https://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5))
见：http://blog.csdn.net/sunny2038/article/details/9137759
cv.WaitKey()函数的功能是程序暂停:
http://blog.csdn.net/gxiaob/article/details/9027799/
最大类间方差法ostu：
http://blog.csdn.net/abcsunl/article/details/60959914
# -*- coding: utf-8 -*-
from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
from matplotlib import pyplot as plt
# define the dictionary to 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
}
img = cv2.imread('/Users/k/Documents/example.jpg')
print(img.shape)
cv2.imshow("ori", img)
img = imutils.resize(img, height=500)
cv2.imshow("cur", img)
print(img.shape)
#cv2.waitKey(0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
edge = cv2.Canny(blur, 50, 200, 255)
cv2.imshow("edge", edge)
#cv2.waitKey(0)
#上面的两个cv2.waitKey(0)要注释掉，才能运行这行下面的program
#最后有一个cv2.waitKey(0)就够了
cnts = cv2.findContours(edge.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
displayCnt = approx
break
warped = four_point_transform(gray, displayCnt.reshape(4, 2))
output = four_point_transform(img, displayCnt.reshape(4, 2))
'''
cv2.imshow("warped",warped)
cv2.imshow("output",output)
cv2.waitKey(0)
'''
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)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
digitCnts = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w >= 15 and (h >= 30 and h <= 40):
digitCnts.append(c)
digitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0]
digits = []
for c in digitCnts:
(x, y, w, h) = cv2.boundingRect(c)
roi = thresh[y:y + h, x:x + w]
(roiH, roiW) = roi.shape
(dW, dH) = (int(roiW * 0.25), int(roiH * 0.15))
dHC = int(roiH * 0.05)
# 七个矩形，以top为例，左上角是（0，0），右下角是（w，dH）
# 这个坐标系和正常的不太一样
segments = [
((0, 0), (w, dH)), # top
((0, 0), (dW, h // 2)),
((w - dW, 0), (w, h // 2)),
((0, (h // 2) - dHC), (w, (h // 2) + dHC)),
((0,h//2),(dW,h)),
((w - dW, h // 2), (w, h)),
((0, h - dH), (w, h))
]
on = [0] * len(segments)
for (i, ((xA, yA), (xB, yB))) in enumerate(segments):
segROI = roi[yA:yB, xA:xB]
total = cv2.countNonZero(segROI)
area = (xB - xA) * (yB - yA)
if total / float(area) > 0.5:
on[i] = 1
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)
print(u"{}{}.{} \u00b0C".format(*digits))
cv2.imshow("Input", img)
cv2.imshow("Output", output)
cv2.waitKey(0)