OpenCV3与深度学习实例：使用MobileNet SSD检测物体

 

#coding:utf-8
import numpy as np
import argparse
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=False,default='datas/images/people.jpg',
	help="path to input image")
# 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", "trai