Lying position recognition model training.

classify_image.py

+# USAGE
+# python classify_image.py --image images/soccer_ball.jpg --model vgg16
+
+# 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 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)
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