3种方法用yolov3pythoncv2实现视频目标检测

　　1 说明：
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　　1.1 技术要点：yolov3+python+cv2（OpenCV）实现目标检测，本文由浅入深，代码注释，小白秒懂，值得收藏。
　　1.2 3种方法：摄像头视频目标检测、小视频cv2目标检测，和视频目标检测生成avi视频。
　　1.3 yolo的基本介绍。
　　pic1：单张静态图片目标检测
　　1.4 单张静态图片目标检测，我已经讲过，可参考文章
　　《目标检测和识别：Python+OpenCV+Yolov3》，文章中三个文件：yolov3.cfg、coco.names和yolov3.weights.下载已经告知，本文省略。建议先看这篇文章后，再看本文。
　　2 效果图：
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　　2.1 摄像头实时目标检测
　　bottle=矿泉水瓶；cell phone=手机；person=人
　　2.2 读取视频实时目标检测：
　　person=人；
　　2.3 目标检测后生成avi视频：
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　　3 摄像头实时目标检测代码：#代码来源，进行注释和分析 #https://blog.csdn.net/qq_39567427/article/details/105451962?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf  #摄像头实时目标检测  #第1步：导入模块 import cv2 import numpy as np  #第2步：加载yolov3的模型和三个文件 #注意路径 net = cv2.dnn.readNet(＂/home/xgj/Desktop/yolov3/yolov3.weights＂, ＂/home/xgj/Desktop/yolov3/yolov3.cfg＂) #名字分类的空的列表 classes = [] with open(＂/home/xgj/Desktop/yolov3/coco.names＂, ＂r＂) as f:     classes = [line.strip() for line in f.readlines()] layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]  #第3步：初始化相关参数 #颜色随机产生 colors = np.random.uniform(0, 255, size=(len(classes), 3)) # Initialize frame rate calculation frame_rate_calc = 1 #获取频率 freq = cv2.getTickFrequency() # 0提示读取摄像头 # 如果格式是＂xxx/xxx/xxx.mp4＂就是读取本地视频文件 cap = cv2.VideoCapture(0)  #第4步：循环 while True:     # Start timer (for calculating frame rate)     t1 = cv2.getTickCount()     #获取读取摄像头资料     ret, frame = cap.read()      #以下与单张静态图片类似     #获取高、宽和通道     height, width, channels = frame.shape     # Detecting objects，侦测对象相关设置，与静态图片类似     blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)     net.setInput(blob)     outs = net.forward(output_layers)      # Showing informations on the screen     #屏幕展示信息设置     class_ids = []     confidences = []     boxes = []     #outs是上面获取的     for out in outs:         for detection in out:             scores = detection[5:]             class_id = np.argmax(scores)             confidence = scores[class_id]             if confidence > 0.5:                 # Object detected                 center_x = int(detection[0] * width)                 center_y = int(detection[1] * height)                  w = int(detection[2] * width)                 h = int(detection[3] * height)                  # Rectangle coordinates                 x = int(center_x - w / 2)                 y = int(center_y - h / 2)                  boxes.append([x, y, w, h])                 confidences.append(float(confidence))                 class_ids.append(class_id)      indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)      font = cv2.FONT_HERSHEY_SIMPLEX     for i in range(len(boxes)):         if i in indexes:             x, y, w, h = boxes[i]             label = str(classes[class_ids[i]])             color = colors[i]             cv2.rectangle(frame, (x, y), (x + w, y + h), color, 1)             cv2.putText(frame, label, (x, y - 20), font, 0.7, color, 1)          cv2.putText(frame,＂FPS: {0:.2f}＂.format(frame_rate_calc), (30,50),font, 0.7, (255,255,0), 1)     cv2.namedWindow(＂Image＂, cv2.WINDOW_NORMAL)     cv2.resizeWindow(＂Image＂, 960, 540)     cv2.imshow(＂Image＂, frame)     #以上与单张静态图片类似      # Calculate framerate     t2 = cv2.getTickCount()     time1 = (t2-t1)/freq     frame_rate_calc= 1/time1          # Press ＂q＂ to quit，默认为1     if cv2.waitKey(1) == ord(＂q＂):         break  #第5步：结束关闭 cap.release() cv2.destroyAllWindows()
　　4 读取本地视频进行目标检测：
　　代码基本与上面相同，仅需给一下这一行代码为cap = cv2.VideoCapture(＂/home/xgj/Desktop/yolov3/333.mp4＂)   #上面注释里有介绍
　　5 高级一点的视频标准目标检测，并合成新的视频：
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　　5.1 本机环境：python3.8+OpenCV4.2.0+华为笔记本电脑+deepin-linux操作系统。
　　5.2 代码说明：#参考文章，对代码进行注释和适当修改 #https://my.oschina.net/u/1464083/blog/2906860 #本代码：sp-3.py
　　5.3 代码：#第1步：导入模块 import numpy as np import argparse import imutils import time import cv2 import os  #第2步：参数构建 #实例化 ap = argparse.ArgumentParser() #当前本代码同目录下的input文件夹：原视频位置 ap.add_argument(＂-i＂, ＂--input＂, required=True,     help=＂path to input video＂) #当前本代码同目录下的output文件夹：目标检测标定后的合成视频位置 ap.add_argument(＂-o＂, ＂--output＂, required=True,     help=＂path to output video＂) #当前本代码同目录下的yolov3文件夹 ap.add_argument(＂-y＂, ＂--yolov3＂, required=True,     help=＂base path to YOLO directory＂) #参数定义，默认值 ap.add_argument(＂-c＂, ＂--confidence＂, type=float, default=0.5,     help=＂minimum probability to filter weak detections＂) ap.add_argument(＂-t＂, ＂--threshold＂, type=float, default=0.3,     help=＂threshold when applyong non-maxima suppression＂) args = vars(ap.parse_args())  #第3步：加载yolov3的3个文件 #注意：三个文件在yolov3文件下 weightsPath = os.path.sep.join([args[＂yolov3＂], ＂yolov3.weights＂]) configPath = os.path.sep.join([args[＂yolov3＂], ＂yolov3.cfg＂]) labelsPath = os.path.sep.join([args[＂yolov3＂], ＂coco.names＂]) #读取标签名文件 LABELS = open(labelsPath).read().strip().split(＂ ＂)   # initialize a list of colors to represent each possible class label np.random.seed(42) #颜色随机设置 COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),dtype=＂uint8＂)  print(＂[INFO] loading YOLO from disk...