我正在寻找一些编码解决方案/帮助，从液晶显示器提取数字，然后从图像输出值。在

下面是代码和示例图像，以及到目前为止我是如何得到的，但是我需要一些进一步的帮助来从图像中提取“数字”并输出值。在

我从前面的例子中做了个数字查找表

# 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("Multimeter_1.jpg")

# pre-process the image by converting it to

# graycale, blurring it, and computing an edge map

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

blur = cv2.GaussianBlur(gray, (7,7), 0)

thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,87,9)

# find contours in the edge map,

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

cnts = cnts[0] if len(cnts) == 2 else cnts[1]

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

rect = None

# loop over the contours

for c in cnts:

# approximate the contour

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

#cv2.rectangle(image, (x, y), (x+w, y+h), (36, 255, 12), 1)

cv2.drawContours(image, [c], -1, (36,255,12),3)

rect = c

peri = cv2.arcLength(c, True)

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

if len(approx) == 4:

rect = approx

break

# extract the display, apply a perspective transform

warped = four_point_transform(thresh, rect.reshape(4, 2))

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

# Warp the image and perform morphology to clean it

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

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

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

# pre-process the image by converting it to

# graycale, blurring it, and computing an edge map

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

blur = cv2.GaussianBlur(gray, (7,7), 0)

thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,87,9)

# find contours in the edge map,

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

cnts = cnts[0] if len(cnts) == 2 else cnts[1]

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

rect = None

# loop over the contours

for c in cnts:

# approximate the contour

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

#cv2.rectangle(image, (x, y), (x+w, y+h), (36, 255, 12), 1)

cv2.drawContours(image, [c], -1, (36,255,12),3)

rect = c

peri = cv2.arcLength(c, True)

approx = cv2.approxPolyDP(c, 0.05 * peri, True)

if len(approx) == 4:

rect = approx

break

# extract the display, apply a perspective transform

warped = four_point_transform(thresh, rect.reshape(4, 2))

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

# Warp the image and perform morphology to clean it

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

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

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

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:

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

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

if w >= 25 and (h >= 50 and h <= 60):

digitCnts.append(c)

cv2.imshow("Multimeter", image)

cv2.imshow("Threshed", thresh)

cv2.imwrite("Threshed.jpg",thresh)

cv2.waitKey(0)

cv2.destroyAllWindows()

有人能在python，特别是在Open CV中非常有经验的人帮我解决这个问题吗。这是一个关于图像处理部分的学校作业，我决定使用Python和OpenCV，所以这不是一个编程作业，代码将被用来做和解释实际的图像处理。我只需要帮助从树上的图像中提取数字。我还提供了从液晶显示器的原始图像，以防有更好的方法来实现我所寻找的。在