keras ResNet50 InceptionV3 Xception VGG16 VGG19

先下载模型，再到各源码中修改源码中模型路径

# import the necessary packages
from keras.applications import ResNet50
from keras.applications import InceptionV3
from keras.applications import Xception  # tensorflow only
from keras.applications import VGG16
from keras.applications import VGG19
from keras.applications import imagenet_utils  # 模块中有一些函数可以方便的进行输入图像预处理和解码输出分类
from keras.applications.inception_v3 import preprocess_input
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
import numpy as np
import argparse
import cv2


# 解析命令行参数
# construct the argument(论据) parse and parse(解析) the arguments
ap = argparse.ArgumentParser()

ap.add_argument('-i','--image',default='./image/DSC_0001.JPG',help='path to input image') # '--image',是要分类的输入图片的路径
# ap.add_argument('/Users/qyk/Desktop/DSC_0001.JPG')
ap.add_argument('-model','--model',type=str,default='vgg16',help='name of pre-trained network to use') # '--model'，指定想要使用的与训练模型
args = vars(ap.parse_args()) # 返回对象object的属性和属性值的字典对象
print(args)

# define a dictionary that maps model names to their classes inside keras
MODELS = {
'vgg16': VGG16,
'vgg19': VGG19,
'inception': InceptionV3,
'xception': Xception,
'resnet': ResNet50

}

# ensure a valid model name was supplied via command line argment
if args['model'] not in MODELS.keys():
raise AssertionError("The --model command line argument should be a key in the 'MODELS' dictionary")


# initialize the input image shape (224X224 pixels) along with the pre-processing function (this might need to be changed
# based on which model we use to classify our image)
# 经典的CNN输入图像的尺寸，是224×224、227×227、256×256和299×299，但也可以是其他尺寸。
# VGG16，VGG19和ResNet均接受224×224输入图像，而Inception V3和Xception需要299×299像素输入
inputShape = (224, 224)

preprocess = imagenet_utils.preprocess_input

# if we are using the InceptionV3 or Xception networks, then we
# need to set the input shape to (299x299) [rather than (224x224)]
# and use a different image processing function
if args["model"] in ("inception", "xception"):

inputShape = (299, 299)

preprocess = preprocess_input


# 从磁盘加载预训练的模型weight(权重)并实例化模型
# load our the network weights from disk (NOTE: if this is the
# first time you are running this script for a given network, the
# weights will need to be downloaded first -- depending on which
# network you are using, the weights can be 90-575MB, so be
# patient; the weights will be cached and subsequent runs of this
# script will be *much* faster)
print("[INFO] loading {}...".format(args["model"]))

Network = MODELS[args["model"]] # 从--model命令行参数得到model的名字，通过MODELS词典映射到相应的类
model = Network(weights="imagenet") # 然后使用预训练的ImageNet权重实例化卷积神经网络

# load the input image using the Keras helper utility while ensuring
# the image is resized to `inputShape`, the required input dimensions
# for the ImageNet pre-trained network
print("[INFO] loading and pre-processing image...")

image = load_img(args["image"], target_size=inputShape) # 从磁盘加载输入图像，inputShape调整图像的宽度和高度
image = img_to_array(image) # 将图像从PIL/Pillow实例转换为NumPy数组,输入图像现在表示为(inputShape[0],inputShape[1],3)的NumPy数组

# our input image is now represented as a NumPy array of shape
# (inputShape[0], inputShape[1], 3) however we need to expand the
# dimension by making the shape (1, inputShape[0], inputShape[1], 3)
# so we can pass it through thenetwork
image = np.expand_dims(image, axis=0) # 向矩阵添加一个额外的维度(颜色通道),形状(1,inputShape[0],inputShape[1],3)

# pre-process the image using the appropriate function based on the
# model that has been loaded (i.e., mean subtraction, scaling, etc.)
image = preprocess(image,mode='tf') # 调用相应的预处理功能来执行数据归一化

# 调用CNN中.predict得到预测结果。根据这些预测结果，将它们传递给ImageNet辅助函数decode_predictions，
# 会得到ImageNet类标签名字(id转换成名字，可读性高)以及与标签相对应的概率
# classify the image
print("[INFO] classifying image with '{}'...".format(args["model"]))

preds = model.predict(image)

P = imagenet_utils.decode_predictions(preds)

# loop over the predictions and display the rank-5 predictions +
# probabilities to our terminal
for (i, (imagenetID, label, prob)) in enumerate(P[0]):
print("{}. {}: {:.2f}%".format(i + 1, label, prob * 100))

# load the image via OpenCV, draw the top prediction on the image,
# and display the image to our screen
orig = cv2.imread(args["image"])

(imagenetID, label, prob) = P[0][0]

cv2.putText(orig, "Label: {}, {:.2f}%".format(label, prob * 100),

(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)

cv2.imshow("Classification", orig)

cv2.waitKey(0)