Opencv對視頻進行目標檢測_視頻

在Opencv3.3版本中整合了deeplearning功能。其實現了對caffe和tensorflow兩個架構的推理，但不支援訓練。本文使用caffe訓練的檔案對目標進行檢測。整個思路是首先讀取視頻檔案，然後載入模型檔案，最後讀取到視頻的每一幀對其進行檢測。 系統: ubuntu16.04 python:2.7 模型檔案：MobileNet-SSD 任意視頻檔案 1.安裝 openCV

如果安裝好了openCV可以跳過這一小節，這一小節只是把一些命令複製出來，實際安裝可能會存在錯誤。
install opencv3.3

Step #1: Install OpenCV dependencies on Ubuntu 16.04

Ubuntu 16.04:How to install OpenCV$ sudo apt-get update$ sudo apt-get upgrade

$ sudo apt-get install build-essential cmake pkg-config

$ sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev

$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev$ sudo apt-get install libxvidcore-dev libx264-dev

$ sudo apt-get install libgtk-3-dev

$ sudo apt-get install libatlas-base-dev gfortran

$ sudo apt-get install python2.7-dev python3.5-dev

Step #2: Download the OpenCV source

$ cd ~$ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.3.0.zip$ unzip opencv.zip

Step #3: Setup your Python environment — Python 2.7 or Python 3

$ cd ~$ wget https://bootstrap.pypa.io/get-pip.py$ sudo python get-pip.py

Step #4: Configuring and compiling OpenCV on Ubuntu 16.04

$ cd ~/opencv-3.3.0/$ mkdir build$ cd build$ cmake -D CMAKE_BUILD_TYPE=RELEASE \    -D CMAKE_INSTALL_PREFIX=/usr/local \    -D INSTALL_PYTHON_EXAMPLES=ON \    -D INSTALL_C_EXAMPLES=OFF \    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.3.0/modules \    -D PYTHON_EXECUTABLE=~/.virtualenvs/cv/bin/python \    -D BUILD_EXAMPLES=ON ..

$ make -j4

$ sudo make install$ sudo ldconfig

Step #6: Testing your OpenCV install

$ cd ~$ workon cv$ pythonPython 2.7.12 (default, Nov 19 2016, 06:48:10) [GCC 5.4.0 20160609] on linux2Type "help", "copyright", "credits" or "license" for more information.>>> import cv2>>> cv2.__version__'3.3.0'>>>

進行檢測

需要修改代碼中的幾個地方，change the path “–prototxt”,”–model, videoPath”

#import the necessary packagesfrom imutils.video import VideoStreamfrom imutils.video import FPSimport numpy as npimport argparseimport imutilsimport timeimport cv2videoPath = "/home/user/Desktop/test.mp4"# construct the argument parse and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-p", "--prototxt", default='/home/user/Desktop/MobileNetSSD_deploy.prototxt',    help="path to Caffe 'deploy' prototxt file")ap.add_argument("-m", "--model", default="/home/user/Desktop/MobileNetSSD_deploy.caffemodel",    help="path to Caffe pre-trained model")ap.add_argument("-c", "--confidence", type=float, default=0.2,    help="minimum probability to filter weak detections")args = vars(ap.parse_args())# detect, then generate a set of bounding box colors for each classCLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",    "sofa", "train", "tvmonitor"]COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))# load our serialized model from diskprint("[INFO] loading model...")net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])# initialize the video stream, allow the cammera sensor to warmup,# and initialize the FPS counterprint('[INFO] starting video stream...')vs = cv2.VideoCapture(videoPath)time.sleep(2.0)fps = FPS().start()print('open ',vs.isOpened())#loop over the frames from the video streamwhile True:    #grap the frame from the threaded video stream and resize it    #to have a maximum width of 400 pixels    ret,frame = vs.read()    print('shape : ',type(frame))    if frame is None:        break    frame = imutils.resize(frame,width=400)    #grab the frame dimensions and convert it to a blob    (h,w) = frame.shape[:2]    bolb = cv2.dnn.blobFromImage(frame,0.007843,(300,300),127.5)    #pass the blob through the network and obtain the detections and predictions    net.setInput(bolb)    detections = net.forward()    for i in np.arange(0, detections.shape[2]):        # extract the confidence (i.e., probability) associated with the        # prediction        confidence = detections[0, 0, i, 2]        # filter out weak detections by ensuring the `confidence` is        # greater than the minimum confidence        if confidence > args["confidence"]:            # extract the index of the class label from the `detections`,            # then compute the (x, y)-coordinates of the bounding box for            # the object            idx = int(detections[0, 0, i, 1])            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])            (startX, startY, endX, endY) = box.astype("int")            # display the prediction            label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)            print("[INFO] {}".format(label))            cv2.rectangle(frame, (startX, startY), (endX, endY),                          COLORS[idx], 2)            y = startY - 15 if startY - 15 > 15 else startY + 15            cv2.putText(frame, label, (startX, y),                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)    #show the output frame    cv2.imshow("Frame",frame)    key = cv2.waitKey(1)& 0xff    if key == ord('q'):        break    fps.update()fps.stop()vs.release()cv2.destroyAllWindows()

如果openCV載入不到視頻檔案，有可能是缺少相對應的ffmpeg，或者與老版本衝突，可以卸載老版本和相對應的版本依賴。 參考文獻 https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/ https://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/ https://github.com/chuanqi305/MobileNet-SSD