X2TestAccuracy
双目模式测试相机精度。以 Win 10 系统为例，操作步骤如下：
RVC 相机精度检测步骤
1. 搭建 Python 编译环境
此步骤可参考 Python SDK 配置步骤。
① 安装 Python。适配版本：Python 3.7—Python 3.10，推荐版本：Python 3.7.9。
② 安装 RyRVC 模块。在命令窗口输入：
③ 安装 NumPy。
④ 安装 OpenCV-Python。
⑤ 安装 Matplotlib。
2. 选择合适的标定板
根据相机工作距离，确定合适的标定板大小，继而确定标定板圆心距与规格。
以 A6 标定板为例。A6:0.02 指的是圆心距 0.02 m，需根据标定板上标注的参数，选择下图所示的标定板。标定板详细规格及图示见 RVC 标定板尺寸图。
| 标定板规格 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | A10 |
|---|---|---|---|---|---|---|---|---|---|
| 圆心距 (m) | 0.08 | 0.056 | 0.04 | 0.028 | 0.02 | 0.014 | 0.01 | 0.007 | 0.0048 |
3. 调整拍摄参数
对标定板进行 2D、3D 拍摄，在 RVCManager 中调试拍摄参数，直至达到满意的效果，记录此时的拍摄参数。
进入 RVC 安装目录下的文件夹：/RVCSDK/examples/Python，找到双目模式精度测试例程 X2TestAccuracy.py，定位至下列代码，改成前面记录的拍摄参数。
# Set capture parameters
cap_opt = RVC.X2_CaptureOptions()
# Transform point map's coordinate to left/right(RVC.CameraID_Left/RVC.CameraID_Right) camera or reference
# plane(RVC.CameraID_NONE)
cap_opt.transform_to_camera = RVC.CameraID_Left
# Set 2d and 3d exposure time. Capture with white light, range [11, 100]ms, others [3, 100]ms.
cap_opt.exposure_time_2d = 20
cap_opt.exposure_time_3d = 20
# Set 2d and 3d gain. the default value is 0. The gain value of each series cameras is different, you can call function GetGainRange() to get specific range.
cap_opt.gain_2d = 0
cap_opt.gain_3d = 0
# Set 2d and 3d gamma. the default value is 1. The gamma value of each series cameras is different, you can call function GetGammaRange() to get specific range.
cap_opt.gamma_2d = 1
cap_opt.gamma_3d = 1
# range in [0, 10], default = 5. The contrast of point less than this value will be treat * as invalid point and be removed.
cap_opt.light_contrast_threshold = 2
# edge control after point matching, range in [0, 10], default = 2. The big the value, the more edge * noise to be
# removed.
cap_opt.edge_noise_reduction_threshold = 0
cap_opt.capture_mode = RVC.CaptureMode_Ultra
4. 热机
在 RVCManager 中，选择连续拍摄模式，循环拍摄 30 分钟进行热机。
5. 运行精度检测例程
执行命令：
此命令代表脚本名称为 X2TestAccuracy.py、Gamma 值为 1、标定板规格为 A6、精度测试次数为 10，可根据实际情况修改参数。请确保标定板规格准确，否则会导致测量误差。
6. 查看测试结果
精度检测结果将以折线图像显示，图像竖轴为精度偏差值，横轴为测试次数。若精度偏差值较大（如达到毫米级别），请联系技术支持。
示例代码
# Copyright (c) RVBUST, Inc - All rights reserved.
import PyRVC as RVC
import sys
import os
import numpy as np
import cv2
from Utils.CaliBoardUtils import *
from Utils.Tools import *
def App(gamma, pattern_type, capture_times):
# Initialize RVC X system
RVC.SystemInit()
# Choose RVC X Camera type (USB, GigE or All)
opt = RVC.SystemListDeviceTypeEnum.All
# Scan all RVC X Camera devices
ret, devices = RVC.SystemListDevices(opt)
print("RVC X Camera devices number:", len(devices))
# Find whether any RVC X Camera is connected or not
if len(devices) == 0:
print("Can not find any RVC X Camera!")
RVC.SystemShutdown()
exit(1)
# Create a RVC X Camera
x = RVC.X2.Create(devices[0])
# Test RVC X Camera is valid or not
if x.IsValid() == False:
print("RVC X Camera is not valid!")
RVC.X2.Destroy(x)
RVC.SystemShutdown()
exit(1)
#PrintCaptureMode(devices[0])
# Open RVC X Camera
ret1 = x.Open()
# Test RVC X Camera is opened or not
if x.IsOpen() == False:
print("RVC X Camera is not opened!")
