Upload utils.py

utils.py

@@ -0,0 +1,53 @@
+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()