OpenCV实战使用GoogLeNet实现图像分类

模型参数文件：
bvlc_googlenet.caffemodel
网络结构文件：
bvlc_googlenet.prototxt
分类文件：
label.txt

https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet

import numpy as np
import cv2

# image = cv2.imread('tower.jpg')
# image = cv2.imread('cat.jpg')
image = cv2.imread('dog.jpg')
config = 'model/bvlc_googlenet.prototxt'
model = 'model/bvlc_googlenet.caffemodel'

net = cv2.dnn.readNetFromCaffe(config, model)
blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))
net.setInput(blob)
prob = net.forward()

classes = open('model/label.txt', 'rt').read().strip().split('\n')
rowIndex = np.argsort(prob[0])[::-1][0]

result = 'result:{}, {:.0f}%'.format(classes[rowIndex], prob[0][rowIndex]*100)
cv2.putText(image, result, (25, 45), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow('result', image)
cv2.waitKey()
cv2.destroyAllWindows()