shallownet_load.py

from pyimagesearch.preprocessing import SimplePreprocessor
from pyimagesearch.preprocessing import ImageToArrayPreprocessor
from pyimagesearch.datasets import SimpleDatasetLoader
from keras.models import load_model
from imutils import paths
import numpy as np
import cv2
import argparse


ap=argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to input dataset")
ap.add_argument("-m", "--model", required=True,
help="path to pre-trained model")

args=vars(ap.parse_args());
classLabels=["cat","dog","panda"]


print("[INFO] sampling images...")
imagePaths = np.array(list(paths.list_images(args["dataset"])))
idxs = np.random.randint(0, len(imagePaths), size=(10,))
print(idxs)
imagePaths = imagePaths[idxs]
print(imagePaths)

sp=SimplePreprocessor(32,32)
iap=ImageToArrayPreprocessor()
sdl=SimpleDatasetLoader([sp,iap])
(data,labels)=sdl.load(imagePaths)
data=data.astype("float")/255.0


print("[info] loading model....")
model=load_model(args["model"])
print("[info]pridicting.....")
preds=model.predict(data,batch_size=32).argmax(axis=1)
for (i,imagePath) in enumerate(imagePaths):
	image=cv2.imread(imagePath)
	cv2.putText(image, "Label: {}".format(classLabels[preds[i]]),(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
	cv2.imshow("Image", image)
	cv2.waitKey(0)