utils/predict.py

import cv2
import numpy as np
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
from tensorflow.keras.optimizers import Adam

from .constants import LEARNING_RATE

def get_model():
    """
    Download the model from the Hugging Face Hub and compile it.
    """
    model = from_pretrained_keras("keras-io/video-vision-transformer")

    model.compile(
        optimizer=Adam(learning_rate=LEARNING_RATE),
        loss="sparse_categorical_crossentropy",
        # metrics=[
        #     keras.metrics.SparseCategoricalAccuracy(name="accuracy"),
        #     keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"),
        # ],
    )

    return model


model = get_model()
labels = ['liver', 'kidney-right', 'kidney-left', 'femur-right', 'femur-left', 'bladder', 'heart', 'lung-right', 'lung-left', 'spleen', 'pancreas']


def predict_label(path):
    frames = load_video(path)
    dataloader = prepare_dataloader(frames)
    prediction = model.predict(dataloader)[0]
    label = np.argmax(prediction, axis=0)
    label = labels[label]

    return label


def load_video(path):
    """
    Load video from path and return a list of frames. 
    The video is converted to grayscale because it is the format expected by the model.
    """
    cap = cv2.VideoCapture(path)
    frames = []
    try:
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            frames.append(frame)
    finally:
        cap.release()
    return np.array(frames)


def prepare_dataloader(video):
    video = tf.expand_dims(video, axis=0)
    dataset = tf.data.Dataset.from_tensor_slices((video, np.array([0])))

    dataloader = (
        dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
        .batch(1)
        .prefetch(tf.data.AUTOTUNE)
    )
    return dataloader


@tf.function
def preprocess(frames: tf.Tensor, label: tf.Tensor):
    """Preprocess the frames tensors and parse the labels."""
    # Preprocess images
    frames = tf.image.convert_image_dtype(
        frames[
            ..., tf.newaxis
        ],  # The new axis is to help for further processing with Conv3D layers
        tf.float32,
    )
    # Parse label
    label = tf.cast(label, tf.float32)
    return frames, label