_save_count
global MAX_SAVE_FILE_NUM global MAX_SAVE_FILE_NUM
global g_saveFileType global g_saveFileType
global g_map_lock global g_map_lock
global car_yaw_cal_angle
print ("开启线程：" + self.name) print ("开启线程：" + self.name)
isStop = 0 isStop = 0
index = self.begin index = self.begin
...@@ -637,7 +652,7 @@ class pcdThread (threading.Thread): ...@@ -637,7 +652,7 @@ class pcdThread (threading.Thread):
# exportCenterBL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, generate_Trans) # exportCenterBL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, generate_Trans)
lidar_loc_ex,out_BL_ex,exportCenterBL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, self.trans,self.kit2ori) lidar_loc_ex,out_BL_ex,exportCenterBL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, self.trans,self.kit2ori)
# print(exportCenterBL) # print(exportCenterBL)
angle2 = car_yaw_cal(generate_car_yaw_cal_angle,bbox[2]) angle2 = car_yaw_cal(car_yaw_cal_angle[self.dir_g],bbox[2])
g_map_lock.acquire() g_map_lock.acquire()
mapInfoExport = get_map_data(exportCenterBL[0],exportCenterBL[1],angle2) mapInfoExport = get_map_data(exportCenterBL[0],exportCenterBL[1],angle2)
g_map_lock.release() g_map_lock.release()
...@@ -698,30 +713,14 @@ class pcdThread (threading.Thread): ...@@ -698,30 +713,14 @@ class pcdThread (threading.Thread):
print ("退出线程：" + self.name) print ("退出线程：" + self.name)
if __name__ == '__main__':
if __name__ == '__main__':
# origin_root_path = "/media/sf_shared/nodes/" #读取数据的总目录 # origin_root_path = "/media/sf_shared/nodes/" #读取数据的总目录
# generate_root_path = "/media/sf_shared/5-3/" # generate_root_path = "/media/sf_shared/5-3/"
origin_root_path = "/host/home/sata2/datasets/N19_annotations/" #读取数据的总目录 origin_root_path = "/host/home/sata2/datasets/N19_annotations/" #读取数据的总目录
generate_root_path = "/host/home/sata2/oscar/jfxmap_python/script/data/5-3/" generate_root_path = "/host/home/sata2/oscar/jfxmap_python/script/generate/"
generate_car_yaw_cal_angle = 79.89299572540227 # generate_car_yaw_cal_angle = 79.89299572540227
generate_pcd_path = os.path.join(generate_root_path,"pcd/")
if os.path.exists(generate_pcd_path) == False:
os.mkdir(generate_pcd_path)
if os.path.isdir(generate_pcd_path) == False:
os.remove(generate_pcd_path)
os.mkdir(generate_pcd_path)
generate_child_dir = "N5_3"
generate_cfg_path = generate_root_path + "config/5-3.yaml"
generate_cfg = read_yaml(generate_cfg_path)
generate_Trans=generate_cfg.TRACKING.TRANS
generate_Trans = np.array(generate_Trans)
generate_kitti2origin=generate_cfg.POINTPILLARS.KITTI2ORIGIN
generate_kitti2origin = np.array(generate_kitti2origin)
# p_dir = "/home/oscar/ros/git/jfxmap_python/jfxmap/" # p_dir = "/home/oscar/ros/git/jfxmap_python/jfxmap/"
p_dir = "/host/home/sata2/oscar/jfxmap_python/jfxmap/" p_dir = "/host/home/sata2/oscar/jfxmap_python/jfxmap/"
...@@ -729,22 +728,11 @@ if __name__ == '__main__': ...@@ -729,22 +728,11 @@ if __name__ == '__main__':
ret = init_jfxmap(p_dir,f_path) ret = init_jfxmap(p_dir,f_path)
print(ret) print(ret)
base_file_path = os.path.join(generate_root_path, "base/1633640721.417269000.pcd") dirsAll_g = os.listdir(generate_root_path)
g_xyzi,g_pcd,g_converted_pcd = parse_pandarmind_pcd(base_file_path, rotMat[generate_child_dir]) dirs_g =[]
# 计算点云图的地面平面 for file_g in dirsAll_g:
# converted_points = np.array(g_converted_pcd.points) if os.path.isdir(generate_root_path + file_g) == True:
# b = np.where( (converted_points[:,2] <= -5.0) & (converted_points[:,2] > -7) ) dirs_g.append(file_g)
# cropped_cropped = converted_points[b]
# print(cropped_cropped.shape)
# seg,m = ground_segmentation(data=cropped_cropped[:,:3])
m = [ 1.11119401e-03, -8.66741252e-04, 1.60358817e-01, 9.87057781e-01]
save_cloud_list = [] #保存点云的数组
save_json = {}
save_json["annotations"] = []
save_json_list = []
cloud_box_count = [] #统计box的数据
dir_pcd_list = {} dir_pcd_list = {}
