Building a CNN model for labeling video data – Labeling Video Data-1

In this section, we will explore the process of building CNN models to label video data. We learned the basic concepts of CNN in Chapter 6. Now, we will delve into the CNN architecture, training, and evaluation techniques required to create effective models for video data analysis and labeling. By understanding the key concepts and techniques, you will be equipped to leverage CNNs to automatically label video data, enabling efficient and accurate analysis in various applications.

A typical CNN contains convolutional layers, pooling layers, and fully connected layers. These layers extract and learn spatial features from video frames, allowing the model to understand patterns and structures. Additionally, the concept of parameter sharing contributes to the efficiency of CNNs in handling large-scale video datasets.

Let’s see an example of how to build a supervised CNN model for video data using Python and the TensorFlow library. We will use this trained CNN model to predict either “dance” or “brushing” labels for the videos in the Kinetics dataset. Remember to replace the path to the dataset with the actual path on your system. We’ll explain each step in detail along with the code:

1. Import the libraries: First, we need to import the necessary libraries – TensorFlow, Keras, and any additional libraries required for data preprocessing and model evaluation:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import os
import numpy as np
import cv2
from sklearn.model_selection import train_test_split

2. Data preprocessing: Next, we need to preprocess the video data before feeding it into the CNN model. The preprocessing steps may vary, depending on the specific requirements of your dataset. Here, we’ll provide a general outline of the steps involved:

I. Load the video data: Load the video data from a publicly available dataset or your own dataset. You can use libraries such as OpenCV or scikit-video to read the video files.

II. Extract the frames: Extract individual frames from the video data. Each frame will be treated as image input to the CNN model.

III. Resize the frames: Resize the frames to a consistent size suitable for the CNN model. This step ensures that all frames have the same dimensions, which is a requirement for CNN models.

Let’s create a Python function to load videos from a directory path:

Function to load videos from a directory

def load_videos_from_directory(directory, max_frames=100):
video_data = []
labels = []
# Extract label from directory name
label = os.path.basename(directory)
for filename in os.listdir(directory):
if filename.endswith(‘.mp4’):
file_path = os.path.join(directory, filename)
# Read video frames
cap = cv2.VideoCapture(file_path)
frames = []
frame_count = 0
while True:
ret, frame = cap.read()
if not ret or frame_count >= max_frames:
break
# Preprocess frame (resize, normalize, etc.)
frame = cv2.resize(frame, (64, 64))
frame = frame.astype(“float32”) / 255.0
frames.append(frame)
frame_count += 1
cap.release()
# Pad or truncate frames to max_frames
frames = frames + [np.zeros_like(frames[0])] * /
(max_frames – len(frames))
video_data.append(frames)
labels.append(label)
return np.array(video_data), np.array(labels)