RVC.X2.Destroy(x)
RVC.SystemShutdown()
exit(1)
# Print ExposureTime Range
_, exp_range_min, exp_range_max = x.GetExposureTimeRange()
print("ExposureTime Range:[{}, {}]".format(exp_range_min, exp_range_max))
# Set capture parameters
cap_opt = RVC.X2_CaptureOptions()
# Transform point map's coordinate to left/right(RVC.CameraID_Left/RVC.CameraID_Right) camera or reference
# plane(RVC.CameraID_NONE)
cap_opt.transform_to_camera = RVC.CameraID_Left
# Set 2d and 3d exposure time. Capture with white light, range [11, 100]ms, others [3, 100]ms.
cap_opt.exposure_time_2d = 20
cap_opt.exposure_time_3d = 20
# Set 2d and 3d gain. the default value is 0. The gain value of each series cameras is different, you can call function GetGainRange() to get specific range.
cap_opt.gain_2d = 0
cap_opt.gain_3d = 0
# Set 2d and 3d gamma. the default value is 1. The gamma value of each series cameras is different, you can call function GetGammaRange() to get specific range.
cap_opt.gamma_2d = 1
cap_opt.gamma_3d = 1
# range in [0, 10], default = 5. The contrast of point less than this value will be treat * as invalid point and be removed.
cap_opt.light_contrast_threshold = 2
# edge control after point matching, range in [0, 10], default = 2. The big the value, the more edge * noise to be
# removed.
cap_opt.edge_noise_reduction_threshold = 0
# Create saving address of image and point map.
save_dir = "Data"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
valid_cnt = 0
fail_cnt = 0
err_list = []
i = 0
while valid_cnt < capture_times and fail_cnt < 5:
# Capture a point map and a image.
ret2 = x.Capture(cap_opt)
if ret2 == True:
# Get image data. choose left or right side. the point map is map to left image.
img = x.GetImage(RVC.CameraID_Left)
# Convert image to array and save it.
img = np.array(img, copy=False)
pm = np.array(x.GetPointMap(), copy=False).reshape(-1, 3)
if 0 == i:
# Save image and point map data, only save once to save time and space
cv2.imwrite("{}/test.png".format(save_dir), img)
print("Save image successed!")
# Save point map (m) to file.
if x.GetPointMap().Save("{}/test.ply".format(save_dir), RVC.PointMapUnitEnum.Meter):
print("Save point map successed!")
else:
print("Save point map failed!")
# Calculate accuracy by image and point cloud.
ret3, error_mm = CalcAccuracy(img,
pm,
gamma=gamma,
m_type=pattern_type)
if ret3:
valid_cnt += 1
mean_err = np.mean(error_mm)
err_list.append(mean_err)
max_err = error_mm[np.argmax(np.abs(error_mm))]
print(f"{valid_cnt} times accuracy test, mean error: {mean_err} mm, max_error: {max_err} mm")
else:
fail_cnt += 1
else:
print(RVC.GetLastErrorMessage())
fail_cnt += 1
i += 1
if len(err_list) > 0:
PlotError(err_list)
else:
print("no valid result, please adjust param as suggested!")
# Close RVC X Camera
x.Close()
# Destroy RVC X Camera
RVC.X2.Destroy(x)
# Shut Down RVC X System
RVC.SystemShutdown()
if __name__ == "__main__":
"""
This demo shows how to get X2 accuracy.
argv[1]: gamma (0~1), for adjust image brightness. gamma>1 will increse image brightness
and gamma<1 wille decrease image brightness
argv[2]: pattern_type, type of cali board pattern used. You can choose A2, A3, ..., A10
Center distance of circles on cali borad (m):
A2:0.08
A3:0.056
A4:0.04
A5:0.028
A6:0.02
A7:0.014
A8:0.01
A9:0.007
A10:0.0048
argv[3]: capture times, 10 - 100 recommended. Average error of N capture times will be returned
as result
Please select a cali board pattern of appropriate size according to the working distance
of 3D camera. For details, please consult technical support.
"""
if len(sys.argv) != 4:
sys.exit("Invalid arguments: Usage: python3 X2TestAccuracy.py 1 A4 10")
gamma = sys.argv[1]
pattern_type = sys.argv[2]
capture_times = sys.argv[3]
print("gamma: {}, pattern_type: {}, capture_times: {}".format(
gamma, pattern_type, capture_times))
gamma = float(gamma)
capture_times = int(capture_times)
App(gamma, pattern_type, capture_times)