klasifkasi.py

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

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())

MODELS = {
	"vgg16": VGG16,
	"vgg19": VGG19,
	"inception": InceptionV3,
	"xception": Xception, # TensorFlow ONLY
	"resnet": ResNet50
}


if args["model"] not in MODELS.keys():
	raise AssertionError("The --model command line argument should "
		"be a key in the `MODELS` dictionary")

inputShape = (224, 224)
preprocess = imagenet_utils.preprocess_input

if args["model"] in ("inception", "xception"):
	inputShape = (299, 299)
	preprocess = preprocess_input

print("[INFO] loading {}...".format(args["model"]))
Network = MODELS[args["model"]]
model = Network(weights="imagenet")

print("[INFO] loading and pre-processing image...")
image = load_img(args["image"], target_size=inputShape)
image = img_to_array(image)

image = np.expand_dims(image, axis=0)

image = preprocess(image)

# classify the image
print("[INFO] classifying image with '{}'...".format(args["model"]))
preds = model.predict(image)
P = imagenet_utils.decode_predictions(preds)
for (i, (imagenetID, label, prob)) in enumerate(P[0]):
	print("{}. {}: {:.2f}%".format(i + 1, label, prob * 100))

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)