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scripts/examples: Add facial landmarks examples.
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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 Face Landmark Detection model for multiple faces.
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#
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# NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time.
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import csi
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import time
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import ml
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from ml.preprocessing import Normalization
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from ml.postprocessing.mediapipe import BlazeFace
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from ml.postprocessing.mediapipe import FaceLandmarks
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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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face_detection = ml.Model("/rom/blazeface_front_128.tflite", postprocess=BlazeFace(threshold=0.4))
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print(face_detection)
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# Load built-in face landmark model
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face_landmarks = ml.Model("/rom/face_landmarks_192.tflite", postprocess=FaceLandmarks(threshold=0.4))
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print(face_landmarks)
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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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for r, score, keypoints in face_detection.predict([img]):
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# rect is (x, y, w, h) - enlarge by 2x for face landmarks model
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wider_rect = (r[0] - r[2] // 2, r[1] - r[3] // 2, r[2] * 2, r[3] * 2)
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# Operate on just the ROI of the detected face
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n = Normalization(roi=wider_rect)
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# marks is a list of ((x, y, w, h), score, keypoints) tuples
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for r, score, keypoints in face_landmarks.predict([n(img)]):
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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 (468, 3) where each keypoint is (x, y, z)
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ml.utils.draw_keypoints(img, keypoints, radius=0, color=(255, 0, 0))
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print(clock.fps(), "fps")
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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 Face Landmark Detection model for a single face.
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#
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# NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time.
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import csi
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import time
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import ml
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from ml.preprocessing import Normalization
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from ml.postprocessing.mediapipe import BlazeFace
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from ml.postprocessing.mediapipe import FaceLandmarks
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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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face_detection = ml.Model("/rom/blazeface_front_128.tflite", postprocess=BlazeFace(threshold=0.4))
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print(face_detection)
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# Load built-in face landmark model
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face_landmarks = ml.Model("/rom/face_landmarks_192.tflite", postprocess=FaceLandmarks(threshold=0.4))
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print(face_landmarks)
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# Tracking vars.
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n = None
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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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if n is None:
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# faces is a list of ((x, y, w, h), score, keypoints) tuples
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for r, score, keypoints in face_detection.predict([img]):
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# rect is (x, y, w, h) - enlarge by 2x for face landmarks model
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wider_rect = (r[0] - r[2] // 2, r[1] - r[3] // 2, r[2] * 2, r[3] * 2)
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# Operate on just the ROI of the detected face
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n = Normalization(roi=wider_rect)
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else:
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# marks is a list of ((x, y, w, h), score, keypoints) tuples
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marks = face_landmarks.predict([n(img)])
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# No faces detected, reset the tracker.
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if not marks:
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n = None
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continue
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# Draw bounding boxes around the detected faces and keypoints.
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for r, score, keypoints in marks:
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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 (468, 3) where each keypoint is (x, y, z)
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ml.utils.draw_keypoints(img, keypoints, radius=0, color=(255, 0, 0))
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# Center new_wider_rect on face for tracking
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new_wider_rect = (r[0] + (r[2] // 2) - (wider_rect[2] // 2),
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r[1] + (r[3] // 2) - (wider_rect[3] // 2),
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wider_rect[2],
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wider_rect[3])
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# Operate on just the ROI of the detected face
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n = Normalization(roi=new_wider_rect)
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print(clock.fps(), "fps")
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