# This work is licensed under the MIT license. # Copyright (c) 2013-2025 OpenMV LLC. All rights reserved. # https://github.com/openmv/openmv/blob/master/LICENSE # # This example shows off Google's MediaPipe BlazeFace face detection model. import csi import time import ml from ml.postprocessing import mediapipe_face_detection_postprocess # Initialize the sensor. csi0 = csi.CSI() csi0.reset() csi0.pixformat(csi.RGB565) csi0.framesize(csi.VGA) csi0.window((400, 400)) # Load built-in face detection model model = ml.Model("/rom/blazeface_front_128.tflite") print(model) # Create the face detection post-processor. This post-processor dynamically # generates anchors for the model input size which should only be done once. face_detection_postprocess = mediapipe_face_detection_postprocess(threshold=0.6) clock = time.clock() while True: clock.tick() img = csi0.snapshot() # faces is a list of ((x, y, w, h), score, keypoints) tuples faces = model.predict([img], callback=face_detection_postprocess) # Draw bounding boxes around the detected faces and keypoints. if faces: for r, score, keypoints in faces[0]: 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) for kp in keypoints.tolist(): img.draw_circle(int(kp[0]), int(kp[1]), 4, color=(255, 0, 0)) print(clock.fps(), "fps")