OpenCV-Python 识别万用表七段数码管电流值

项目简介

利用 OpenCV 和笔记本 PC 的摄像头（或者外接摄像头）识别万用表电流值。

步骤：

1、定位屏幕

照片要光线均匀，无大面积阴影。
另外，这款万用表轮廓不明显，故贴纸使轮廓更分明。

首先对图像做：灰度化、高斯滤波、边缘识别。
在处理后的图片上识别轮廓，并提取最大轮廓，即为屏幕区域。
得到：

2、提取屏幕，并做一系列图像处理，得到黑白屏幕

透视变换，提取屏幕区域：

图片二值化、形态学变换（腐蚀、膨胀）：

3、提取数字，并分割

提取图片数字区域，消除边缘阴影，然后使用垂直投影法分割数字。

4、识别数字

进一步提取单个数字中每一根数码管的大致区域，通过统计该区域白色像素占比，若占比超过40%（有误差），则认为该管为亮。然后查表，判断数字。

结果：

代码附上：

# import the necessary packages
from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
import numpy as np
from openpyxl import Workbook
from openpyxl import load_workbook
from openpyxl.writer.excel import ExcelWriter# 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, 0, 1): 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, 1, 1, 0, 0, 1, 0): 7,(1, 1, 1, 1, 1, 1, 1): 8,(1, 1, 1, 1, 0, 1, 1): 9
}def get_vvList(list_data):#取出list中像素存在的区间vv_list=list()v_list=list()for index,i in enumerate(list_data):if i>0:v_list.append(index)else:if v_list:vv_list.append(v_list)#list的clear与[]有区别v_list=[]return vv_list# load the example image
image = cv2.imread("15.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, 128, 200)# find contours in the edge map, then sort them by their
# size in descending order
cnts,hi = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# the largest contour(default: target) is in the first place after sorting
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
cv2.drawContours(image,cnts,0,(0,0,255),3)
displayCnt = None
# loop over the contours
for c in cnts:# approximate the contourperi = cv2.arcLength(c, True)approx = cv2.approxPolyDP(c, 0.02 * peri, True)# if the contour has four vertices, then we have found# the thermostat displayif len(approx) == 4:displayCnt = approxbreak
# 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))#
thresh = cv2.threshold(warped, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]# kernel = np.ones((5,5),np.uint8)
# erosion = cv2.erode(thresh,kernel)
# dst = cv2.dilate(erosion,kernel)
# dst1 = cv2.dilate(dst,kernel)kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel,2)ROI = thresh.copy()[20:103, 20:168]
# 黑白反转
where_0 = np.where(ROI == 0)
where_255 = np.where(ROI == 255)
ROI[where_0] = 255
ROI[where_255] = 0
print(thresh.shape)rows, cols = ROI.shape
ver_list = [0]*cols
for j in range(cols):for i in range(rows):if ROI.item(i,j)==0:ver_list[j]=ver_list[j]+1
'''
对ver_list中的元素进行筛选，可以去除一些噪点qq
'''
ver_arr=np.array(ver_list)
ver_arr[np.where(ver_arr<1)]=0
ver_list=ver_arr.tolist()#绘制垂直投影
img_white=np.ones(shape=(rows,cols),dtype=np.uint8)*255
for j in range(cols):pt1=(j,rows-1)pt2=(j,rows-1-ver_list[j])cv2.line(img_white,pt1,pt2,(0,),1)
cv2.imshow('垂直投影',img_white)digits = []#切割单一字符
vv_list=get_vvList(ver_list)
for i in vv_list:img_ver=ROI.copy()[:,i[0]:i[-1]]where_0 = np.where(img_ver == 0)where_255 = np.where(img_ver == 255)img_ver[where_0] = 255img_ver[where_255] = 0(roiH, roiW) = img_ver.shape(dW, dH) = (int(roiW * 0.3), int(roiH * 0.1))dHC = int(roiH * 0.05)# define the set of 7 segmentssegments = [((0, 0), (roiW, dH)),  # top((0, 0), (dW, roiH // 2)),  # top-left((roiW - dW, 0), (roiW, roiH // 2)),  # top-right((0, (roiH // 2) - dHC), (roiW, (roiH // 2) + dHC)),  # center((0, roiH // 2), (dW, roiH)),  # bottom-left((roiW - dW, roiH // 2), (roiW, roiH)),  # bottom-right((0, roiH - dH), (roiW, roiH))  # bottom]on = [0] * len(segments)# loop over the segmentsfor (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 segmentsegROI = img_ver[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"proportion = total / float(area)print(proportion)if proportion > 0.35:on[i] = 1# lookup the digit and draw it on the image# special: digit = 1if roiH > 5 * roiW:digit = 1else:digit = DIGITS_LOOKUP[tuple(on)]print(digit)digits.append(digit)cv2.imshow('单一字符', img_ver)cv2.waitKey(0)cv2.putText(warped, str(digits), (15, 15),cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 0), 2)
print(digits)cv2.imshow("Output1", image)
cv2.imshow("Output2", warped)
cv2.imshow("Output3", thresh)cv2.waitKey(0)

还有一份带简单UI界面，摄像头实时识别版，且有绘图功能，如需要联系我：

参考：
使用 OpenCV 和 Python 识别数字
文本分割之垂直投影法基于OpenCV(python)的实现

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