def processing_image(image):
aap = AspectAwarePreprocessor(64, 64)
iap = ImageToArrayPreprocessor()
sdl_nivel = SimpleDatasetLoader(preprocessors=[aap, iap])
X_test = sdl_nivel.preprocess(image)
X_test = X_test.astype("float") / 255.0
return X_test
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
args = vars(ap.parse_args())
# grab the list of images that we’ll be describing, then extract
# the class label names from the image paths
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths]
classNames = [str(x) for x in np.unique(classNames)]
# initialize the image preprocessors
aap = AspectAwarePreprocessor(64, 64)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities
# to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[aap, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
# validation data
(trainPaths, valPaths, trainLabels,
valLabels) = train_test_split(trainPaths,
trainLabels,
test_size=config.NUM_VAL_IMAGES,
stratify=trainLabels,
random_state=42)
# construct a list pairing the training, validation, and testing
# image paths along with their corresponding labels and output HDF5 files
datasets = [("train", trainPaths, trainLabels, config.TRAIN_HDF5),
("val", valPaths, valLabels, config.VAL_HDF5),
("test", testPaths, testLabels, config.TEST_HDF5)]
# initialize the image preprocessor and the lists of RGB channel averages
aaPreprocessor = AspectAwarePreprocessor(256, 256)
(R, G, B) = ([], [], [])
# loop over the dataset tuples
for (datasetType, paths, labels, outputPath) in datasets:
# create HDF5 writer
print("[INFO] building " + str(outputPath) + " ...")
writer = HDF5DatasetWriter((len(paths), 256, 256, 3), outputPath)
# initialize the progress bar
widgets = [
"Building Dataset: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
model_path='../saved_models/{}_fine_tune.model'.format(db_name)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
# grab the list of images that we'll be describing, then extract
# the class label names from the image paths
print("[INFO] loading images...")
imagePaths = list(paths.list_images(db_path))
classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths]
classNames = [str(x) for x in np.unique(classNames)]
# initialize the image preprocessors
aap = AspectAwarePreprocessor(224, 224)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities to
# the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[aap, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels,
test_size=0.25, random_state=42)
# convert the labels from integers to vectors
trainY = LabelBinarizer().fit_transform(trainY)