dirsAll = os.listdir(origin_root_path) dirsAll = os.listdir(origin_root_path)
...@@ -768,123 +756,94 @@ if __name__ == '__main__': ...@@ -768,123 +756,94 @@ if __name__ == '__main__':
anno[fileuri] = an['labels_box3D'] anno[fileuri] = an['labels_box3D']
fp.close() fp.close()
dir_pcd_list[dir].append(anno) dir_pcd_list[dir].append(anno)
for i in range(Thread_NUM):
list = [] for dir_g in dirs_g:
save_cloud_list.append(list) pcd_list = glob.glob(os.path.join(generate_root_path + dir_g,"*.pcd"))
jsn_list = [] if len(pcd_list) <= 0 :
save_json_list.append(jsn_list) continue
box_info_cout = {'big': 0, 'little':0,'pedestrian':0,'mid':0,'cyclist':0} yaml_list = glob.glob(os.path.join(generate_root_path + dir_g,"*.yaml"))
cloud_box_count.append(box_info_cout) if len(yaml_list) <= 0 :
thread = pcdThread(i, "Thread"+ str(i) ,i,Thread_NUM,dirs,dir_pcd_list,save_cloud_list[i],save_json_list[i],generate_Trans,generate_kitti2origin,cloud_box_count[i]) continue
thread.start() plane_list = glob.glob(os.path.join(generate_root_path + dir_g,"*.json"))
threads.append(thread) if len(plane_list) <= 0 :
for t in threads: continue
t.join()
for _ind,cloud_i in enumerate(save_cloud_list): generate_pcd_path = os.path.join(generate_root_path + dir_g,"pcd/")
for cloud in cloud_i: if os.path.exists(generate_pcd_path) == False:
if len(cloud["boxes"]) >=10: os.mkdir(generate_pcd_path)
Save_Cloud_File(cloud,save_json_list[_ind]) if os.path.isdir(generate_pcd_path) == False:
for t_idx in save_json_list: os.remove(generate_pcd_path)
for jsn in t_idx: os.mkdir(generate_pcd_path)
save_json["annotations"].append(jsn)
generate_child_dir = dir_g
jsn_path = os.path.join(generate_root_path,generate_child_dir + ".json")
with open(jsn_path,'w') as file_obj: generate_cfg_path = os.path.join(generate_root_path + dir_g, yaml_list[0])
json.dump(save_json,file_obj,cls=NumpyEncoder,indent=4) generate_cfg = read_yaml(generate_cfg_path)
totel_count = {'big': 0, 'little':0,'pedestrian':0,'mid':0,'cyclist':0} generate_Trans=generate_cfg.TRACKING.TRANS
for count in cloud_box_count: generate_Trans = np.array(generate_Trans)
totel_count['big'] += count['big'] generate_kitti2origin=generate_cfg.POINTPILLARS.KITTI2ORIGIN
totel_count['little'] += count['little'] generate_kitti2origin = np.array(generate_kitti2origin)
totel_count['pedestrian'] += count['pedestrian']
totel_count['mid'] += count['mid']
totel_count['cyclist'] += count['cyclist']
cloud_box_count.append(totel_count) base_file_path = os.path.join(generate_root_path + dir_g, pcd_list[0])
count_jsn_path = os.path.join(generate_root_path,"count.json") g_xyzi,g_pcd,g_converted_pcd = parse_pandarmind_pcd(base_file_path, rotMat[generate_child_dir])
with open(count_jsn_path,'w') as file_o: # 计算点云图的地面平面
json.dump(cloud_box_count,file_o,cls=NumpyEncoder,indent=4) # converted_points = np.array(g_converted_pcd.points)
# b = np.where( (converted_points[:,2] <= -5.0) & (converted_points[:,2] > -7) )
# isStop = 0 # cropped_cropped = converted_points[b]
# index = 0 # print(cropped_cropped.shape)
# while isStop == 0:
# if g_save_count >= MAX_SAVE_FILE_NUM: # seg,m = ground_segmentation(data=cropped_cropped[:,:3])
# print("finish g_save_count = ",g_save_count) # m = [ 1.11119401e-03, -8.66741252e-04, 1.60358817e-01, 9.87057781e-01]
# break jsn_file = os.path.join(generate_root_path + dir_g, plane_list[0])
# isDeal = 0 m = []
# for dir in dirs: with open(jsn_file, 'r',encoding='utf-8',errors='ignore') as fp:
# if len(dir_pcd_list[dir][0]) > index: m = json.load(fp)
# isDeal = 1
# pcd_file = dir_pcd_list[dir][0][index]
# anno = dir_pcd_list[dir][1] save_cloud_list = [] #保存点云的数组
# child_dir = dir save_json = {}
# if pcd_file not in anno: save_json["annotations"] = []
# print('the anno is null : ', pcd_file) save_json_list = []
# continue cloud_box_count = [] #统计box的数据
# # print(pcd_file)
# boxx = anno[pcd_file] for i in range(Thread_NUM):
