Pytorch学习系列之五 ：OpenCV加载pytorch所生成的ONNX模型进行推理（彩蛋： pyttsx3文字转语音包的使用）

import cv2 as cv
import numpy as np
import pyttsx3 #文字转语音的功能包


def mnist_onnx_demo():
    mnist_net = cv.dnn.readNetFromONNX("cnn_mnist.onnx")
    image = cv.imread("C:/Users/Administrator/Desktop/666.png")
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    cv.imshow("input", gray)
    '''
    '''
    #将单通道的灰度图转为[-1,1]的区间，详情可参考#https://blog.csdn.net/weixin_42216109/article/details/103010206
    #关于cv.dnn.blobFromImage的参数说明
    blob = cv.dnn.blobFromImage(gray, 1.0/255.0, (28, 28), (127.0)) / 0.5
    print(blob.shape)
    mnist_net.setInput(blob)
    result = mnist_net.forward()
    pred_label = np.argmax(result, 1)
    print("predit label : %d"%pred_label)
    #初始化语音转文字的模块
    engine = pyttsx3.init()
    engine.say(str(pred_label) + "预测的结果出来啦!!! I am very happy")
    engine.runAndWait()
    cv.waitKey(0)
    cv.destroyAllWindows()

if __name__ == "__main__":
    mnist_onnx_demo()