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scripts/examples: Add BlazeFace detection example.
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# This work is licensed under the MIT license.
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# Copyright (c) 2013-2025 OpenMV LLC. All rights reserved.
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# https://github.com/openmv/openmv/blob/master/LICENSE
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#
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# This example shows off Google's MediaPipe BlazeFace face detection model.
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import csi
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import time
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import ml
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from ml.postprocessing import mediapipe_face_detection_postprocess
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# Initialize the sensor.
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csi0 = csi.CSI()
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csi0.reset()
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csi0.pixformat(csi.RGB565)
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csi0.framesize(csi.VGA)
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csi0.window((400, 400))
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# Load built-in face detection model
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model = ml.Model("/rom/blazeface_front_128.tflite")
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print(model)
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# Create the face detection post-processor. This post-processor dynamically
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# generates anchors for the model input size which should only be done once.
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face_detection_postprocess = mediapipe_face_detection_postprocess(threshold=0.6)
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clock = time.clock()
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while True:
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clock.tick()
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img = csi0.snapshot()
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# faces is a list of ((x, y, w, h), score, keypoints) tuples
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faces = model.predict([img], callback=face_detection_postprocess)
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# Draw bounding boxes around the detected faces and keypoints.
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if faces:
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for r, score, keypoints in faces[0]:
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ml.utils.draw_predictions(img, [r], ["face"], [(0, 0, 255)], format=None)
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# keypoints is a ndarray of shape (6, 2)
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# 0 - right eye (x, y)
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# 1 - left eye (x, y)
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# 2 - nose (x, y)
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# 3 - mouth (x, y)
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# 4 - right ear (x, y)
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# 5 - left ear (x, y)
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for kp in keypoints.tolist():
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img.draw_circle(int(kp[0]), int(kp[1]), 4, color=(255, 0, 0))
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print(clock.fps(), "fps")
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