FeatureExtraction.py

import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.resnet import ResNet152
from tensorflow.keras.layers import AveragePooling2D, Flatten
from tensorflow.keras.models import Model
from tqdm import tqdm
import os

class FeatureExtractor:
    def __init__(self, img_shape, channels):
        self.seq_length = 40  # Number of frames to process
        self.height = img_shape[0]
        self.width = img_shape[1]
        self.channels = channels

        # Load ResNet152 model without the top fully connected layer
        self.base_model = ResNet152(include_top=False, input_shape=(224, 224, 3), weights='imagenet')

        # Freeze the base model layers
        for layer in self.base_model.layers:
            layer.trainable = False

        # Adding an Average Pooling layer followed by Flatten
        self.op = self.base_model.output
        self.x_model = AveragePooling2D((7, 7), name='avg_pool')(self.op)
        self.x_model = Flatten()(self.x_model)

        # Create the feature extraction model
        self.model = Model(self.base_model.input, self.x_model)

    def extract_feature(self, frames_buffer):
        x_op = np.zeros((2048, 40))  # Shape (features_dim, seq_length)
        for i in range(len(frames_buffer)):
            x_t = frames_buffer[i]
            x_t = cv2.resize(x_t, (224, 224))  # Resize each frame to the required input size
            x_t = np.expand_dims(x_t, axis=0)  # Add batch dimension
            x = self.model.predict(x_t)
            x_op[:, i] = x

        return x_op