522 lines
20 KiB
Markdown
522 lines
20 KiB
Markdown
---
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layout: forward
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target: https://developers.google.com/mediapipe/solutions/vision/face_detector/
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title: Face Detection
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parent: MediaPipe Legacy Solutions
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nav_order: 1
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---
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# MediaPipe Face Detection
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{: .no_toc }
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<details close markdown="block">
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<summary>
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Table of contents
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</summary>
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{: .text-delta }
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1. TOC
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{:toc}
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</details>
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---
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**Attention:** *Thank you for your interest in MediaPipe Solutions.
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As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
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Solution. For more information, see the
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[MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
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site.*
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----
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## Overview
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MediaPipe Face Detection is an ultrafast face detection solution that comes with
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6 landmarks and multi-face support. It is based on
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[BlazeFace](https://arxiv.org/abs/1907.05047), a lightweight and well-performing
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face detector tailored for mobile GPU inference. The detector's super-realtime
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performance enables it to be applied to any live viewfinder experience that
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requires an accurate facial region of interest as an input for other
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task-specific models, such as 3D facial keypoint estimation (e.g.,
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[MediaPipe Face Mesh](./face_mesh.md)), facial features or expression
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classification, and face region segmentation. BlazeFace uses a lightweight
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feature extraction network inspired by, but distinct from
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[MobileNetV1/V2](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html),
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a GPU-friendly anchor scheme modified from
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[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325), and an
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improved tie resolution strategy alternative to non-maximum suppression. For
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more information about BlazeFace, please see the [Resources](#resources)
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section.
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## Solution APIs
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### Configuration Options
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Naming style and availability may differ slightly across platforms/languages.
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#### model_selection
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An integer index `0` or `1`. Use `0` to select a short-range model that works
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best for faces within 2 meters from the camera, and `1` for a full-range model
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best for faces within 5 meters. For the full-range option, a sparse model is
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used for its improved inference speed. Please refer to the
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[model cards](./models.md#face_detection) for details. Default to `0` if not
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specified.
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Note: Not available for JavaScript (use "model" instead).
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#### model
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A string value to indicate which model should be used. Use "short" to
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select a short-range model that works best for faces within 2 meters from the
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camera, and "full" for a full-range model best for faces within 5 meters. For
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the full-range option, a sparse model is used for its improved inference speed.
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Please refer to the model cards for details. Default to empty string.
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Note: Valid only for JavaScript solution.
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#### selfie_mode
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A boolean value to indicate whether to flip the images/video frames
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horizontally or not. Default to `false`.
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Note: Valid only for JavaScript solution.
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#### min_detection_confidence
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Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
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detection to be considered successful. Default to `0.5`.
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### Output
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Naming style may differ slightly across platforms/languages.
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#### detections
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Collection of detected faces, where each face is represented as a detection
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proto message that contains a bounding box and 6 key points (right eye, left
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eye, nose tip, mouth center, right ear tragion, and left ear tragion). The
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bounding box is composed of `xmin` and `width` (both normalized to `[0.0, 1.0]`
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by the image width) and `ymin` and `height` (both normalized to `[0.0, 1.0]` by
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the image height). Each key point is composed of `x` and `y`, which are
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normalized to `[0.0, 1.0]` by the image width and height respectively.
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### Python Solution API
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Please first follow general [instructions](../getting_started/python.md) to
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install MediaPipe Python package, then learn more in the companion
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[Python Colab](#resources) and the usage example below.
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Supported configuration options:
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* [model_selection](#model_selection)
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* [min_detection_confidence](#min_detection_confidence)
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```python
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import cv2
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import mediapipe as mp
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mp_face_detection = mp.solutions.face_detection
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mp_drawing = mp.solutions.drawing_utils
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# For static images:
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IMAGE_FILES = []
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with mp_face_detection.FaceDetection(
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model_selection=1, min_detection_confidence=0.5) as face_detection:
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for idx, file in enumerate(IMAGE_FILES):
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image = cv2.imread(file)
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# Convert the BGR image to RGB and process it with MediaPipe Face Detection.
