Imagenet VGG-19图片识别实例展示

本文介绍如何使用预训练的VGG19模型进行图像分类。通过加载模型权重,搭建卷积神经网络,并对图像进行预处理,最终实现对输入图像的分类预测。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

资源

1.相关的vgg模型下载网址

2.ImageNet 1000种分类以及排列

https://github.com/sh1r0/caffe-Android-demo/blob/master/app/src/main/assets/synset_words.txt(如果下载单个txt格式不对的话就整包下载)

 

完整代码如下:

import numpy as np
import scipy.misc
import scipy.io as sio
import tensorflow as  tf
import os


##卷积层
def _conv_layer(input, weight, bias):
    conv = tf.nn.conv2d(input, tf.constant(weight), strides=(1, 1, 1, 1), padding='SAME')
    return tf.nn.bias_add(conv, bias)


##池化层
def _pool_layer(input):
    return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')


##全链接层
def _fc_layer(input, weights, bias):
    shape = input.get_shape().as_list()
    dim = 1
    for d in shape[1:]:
        dim *= d
    x = tf.reshape(input, [-1, dim])
    fc = tf.nn.bias_add(tf.matmul(x, weights), bias)
    return fc


##softmax输出层
def _softmax_preds(input):
    preds = tf.nn.softmax(input, name='prediction')
    return preds


##图片处里前减去均值
def _preprocess(image, mean_pixel):
    return image - mean_pixel


##加均值  显示图片
def _unprocess(image, mean_pixel):
    return image + mean_pixel


##读取图片 并压缩
def _get_img(src, img_size=False):
    img = scipy.misc.imread(src, mode='RGB')
    if not (len(img.shape) == 3 and img.shape[2] == 3):
        img = np.dstack((img, img, img))
    if img_size != False:
        img = scipy.misc.imresize(img, img_size)
    return img.astype(np.float32)


##获取名列表
def list_files(in_path):
    files = []
    for (dirpath, dirnames, filenames) in os.walk(in_path):
        # print("dirpath=%s, dirnames=%s, filenames=%s"%(dirpath, dirnames, filenames))
        files.extend(filenames)
        break

    return files


##获取文件路径列表dir+filename
def _get_files(img_dir):
    files = list_files(img_dir)
    return [os.path.join(img_dir, x) for x in files]

##获得图片lable列表
def _get_allClassificationName(file_path):
    f = open(file_path, 'r')
    lines = f.readlines()
    f.close()
    return lines

##构建cnn前向传播网络
def net(data, input_image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2',
        'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',

        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2',
        'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',

        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',
        'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5',

        'fc6', 'relu6',
        'fc7', 'relu7',
        'fc8', 'softmax'
    )

    weights = data['layers'][0]
    net = {}
    current = input_image
    for i, name in enumerate(layers):
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            kernels = np.transpose(kernels, (1, 0, 2, 3))
            bias = bias.reshape(-1)
            current = _conv_layer(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current)
        elif kind == 'pool':
            current = _pool_layer(current)
        elif kind == 'soft':
            current = _softmax_preds(current)

        kind2 = name[:2]
        if kind2 == 'fc':
            kernels1, bias1 = weights[i][0][0][0][0]

            kernels1 = kernels1.reshape(-1, kernels1.shape[-1])
            bias1 = bias1.reshape(-1)
            current = _fc_layer(current, kernels1, bias1)

        net[name] = current
    assert len(net) == len(layers)
    return net, mean_pixel, layers


if __name__ == '__main__':
    imagenet_path = 'data/imagenet-vgg-verydeep-19.mat'
    image_dir = 'images/'

    data = sio.loadmat(imagenet_path)  ##加载ImageNet mat模型
    mean = data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))  ##获取图片像素均值

    lines = _get_allClassificationName('data/synset_words.txt')  ##加载ImageNet mat标签
    images = _get_files(image_dir)  ##获取图片路径列表
    with tf.Session() as sess:
        for i, imgPath in enumerate(images):
            image = _get_img(imgPath, (224, 224, 3))  ##加载图片并压缩到标准格式=>224 224

            image_pre = _preprocess(image, mean_pixel)
            # image_pre = image_pre.transpose((2, 0, 1))
            image_pre = np.expand_dims(image_pre, axis=0)

            image_preTensor = tf.convert_to_tensor(image_pre)
            image_preTensor = tf.to_float(image_preTensor)

