predict.py

import cv2
# import imageio
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 predict_label(path):
    frames = load_video(path)
    model = get_model()
    prediction = model.predict(tf.expand_dims(example, axis=0))[0]
    label = np.argmax(pred, axis=0)

    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 get_model():
    """
    Download the model from the Hugging Face Hub and compile it.
    """
    model = from_pretrained_keras("pablorodriper/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