from pgmpy.models import BayesianModel
from pgmpy.factors.discrete import TabularCPD
from pgmpy.inference import VariableElimination
# 通过边来定义贝叶斯模型
model = BayesianModel([('D', 'G'), ('I', 'G'), ('G', 'L'), ('I', 'S')])
# 定义条件概率分布
cpd_d = TabularCPD(variable='D', variable_card=2, values=[[0.6, 0.4]])
cpd_i = TabularCPD(variable='I', variable_card=2, values=[[0.7, 0.3]])
# variable：变量
# variable_card：基数
# values：变量值
# evidence：
cpd_g = TabularCPD(variable='G', variable_card=3,
values=[[0.3, 0.05, 0.9, 0.5],
[0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2]],
evidence=['I', 'D'],
evidence_card=[2, 2])
cpd_l = TabularCPD(variable='L', variable_card=2,
values=[[0.1, 0.4, 0.99],
[0.9, 0.6, 0.01]],
evidence=['G'],
evidence_card=[3])
cpd_s = TabularCPD(variable='S', variable_card=2,
values=[[0.95, 0.2],
[0.05, 0.8]],
evidence=['I'],
evidence_card=[2])
# 将有向无环图与条件概率分布表关联
model.add_cpds(cpd_d, cpd_i, cpd_g, cpd_l, cpd_s)
# 验证模型：检查网络结构和CPD，并验证CPD是否正确定义和总和为1
model.check_model()
# 获得G点的概率表
print(model.get_cpds('G'))
# 获得G点的基数
print(model.get_cardinality('G'))
# 获取贝叶斯网络的局部依赖关系
print(model.local_independencies(['D','I','S','G','L']))
# 贝叶斯推理:变量消除
infer = VariableElimination(model)
print(infer.query(['G']), ['G'])
print(infer.query(['G'], evidence={'D':0, 'I':1})['G'])
print(infer.map_query('G'))
ValueError: values must be of shape (2, 1). Got shape: (1, 2)
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HALCON算子错误
当在函数 'main' 中调用 'gen_circle_contour_xld'时.line: 16.
Wrong number of values of control parameter 1 (HALCON错误代码: 1401)
代码如下
read_image (Image, 'C:/Users/zhang/Desktop/屏幕截图 2021-09-21 232135.png')
*read_image (Image, 'C:/Users/zhang/Desktop/20180831114255361.png') 这是原图，换了一张图后出现了错误
get_image_size (Image, Width, Height)
dev_open_window (0, 0, Width/2, Height/2, 'black', WindowHandle)
dev_display (Image)
*均值滤波
mean_image (Image, ImageMean, 3, 3)
*灰度范围，结果为掩码矩形内灰度最大值减最小值
gray_range_rect (ImageMean, ImageResult, 8, 8)
threshold (ImageResult, Regions, 40, 138)
connection (Regions, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 31134.7, 50000)
*生成区域的轮廓
gen_contour_region_xld (SelectedRegions, Contours, 'border')
fit_circle_contour_xld (Contours, 'algebraic', -1, 0, 0, 3, 2, Row, Column, Radius, StartPhi, EndPhi, PointOrder)
gen_circle_contour_xld (ContCircle, Row, Column, Radius, 0, 6.28318, 'positive', 1)
gen_circle (Circle, Row, Column, Radius-60)
gen_circle (Circle1, Row, Column, Radius-5)
difference (Circle1, Circle, RegionDifference)
dev_open_window (0, 0, 900, 60, 'black', WindowHandle1)
*直角坐标转极坐标
polar_trans_image_ext (Image, PolarTransImage, Row, Column, 0, 6.28319, Radius-60, Radius-5, 900, 60, 'bilinear')
rotate_image (PolarTransImage, ImageRotate, 180, 'constant')
*以下是通过助手实现光学字符识别并自动生成的代码
create_text_model_reader ('manual', [], TextModel)
set_text_model_param (TextModel, 'char_width', 17)
set_text_model_param (TextModel, 'char_height', 26)
set_text_model_param (TextModel, 'stroke_width', 5.9)
set_text_model_param (TextModel, 'return_punctuation', 'false')
set_text_model_param (TextModel, 'uppercase_only', 'true')
set_text_model_param (TextModel, 'fragment_size_min', 17)
set_text_model_param (TextModel, 'eliminate_border_blobs', 'true')
set_text_model_param (TextModel, 'base_line_tolerance', 0.2)
set_text_model_param (TextModel, 'max_line_num', 1)
read_ocr_class_mlp ('Industrial_0-9A-Z_NoRej.omc', OcrHandle)
gen_rectangle1 (ROI_OCR_01_0, 2.77273, 86.9906, 31.1364, 290.457)
TmpCtrl_Orientation := 0
dev_display (ImageRotate)
set_display_font (WindowHandle1, 16, 'mono', 'true', 'false')
for i:=0 to |SymbolNames_OCR_01_0|-1 by 1
disp_message (WindowHandle1, SymbolNames_OCR_01_0[i], 'image', 30, 10*i+88, 'white', 'false')
endfor
clear_text_model (TextModel)
clear_ocr_class_mlp (OcrHandle)