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
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from preprocessing import SimplePreprocessor
from datasets import SimpleDatasetLoader
from imutils import paths
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True)
args = vars(ap.parse_args())
print("[INFO] loading images ...")
imagePaths = list(paths.list_images(args["dataset"]))
sp = SimplePreprocessor(32, 32)
sdl = SimpleDatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.reshape((data.shape[0], 3072))
le = LabelEncoder()
labels = le.fit_transform(labels)
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=5)
#! Regularizers
# loop over our set of regularizers
for r in (None, "l1", "l2"):
print("[INFO] training model with '{}' penalty".format(r))
"--dataset",
required=False,
default=
"/home/vamsimocherla/Research/DeepLearning/DrAdrian/0-StarterBundle/SB_Code/datasets/animals",
help="path to input dataset")
args = vars(ap.parse_args())
# grab the list of images that we’ll be describing
print("[INFO] loading images...")
image_paths = list(paths.list_images(args["dataset"]))
# initialize the image processors
sp = SimplePreprocessor(32, 32)
ip = ImageToArrayPreprocessor()
# load dataset
sdl = SimpleDatasetLoader(preprocessors=[sp, ip])
# scale raw pixel intensities to range [0, 1]
data, labels = sdl.load(image_paths, verbose=500)
data = data.astype("float") / 255.0
# split data into training and testing
trainX, testX, trainY, testY = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
# one-hot encoding - convert labels to vectors
trainY = LabelBinarizer().fit_transform(trainY)
testY = LabelBinarizer().fit_transform(testY)
# initialize the optimizer and model
print("[INFO] compiling model")
classLabels = ["cat", "dog", "panda"]
# grab the list of images in the dataset then randomly sample
# indexes into the image paths list
print("[INFO] sampling images...")
imagePaths = np.array(list(paths.list_images(args["dataset"])))
idxs = np.random.randint(0, len(imagePaths), size=(10,))
imagePaths = imagePaths[idxs]
# initialize the image preprocessors
sp = SimplePreprocessor(32, 32)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities
# to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths)
data = data.astype("float") / 255.0
# load the pre-trained network
print("[INFO] loading pre-trained network...")
model = load_model(args["model"])
# make predictions on the images
print("[INFO] predicting...")
preds = model.predict(data, batch_size=32).argmax(axis=1)
# loop over the sample images
for (i, imagePath) in enumerate(imagePaths):
# load the example image, draw the prediction, and display it
# to our screen
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
options = parse_args(argv)
print("[INFO] loading images...")
loader = SimpleDatasetLoader(preprocessors=[
SimplePreprocessor(width=img_cols, height=img_rows),
ImageToArrayPreprocessor(),
])
data, labels = loader.load(
driving_log_path=options.driving_log,
data_path=options.dataset,
verbose=True,
)
data = data.astype('float32')
import ipdb
ipdb.set_trace()
# # horizontal reflection for augmentation
# data = np.append(data, data[:, :, ::-1], axis=0)
# labels = np.append(labels, -labels, axis=0)
# split train and validation
data, labels = shuffle(data, labels)
x_train, x_test, y_train, y_test = train_test_split(
data,
labels,
random_state=13,
test_size=0.1,
)
# x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
# x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
lb = LabelBinarizer()
y_train = lb.fit_transform(y_train)
y_test = lb.transform(y_test)
label_names = ['straight', 'left', 'right']
aug = ImageDataGenerator(
rotation_range=1,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2,
horizontal_flip=False,
fill_mode="nearest",
)
print('[INFO] compiling model...')
# model = NvidiaNet.build(width=img_cols, height=img_rows, depth=1)
# model = TinyNet.build(width=img_cols, height=img_rows, depth=1)
# model = ShallowNet.build(width=img_cols, height=img_rows, depth=1, classes=len(label_names))
model = MiniVGGNet.build(width=img_cols,
height=img_rows,
depth=1,
classes=len(label_names))
opt = SGD(lr=learning_rate,
momentum=0.9,
decay=learning_rate / nb_epoch,
nesterov=True)
# opt = SGD(lr=learning_rate)
# opt = Adam(lr=learning_rate)
# model.compile(
# loss='mean_squared_error',
# metrics=["accuracy"],
# optimizer=opt,
# )
model.compile(
loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer=opt,
)
history = model.fit_generator(
aug.flow(x_train, y_train, batch_size=batch_size),
# history = model.fit(
# x_train, y_train,
nb_epoch=nb_epoch,
# batch_size=batch_size,
steps_per_epoch=(len(x_train) // batch_size),
verbose=1,
validation_data=(x_test, y_test),
)
predictions = model.predict(x_test, batch_size=batch_size)
print(
classification_report(
y_test.argmax(axis=1),
predictions.argmax(axis=1),
target_names=label_names,
))
plt.style.use("ggplot")
fig, ax_acc = plt.subplots(1, 1)
ax_acc.set_xlabel("Epoch #")
ax_loss = ax_acc.twinx()
ax_loss.grid(None)
ax_loss.set_ylabel("Loss")
ax_acc.grid(None)
ax_acc.set_ylabel("Accuracy")
ax_acc.set_ylim([0, 1])
ax_loss.plot(np.arange(0, nb_epoch),
history.history["loss"],
label="train_loss")
ax_loss.plot(np.arange(0, nb_epoch),
history.history["val_loss"],
label="val_loss")
ax_acc.plot(np.arange(0, nb_epoch),
history.history["acc"],
label="train_acc")
ax_acc.plot(np.arange(0, nb_epoch),
history.history["val_acc"],
label="val_acc")
fig.suptitle("Training Loss and Accuracy")
fig.legend()
plt.show()
model.save(options.model)
return 0