            # Test pretrained model
            nets, mean_pixel, layers = net(data, image_preTensor)

            preds = nets['softmax']

            predsSortIndex = np.argsort(-preds[0].eval())
            print('#####%s#######' % imgPath)
            for i in range(3):   ##输出前3种分类
                nIndex = predsSortIndex
                classificationName = lines[nIndex[i]] ##分类名称
                problity = preds[0][nIndex[i]]   ##某一类型概率

                print('%d.ClassificationName=%s  Problity=%f' % ((i + 1), classificationName, problity.eval()))

 

#####images/cat1.jpg#######
1.ClassificationName=n02123045 tabby, tabby cat
  Problity=0.219027
2.ClassificationName=n02123159 tiger cat
  Problity=0.091527
3.ClassificationName=n02445715 skunk, polecat, wood pussy
  Problity=0.028864

 

#####images/cat2.jpg#######
1.ClassificationName=n02123045 tabby, tabby cat
  Problity=0.337648
2.ClassificationName=n02123159 tiger cat
  Problity=0.171013
3.ClassificationName=n02124075 Egyptian cat
  Problity=0.059857

 

#####images/cat_two.jpg#######
1.ClassificationName=n03887697 paper towel
  Problity=0.178623
2.ClassificationName=n02111889 Samoyed, Samoyede
  Problity=0.119629
3.ClassificationName=n02098286 West Highland white terrier
  Problity=0.060589

 

#####images/dog1.jpg#######
1.ClassificationName=n02096585 Boston bull, Boston terrier
  Problity=0.403131
2.ClassificationName=n02108089 boxer
  Problity=0.184223
3.ClassificationName=n02093256 Staffordshire bullterrier, Staffordshire bull terrier
  Problity=0.101937

### 使用VGG19模型进行植物病虫害识别 #### 数据准备 为了训练和评估VGG19模型,在文献中提到的研究采用了由1965年的真实植物病虫害图像组成的数据集,这些图像被划分为8个不同的类别[^1]。确保所使用的数据集具有足够的多样性和代表性对于模型的成功至关重要。 #### 模型架构理解 VGG19是在AlexNet的基础上发展起来的一种更深的卷积神经网络结构。它通过增加网络层数来提升特征提取能力,从而提高了分类性能[^3]。具体来说,VGG19拥有19层权重层(包括卷积层和全连接层),这使得它可以捕捉到更复杂的模式并适用于多种视觉任务。 #### 训练过程概述 当利用MATLAB中的App Designer工具创建交互界面时,可以通过集成MATLAB深度学习模块轻松调用预训练好的VGG19模型来进行迁移学习或者微调操作[^2]。以下是简化版的工作流程: - **加载预训练模型**:可以从MATLAB内置函数`vgg19`获取已经过ImageNet大规模图片库训练过的参数初始化。 ```matlab net = vgg19; ``` - **调整最后一层**:由于原始VGG19的最后一层是为了处理ImageNet上的千类物体而设计的,因此需要将其替换为适合当前特定问题的新一层——比如针对上述提及的八个病虫害种类设置输出节点数目的softmax分类器。 ```matlab % 假设有8种不同类型的病虫害标签 numClasses = 8; layersTransfer = [ net.Layers(1:end-3); % 移除最后三层 fullyConnectedLayer(numClasses, 'WeightLearnRateFactor', 20, 'BiasLearnRateFactor', 20); softmaxLayer; classificationLayer]; ``` - **定义训练选项**:设定合适的超参数如批量大小、迭代次数等,并启动训练循环直到收敛为止。 ```matlab options = trainingOptions('sgdm',... 'InitialLearnRate', 0.0001,... 'MaxEpochs', 20,... 'MiniBatchSize', 32,... 'Shuffle', 'every-epoch',... 'ValidationData', validationSet,... % 提供验证集合 'Plots', 'training-progress'); trainedNet = trainNetwork(trainingImages, trainingLabels, layersTransfer, options); ``` 完成以上步骤后即可得到一个能够有效区分各类植物病虫害状况的VGG19模型实例
评论 8
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值