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import numpy as np
import matplotlib.pyplot as plt
from progressbar import ProgressBar
import matplotlib.image as mpimg
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array, load_img
def get_image_arrays(image_column, image_path):
progressBar = ProgressBar()
X = []
for image_id in progressBar(image_column.values):
image = load_img(image_path + image_id, target_size=(224, 224))
image_array = img_to_array(image)
X.append(image_array)
X_array = np.asarray(X, dtype='float32')
X_array /= 255.
return X_array
def get_image_predictions(image_array, model_path):
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model on random input data.
input_shape = input_details[0]['shape']
input_data = image_array
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
return output_data
def show_image(image_id, image_path):
image_id_dict = dict(image_id).values()
image_id_string = list(image_id_dict)[0]
img = mpimg.imread(image_path + image_id_string)
plt.imshow(img, interpolation='nearest', aspect='auto')
plt.show()
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