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results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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# Draw face detections of each face.
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if not results.detections:
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continue
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annotated_image = image.copy()
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for detection in results.detections:
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print('Nose tip:')
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print(mp_face_detection.get_key_point(
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detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
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mp_drawing.draw_detection(annotated_image, detection)
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
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# For webcam input:
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cap = cv2.VideoCapture(0)
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with mp_face_detection.FaceDetection(
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model_selection=0, min_detection_confidence=0.5) as face_detection:
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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continue
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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results = face_detection.process(image)
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# Draw the face detection annotations on the image.
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image.flags.writeable = True
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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if results.detections:
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for detection in results.detections:
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mp_drawing.draw_detection(image, detection)
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# Flip the image horizontally for a selfie-view display.
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cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cap.release()
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```
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### JavaScript Solution API
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Please first see general [introduction](../getting_started/javascript.md) on
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MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
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and the following usage example.
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Supported face detection options:
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* [selfieMode](#selfie_mode)
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* [model](#model)
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* [minDetectionConfidence](#min_detection_confidence)
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```html
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/face_detection/face_detection.js" crossorigin="anonymous"></script>
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</head>
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<body>
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<div class="container">
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<video class="input_video"></video>
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<canvas class="output_canvas" width="1280px" height="720px"></canvas>
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</div>
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</body>
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</html>
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```
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```javascript
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<script type="module">
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const videoElement = document.getElementsByClassName('input_video')[0];
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const canvasElement = document.getElementsByClassName('output_canvas')[0];
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const canvasCtx = canvasElement.getContext('2d');
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const drawingUtils = window;
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function onResults(results) {
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// Draw the overlays.
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canvasCtx.save();
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canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
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canvasCtx.drawImage(
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results.image, 0, 0, canvasElement.width, canvasElement.height);
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if (results.detections.length > 0) {
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drawingUtils.drawRectangle(
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canvasCtx, results.detections[0].boundingBox,
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{color: 'blue', lineWidth: 4, fillColor: '#00000000'});
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drawingUtils.drawLandmarks(canvasCtx, results.detections[0].landmarks, {
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color: 'red',
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radius: 5,
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});
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}
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canvasCtx.restore();
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}
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const faceDetection = new FaceDetection({locateFile: (file) => {
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return `https://cdn.jsdelivr.net/npm/@mediapipe/face_detection@0.0/${file}`;
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}});
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faceDetection.setOptions({
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model: 'short',
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minDetectionConfidence: 0.5
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});
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faceDetection.onResults(onResults);
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const camera = new Camera(videoElement, {
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onFrame: async () => {
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await faceDetection.send({image: videoElement});
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},
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width: 1280,
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height: 720
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});
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camera.start();
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</script>
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```
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### Android Solution API
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Please first follow general
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[instructions](../getting_started/android_solutions.md) to add MediaPipe Gradle
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dependencies and try the Android Solution API in the companion
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[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/facedetection),
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and learn more in the usage example below.
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Supported configuration options:
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* [staticImageMode](#static_image_mode)
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* [modelSelection](#model_selection)
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#### Camera Input
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```java
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// For camera input and result rendering with OpenGL.
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FaceDetectionOptions faceDetectionOptions =
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FaceDetectionOptions.builder()
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.setStaticImageMode(false)
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.setModelSelection(0).build();
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FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
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faceDetection.setErrorListener(
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(message, e) -> Log.e(TAG, "MediaPipe Face Detection error:" + message));
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// Initializes a new CameraInput instance and connects it to MediaPipe Face Detection Solution.
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CameraInput cameraInput = new CameraInput(this);
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cameraInput.setNewFrameListener(
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textureFrame -> faceDetection.send(textureFrame));
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// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceDetectionResult> instance
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// that provides the interfaces to run user-defined OpenGL rendering code.
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// See mediapipe/examples/android/solutions/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultGlRenderer.java
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// as an example.
