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我一直在尝试用 python 制作一个 ANPR 程序，我发现这个库理论上可以解决问题。然而，在测试时，代码工作正常，可以在这里看到。唯一的问题是，我最需要的是 ANPR 自己的读数，就像你在java anpr上一样，我在获取它们时遇到了问题，打印它们。有谁知道如何获得这些读数？

谢谢。

代码：

            #!/usr/bin/python2
import cv
import cv2
import copy
import numpy
import math
import os
# import matplotlib.pyplot as plt # crashes when using opencv windows
import Gnuplot

gp = Gnuplot.Gnuplot()
gp("set style data lines")

gost_width = [520.0, 408.0]
gost_height = 112.0
gost_ratio = [gost_height / w for w in gost_width]
min_error = 0.15;

path = "C:/Users/Arengorn/Desktop/testsnaps/"
img_index = 0
min_index = 0
max_img_index = len(os.listdir(path)) - 1
line_index = 0

def rms(values):
        rms = 0.0
        for v in values:
                rms += v**2;
        rms = math.sqrt(rms / len(values))

        return rms

def sma(values, size):
        new_values = []
        for i in range(len(values) - size):
                new_values.append(sum(values[i:i+size]) / float(size))
        return new_values

def compute():
        img_src = cv2.imread(path + "%d1.jpg" % img_index)
        img = cv2.cvtColor(img_src, cv.CV_RGB2GRAY)
        img = cv2.blur(img, (3, 3))
        #img = cv2.Canny(img, 400, 300)
        #retval, img = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)
        img = cv2.adaptiveThreshold(img, 255,
                                                                cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                                                cv2.THRESH_BINARY,
                                                                31, -10)
        contours, _ = cv2.findContours(copy.copy(img),
                                                                   cv.CV_RETR_LIST,
                                                                   cv.CV_CHAIN_APPROX_SIMPLE)
        for contour in contours:
                rect = cv2.boundingRect(contour)
                ratio = float(rect[3]) / rect[2]
                if rect[3] * rect[2] > 800:
                        for gr in gost_ratio:
                                if abs(ratio - gr) < min_error:
                                        cv2.rectangle(img_src,
                                                                  (rect[0], rect[1]),
                                                                  (rect[0] + rect[2], rect[1] + rect[3]),
                                                                  (255, 0, 0), 2)
                                        break

        edge_matrix = numpy.array([[-0.0, -1.0, 0.0, 1.0, 0.0],
                                   [-1.0, -2.0, 0.0, 2.0, 1.0],
                                   [-0.0, -1.0, 0.0, 1.0, 0.0]])
        img = cv2.filter2D(img, -1, edge_matrix)
        py = []
        for y in range(img.shape[0]):
                py.append(rms(img[y, :]))
        pys = sma(py, 3)
        pyp = []
        for i in range(len(pys) - 1):
            pyp.append(abs(pys[i + 1] - pys[i]))
        pyps = sma(pyp, 1)
        #print (pyps)

        line_index = pys.index(max(pys))        
        cv2.line(img_src, (0, line_index), (img.shape[1] - 1, line_index), (255, 0, 0))
        cv2.imshow("img", img_src)
        cv2.imshow("img2", img)

        gp.plot(pys)


compute()

key = 0
while key != 27:
        key = cv2.waitKey()

        if key == 65363:
                img_index += 1
                if img_index > max_img_index:
                        img_index = 0
                compute()
        elif key == 65361:
                img_index -= 1
                if img_index < 0:
                        img_index = max_img_index
                compute()
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