opencv+python识别七段数码显示器的数字(数字识别)
程序员文章站
2022-03-07 15:28:30
目录一、什么是七段数码显示器二、创建opencv数字识别器一、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...
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。
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。
二、创建opencv数字识别器
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,更多相关opencv数字识别内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!
上一篇: 从网红变长红,新品如何穿越周期的成长?
推荐阅读
-
Python(TensorFlow框架)实现手写数字识别系统的方法
-
基于MNIST手写数字数据集的数字识别小程序
-
c#实现识别图片上的验证码数字
-
手写数字识别 ----在已经训练好的数据上根据28*28的图片获取识别概率(基于Tensorflow,Python)
-
C++编程模板匹配超详细的识别手写数字实现示例
-
Python机器学习!识别图中最难的数字!验证码?验证码是小儿科!
-
Python(TensorFlow框架)实现手写数字识别系统的方法
-
【机器学习】【KNN】线性扫描算法,python实现识别手写数字的系统
-
机器学习实战学习笔记(二)-KNN算法(2)-使用KNN算法进行手写数字的识别
-
机器学习_KNN实验(手写数字的识别)