使用Imagenet VGG-19模型进行图片识别

该程序参考  http://blog.csdn.net/wyl1987527/article/details/68168115 编写

资源：

1.相关的vgg模型下载网址

http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

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 npimport scipy.miscimport scipy.io as sioimport tensorflow as  tfimport 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+filenamedef _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, layersif __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()))        sess.close()

#####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

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