# python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
   help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
   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())

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["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 disk
print("[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 counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

# loop over the frames from the video stream
while True:
   # grab the frame from the threaded video stream and resize it
   # to have a maximum width of 400 pixels
   frame = vs.read()
   frame = imutils.resize(frame, width=400)

   # grab the frame dimensions and convert it to a blob
   (h, w) = frame.shape[:2]
   blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
      0.007843, (300, 300), 127.5)

   # pass the blob through the network and obtain the detections and
   # predictions
   net.setInput(blob)
   detections = net.forward()

   # loop over the detections
   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")

         # draw the prediction on the frame
         label = "{}: {:.2f}%".format(CLASSES[idx],
            confidence * 100)
         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 the `q` key was pressed, break from the loop
   if key == ord("q"):
      break

   # update the FPS counter
   fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

windows下运行，使用的编辑器是pycharm,运行中遇到以下问题：

1. No module named ‘imutils’

安装方法：pip install imutils

2. No module named cv2

安装方法：pip install opencv-python

3. real_time_object_detection.py: error: the following arguments are required: -p/–prototxt, -m/–model

解决方法 ：使用cmd 命令 进入real_time_object_detection.py所在文件夹，命令行模式下运行python real_time_object_detection.py --prototxt=MobileNetSSD_deploy.prototxt.txt --model=MobileNetSSD_deploy.caffemodel

下面是运行结果：

大家可以直接去下面的网站下载，不过需要梯子：

https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/#post_downloads

也可以到我上传的资源下载：

https://download.csdn.net/download/qq_44777811/12264432