mediapipe/mediapipe2/examples/desktop/youtube8m

Steps to run the YouTube-8M feature extraction graph

1. Checkout the repository and follow the installation instructions to set up MediaPipe.

   git clone https://github.com/google/mediapipe.git
   cd mediapipe
   

2. Download the PCA and model data.

   mkdir /tmp/mediapipe
   cd /tmp/mediapipe
   curl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb
   curl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb
   curl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb
   curl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb
   curl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
   tar -xvf /tmp/mediapipe/inception-2015-12-05.tgz
   

3. Get the VGGish frozen graph.

   Note: To run step 3 and step 4, you must have Python 2.7 or 3.5+ installed with the TensorFlow 1.14+ package installed.

   # cd to the root directory of the MediaPipe repo
   cd -
   
   pip3 install tf_slim
   python -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph
   

4. Generate a MediaSequence metadata from the input video.

   Note: the output file is /tmp/mediapipe/metadata.pb

   # change clip_end_time_sec to match the length of your video.
   python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \
     --path_to_input_video=/absolute/path/to/the/local/video/file \
     --clip_end_time_sec=120
   

5. Run the MediaPipe binary to extract the features.

   bazel build -c opt --linkopt=-s \
     --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \
     mediapipe/examples/desktop/youtube8m:extract_yt8m_features
   
   GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
     --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \
     --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb  \
     --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb
   

6. [Optional] Read the features.pb in Python.

   import tensorflow as tf
   
   sequence_example = open('/tmp/mediapipe/features.pb', 'rb').read()
   print(tf.train.SequenceExample.FromString(sequence_example))
   

Steps to run the YouTube-8M inference graph with the YT8M dataset

1. Download the YT8M dataset

   For example, download one shard of the training data:

   curl http://us.data.yt8m.org/2/frame/train/trainpj.tfrecord --output /tmp/mediapipe/trainpj.tfrecord
   

2. Copy the baseline model (model card) to local.

   curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz
   
   tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe
   

3. Build and run the inference binary.

   bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
   mediapipe/examples/desktop/youtube8m:model_inference
   
   GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \
     --calculator_graph_config_file=mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt \
     --input_side_packets=tfrecord_path=/tmp/mediapipe/trainpj.tfrecord,record_index=0,desired_segment_size=5 \
     --output_stream=annotation_summary \
     --output_stream_file=/tmp/summary \
     --output_side_packets=yt8m_id \
     --output_side_packets_file=/tmp/yt8m_id
   

Steps to run the YouTube-8M model inference graph with Web Interface

1. Copy the baseline model (model card) to local.

   curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz
   
   tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe
   

2. Build the inference binary.

   bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
     mediapipe/examples/desktop/youtube8m:model_inference
   

3. Run the python web server.

   Note: pip3 install absl-py

   python mediapipe/examples/desktop/youtube8m/viewer/server.py --root `pwd`
   

   Navigate to localhost:8008 in a web browser.

Steps to run the YouTube-8M model inference graph with a local video

1. Make sure you have the features.pb from the feature extraction pipeline.

2. Copy the baseline model (model card) to local.

   curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz
   
   tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe
   

3. Build and run the inference binary.

   bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
     mediapipe/examples/desktop/youtube8m:model_inference
   
   # segment_size is the number of seconds window of frames.
   # overlap is the number of seconds adjacent segments share.
   GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \
     --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \
     --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4
   

4. View the annotated video.