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opencv+python识别七段数码显示器的数字(数字识别)

本文主要介绍了opencv+python识别七段数码显示器的数字(数字识别)，文中通过示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

一、什么是七段数码显示器

七段LCD数码显示器有很多叫法：段码液晶屏、段式液晶屏、黑白笔段屏、段码LCD液晶屏、段式显示器、TN液晶屏、段码液晶显示器、段码屏幕、笔段式液晶屏、段码液晶显示屏、段式LCD、笔段式LCD等。

如下图，每个数字都由一个七段组件组成。

图 2：单个七段显示器的示例。每个段都可以“打开”或“关闭”以表示特定的数字。

七段显示器总共可以呈现 128 种可能的状态：

我们要识别其中的0-9，如果用深度学习的方式有点小题大做，并且如果要进行应用还有很多前序工作需要进行，比如要确认识别什么设备的，怎么找到数字区域并进行分割等等。

我们这里进行使用空调恒温器进行识别，首先整理下流程。

1、定位恒温器上的 LCD屏幕。

2、提取 LCD的图像。

3、提取数字区域

4、识别数字。

我们创建名称为recognize_digits.py的文件，代码如下。仅思路供参考（因为代码中的一些参数只适合测试图片）

# import the necessary packages

from imutils.perspective import four_point_transform

from imutils import contours

import imutils

import cv2

# define the dictionary of digit segments so we can identify

# each digit on the thermostat

DIGITS_LOOKUP = {

    (1, 1, 1, 0, 1, 1, 1): 0,

    (0, 0, 1, 0, 0, 1, 0): 1,

    (1, 0, 1, 1, 1, 1, 0): 2,

    (1, 0, 1, 1, 0, 1, 1): 3,

    (0, 1, 1, 1, 0, 1, 0): 4,

    (1, 1, 0, 1, 0, 1, 1): 5,

    (1, 1, 0, 1, 1, 1, 1): 6,

    (1, 0, 1, 0, 0, 1, 0): 7,

    (1, 1, 1, 1, 1, 1, 1): 8,

    (1, 1, 1, 1, 0, 1, 1): 9

}

# load the example image

image = cv2.imread("example.jpg")#

# pre-process the image by resizing it, converting it to

# graycale, blurring it, and computing an edge map

image = imutils.resize(image, height=500)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

blurred = cv2.GaussianBlur(gray, (5, 5), 0)

edged = cv2.Canny(blurred, 50, 200, 255)

# find contours in the edge map, then sort them by their

# size in descending order

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cnts = imutils.grab_contours(cnts)

cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

displayCnt = None

# loop over the contours

for c in cnts:

    # approximate the contour

    peri = cv2.arcLength(c, True)

    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    # if the contour has four vertices, then we have found

    # the thermostat display

    if len(approx) == 4:

        displayCnt = approx

        break

# extract the thermostat display, apply a perspective transform

# to it

warped = four_point_transform(gray, displayCnt.reshape(4, 2))

output = four_point_transform(image, displayCnt.reshape(4, 2))

# threshold the warped image, then apply a series of morphological

# operations to cleanup the thresholded image

thresh = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5))

thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

# find contours in the thresholded image, then initialize the

# digit contours lists

cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cnts = imutils.grab_contours(cnts)

digitCnts = []

# loop over the digit area candidates

for c in cnts:

    # compute the bounding box of the contour

    (x, y, w, h) = cv2.boundingRect(c)

    # if the contour is sufficiently large, it must be a digit

    if w >= 15 and (h >= 30 and h <= 40):

        digitCnts.append(c)

# sort the contours from left-to-right, then initialize the

# actual digits themselves

digitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0]

digits = []

# loop over each of the digits

for c in digitCnts:

    # extract the digit ROI

    (x, y, w, h) = cv2.boundingRect(c)

    roi = thresh[y:y + h, x:x + w]

    # compute the width and height of each of the 7 segments

    # we are going to examine

    (roiH, roiW) = roi.shape

    (dW, dH) = (int(roiW * 0.25), int(roiH * 0.15))

    dHC = int(roiH * 0.05)

    # define the set of 7 segments

    segments = [

        ((0, 0), (w, dH)),  # top

        ((0, 0), (dW, h // 2)), # top-left

        ((w - dW, 0), (w, h // 2)), # top-right

        ((0, (h // 2) - dHC) , (w, (h // 2) + dHC)), # center

        ((0, h // 2), (dW, h)), # bottom-left

        ((w - dW, h // 2), (w, h)), # bottom-right

        ((0, h - dH), (w, h))   # bottom

]

    on = [0] * len(segments)

    # loop over the segments

    for (i, ((xA, yA), (xB, yB))) in enumerate(segments):

        # extract the segment ROI, count the total number of

        # thresholded pixels in the segment, and then compute

        # the area of the segment

        segROI = roi[yA:yB, xA:xB]

        total = cv2.countNonZero(segROI)

        area = (xB - xA) * (yB - yA)

        # if the total number of non-zero pixels is greater than

        # 50% of the area, mark the segment as "on"

        if total / float(area) > 0.5:

            on[i]= 1

    # lookup the digit and draw it on the image

    digit = DIGITS_LOOKUP[tuple(on)]

    digits.append(digit)

    cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 1)

    cv2.putText(output, str(digit), (x - 10, y - 10),

        cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)

# display the digits

print(u"{}{}.{} \u00b0C".format(*digits))

cv2.imshow("Input", image)

cv2.imshow("Output", output)

cv2.waitKey(0)

原始图片

边缘检测

识别的结果图片

到此这篇关于opencv+python识别七段数码显示器的数字(数字识别)的文章就介绍到这了

原文链接：https://blog.csdn.net/bashendixie5/article/details/122361536