keras----resnet-vgg-xception-inception

来源：

https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/

classify_image.py

#encoding:utf8
import  keras

# 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
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

print "hello, keras. "

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to the input image")
ap.add_argument("-model", "--model", type=str, default="vgg16",
    help="name of pre-trained network to use")
args = vars(ap.parse_args())

# define a dictionary that maps model names to their classes
# inside Keras
MODELS = {
    "vgg16": VGG16,
    "vgg19": VGG19,
    "inception": InceptionV3,
    "xception": Xception, # TensorFlow ONLY
    "resnet": ResNet50
}

# esnure a valid model name was supplied via command line argument
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)
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    

# Net, ResNet, Inception, and Xception with KerasPython

# 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
# 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

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to the input image")
ap.add_argument("-model", "--model", type=str, default="vgg16",
    help="name of pre-trained network to use")
args = vars(ap.parse_args())

# define a dictionary that maps model names to their classes
# inside Keras
MODELS = {
    "vgg16": VGG16,
    "vgg19": VGG19,
    "inception": InceptionV3,
    "xception": Xception, # TensorFlow ONLY
    "resnet": ResNet50
}

# esnure a valid model name was supplied via command line argument
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)
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

# 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"]]
model = Network(weights="imagenet")

# 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"]]
model = Network(weights="imagenet")   

# 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)
image = img_to_array(image)

# 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)

# 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) 

# 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)    

print  "finished  .  all.   "

classfy.sh

python classify_image.py --image /home/sea/Downloads/images/a.jpg  --model  vgg19

1. tobacco_shop: 19.85%
2. confectionery: 12.88%
3. bakery: 11.10%
4. barbershop: 4.98%
5. restaurant: 4.29%
finished  .  all.