Example: 03-Machine-Learning/00-TensorFlow/face_landmarks_multi_face.py

# 本作品采用MIT许可证授权。
# Copyright (c) 2013-2025 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# This example shows off Google's MediaPipe Face Landmark Detection model for multiple faces.
#
# NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time.

import csi
import time
import ml
from ml.preprocessing import Normalization
from ml.postprocessing.mediapipe import BlazeFace
from ml.postprocessing.mediapipe import FaceLandmarks

# Initialize the sensor.
csi0 = csi.CSI()
csi0.reset()
csi0.pixformat(csi.RGB565)
csi0.framesize(csi.VGA)

# BlazeFace requires a square image for the best results.
# FaceLandmarks works with non-square images from BlazeFace crops.
csi0.window((400, 400))

# Load built-in face detection model
face_detection = ml.Model("/rom/blazeface_front_128.tflite", postprocess=BlazeFace(threshold=0.4))
print(face_detection)

# Load built-in face landmark model
face_landmarks = ml.Model("/rom/face_landmarks_192.tflite", postprocess=FaceLandmarks(threshold=0.4))
print(face_landmarks)

clock = time.clock()
while True:
    clock.tick()
    img = csi0.snapshot()

    # faces is a list of ((x, y, w, h), score, keypoints) tuples
    for r, score, keypoints in face_detection.predict([img]):
        # rect is (x, y, w, h) - enlarge by 2x for face landmarks model
        wider_rect = (r[0] - r[2] // 2, r[1] - r[3] // 2, r[2] * 2, r[3] * 2)
        # Operate on just the ROI of the detected face
        n = Normalization(roi=wider_rect)

        # marks is a list of ((x, y, w, h), score, keypoints) tuples
        for r, score, keypoints in face_landmarks.predict([n(img)]):
            ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)

            # keypoints is a ndarray of shape (468, 3) where each keypoint is (x, y, z)
            ml.utils.draw_keypoints(img, keypoints, radius=0, color=(255, 0, 0))

    print(clock.fps(), "fps")