2021SC@SDUSC

概述

此次文章主要分析finemapping_vertical.py文件

 调用的库

#coding=utf-8
from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
from keras.models import Model, Sequential
from keras.layers.advanced_activations import PReLU
from keras.optimizers import adam
import numpy as np

import cv2

由代码可知，主要调用的库为keras、cv2

keras

Keras是一个高层神经网络API，Keras由纯Python编写而成并基Tensorflow、Theano以及CNTK后端。Keras 为支持快速实验而生，能够把你的idea迅速转换为结果

以下是keras的框架架构

代码分析

def getModel():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    #将此形状更改为[None，None，3]以启用仲裁形状输入
    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = Activation("relu", name='relu1')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = Activation("relu", name='relu3')(x)
    x = Flatten()(x)
    output = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(output)
    model = Model([input], [output])
    return model

def gettest_model():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    B = Activation("relu", name='relu1')(A)
    C = MaxPool2D(pool_size=2)(B)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    K = Activation("relu", name='relu3')(x)


    x = Flatten()(K)
    dense = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(dense)
    x = Model([input], [output])
    x.load_weights("./model/model12.h5")
    ok = Model([input], [dense])

    for layer in ok.layers:
        print(layer)

    return ok

首先将图像的形状更改为[None, None,3]以启用总裁形状输入，然后使用二维卷积Con2v，及配合激活函数Activation对传入的图像构建神经网络系统并定义模型结构。激活函数在深度学习中扮演着非常重要的角色，它给网络赋予了非线性，从而使得神经网络能够拟合任意复杂的函数。非线性激活函数可以使神经网络随意逼近复杂函数，没有激活函数带来的非线性，多层神经网络和单层无异。

model = getModel()
model.load_weights("./model/model12.h5")

传入图片，进行模型结构的构建，并将构建好的模型返回到model变量中

def finemappingVertical(image):
    resized = cv2.resize(image,(66,16))
    resized = resized.astype(np.float)/255
    res= model.predict(np.array([resized]))[0]
    print("keras_predict",res)
    res  =res*image.shape[1]
    res = res.astype(np.int)
    H,T = res
    H-=3
    #3 79.86
    #4 79.3
    #5 79.5
    #6 78.3


    #T
    #T+1 80.9
    #T+2 81.75
    #T+3 81.75



    if H<0:
        H=0
    T+=2;

    if T>= image.shape[1]-1:
        T= image.shape[1]-1

    image = image[0:35,H:T+2]

    image = cv2.resize(image, (int(136), int(36)))
    return image

使用python的openCV的cv2库对其进行裁剪，最终裁剪为136*36的大小，并返回这个图片。该函数的目的便是裁剪图片，使图片的识别的效率更高

标签：HyperLPR,name,image,系统分析,shape,input,model,车牌,Activation
来源： https://blog.csdn.net/m0_57704837/article/details/120693970