def predict_classes(img: np.ndarray, classifier: MobileNetV2) -> dict:
"""Predict classification of an image.
Args:
img (np.ndarray): Image, preprocessed for MobileNetv2
classifier (MobileNetV2): Instance of classifier.
Returns:
dict: Predicted classes and their assigned probability.
"""
predictions = classifier.predict(img)
prediction = decode_predictions(predictions)
classes = dict([(i[1], float(i[2])) for i in prediction[0]])
return classes
import tensorflow as tf
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 as Net
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
import numpy as np
import time
model = Net(weights='imagenet')
img_path = 'images/grace_hopper.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])
times = []
for i in range(20):
start_time = time.time()
preds = model.predict(x)
delta = (time.time() - start_time)
times.append(delta)
mean_delta = np.array(times).mean()
fps = 1 / mean_delta
print('average(sec):{:.2f},fps:{:.2f}'.format(mean_delta, fps))
