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jdbck.txt

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+# USAGE
+# python test_network.py --model output/simple_neural_network.hdf5 --test-images test_images
+
+# import the necessary packages
+from __future__ import print_function
+from keras.models import load_model
+from imutils import paths
+import numpy as np
+import argparse
+import imutils
+import cv2
+
+def image_to_feature_vector(image, size=(32, 32)):
+	# resize the image to a fixed size, then flatten the image into
+	# a list of raw pixel intensities
+	return cv2.resize(image, size).flatten()
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", required=True,
+	help="path to output model file")
+ap.add_argument("-t", "--test-images", required=True,
+	help="path to the directory of testing images")
+ap.add_argument("-b", "--batch-size", type=int, default=32,
+	help="size of mini-batches passed to network")
+args = vars(ap.parse_args())
+
+# initialize the class labels for the Kaggle dogs vs cats dataset
+CLASSES = ["cat", "dog"]
+
+# load the network
+print("[INFO] loading network architecture and weights...")
+model = load_model(args["model"])
+print("[INFO] testing on images in {}".format(args["test_images"]))
+
+# loop over our testing images
+for imagePath in paths.list_images(args["test_images"]):
+	# load the image, resize it to a fixed 32 x 32 pixels (ignoring
+	# aspect ratio), and then extract features from it
+	print("[INFO] classifying {}".format(
+		imagePath[imagePath.rfind("/") + 1:]))
+	image = cv2.imread(imagePath)
+	features = image_to_feature_vector(image) / 255.0
+	features = np.array([features])
+
+	# classify the image using our extracted features and pre-trained
+	# neural network
+	probs = model.predict(features)[0]
+	prediction = probs.argmax(axis=0)
+
+	# draw the class and probability on the test image and display it
+	# to our screen
+	label = "{}: {:.2f}%".format(CLASSES[prediction],
+		probs[prediction] * 100)
+	cv2.putText(image, label, (10, 35), cv2.FONT_HERSHEY_SIMPLEX,
+		1.0, (0, 255, 0), 3)
+	cv2.imshow("Image", image)
+	cv2.waitKey(0)