multiclass_classification_predict.py

# import the necessary packages
from keras.models import load_model
import argparse
import pickle
import cv2
import os

test_image_path = "/test_image/cats.jpg"

model_path = "/simple_multiclass_classifcation_model.model"
label_binarizer_path = "/simple_multiclass_classifcation_lb.pickle"

image = cv2.imread(test_image_path)
output = image.copy()
image = cv2.resize(image, (32,32))

# scale the pixel values to [0, 1]
image = image.astype("float") / 255.0
image = image.flatten()
print ("image after flattening",len(image))
image = image.reshape((1, image.shape[0]))
print ("image--reshape",image.shape)

# load the model and label binarizer
print("[INFO] loading network and label binarizer...")
model = load_model(model_path)
lb = pickle.loads(open(label_binarizer_path, "rb").read())

# # make a prediction on the image
print (image.shape)
preds = model.predict(image)

# find the class label index with the largest corresponding
# probability
print ("preds.argmax(axis=1)",preds.argmax(axis=1))
i = preds.argmax(axis=1)[0]
print (i)
label = lb.classes_[i]

# draw the class label + probability on the output image
text = "{}: {:.2f}%".format(label, preds[0][i] * 100)
cv2.putText(output, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
	(0, 0, 255), 2)

# show the output image
cv2.imshow("Image", output)
cv2.waitKey(0)