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Checkout the repository and follow the installation instructions to set up MediaPipe.
git clone https://github.com/google/mediapipe.git cd mediapipe -
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 -
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
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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 -
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
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[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))
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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
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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
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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
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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
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Build the inference binary.
bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \ mediapipe/examples/desktop/youtube8m:model_inference -
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.
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Make sure you have the features.pb from the feature extraction pipeline.
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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
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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
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View the annotated video.