def findMaxGradientValueOnNormal(gradient_magnitude, gradient_orientation, p1, p2, normal_orientation):
#Get integer coordinates on the straight line between p1 and p2
pts = SIGBTools.getLineCoordinates(p1, p2)
values = gradient_magnitude[pts[:,1],pts[:,0]]
#orientations = gradient_orientation[pts[:,1],pts[:,0]]
#normal_angle = np.arctan2(normal_orientation[1], normal_orientation[0]) * (180 / math.pi)
# orientation_difference = abs(orientations - normal_angle)
# print orientation_difference[0:10]
# max_index = 0 #np.argmax(values)
# max_value = 0
# for index in range(len(values)):
# if orientation_difference[index] < 20:
# if values[index] > max_value:
# max_index = index
# max_value = values[index]
#print orientations[max_index], normal_angle
max_index = np.argmax(values)
return pts[max_index]
def findMaxGradientValueOnNormal(gm, gd,resolution,p1,p2): pts = SIGBTools.getLineCoordinates(p1, p2) temp=pts/resolution normVal=gm[temp[:,1],temp[:,0]] found=False temp=len(normVal) accuracy=6 bestAngle=360.0 if temp<accuracy: accuracy=temp while found!=True | accuracy>1: maxValueIndexes=np.where(normVal==max(normVal)) maxValueIndex=maxValueIndexes[0][0] maxX=pts[maxValueIndex][0] maxY=pts[maxValueIndex][1] vect=np.subtract(p1,p2) endX=vect[0] endY=vect[1] length=math.sqrt(math.pow(endX, 2)+math.pow(endY, 2)) p1NormX,p1NormY=endX/length, endY/length length=math.sqrt(math.pow(maxX, 2)+math.pow(maxY, 2)) p2NormX,p2NormY=maxX/length, maxY/length dot=np.dot((p1NormX,p1NormY),(p2NormX,p2NormY)) angle=math.degrees(math.acos(dot)) if angle>180: angle=math.fabs(angle-360) if angle<20: found=True retX,retY=maxX,maxY return retX, retY else: if angle<bestAngle: bestAngle=angle retX,retY=maxX,maxY normVal=np.delete(normVal, maxValueIndex, axis=0) accuracy=accuracy-1 return retX, retY