＂) net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]   #第4步：获取输入的原视频文件 vs = cv2.VideoCapture(args[＂input＂]) writer = None (W, H) = (None, None)  # try to determine the total number of frames in the video file try:     prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2()          else cv2.CAP_PROP_FRAME_COUNT     total = int(vs.get(prop))     #测算本视频内的目标检测识别后的总框架     print(＂[INFO] {} total frames in video＂.format(total))   # an error occurred while trying to determine the total # number of frames in the video file except:     print(＂[INFO] could not determine # of frames in video＂)     print(＂[INFO] no approx. completion time can be provided＂)     total = -1   #第5步：循环 # loop over frames from the video file stream while True:     # read the next frame from the file     (grabbed, frame) = vs.read()       # if the frame was not grabbed, then we have reached the end     # of the stream     if not grabbed:         break       # if the frame dimensions are empty, grab them     if W is None or H is None:         (H, W) = frame.shape[:2]      # construct a blob from the input frame and then perform a forward     # pass of the YOLO object detector, giving us our bounding boxes     # and associated probabilities     blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),         swapRB=True, crop=False)     net.setInput(blob)     start = time.time()     layerOutputs = net.forward(ln)     end = time.time()      # initialize our lists of detected bounding boxes, confidences,     # and class IDs, respectively     boxes = []     confidences = []     classIDs = []       # loop over each of the layer outputs     for output in layerOutputs:         # loop over each of the detections         for detection in output:              scores = detection[5:]             classID = np.argmax(scores)             confidence = scores[classID]               if confidence > args[＂confidence＂]:                 box = detection[0:4] * np.array([W, H, W, H])                 (centerX, centerY, width, height) = box.astype(＂int＂)                  x = int(centerX - (width / 2))                 y = int(centerY - (height / 2))                   boxes.append([x, y, int(width), int(height)])                 confidences.append(float(confidence))                 classIDs.append(classID)      #绑定box     idxs = cv2.dnn.NMSBoxes(boxes, confidences, args[＂confidence＂],         args[＂threshold＂])       # ensure at least one detection exists     #确保一个侦测存在     if len(idxs) > 0:         # loop over the indexes we are keeping         for i in idxs.flatten():             # extract the bounding box coordinates             (x, y) = (boxes[i][0], boxes[i][1])             (w, h) = (boxes[i][2], boxes[i][3])               # draw a bounding box rectangle and label on the frame             color = [int(c) for c in COLORS[classIDs[i]]]             cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)             text = ＂{}: {:.4f}＂.format(LABELS[classIDs[i]],                 confidences[i])             cv2.putText(frame, text, (x, y - 5),                 cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)        # check if the video writer is None     #写入新的视频文件     if writer is None:         # initialize our video writer         fourcc = cv2.VideoWriter_fourcc(*＂MJPG＂)         writer = cv2.VideoWriter(args[＂output＂], fourcc, 30,             (frame.shape[1], frame.shape[0]), True)           # some information on processing single frame         if total > 0:             elap = (end - start)             print(＂[INFO] single frame took {:.4f} seconds＂.format(elap))             print(＂[INFO] estimated total time to finish: {:.4f} seconds＂.format(                 elap * total))       # write the output frame to disk     writer.write(frame)  #第6步：结束和收尾 # release the file pointers print(＂[INFO] cleaning up...＂) writer.release() vs.release()
　　5.4 运行：#第7步：使用方法 #在本代码sp-3.py的目录下打开终端 #本机输入 #python3.8 sp-3.py --input videos/222.mp4 --output output/2222.avi --yolov3 yolov3
　　5.5 图：
　　视频是一首完整的MV，有点大，时间有一点久
　　6 小结：
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　　6.1 摄像头视频，有点卡，小bug。
　　6.2 合成视频，有点慢，xiaobug。
　　7 yolo介绍：
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　　7.1 yolo是目前比较流行的目标检测算法，速度快结构简单。
　　7.2 YOLO 是 2016 年提出来的目标检测算法，当前较为火热。
　　7.3 yolo=You Only Look Once:
　　Unified, Real-Time Object Detection，
　　即：You Only Look Once说的是只需要一次CNN运算，
　　Unified指的是这是一个统一的框架，提供end-to-end的预测，
　　而Real-Time体现是Yolo算法速度快，达到实时。
　　7.4 yolov3比较成熟，但目前已经出现yolov4和yolov5（被认为是v4.5版本）。