146 lines
7.2 KiB
Markdown
146 lines
7.2 KiB
Markdown
---
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layout: default
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title: KNIFT (Template-based Feature Matching)
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parent: Solutions
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nav_order: 10
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---
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# MediaPipe KNIFT
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Overview
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MediaPipe KNIFT is a template-based feature matching solution using KNIFT
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(Keypoint Neural Invariant Feature Transform).
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:-----------------------------------------------------------------------: |
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*Fig 1. Matching a real Stop Sign with a Stop Sign template using KNIFT.* |
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In many computer vision applications, a crucial building block is to establish
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reliable correspondences between different views of an object or scene, forming
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the foundation for approaches like template matching, image retrieval and
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structure from motion. Correspondences are usually computed by extracting
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distinctive view-invariant features such as
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[SIFT](https://en.wikipedia.org/wiki/Scale-invariant_feature_transform) or
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[ORB](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_orb/py_orb.html#orb-in-opencv)
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from images. The ability to reliably establish such correspondences enables
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applications like image stitching to create panoramas or template matching for
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object recognition in videos.
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KNIFT is a general purpose local feature descriptor similar to SIFT or ORB.
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Likewise, KNIFT is also a compact vector representation of local image patches
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that is invariant to uniform scaling, orientation, and illumination changes.
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However unlike SIFT or ORB, which were engineered with heuristics, KNIFT is an
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[embedding](https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture)
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learned directly from a large number of corresponding local patches extracted
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from nearby video frames. This data driven approach implicitly encodes complex,
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real-world spatial transformations and lighting changes in the embedding. As a
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result, the KNIFT feature descriptor appears to be more robust, not only to
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[affine distortions](https://en.wikipedia.org/wiki/Affine_transformation), but
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to some degree of
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[perspective distortions](https://en.wikipedia.org/wiki/Perspective_distortion_\(photography\))
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as well.
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For more information, please see
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[MediaPipe KNIFT: Template-based feature matching](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)
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in Google Developers Blog.
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:-------------------------------------------------------------------------------------: |
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*Fig 2. Matching US dollar bills using KNIFT.* |
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## Example Apps
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### Matching US Dollar Bills
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In MediaPipe, we've already provided an
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[index file](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_index.pb)
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pre-computed from the 3 template images (of US dollar bills) shown below. If
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you'd like to use your own template images, see
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[Matching Your Own Template Images](#matching-your-own-template-images).
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Please first see general instructions for
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[Android](../getting_started/building_examples.md#android) on how to build MediaPipe examples.
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../tools/visualizer.md).
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* Graph:
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[`mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1tSWRfes9rAM4NrzmJBplguNQQvaeBZSa)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/templatematchingcpu:templatematchingcpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/templatematchingcpu/BUILD)
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Note: MediaPipe uses OpenCV 3 by default. However, because of
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[issues](https://github.com/opencv/opencv/issues/11488) between NDK 17+ and
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OpenCV 3 when using
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[knnMatch](https://docs.opencv.org/3.4/db/d39/classcv_1_1DescriptorMatcher.html#a378f35c9b1a5dfa4022839a45cdf0e89),
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for this example app please use the following commands to temporarily switch to
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OpenCV 4, and switch back to OpenCV 3 afterwards.
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```bash
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# Switch to OpenCV 4
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sed -i -e 's:3.4.3/opencv-3.4.3:4.0.1/opencv-4.0.1:g' WORKSPACE
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sed -i -e 's:libopencv_java3:libopencv_java4:g' third_party/opencv_android.BUILD
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# Build and install app
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bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/templatematchingcpu
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adb install -r bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/templatematchingcpu/templatematchingcpu.apk
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# Switch back to OpenCV 3
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sed -i -e 's:4.0.1/opencv-4.0.1:3.4.3/opencv-3.4.3:g' WORKSPACE
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sed -i -e 's:libopencv_java4:libopencv_java3:g' third_party/opencv_android.BUILD
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```
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Tip: The example uses the TFLite
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[XNNPACK delegate](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/xnnpack)
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by default for faster inference. Users can change the
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[option in TfLiteInferenceCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/calculators/tflite/tflite_inference_calculator.proto)
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to run regular TFLite inference.
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### Matching Your Own Template Images
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* Step 1: Put all template images in a single directory.
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* Step 2: To build the index file for all templates in the directory, run
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```bash
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bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 \
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mediapipe/examples/desktop/template_matching:template_matching_tflite
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```
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```bash
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bazel-bin/mediapipe/examples/desktop/template_matching/template_matching_tflite \
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--calculator_graph_config_file=mediapipe/graphs/template_matching/index_building.pbtxt \
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--input_side_packets="file_directory=<template image directory>,file_suffix=png,output_index_filename=<output index filename>"
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```
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The output index file includes the extracted KNIFT features.
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* Step 3: Replace
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[mediapipe/models/knift_index.pb](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_index.pb)
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with the index file you generated, and update
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[mediapipe/models/knift_labelmap.txt](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_labelmap.txt)
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with your own template names.
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* Step 4: Build and run the app using the same instructions in
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[Matching US Dollar Bills](#matching-us-dollar-bills).
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## Resources
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* Google Developers Blog:
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[MediaPipe KNIFT: Template-based feature matching](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)
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* [TFLite model for up to 200 keypoints](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float.tflite)
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* [TFLite model for up to 400 keypoints](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_400.tflite)
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* [TFLite model for up to 1000 keypoints](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_1k.tflite)
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* [Model card](https://mediapipe.page.link/knift-mc)
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