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python - Python Hough Lines 实现，提高时间效率

问题描述

所以我试图在python中实现霍夫变换线算法，我发现很难让它变得高效。

这是我的实现：

import numpy as np
def houghLines(edges, dTheta, threshold):
    imageShape = edges.shape
    imageDiameter = (imageShape[0]**2 + imageShape[1]**2)**0.5
    rhoRange = [i for i in range(int(imageDiameter)+1)]
    thetaRange = [dTheta*i for i in range(int(-np.pi/(2*dTheta)), int(np.pi/dTheta))]
    cosTheta = [np.cos(theta) for theta in thetaRange]
    sinTheta = [np.sin(theta) for theta in thetaRange]
    countMatrix = np.zeros([len(rhoRange), len(thetaRange)])
    eds = [(x,y) for (x,y), value in np.ndenumerate(edges) if value > 0]
    for thetaIndex in range(len(thetaRange)):
        theta = thetaRange[thetaIndex]
        cos = cosTheta[thetaIndex]
        sin = sinTheta[thetaIndex]
        for x, y in eds:
            targetRho = x*cos + y*sin
            closestRhoIndex = int(round(targetRho))
            countMatrix[closestRhoIndex, thetaIndex] += 1
    lines = [(p,thetaRange[t]) for (p,t), value in np.ndenumerate(countMatrix) if value > threshold]
    return lines

它可以工作，但速度很慢，比 opencv 实现慢 100 倍。

我该如何改进它？

标签： pythonimage-processinghough-transformhoughlines

解决方案

答案是使用 numba。这就是代码现在的样子：

import numpy as np
from numba import jit
@jit(nopython=True)
def houghLines(edges, dTheta, threshold):
    imageShape = edges.shape
    imageDiameter = (imageShape[0]**2 + imageShape[1]**2)**0.5
    rhoRange = [i for i in range(int(imageDiameter)+1)]
    thetaRange = [dTheta*i for i in range(int(-np.pi/(2*dTheta)), int(np.pi/dTheta))]
    cosTheta = []
    sinTheta = []
    for theta in thetaRange:
        cosTheta.append(np.cos(theta))
        sinTheta.append(np.sin(theta))
    countMatrixSize = (len(rhoRange), len(thetaRange))
    countMatrix = np.zeros(countMatrixSize)

    eds = []
    for (x,y), value in np.ndenumerate(edges):
        if value > 0:
            eds.append((x,y))

    for thetaIndex in range(len(thetaRange)):
        theta = thetaRange[thetaIndex]
        cos = cosTheta[thetaIndex]
        sin = sinTheta[thetaIndex]
        for x, y in eds:
            targetRho = x*cos + y*sin
            closestRhoIndex = int(round(targetRho))
            countMatrix[closestRhoIndex, thetaIndex] += 1
    lines = []
    for (p,t), value in np.ndenumerate(countMatrix):
        if value > threshold:
            lines.append((p,thetaRange[t]))
    return lines

这使它至少快了 50 倍。

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