This example demonstrates video classification, an important use-case with applications in recommendations, security, and so on. We will be using the UCF101 dataset to build our video classifier. The dataset consists of videos categorized into different actions, like cricket shot, punching, biking, etc. This dataset is commonly used to build action recognizers, which are an application of video classification.
A video consists of an ordered sequence of frames. Each frame contains spatial information, and the sequence of those frames contains temporal information. To model both of these aspects, we use a hybrid architecture that consists of convolutions (for spatial processing) as well as recurrent layers (for temporal processing). Specifically, we’ll use a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) consisting of GRU layers. This kind of hybrid architecture is popularly known as a CNN-RNN.
This example requires TensorFlow 2.5 or higher, as well as TensorFlow Docs, which can be installed using the following command:
!pip install -q git+https://github.com/tensorflow/docs
Data collection
In order to keep the runtime of this example relatively short, we will be using a subsampled version of the original UCF101 dataset. You can refer to this notebook to know how the subsampling was done.
!!wget -q https://github.com/sayakpaul/Action-Recognition-in-TensorFlow/releases/download/v1.0.0/ucf101_top5.tar.gz
!tar xf ucf101_top5.tar.gz
Setup
import os
import keras
from imutils import paths
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import imageio
import cv2
from IPython.display import Image
Define hyperparameters
IMG_SIZE = 224
BATCH_SIZE = 64
EPOCHS = 10
MAX_SEQ_LENGTH = 20
NUM_FEATURES = 2048
Data preparation
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
print(f"Total videos for training: {len(train_df)}")
print(f"Total videos for testing: {len(test_df)}")
train_df.sample(10)
Total videos for training: 594
Total videos for testing: 224
| video_name | tag | |
|---|---|---|
| 492 | v_TennisSwing_g10_c03.avi | TennisSwing |
| 536 | v_TennisSwing_g16_c05.avi | TennisSwing |
| 413 | v_ShavingBeard_g16_c05.avi | ShavingBeard |
| 268 | v_Punch_g12_c04.avi | Punch |
| 288 | v_Punch_g15_c03.avi | Punch |
| 30 | v_CricketShot_g12_c03.avi | CricketShot |
| 449 | v_ShavingBeard_g21_c07.avi | ShavingBeard |
| 524 | v_TennisSwing_g14_c07.avi | TennisSwing |
| 145 | v_PlayingCello_g12_c01.avi | PlayingCello |
| 566 | v_TennisSwing_g21_c03.avi | TennisSwing |
One of the many challenges of training video classifiers is figuring out a way to feed the videos to a network. This blog post discusses five such methods. Since a video is an ordered sequence of frames, we could just extract the frames and put them in a 3D tensor. But the number of frames may differ from video to video which would prevent us from stacking them into batches (unless we use padding). As an alternative, we can save video frames at a fixed interval until a maximum frame count is reached. In this example we will do the following:
- Capture the frames of a video.
- Extract frames from the videos until a maximum frame count is reached.
- In the case, where a video’s frame count is lesser than the maximum frame count we will pad the video with zeros.
Note that this workflow is identical to problems involving texts sequences. Videos of the UCF101 dataset is known to not contain extreme variations in objects and actions across frames. Because of this, it may be okay to only consider a few frames for the learning task. But this approach may not generalize well to other video classification problems. We will be using OpenCV’s VideoCapture() method to read frames from videos.
# The following two methods are taken from this tutorial:
# https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub
def crop_center_square(frame):
y, x = frame.shape[0:2]
min_dim = min(y, x)
start_x = (x // 2) - (min_dim // 2)
start_y = (y // 2) - (min_dim // 2)
return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]
def load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):
cap = cv2.VideoCapture(path)
frames = []
try:
while True:
ret, frame = cap.read()
if not ret:
break
frame = crop_center_square(frame)
frame = cv2.resize(frame, resize)
frame = frame[:, :, [2, 1, 0]]
frames.append(frame)
if len(frames) == max_frames:
break
finally:
cap.release()
return np.array(frames)
We can use a pre-trained network to extract meaningful features from the extracted frames. The Keras Applications module provides a number of state-of-the-art models pre-trained on the ImageNet-1k dataset. We will be using the InceptionV3 model for this purpose.
