test_network.py

# USAGE
# python test_network.py --model santa_not_santa.model --image images/examples/santa_01.png

# import the necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import argparse
import imutils
import cv2
import sys

def test_network_classifier(image, model):
	# construct the argument parse and parse the arguments
	# ap = argparse.ArgumentParser()
	# ap.add_argument("-i", "--image", required=True,
	# 	help="path to input image")
	# args = vars(ap.parse_args())

	# load the image
	image = cv2.imread(image)
	#orig = image.copy()

	# pre-process the image for classification
	image = cv2.resize(image, (28, 28))
	image = image.astype("float") / 255.0
	image = img_to_array(image)
	image = np.expand_dims(image, axis=0)

	# load the trained convolutional neural network
	print("[INFO] loading network...")
	model = load_model(model)


	# print(model.predict(image)[0])
	y_prob = model.predict(image)
	# print(y_prob.shape)
	# print(y_prob)
	y_classes = y_prob.argmax()
	#print("Predicted object class: ", y_classes)

	return y_classes


# test_network_classifier()
# print(len(model.predict(image)))

# print(model.predict(image)[0])

# classify the input image


# (notSanta, santa) = model.predict(image)[0]
#
# # build the label
# label = "Santa" if santa > notSanta else "Not Santa"
# proba = santa if santa > notSanta else notSanta
# label = "{}: {:.2f}%".format(label, proba * 100)
#
# draw the label on the image
# output = imutils.resize(orig, width=400)
# cv2.putText(output, 'YES', (10, 25),  cv2.FONT_HERSHEY_SIMPLEX,
# 	0.7, (0, 255, 0), 2)
#
# # show the output image
# cv2.imshow("Output", output)
# cv2.waitKey(0)