import numpy as np
import argparse
from GpuConfig import GpuMemoryAllocate
from keras.callbacks import ModelCheckpoint
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help = "path to the dataset")
ap.add_argument("-w", "--weights", required=True, help="path to best model weights file")
args = vars(ap.parse_args())
epoch = 50
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
sp = SimplePreprocessor(32, 32)
iap = ImageArrayPreprocessor()
sd1 = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sd1.load(imagePaths, Verbose=500)
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
labelNames = ["cat", "dog"]
labelsOneHot_train = []
labelsOneHot_test = []
for label in trainY:
labelOneHot = [1, 0] if label == 'cats' else [0, 1]
import cv2
from ImageTools import SimplePreprocessor
from keras.preprocessing.image import img_to_array
image = cv2.imread('/home/a/animals/training_set/training_set/cats/cat.2.jpg')
image_array = img_to_array(image)
cv2.imshow('image', image)
cv2.waitKey()
print(image_array.shape)
print(image.shape)
p = SimplePreprocessor(32, 32)
image = p.preprocess(image)
cv2.imshow('image', image)
cv2.waitKey()