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load-and-test-model.py
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load-and-test-model.py
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import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.python.keras import backend as K
import pathlib
import time
class_names=np.genfromtxt('saved_model/bc_model/classes.txt', dtype='str', delimiter="\n")
img_height=960
img_width=600
crop_x=575
crop_y=550
cropper = tf.keras.layers.experimental.preprocessing.CenterCrop(height=crop_y, width=crop_x)
model = tf.keras.models.load_model('saved_model/bc_model')
# replace this with your own test image
testbrass_path="./data/gfl-test-case4.jpg"
start_time=time.time() * 1000
img = keras.preprocessing.image.load_img(
testbrass_path, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
img_array = cropper(img_array)
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
end_time=time.time() * 1000
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
print(end_time-start_time)