def build_feature_extractor():
feature_extractor = keras.applications.InceptionV3(
weights="imagenet",
include_top=False,
pooling="avg",
input_shape=(IMG_SIZE, IMG_SIZE, 3),
)
preprocess_input = keras.applications.inception_v3.preprocess_input
inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))
preprocessed = preprocess_input(inputs)
outputs = feature_extractor(preprocessed)
return keras.Model(inputs, outputs, name="feature_extractor")
feature_extractor = build_feature_extractor()
The labels of the videos are strings. Neural networks do not understand string values, so they must be converted to some numerical form before they are fed to the model. Here we will use the StringLookup layer encode the class labels as integers.
label_processor = keras.layers.StringLookup(
num_oov_indices=0, vocabulary=np.unique(train_df["tag"])
)
print(label_processor.get_vocabulary())
['CricketShot', 'PlayingCello', 'Punch', 'ShavingBeard', 'TennisSwing']
Finally, we can put all the pieces together to create our data processing utility.
def prepare_all_videos(df, root_dir):
num_samples = len(df)
video_paths = df["video_name"].values.tolist()
labels = df["tag"].values
labels = keras.ops.convert_to_numpy(label_processor(labels[..., None]))
# `frame_masks` and `frame_features` are what we will feed to our sequence model.
# `frame_masks` will contain a bunch of booleans denoting if a timestep is
# masked with padding or not.
frame_masks = np.zeros(shape=(num_samples, MAX_SEQ_LENGTH), dtype="bool")
frame_features = np.zeros(
shape=(num_samples, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32"
)
# For each video.
for idx, path in enumerate(video_paths):
# Gather all its frames and add a batch dimension.
frames = load_video(os.path.join(root_dir, path))
frames = frames[None, ...]
# Initialize placeholders to store the masks and features of the current video.
temp_frame_mask = np.zeros(
shape=(
1,
MAX_SEQ_LENGTH,
),
dtype="bool",
)
temp_frame_features = np.zeros(
shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32"
)
# Extract features from the frames of the current video.
for i, batch in enumerate(frames):
video_length = batch.shape[0]
length = min(MAX_SEQ_LENGTH, video_length)
for j in range(length):
temp_frame_features[i, j, :] = feature_extractor.predict(
batch[None, j, :], verbose=0,
)
temp_frame_mask[i, :length] = 1 # 1 = not masked, 0 = masked
frame_features[idx,] = temp_frame_features.squeeze()
frame_masks[idx,] = temp_frame_mask.squeeze()
return (frame_features, frame_masks), labels
train_data, train_labels = prepare_all_videos(train_df, "train")
test_data, test_labels = prepare_all_videos(test_df, "test")
print(f"Frame features in train set: {train_data[0].shape}")
print(f"Frame masks in train set: {train_data[1].shape}")
Frame features in train set: (594, 20, 2048)
Frame masks in train set: (594, 20)
The above code block will take ~20 minutes to execute depending on the machine it’s being executed.
The sequence model
Now, we can feed this data to a sequence model consisting of recurrent layers like GRU.
# Utility for our sequence model.
def get_sequence_model():
class_vocab = label_processor.get_vocabulary()
frame_features_input = keras.Input((MAX_SEQ_LENGTH, NUM_FEATURES))
mask_input = keras.Input((MAX_SEQ_LENGTH,), dtype="bool")
# Refer to the following tutorial to understand the significance of using `mask`:
# https://keras.io/api/layers/recurrent_layers/gru/
x = keras.layers.GRU(16, return_sequences=True)(
frame_features_input, mask=mask_input
)
x = keras.layers.GRU(8)(x)
x = keras.layers.Dropout(0.4)(x)
x = keras.layers.Dense(8, activation="relu")(x)
output = keras.layers.Dense(len(class_vocab), activation="softmax")(x)
rnn_model = keras.Model([frame_features_input, mask_input], output)
rnn_model.compile(
loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
)
