使用OpenCV_python中的DNN调用CaffeModel识别图像只需30行代码

#coding:utf-8
from __future__ import print_function
import numpy as np
import cv2
from cv2 import dnn
import sys

cm_path='caffemodel/'

def get_class_list():
with open(cm_path+'bvlc_googlenet_synset_words_cn1.txt', 'rt') as f:
return [x[x.find(" ") + 1:] for x in f]

def dnn_show_result(prob,classes,n):
y = sorted(prob[0],reverse = True) #从大到小排序
z=range(n)  #前n名
for i in range(0, n):
z[i]=np.where(prob[0] == y[i])[0][0]
print(u"第",i+1,u"匹配：", classes[z[i]],end='')
print(u"类所在行：", z[i]+1,"  ",u"可能性:", y[i])

if __name__ == "__main__":
if len(sys.argv) < 2:
print ("USAGE: googlenet_z.py images/tiger.jpg")
sys.exit()
fn=sys.argv[1]
blob = dnn.blobFromImage(cv2.imread(fn), 1, (224, 224), (104, 117, 123))
print("Input:", blob.shape, blob.dtype)

net = dnn.readNetFromCaffe(cm_path+'bvlc_googlenet.prototxt', cm_path+'bvlc_googlenet.caffemodel')
net.setInput(blob)
prob = net.forward()
print("Output:", prob.shape, prob.dtype)

classes = get_class_list()
dnn_show_result(prob,classes,3)