Example: 03-Machine-Learning/00-TensorFlow/blazeface_detector.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 Detection model.

import csi
import time
import ml
from ml.postprocessing.mediapipe import BlazeFace

# 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.
csi0.window((400, 400))

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

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 model.predict([img]):
        ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)

        # keypoints is a ndarray of shape (6, 2)
        # 0 - right eye (x, y)
        # 1 - left eye (x, y)
        # 2 - nose (x, y)
        # 3 - mouth (x, y)
        # 4 - right ear (x, y)
        # 5 - left ear (x, y)
        ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))

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