python – 无法使用sort_contors构建七段OCR

我正在尝试构建一个用于识别七段显示的OCR,如下所述

使用开放式CV的预处理工具我在这里得到它

现在我正在尝试按照本教程 – https://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/

但就此而言

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

我收到错误 –

使用THRESH_BINARY_INV解决了错误,但仍然没有OCR工作任何修复都会很好

在sort_contours中输入文件“/Users/ms/anaconda3/lib/python3.6/site-packages/imutils/contours.py”,第25行
????key = lambda b：b1 [i],reverse = reverse))

ValueError：没有足够的值来解包(预期2,得到0)

任何想法如何解决这个问题,让我的OCR成为一个有效的模型

我的整个代码是：

import numpy as np 
import cv2
import imutils
# 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,(0,0): 1,(1,0): 2,1): 3,0): 4,1): 5,1): 6,0): 7,1): 8,1): 9
}

# load image
image = cv2.imread('d4.jpg')
# create hsv
hsv = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)

 # set lower and upper color limits
low_val = (60,180,160)
high_val = (179,255,255)
# Threshold the HSV image 
mask = cv2.inRange(hsv,low_val,high_val)
# find contours in mask
ret,cont,hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

# select the largest contour
largest_area = 0
for cnt in cont:
    if cv2.contourArea(cnt) > largest_area:
        cont = cnt
        largest_area = cv2.contourArea(cnt)

# get the parameters of the boundingbox
x,y,w,h = cv2.boundingRect(cont)

# create and show subimage
roi = image[y:y+h,x:x+w]
cv2.imshow("Result",roi)


#  draw box on original image and show image
cv2.rectangle(image,(x,y),(x+w,y+h),255),2)
cv2.imshow("Image",image)

grayscaled = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)
retval,threshold = cv2.threshold(grayscaled,10,cv2.THRESH_BINARY)
retval2,threshold2 = cv2.threshold(grayscaled,125,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imshow('threshold',threshold2)
cv2.waitKey(0)
cv2.destroyAllWindows()
# find contours in the thresholded image,then initialize the
# digit contours lists
cnts = cv2.findContours(threshold2.copy(),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,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,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,(dW,h // 2)),# top-left
        ((w - dW,# top-right
        ((0,(h // 2) - dHC),(h // 2) + dHC)),# center
        ((0,h // 2),h)),# bottom-left
        ((w - dW,# bottom-right
        ((0,h - dH),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 + w,y + h),1)
    cv2.putText(output,str(digit),(x - 10,y - 10),cv2.FONT_HERSHEY_SIMPLEX,0.65,2)
# display the digits
print(u"{}{}.{}{}.{}{} u00b0C".format(*digits))
cv2.imshow("Input",image)
cv2.imshow("Output",output)
cv2.waitKey(0)

帮助将很好地修复我的OCR