return rnn_model
# Utility for running experiments.
def run_experiment():
filepath = "/tmp/video_classifier/ckpt.weights.h5"
checkpoint = keras.callbacks.ModelCheckpoint(
filepath, save_weights_only=True, save_best_only=True, verbose=1
)
seq_model = get_sequence_model()
history = seq_model.fit(
[train_data[0], train_data[1]],
train_labels,
validation_split=0.3,
epochs=EPOCHS,
callbacks=[checkpoint],
)
seq_model.load_weights(filepath)
_, accuracy = seq_model.evaluate([test_data[0], test_data[1]], test_labels)
print(f"Test accuracy: {round(accuracy * 100, 2)}%")
return history, seq_model
_, sequence_model = run_experiment()
Epoch 1/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.3058 - loss: 1.5597
Epoch 1: val_loss improved from inf to 1.78077, saving model to /tmp/video_classifier/ckpt.weights.h5
13/13 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step - accuracy: 0.3127 - loss: 1.5531 - val_accuracy: 0.1397 - val_loss: 1.7808
Epoch 2/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.5216 - loss: 1.2704
Epoch 2: val_loss improved from 1.78077 to 1.78026, saving model to /tmp/video_classifier/ckpt.weights.h5
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5226 - loss: 1.2684 - val_accuracy: 0.1788 - val_loss: 1.7803
Epoch 3/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6189 - loss: 1.1656
Epoch 3: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6174 - loss: 1.1651 - val_accuracy: 0.2849 - val_loss: 1.8322
Epoch 4/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6518 - loss: 1.0645
Epoch 4: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.6515 - loss: 1.0647 - val_accuracy: 0.2793 - val_loss: 2.0419
Epoch 5/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6833 - loss: 0.9976
Epoch 5: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6843 - loss: 0.9965 - val_accuracy: 0.3073 - val_loss: 1.9077
Epoch 6/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7229 - loss: 0.9312
Epoch 6: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7241 - loss: 0.9305 - val_accuracy: 0.3017 - val_loss: 2.1513
Epoch 7/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.8023 - loss: 0.9132
Epoch 7: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.8035 - loss: 0.9093 - val_accuracy: 0.3184 - val_loss: 2.1705
Epoch 8/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.8127 - loss: 0.8380
Epoch 8: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.8128 - loss: 0.8356 - val_accuracy: 0.3296 - val_loss: 2.2043
Epoch 9/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.8494 - loss: 0.7641
Epoch 9: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.8494 - loss: 0.7622 - val_accuracy: 0.3017 - val_loss: 2.3734
Epoch 10/10
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.8634 - loss: 0.6883
Epoch 10: val_loss did not improve from 1.78026
13/13 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.8649 - loss: 0.6882 - val_accuracy: 0.3240 - val_loss: 2.4410
7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7816 - loss: 1.0624
Test accuracy: 56.7%
Note: To keep the runtime of this example relatively short, we just used a few training examples. This number of training examples is low with respect to the sequence model being used that has 99,909 trainable parameters. You are encouraged to sample more data from the UCF101 dataset using the notebook mentioned above and train the same model.
Inference
def prepare_single_video(frames):
frames = frames[None, ...]
frame_mask = np.zeros(
shape=(
1,
MAX_SEQ_LENGTH,
),
dtype="bool",
)
frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32")
for i, batch in enumerate(frames):
video_length = batch.shape[0]
length = min(MAX_SEQ_LENGTH, video_length)
for j in range(length):
frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])
frame_mask[i, :length] = 1 # 1 = not masked, 0 = masked
return frame_features, frame_mask
def sequence_prediction(path):
class_vocab = label_processor.get_vocabulary()
frames = load_video(os.path.join("test", path))
frame_features, frame_mask = prepare_single_video(frames)
probabilities = sequence_model.predict([frame_features, frame_mask])[0]
for i in np.argsort(probabilities)[::-1]:
print(f" {class_vocab[i]}: {probabilities[i] * 100:5.2f}%")
return frames
# This utility is for visualization.
# Referenced from:
# https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub
def to_gif(images):
converted_images = images.astype(np.uint8)
imageio.mimsave("animation.gif", converted_images, duration=100)
return Image("animation.gif")
test_video = np.random.choice(test_df["video_name"].values.tolist())
print(f"Test video path: {test_video}")
test_frames = sequence_prediction(test_video)
to_gif(test_frames[:MAX_SEQ_LENGTH])
Test video path: v_TennisSwing_g03_c01.avi
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 166ms/step
CricketShot: 46.99%
ShavingBeard: 18.83%
TennisSwing: 14.65%
Punch: 12.41%
PlayingCello: 7.12%
<IPython.core.display.Image object>
Leave a Reply