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import tensorflow as tffrom PIL import Image# im = Image.open('D:\\deng\\ppp\\888.png')# data = list(im.getdata())# result = [(255-x)*1.0/255.0 for x in data]# print(result)# xs = tf.placeholder(tf.float32, name='x_input')# x_image = tf.reshape(xs, [-1, 28, 28, 1])def prediction_num(result):    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        saver = tf.train.import_meta_graph('D:/deng/model2/model.ckpt.meta')        saver.restore(sess, "D:/deng/model2/model.ckpt")  # 这里使用了之前保存的模型参数        pred = tf.get_collection('network-output')[0]        prediction = tf.argmax(pred, 1)        graph = tf.get_default_graph()        xs = graph.get_operation_by_name('x_input').outputs[0]        keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]        #keep_prob = graph.get_operation_by_name('y_inout').outputs[0]        predint = prediction.eval(feed_dict={
   xs: [result], keep_prob: 1.0}, session=sess)        print("recognize result: %d" % predint[0])

from scipy.spatial import distance as distfrom imutils import perspectivefrom imutils import contoursimport numpy as npimport imutilsimport cv2import tensorflow_testfrom PIL import Imagedef midpoint(ptA, ptB):    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)#cap = cv2.VideoCapture("http://192.168.1.1:8080/?action=stream")cap = cv2.VideoCapture(0)#HSV颜色空间的红色区间Redlower=np.array([0, 43, 46])Redupper=np.array([124, 255, 255])# Redlower=np.array([0,43,46])# Redupper=np.array([25,255,255])while(True):     # Capture frame-by-frame    ret, frame = cap.read()    frame = imutils.resize(frame, width=600)    # Our operations on the frame come here    blurred = cv2.GaussianBlur(frame, (7, 7), 0)    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)    #hsv = cv2.GaussianBlur(hsv, (7, 7), 0)    # perform edge detection, then perform a dilation + erosion to    # close gaps in between object edges    # edged = cv2.Canny(hsv, 50, 100)    #inRange()函数用于图像颜色分割    edged = cv2.inRange(hsv,Redlower,Redupper)    edged = cv2.dilate(edged, None, iterations=2)    edged = cv2.erode(edged, None, iterations=2)    #在OpenCV中，可以用findContours()函数从二值图像中查找轮廓。    #可以使用compare(),inrange(),threshold(),adaptivethreshold(),canny()等函数由灰度图或色彩图创建二进制图像。    #RETR_EXTERNAL表示只检测最外层轮廓。CHAIN_APPROX_SIMPLE表示压缩水平方向，垂直方向，对角线方向的元素，只保留    #该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息。    cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,                            cv2.CHAIN_APPROX_SIMPLE)[-2]    #cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)    #cnts = imutils.grab_contours(cnts)    # sort the contours from left-to-right and initialize the    # 'pixels per metric' calibration variable    #(cnts, _) = contours.sort_contours(cnts)    pixelsPerMetric = None    # loop over the contours individually    for c in cnts:        # if the contour is not sufficiently large, ignore it        if cv2.contourArea(c) < 100:            continue        # compute the rotated bounding box of the contour        orig = frame.copy()        box = cv2.minAreaRect(c)        #box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)        box = cv2.boxPoints(box)        box = np.array(box, dtype="int")        # order the points in the contour such that they appear        # in top-left, top-right, bottom-right, and bottom-left        # order, then draw the outline of the rotated bounding        # box        box = perspective.order_points(box)        #绘制轮廓drawContours()函数        #第二个参数表示所有输入轮廓，每一个轮廓存储为一个点向量，即point类型的vector表示        #第三个参数为负数，则绘制所有轮廓        #第四个参数为轮廓颜色        #第五个参数为轮廓线条的粗细度        cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)        # loop over the original points and draw them        for (x, y) in box:            cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)            # unpack the ordered bounding box, then compute the midpoint            # between the top-left and top-right coordinates, followed by            # the midpoint between bottom-left and bottom-right coordinates            (tl, tr, br, bl) = box            #print(box)            (tltrX, tltrY) = midpoint(tl, tr)            (blbrX, blbrY) = midpoint(bl, br)            # compute the midpoint between the top-left and top-right points,            # followed by the midpoint between the top-righ and bottom-right            (tlblX, tlblY) = midpoint(tl, bl)            (trbrX, trbrY) = midpoint(tr, br)            # draw the midpoints on the image            cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)            cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)            cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)            cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)            # draw lines between the midpoints            cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),                     (255, 0, 255), 2)            cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),                     (255, 0, 255), 2)            # compute the Euclidean distance between the midpoints            dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))            dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))            # if the pixels per metric has not been initialized, then            # compute it as the ratio of pixels to supplied metric            # (in this case, inches)            if pixelsPerMetric is None:                # pixelsPerMetric = dB / args["width"]                pixelsPerMetric = dB / 20.5                # compute the size of the object                dimA = dA / pixelsPerMetric                dimB = dB / pixelsPerMetric                D = 921.43 / pixelsPerMetric                # draw the object sizes on the image                cv2.putText(orig, "{:.1f}mm".format(dimA),                            (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,                            0.65, (255, 255, 255), 2)                cv2.putText(orig, "{:.1f}mm".format(dimB),                            (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,                            0.65, (255, 255, 255), 2)                cv2.putText(orig, "{:.1f}mm".format(D),                            (int(trbrX + 20), int(trbrY+20)), cv2.FONT_HERSHEY_SIMPLEX,                            0.65, (255, 255, 255), 2)                # show the output image                cut_img = orig[(int(tl[1])-60): (int(bl[1])+60), (int(tl[0])-70): (int(tr[0])+70)]                #print(cut_img)                if len(cut_img):                    print(cut_img)                    print(len(cut_img))                    resize_img = cv2.resize(cut_img, (28, 28))  # 调整图像尺寸为28*28                    resize_img = cv2.cvtColor(resize_img, cv2.COLOR_RGB2GRAY)                    ret, thresh_img = cv2.threshold(resize_img, 127, 255, cv2.THRESH_BINARY)  # 二值化                    cv2.imwrite('D:/deng/ppp/888.png', thresh_img)                    cv2.imshow('result', thresh_img)                else:                    print("未检测到数字")                im = Image.open('D:/deng/ppp/888.png')                #im = cv2.imread('D:\\deng\\ppp\\888.png')                #im_gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)                data = list(im.getdata())                result = [(255 - x) * 1.0 / 255.0 for x in data]                tensorflow_test.prediction_num(result)                cv2.imshow("Image", orig)                if cv2.waitKey(1) & 0xFF == ord('q'):                    cap.release()                    cv2.destroyAllWindows()                    break

tensorflow 1.13.1

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