如何创建opencv数字识别器

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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 packagesfrom imutils.perspective import four_point_transformfrom imutils import contoursimport imutilsimport 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 imageimage = cv2.imread("example.jpg")## pre-process the image by resizing it, converting it to# graycale, blurring it, and computing an edge mapimage = 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 ordercnts = 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 contoursfor 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 itwarped = 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 imagethresh = 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 listscnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)cnts = imutils.grab_contours(cnts)digitCnts = []# loop over the digit area candidatesfor 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 themselvesdigitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0]digits = [] # loop over each of the digitsfor 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 digitsprint(u"{}{}.{} \u00b0C".format(*digits))cv2.imshow("Input", image)cv2.imshow("Output", output)cv2.waitKey(0)

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