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import tensorflow as tf
import tensorflow_hub as hub
from pathlib import Path
import numpy as np
import config
import os
from logger import logging
FRAME_HT = 224
FRAME_WD = 224
FRAME_NUM = 8
# tensorflow urls to download the model and lables
TENSORFLOW_HUB_URL_LABELS = "https://raw.githubusercontent.com/tensorflow/models/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/kinetics_600_labels.txt"
TENSORFLOW_HUB_URL_MODEL = "https://tfhub.dev/tensorflow/movinet/a2/base/kinetics-600/classification/3"
MODEL_PATH = os.path.join(os.getcwd(), 'models', 'Activity_recognition.h5')
def get_labels() :
"""
Downloads and saves the labels for tensorflow 'movienet' model.
Returns the path of the file 'labels.txt' where the labels are saved.
"""
logging.info(">>> Downloading the labels 'movienet' model... ")
labels_path = tf.keras.utils.get_file(
fname=os.path.join(os.getcwd(), 'labels.txt'),
origin=config.TENSORFLOW_HUB_URL_LABELS
)
labels_path = Path(labels_path)
lines = labels_path.read_text().splitlines()
KINETICS_600_LABELS = np.array([line.strip() for line in lines])
logging.info("Labels retrieved successfully.")
return KINETICS_600_LABELS
def get_model() -> tf.keras.models.Model :
"""
Downloads the tensorflow 'movienet' model.
Returns tensorflow.keras.models.Model object instance.
"""
logging.info(">>> Downloading the 'movienet' model from tensorflow...")
encoder = hub.KerasLayer(TENSORFLOW_HUB_URL_MODEL, trainable=True)
inputs = tf.keras.layers.Input(
shape=[FRAME_NUM, FRAME_HT, FRAME_WD, 3],
dtype=tf.float32,
name='image'
)
outputs = encoder(dict(image=inputs))
model = tf.keras.Model(inputs, outputs, name='movinet')
logging.info("Model downloaded successfully.")
return model
MODEL = get_model()
KINETICS_600_LABELS = get_labels()
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