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SolutionGlSurfaceView<FaceDetectionResult> glSurfaceView =
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new SolutionGlSurfaceView<>(
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this, faceDetection.getGlContext(), faceDetection.getGlMajorVersion());
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glSurfaceView.setSolutionResultRenderer(new FaceDetectionResultGlRenderer());
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glSurfaceView.setRenderInputImage(true);
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faceDetection.setResultListener(
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faceDetectionResult -> {
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if (faceDetectionResult.multiFaceDetections().isEmpty()) {
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return;
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}
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RelativeKeypoint noseTip =
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faceDetectionResult
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.multiFaceDetections()
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.get(0)
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.getLocationData()
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.getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
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Log.i(
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TAG,
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String.format(
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"MediaPipe Face Detection nose tip normalized coordinates (value range: [0, 1]): x=%f, y=%f",
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noseTip.getX(), noseTip.getY()));
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// Request GL rendering.
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glSurfaceView.setRenderData(faceDetectionResult);
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glSurfaceView.requestRender();
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});
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// The runnable to start camera after the GLSurfaceView is attached.
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glSurfaceView.post(
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() ->
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cameraInput.start(
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this,
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faceDetection.getGlContext(),
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CameraInput.CameraFacing.FRONT,
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glSurfaceView.getWidth(),
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glSurfaceView.getHeight()));
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```
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#### Image Input
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```java
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// For reading images from gallery and drawing the output in an ImageView.
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FaceDetectionOptions faceDetectionOptions =
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FaceDetectionOptions.builder()
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.setStaticImageMode(true)
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.setModelSelection(0).build();
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FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
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// Connects MediaPipe Face Detection Solution to the user-defined ImageView
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// instance that allows users to have the custom drawing of the output landmarks
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// on it. See mediapipe/examples/android/solutions/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultImageView.java
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// as an example.
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FaceDetectionResultImageView imageView = new FaceDetectionResultImageView(this);
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faceDetection.setResultListener(
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faceDetectionResult -> {
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if (faceDetectionResult.multiFaceDetections().isEmpty()) {
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return;
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}
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int width = faceDetectionResult.inputBitmap().getWidth();
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int height = faceDetectionResult.inputBitmap().getHeight();
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RelativeKeypoint noseTip =
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faceDetectionResult
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.multiFaceDetections()
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.get(0)
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.getLocationData()
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.getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
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Log.i(
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TAG,
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String.format(
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"MediaPipe Face Detection nose tip coordinates (pixel values): x=%f, y=%f",
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noseTip.getX() * width, noseTip.getY() * height));
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// Request canvas drawing.
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imageView.setFaceDetectionResult(faceDetectionResult);
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runOnUiThread(() -> imageView.update());
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});
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faceDetection.setErrorListener(
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(message, e) -> Log.e(TAG, "MediaPipe Face Detection error:" + message));
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// ActivityResultLauncher to get an image from the gallery as Bitmap.
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ActivityResultLauncher<Intent> imageGetter =
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registerForActivityResult(
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new ActivityResultContracts.StartActivityForResult(),
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result -> {
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Intent resultIntent = result.getData();
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if (resultIntent != null && result.getResultCode() == RESULT_OK) {
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Bitmap bitmap = null;
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try {
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bitmap =
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MediaStore.Images.Media.getBitmap(
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this.getContentResolver(), resultIntent.getData());
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// Please also rotate the Bitmap based on its orientation.
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} catch (IOException e) {
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Log.e(TAG, "Bitmap reading error:" + e);
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}
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if (bitmap != null) {
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faceDetection.send(bitmap);
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}
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}
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});
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Intent pickImageIntent = new Intent(Intent.ACTION_PICK);
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pickImageIntent.setDataAndType(MediaStore.Images.Media.INTERNAL_CONTENT_URI, "image/*");
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imageGetter.launch(pickImageIntent);
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```
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#### Video Input
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```java
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// For video input and result rendering with OpenGL.
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FaceDetectionOptions faceDetectionOptions =
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FaceDetectionOptions.builder()
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.setStaticImageMode(false)
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.setModelSelection(0).build();
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FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
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faceDetection.setErrorListener(
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(message, e) -> Log.e(TAG, "MediaPipe Face Detection error:" + message));
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// Initializes a new VideoInput instance and connects it to MediaPipe Face Detection Solution.
