Keras - 使用 ResNet 模型进行实时预测

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  简述

  ResNet是一个预训练模型。它是使用ImageNet训练的。在ImageNet上预训练的 ResNet 模型权重。它具有以下语法 -

  
  
  keras.applications.resnet.ResNet50 (
  
     include_top = True, 
  
     weights = 'imagenet', 
  
     input_tensor = None, 
  
     input_shape = None, 
  
     pooling = None, 
  
     classes = 1000
  
  )
  
  

  说明

  • include_top指的是网络顶部的全连接层。
  • weights是指 ImageNet 上的预训练。
  • input_tensor是指可选的 Keras 张量，用作模型的图像输入。
  • input_shape是指可选的形状元组。此模型的默认输入大小为 224x224。
  • classes是指可选数量的类来对图像进行分类。

  让我们通过写一个简单的例子来理解模型 -
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  第一步：导入模块

  让我们加载必要的模块，如下所示 -

  
  
  >>> import PIL 
  
  >>> from keras.preprocessing.image import load_img 
  
  >>> from keras.preprocessing.image import img_to_array 
  
  >>> from keras.applications.imagenet_utils import decode_predictions 
  
  >>> import matplotlib.pyplot as plt 
  
  >>> import numpy as np 
  
  >>> from keras.applications.resnet50 import ResNet50 
  
  >>> from keras.applications import resnet50
  
  

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  第 2 步：选择输入

  让我们选择一个输入图像，Lotus，如下所示 -

  
  
  >>> filename = 'banana.jpg' 
  
  >>> ## load an image in PIL format 
  
  >>> original = load_img(filename, target_size = (224, 224)) 
  
  >>> print('PIL image size',original.size)
  
  PIL image size (224, 224) 
  
  >>> plt.imshow(original) 
  
  <matplotlib.image.AxesImage object at 0x1304756d8> 
  
  >>> plt.show()
  
  

  在这里，我们加载了一个图像（banana.jpg）并显示它。
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  第 3 步：将图像转换为 NumPy 数组

  让我们将输入Banana转换为 NumPy 数组，以便可以将其传递到模型中进行预测。

  
  
  >>> #convert the PIL image to a numpy array 
  
  >>> numpy_image = img_to_array(original) 
  
  
  
  >>> plt.imshow(np.uint8(numpy_image)) 
  
  <matplotlib.image.AxesImage object at 0x130475ac8> 
  
  
  
  >>> print('numpy array size',numpy_image.shape) 
  
  numpy array size (224, 224, 3) 
  
  
  
  >>> # Convert the image / images into batch format 
  
  >>> image_batch = np.expand_dims(numpy_image, axis = 0) 
  
  
  
  >>> print('image batch size', image_batch.shape) 
  
  image batch size (1, 224, 224, 3)
  
  >>> 
  
  

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  第 4 步：模型预测

  让我们将输入输入模型以获得预测

  
  
  >>> prepare the image for the resnet50 model >>> 
  
  >>> processed_image = resnet50.preprocess_input(image_batch.copy()) 
  
  
  
  >>> # create resnet model 
  
  >>>resnet_model = resnet50.ResNet50(weights = 'imagenet') 
  
  >>> Downloavding data from https://github.com/fchollet/deep-learning-models/releas
  
  es/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5 
  
  102858752/102853048 [==============================] - 33s 0us/step 
  
  
  
  >>> # get the predicted probabilities for each class 
  
  >>> predictions = resnet_model.predict(processed_image) 
  
  
  
  >>> # convert the probabilities to class labels 
  
  >>> label = decode_predictions(predictions) 
  
  Downloading data from https://storage.googleapis.com/download.tensorflow.org/
  
  data/imagenet_class_index.json 
  
  40960/35363 [==================================] - 0s 0us/step 
  
  
  
  >>> print(label)
  
  

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  输出

  
  
  [
  
     [
  
        ('n07753592', 'banana', 0.99229723), 
  
        ('n03532672', 'hook', 0.0014551596), 
  
        ('n03970156', 'plunger', 0.0010738898), 
  
        ('n07753113', 'fig', 0.0009359837) , 
  
        ('n03109150', 'corkscrew', 0.00028538404)
  
     ]
  
  ]
  
  

  在这里，模型正确地将图像预测为香蕉。