Esempi in Python per expreBuilder

Esempio n. 1

File: testStructural.py Progetto: MrLeylo/OptiCalcRead

	testCase[10].bBox=[8.25,9.25,2,2.75]
	testCase[11].bBox=[10,11,1.75,3.9]
	testCase[12].bBox=[11,11.5,1.25,1.75]
	testCase[13].bBox=[11.75,12,1.25,2]
	testCase[14].bBox=[11.4,11.6,2.75,3.5]
	testCase[15].bBox=[12.5,12.75,1,4]
	for ntc in range(len(testCase)):
		testCase[ntc].center=[(testCase[ntc].bBox[0]+testCase[ntc].bBox[1])/2.0,(testCase[ntc].bBox[2]+testCase[ntc].bBox[3])/2.0]
	for ntc in range(len(testCase)):
		plt.plot([testCase[ntc].bBox[0],testCase[ntc].bBox[1]],[-testCase[ntc].bBox[2],-testCase[ntc].bBox[2]],'r-')
		plt.plot([testCase[ntc].bBox[1],testCase[ntc].bBox[1]],[-testCase[ntc].bBox[2],-testCase[ntc].bBox[3]],'r-')
		plt.plot([testCase[ntc].bBox[1],testCase[ntc].bBox[0]],[-testCase[ntc].bBox[3],-testCase[ntc].bBox[3]],'r-')
		plt.plot([testCase[ntc].bBox[0],testCase[ntc].bBox[0]],[-testCase[ntc].bBox[3],-testCase[ntc].bBox[2]],'r-')
	#plt.show()
	tags=['\sum1','n1','i2','01','=2','(1','x2','21','x2','i2','+2','y1','21','y1','i2',')1']
	dominations,laTexExpr= stru.expreBuilder(testCase,tags)
	plt.figure(5)
	plt.text(0,0.5,'$%s$'%laTexExpr,fontsize=30)
	plt.show()
elif test==2:
	filenom=sys.argv[1]		#Llegir el nom del fitxer InkML de la consola
	Coord=ink2Traces.i2t(filenom)		#Extreure les coordenades donades pel fitxer
	img,byAxis,difs=drawTraces.draw(Coord)		#Mostrar resultat obtingut i montar imatge
	Symb,groupedStrokes=fileSeg.segment(Coord,byAxis,difs)		#Agrupar traces en simbols
	Symb=drawRegions.drawS(Symb)
	testCase=Symb
	plt.figure(4)
	for ntc in range(len(testCase)):
		plt.plot([testCase[ntc].bBox[0],testCase[ntc].bBox[1]],[-testCase[ntc].bBox[2],-testCase[ntc].bBox[2]],'r-')
		plt.plot([testCase[ntc].bBox[1],testCase[ntc].bBox[1]],[-testCase[ntc].bBox[2],-testCase[ntc].bBox[3]],'r-')
		plt.plot([testCase[ntc].bBox[1],testCase[ntc].bBox[0]],[-testCase[ntc].bBox[3],-testCase[ntc].bBox[3]],'r-')

Esempio n. 2

File: testStructural.py Progetto: jojolilian/OptiCalcRead

        ]
    for ntc in range(len(testCase)):
        plt.plot([testCase[ntc].bBox[0], testCase[ntc].bBox[1]],
                 [-testCase[ntc].bBox[2], -testCase[ntc].bBox[2]], 'r-')
        plt.plot([testCase[ntc].bBox[1], testCase[ntc].bBox[1]],
                 [-testCase[ntc].bBox[2], -testCase[ntc].bBox[3]], 'r-')
        plt.plot([testCase[ntc].bBox[1], testCase[ntc].bBox[0]],
                 [-testCase[ntc].bBox[3], -testCase[ntc].bBox[3]], 'r-')
        plt.plot([testCase[ntc].bBox[0], testCase[ntc].bBox[0]],
                 [-testCase[ntc].bBox[3], -testCase[ntc].bBox[2]], 'r-')
    #plt.show()
    tags = [
        '\sum1', 'n1', 'i2', '01', '=2', '(1', 'x2', '21', 'x2', 'i2', '+2',
        'y1', '21', 'y1', 'i2', ')1'
    ]
    dominations, laTexExpr = stru.expreBuilder(testCase, tags)
    plt.figure(5)
    plt.text(0, 0.5, '$%s$' % laTexExpr, fontsize=30)
    plt.show()
elif test == 2:
    filenom = sys.argv[1]  #Llegir el nom del fitxer InkML de la consola
    Coord = ink2Traces.i2t(
        filenom)  #Extreure les coordenades donades pel fitxer
    img, byAxis, difs = drawTraces.draw(
        Coord)  #Mostrar resultat obtingut i montar imatge
    Symb, groupedStrokes = fileSeg.segment(Coord, byAxis,
                                           difs)  #Agrupar traces en simbols
    Symb = drawRegions.drawS(Symb)
    testCase = Symb
    plt.figure(4)
    for ntc in range(len(testCase)):

Esempio n. 3

File: overAll.py Progetto: jojolilian/OptiCalcRead

]
del averages['i2'], averages['\ldots3'], averages['.1'], averages[
    '/1'], averages['-1'], averages['!2']
weights = fp.ponderateByConcentration(
)  #Aplica una ponderacio per concentracio a les features
amp = np.asarray([[Coord[i, j, 0] for j in range(Coord.shape[1])]
                  for i in range(Coord.shape[0])],
                 np.float64).max() - np.asarray(
                     [[Coord[i, j, 0] for j in range(Coord.shape[1])]
                      for i in range(Coord.shape[0])], np.float64).min()
alt = np.asarray([[Coord[i, j, 1] for j in range(Coord.shape[1])]
                  for i in range(Coord.shape[0])],
                 np.float64).max() - np.asarray(
                     [[Coord[i, j, 1] for j in range(Coord.shape[1])]
                      for i in range(Coord.shape[0])], np.float64).min()
print 'Starting to classify:'
foundTags = []
for i in range(len(Symb)):  #Per cada simbol a analitzar li assigna un caracter
    decision = eM.elasticMatching(averages, Symb[i], weights, amp, alt)
    print decision
    foundTags.append(decision)
dominations, laTexExpr = stru.expreBuilder(Symb, foundTags)
print 'Given Tex expression:'
print laTexExpr
print '100% Complete'
print 'Results shown on figures.'
fig = plt.figure(3)
fig.canvas.set_window_title('Expression')
plt.text(0, 0.5, '$%s$' % laTexExpr, fontsize=30)
plt.show()