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VideoInput videoInput = new VideoInput(this);
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videoInput.setNewFrameListener(
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textureFrame -> faceDetection.send(textureFrame));
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// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceDetectionResult> instance
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// that provides the interfaces to run user-defined OpenGL rendering code.
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// See mediapipe/examples/android/solutions/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultGlRenderer.java
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// as an example.
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SolutionGlSurfaceView<FaceDetectionResult> glSurfaceView =
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new SolutionGlSurfaceView<>(
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this, faceDetection.getGlContext(), faceDetection.getGlMajorVersion());
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glSurfaceView.setSolutionResultRenderer(new FaceDetectionResultGlRenderer());
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glSurfaceView.setRenderInputImage(true);
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faceDetection.setResultListener(
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faceDetectionResult -> {
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if (faceDetectionResult.multiFaceDetections().isEmpty()) {
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return;
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}
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RelativeKeypoint noseTip =
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faceDetectionResult
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.multiFaceDetections()
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.get(0)
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.getLocationData()
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.getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
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Log.i(
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TAG,
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String.format(
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"MediaPipe Face Detection nose tip normalized coordinates (value range: [0, 1]): x=%f, y=%f",
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noseTip.getX(), noseTip.getY()));
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// Request GL rendering.
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glSurfaceView.setRenderData(faceDetectionResult);
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glSurfaceView.requestRender();
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});
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ActivityResultLauncher<Intent> videoGetter =
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registerForActivityResult(
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new ActivityResultContracts.StartActivityForResult(),
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result -> {
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Intent resultIntent = result.getData();
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if (resultIntent != null) {
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if (result.getResultCode() == RESULT_OK) {
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glSurfaceView.post(
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() ->
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videoInput.start(
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this,
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resultIntent.getData(),
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faceDetection.getGlContext(),
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glSurfaceView.getWidth(),
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glSurfaceView.getHeight()));
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}
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}
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});
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Intent pickVideoIntent = new Intent(Intent.ACTION_PICK);
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pickVideoIntent.setDataAndType(MediaStore.Video.Media.INTERNAL_CONTENT_URI, "video/*");
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videoGetter.launch(pickVideoIntent);
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```
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## Example Apps
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Please first see general instructions for
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[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
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[desktop](../getting_started/cpp.md) 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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### Mobile
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#### GPU Pipeline
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1DZTCy1gp238kkMnu4fUkwI3IrF77Mhy5)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu:facedetectiongpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectiongpu/BUILD)
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#### CPU Pipeline
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This is very similar to the [GPU pipeline](#gpu-pipeline) except that at the
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beginning and the end of the pipeline it performs GPU-to-CPU and CPU-to-GPU
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image transfer respectively. As a result, the rest of graph, which shares the
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same configuration as the GPU pipeline, runs entirely on CPU.
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1npiZY47jbO5m2YaL63o5QoCQs40JC6C7)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectioncpu:facedetectioncpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectioncpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectioncpu/BUILD)
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### Desktop
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* Running on CPU:
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/face_detection:face_detection_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
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* Running on GPU
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/face_detection:face_detection_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
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### Coral
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Please refer to
|
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[these instructions](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
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to cross-compile and run MediaPipe examples on the
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[Coral Dev Board](https://coral.ai/products/dev-board).
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## Resources
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* Paper:
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[BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://arxiv.org/abs/1907.05047)
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([presentation](https://docs.google.com/presentation/d/1YCtASfnYyZtH-41QvnW5iZxELFnf0MF-pPWSLGj8yjQ/present?slide=id.g5bc8aeffdd_1_0))
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([poster](https://drive.google.com/file/d/1u6aB6wxDY7X2TmeUUKgFydulNtXkb3pu/view))
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* [Models and model cards](./models.md#face_detection)
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* [Web demo](https://code.mediapipe.dev/codepen/face_detection)
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* [Python Colab](https://mediapipe.page.link/face_detection_py_colab)
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