Caffe模型MobileNet与opencv的结合

所需原料:opencv-python + yourmobilenet.caffemodel + yourdeploy.prototxt

利用opencv3.3.1之后推出的dnn模块,支持很多模型的导入.这次我选择Caffe的Mobilenet模型.

import numpy as np
import time
import cv2, os
print('******model*********')
net = cv2.dnn.readNetFromCaffe(r'./mobilenet_deploy.prototxt',
                               r'./model/mbilenet_iter_4055.caffemodel')
labels = ['cha', 'yang', 'xiaotiqin', 'xiong',
          'none', 'zixingche', 'gangqin', 'gou',
          'jingche', 'jita', 'jiuhuche', 'lunchuan',
          'mao']
cap = cv2.VideoCapture(0)
while 1:
    _, frame1 = cap.read()
    print('**********one frame***********')
    frame = cv2.resize(frame1, (224,224)) # 你的网络输入的尺寸
    # grab the frame dimensions and convert it to a blob
    blob = cv2.dnn.blobFromImage(frame, 0.00390625, (224, 224), (112.24, 115.46, 117.84),swapRB=False)
    net.setInput(blob)
    detections = net.forward()
    label = labels[np.argmax(detections)]
    # label = str(np.argmax(detections))
    cv2.putText(frame1, label,(111,111),cv2.FONT_HERSHEY_DUPLEX, 2, [0, 0, 111], 2)
    # show the output frame
    cv2.imshow("Frame", frame1)
    cv2.waitKey(1)

    blob = cv2.dnn.blobFromImage(frame, 0.00390625, (224, 224), (112.24, 115.46, 117.84),swapRB=False)

第二个参数是scale,第三个是送入网络的尺寸,然后是每个通道的均值.