diff --git a/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_multi_face.py b/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_multi_face.py new file mode 100644 index 000000000..f598b7d78 --- /dev/null +++ b/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_multi_face.py @@ -0,0 +1,50 @@ +# 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 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) +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") diff --git a/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_single_face.py b/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_single_face.py new file mode 100644 index 000000000..0246086bf --- /dev/null +++ b/scripts/examples/03-Machine-Learning/00-TensorFlow/face_landmarks_single_face.py @@ -0,0 +1,71 @@ +# 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 Face Landmark Detection model for a single face. +# +# 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) +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) + +# Tracking vars. +n = None + +clock = time.clock() +while True: + clock.tick() + img = csi0.snapshot() + + if n is None: + # 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) + + else: + # marks is a list of ((x, y, w, h), score, keypoints) tuples + marks = face_landmarks.predict([n(img)]) + + # No faces detected, reset the tracker. + if not marks: + n = None + continue + + # Draw bounding boxes around the detected faces and keypoints. + for r, score, keypoints in marks: + 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)) + + # Center new_wider_rect on face for tracking + new_wider_rect = (r[0] + (r[2] // 2) - (wider_rect[2] // 2), + r[1] + (r[3] // 2) - (wider_rect[3] // 2), + wider_rect[2], + wider_rect[3]) + # Operate on just the ROI of the detected face + n = Normalization(roi=new_wider_rect) + + print(clock.fps(), "fps")