# bbox_flag,bboxes = read_all_3d_bboxes(boxx, rotMat[child_dir]) list = []
# if bbox_flag == False or len(bboxes) < 1: save_cloud_list.append(list)
# continue jsn_list = []
# xyzi,pcd,converted_pcd = parse_pandarmind_pcd(pcd_file, rotMat[child_dir]) save_json_list.append(jsn_list)
# xyz = np.array(converted_pcd.points) box_info_cout = {'big': 0, 'little':0,'pedestrian':0,'mid':0,'cyclist':0}
# if xyz.size == 0: cloud_box_count.append(box_info_cout)
# continue; thread = pcdThread(i, "Thread"+ str(i) ,i,Thread_NUM,dirs,dir_pcd_list,save_cloud_list[i],save_json_list[i],generate_Trans,generate_kitti2origin,cloud_box_count[i],dir_g)
# for bbox in bboxes: thread.start()
# # exportCenterBL = get_loc_from_origin([bbox[4][0], bbox[4][1], bbox[4][2]], 0, generate_Trans) threads.append(thread)
# lidar_loc_ex,out_BL_ex,exportCenterBL = get_loc([bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0],bbox[6][1],bbox[6][2],bbox[2]], 0, generate_Trans,generate_kitti2origin) for t in threads:
# # print(exportCenterBL) t.join()
# angle2 = car_yaw_cal(generate_car_yaw_cal_angle,bbox[2]) for _ind,cloud_i in enumerate(save_cloud_list):
# mapInfoExport = get_map_data(exportCenterBL[0],exportCenterBL[1],angle2) for cloud in cloud_i:
# # print("call get ex data isInMap = ",mapInfoExport) if len(cloud["boxes"]) >=10:
# if mapInfoExport[0] != 1: Save_Cloud_File(cloud,save_json_list[_ind])
# continue; for t_idx in save_json_list:
# laneAngle = mapInfoExport[10]; for jsn in t_idx:
# detaAngel = laneAngle - angle2; save_json["annotations"].append(jsn)
# while detaAngel > 180:
# detaAngel -= 360 jsn_path = os.path.join(generate_root_path,generate_child_dir + ".json")
# while detaAngel < -180: with open(jsn_path,'w') as file_obj:
# detaAngel += 360 json.dump(save_json,file_obj,cls=NumpyEncoder,indent=4)
# # print("angle = ",angle2," laneAngle = ",laneAngle," detaAngel = ",detaAngel) totel_count = {'big': 0, 'little':0,'pedestrian':0,'mid':0,'cyclist':0}
# if bbox[0] != "pedestrian" and abs(detaAngel) > 30: for count in cloud_box_count:
# continue; totel_count['big'] += count['big']
# #获取到汽车点云符合条件，可以添加到新点云里 totel_count['little'] += count['little']
# # f1.write('%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n'%(exportCenterBL[0],exportCenterBL[1],bbox[4][0], bbox[4][1], bbox[4][2],bbox[6][0], bbox[6][1], bbox[6][2],angle2,laneAngle)) totel_count['pedestrian'] += count['pedestrian']
# name = pcd_file.split("/")[-1] totel_count['mid'] += count['mid']
# if g_saveFileType == 1: totel_count['cyclist'] += count['cyclist']
# name = name[0:-3] + "bin" cloud_box_count.append(totel_count)
# idx = Check_Add_Cloud_box(save_cloud_list,bbox,g_converted_pcd,name,generate_pcd_path,index) count_jsn_path = os.path.join(generate_root_path,"count.json")
# if idx == -1: with open(count_jsn_path,'w') as file_o:
# continue; json.dump(cloud_box_count,file_o,cls=NumpyEncoder,indent=4)
# flag = in_hull(xyz,bbox[1])
# pickcloud = xyz[flag]
# bottom_center = (bbox[1][5,:] + bbox[1][7,:]) / 2.0
# ground_z = (-m[3] - m[0] * bottom_center[0] - m[1] * bottom_center[1]) / m[2]
# delta_z = ground_z - bottom_center[2]
# dz = np.array([0,0,delta_z])
# pickcloud = np.add(pickcloud,dz)
# # print(pickcloud)
# bbox[1] += dz
# Add_Cloud_box(save_cloud_list,idx,bbox,pickcloud,save_json)
# if isDeal == 1:
# index += 1
# if index % 10 == 0:
# print("while index = ",index)
# # if index >= 10:
# # break
# else:
# isStop = 1
# print("finish all the data")
# for cloud in save_cloud_list:
# Save_Cloud_File(cloud,save_json)
# jsn_path = os.path.join(generate_root_path,generate_child_dir + ".json")
# with open(jsn_path,'w') as file_obj:
# json.dump(save_json,file_obj,cls=NumpyEncoder)
# # for file, num in g_pcd_info.items():
# # print("file = ",file,", box num = ",num)
# print("no save file num = ",g_no_save_count)
......
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