（6）后处理（postrocess）
使用NMSBoxes函数过滤掉重复识别的区域 。
   indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)      for i in indices:        box = boxes[i]        left = box[0]        top = box[1]        width = box[2]        height = box[3]        drawPred(classIds[i], confidences[i], left, top, left + width, top + height)（7）画出检测到的对象
def drawPred(classId, conf, left, top, right, bottom):    # Draw a bounding box.    cv.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))        label = '%.2f' % conf            # Get the label for the class name and its confidence    if classes:        assert(classId < len(classes))        label = '%s:%s' % (classes[classId], label)    #Display the label at the top of the bounding box    labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)    top = max(top, labelSize[1])    cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255))（8）完整源码及检测结果（cv_call_yolo.py）
import cv2cv=cv2import numpy as npimport timenet = cv2.dnn.readNetFromDarknet("yolov3/yolov3.cfg", "yolov3/yolov3.weights")net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)?confThreshold = 0.5  #Confidence thresholdnmsThreshold = 0.4   #Non-maximum suppression thresholdframe=cv2.imread("dog.jpg")classesFile = "coco.names";classes = Nonewith open(classesFile, 'rt') as f:    classes = f.read().rstrip('\n').split('\n')?def getOutputsNames(net):    # Get the names of all the layers in the network    layersNames = net.getLayerNames()    # Get the names of the output layers, i.e. the layers with unconnected outputs    return [layersNames[i - 1] for i in net.getUnconnectedOutLayers()]print(getOutputsNames(net))# Remove the bounding boxes with low confidence using non-maxima suppression?def postprocess(frame, outs):    frameHeight = frame.shape[0]    frameWidth = frame.shape[1]    classIds = []    confidences = []    boxes = []    # Scan through all the bounding boxes output from the network and keep only the    # ones with high confidence scores. Assign the box's class label as the class with the highest score.    classIds = []    confidences = []    boxes = []    for out in outs:        for detection in out:            scores = detection[5:]            classId = np.argmax(scores)            confidence = scores[classId]            if confidence > confThreshold:                center_x = int(detection[0] * frameWidth)                center_y = int(detection[1] * frameHeight)                width = int(detection[2] * frameWidth)                height = int(detection[3] * frameHeight)                left = int(center_x - width / 2)                top = int(center_y - height / 2)                classIds.append(classId)                confidences.append(float(confidence))                boxes.append([left, top, width, height])    # Perform non maximum suppression to eliminate redundant overlapping boxes with    # lower confidences.    print(boxes)    print(confidences)      indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)    for i in indices:        #print(i)        #i = i[0]        box = boxes[i]        left = box[0]        top = box[1]        width = box[2]        height = box[3]        drawPred(classIds[i], confidences[i], left, top, left + width, top + height)?    # Draw the predicted bounding boxdef drawPred(classId, conf, left, top, right, bottom):    # Draw a bounding box.    cv.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))    label = '%.2f' % conf        # Get the label for the class name and its confidence    if classes:        assert(classId < len(classes))        label = '%s:%s' % (classes[classId], label)    #Display the label at the top of the bounding box    labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)    top = max(top, labelSize[1])    cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255))blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), [0,0,0], 1, crop=False)t1=time.time()net.setInput(blob)outs = net.forward(getOutputsNames(net))print(time.time()-t1)postprocess(frame, outs)t, _ = net.getPerfProfile()label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))cv2.imshow("result",frame)?