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.
