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# ====== 33 точки mediapipe ======
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'''
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0 - нос
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1 - внутренний угол левого глаза
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2 - центр левого глаза
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3 - внешний угол левого глаза
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4 - внутренний угол правого глаза
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5 - центр правого глаза
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6 - внешний угол правого глаза
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7 - левое ухо
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8 - правое ухо
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9 - левый угол рта
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10 - правый угол рта
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11 - левое плечо
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12- правое плечо
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13 - левый локоть
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14 - правый локоть
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15 - левое запястье
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16 - правое запястье
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17 - левый мизинец
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18 - правый мизинец
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19 - левый указательный палец
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20 - правй указательный палец
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21- левый большой палец
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22 -правый большой палец
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23 - левое бедро
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24 - правое бедро
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25 - левое колено
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26 - правое колено
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27 - левая голень/лодыжка
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28 - правая голень/лодыжка
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29 - левая пятка
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30 - правая пятка
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31 - левый носок
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32 - правый носок
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'''
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# ====== 17 точек efficienthrnet ======
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'''
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0 - нос
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1 - левый глаз
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2 - правый глаз
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3 - левое ухо
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4 - правое ухо
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5 - левое плечо
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6 - правое плечо
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7 - левый локоть
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8 - правый локоть
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9 - левое запястье
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10 - правое запястье
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11 - левое бедро
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12 - правое бедро
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13 - левое колено
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14 - правое колено
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15 - левая лодыжка
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16 - правая лодыжка
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'''
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import sys
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from pathlib import Path
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REPO_PATH = Path(__file__).parent.parent / "submodules" / "OAK-HumanPoseEstimation"
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sys.path.append(str(REPO_PATH))
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import cv2
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import depthai as dai
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import numpy as np
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import daipipeline
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from daipipeline import create_pipeline, get_model_list
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from poseestimators import get_poseestimator
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class OakPoseDetector:
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EH_TO_MP = {
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0: 0, # nose
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1: 2, # left eye
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2: 5, # right eye
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3: 7, # left ear
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4: 8, # right ear
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5: 11, # left shoulder
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6: 12, # right shoulder
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7: 13, # left elbow
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8: 14, # right elbow
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9: 15, # left wrist
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10: 16, # right wrist
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11: 23, # left hip
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12: 24, # right hip
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13: 25, # left knee
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14: 26, # right knee
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15: 27, # left ankle
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16: 28, # right ankle
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}
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MP_CONNECTIONS = [(EH_TO_MP[0], EH_TO_MP[1]),
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(EH_TO_MP[1], EH_TO_MP[3]),
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(EH_TO_MP[0], EH_TO_MP[2]),
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(EH_TO_MP[2], EH_TO_MP[4]),
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(EH_TO_MP[5], EH_TO_MP[6]),
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(EH_TO_MP[5], EH_TO_MP[7]),
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(EH_TO_MP[7], EH_TO_MP[9]),
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(EH_TO_MP[6], EH_TO_MP[8]),
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(EH_TO_MP[8], EH_TO_MP[10]),
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(EH_TO_MP[5], EH_TO_MP[11]),
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(EH_TO_MP[6], EH_TO_MP[12]),
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(EH_TO_MP[11], EH_TO_MP[12]),
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(EH_TO_MP[11], EH_TO_MP[13]),
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(EH_TO_MP[13], EH_TO_MP[15]),
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(EH_TO_MP[12], EH_TO_MP[14]),
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(EH_TO_MP[14], EH_TO_MP[16])]
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def __init__(self, model_type='efficienthrnet1', shaves=6, detection_threshold=0.2, **kwargs):
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daipipeline.MODELS_FOLDER = str(REPO_PATH / "models")
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self.model_type = model_type
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self.model_list = get_model_list()
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if model_type not in self.model_list:
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raise ValueError(f"Unknown model: {model_type}")
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self.model_config = self.model_list[model_type]
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self.kwargs = kwargs
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self.kwargs['shaves'] = shaves
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self.kwargs['detection_threshold'] = int(detection_threshold * 100)
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if 'decoder' not in self.kwargs:
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self.kwargs['decoder'] = None
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self.pose_estimator = get_poseestimator(self.model_config, **self.kwargs)
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self.pipeline = create_pipeline(self.model_config,
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camera=True,
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passthrough=False,
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**self.kwargs)
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self.device = dai.Device(self.pipeline)
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self.preview_queue = self.device.getOutputQueue("preview", maxSize=1, blocking=False)
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self.pose_queue = self.device.getOutputQueue("pose", maxSize=1, blocking=False)
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def get_frame_and_pose(self):
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preview = self.preview_queue.tryGet()
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if preview is None:
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return None, None
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frame = preview.getCvFrame()
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raw_output = self.pose_queue.tryGet()
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if raw_output is None:
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return frame, None
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# получаем результат в формате модели
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personwise_keypoints = self.pose_estimator.get_pose_data(raw_output)
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if personwise_keypoints.shape[0] == 0:
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return frame, None
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kps = personwise_keypoints[0] # (17, 3) – (x, y, confidence)
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mp_landmarks = np.zeros((33, 4), dtype=np.float32)
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for eh_idx, mp_idx in self.EH_TO_MP.items():
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x, y, conf = kps[eh_idx]
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if conf > 1.0:
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conf = conf / 100.0
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mp_landmarks[mp_idx] = [x, y, 0.0, conf]
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return frame, mp_landmarks
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def draw_landmarks(self, image, landmarks, conf_threshold=0.1):
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if landmarks is None:
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return image.copy()
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vis = image.copy()
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for (i, j) in self.MP_CONNECTIONS:
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if i < len(landmarks) and j < len(landmarks):
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if landmarks[i][3] > conf_threshold and landmarks[j][3] > conf_threshold:
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pt1 = (int(landmarks[i][0]), int(landmarks[i][1]))
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pt2 = (int(landmarks[j][0]), int(landmarks[j][1]))
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cv2.line(vis, pt1, pt2, (0, 255, 0), 2)
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for i, (x, y, z, conf) in enumerate(landmarks):
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if conf > conf_threshold:
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cv2.circle(vis, (int(x), int(y)), 3, (255, 0, 0), -1)
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return vis
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def release(self):
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if hasattr(self, 'device'):
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self.device.close()
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def main():
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detector = OakPoseDetector(model_type='efficienthrnet1', detection_threshold=0.2)
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print("Запуск. Нажмите 'q' для выхода.")
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while True:
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frame, landmarks = detector.get_frame_and_pose()
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if frame is None:
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continue
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vis = detector.draw_landmarks(frame, landmarks) if landmarks is not None else frame
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cv2.imshow("Pose Estimation", vis)
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if cv2.waitKey(1) == ord('q'):
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break
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detector.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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main()
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