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101 lines
3.5 KiB
101 lines
3.5 KiB
import cv2
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import mediapipe as mp
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from mediapipe.tasks import python
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from mediapipe.tasks.python import vision
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YAW_THRESHOLD = 13
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PITCH_THRESHOLD = 5
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def get_aoi(yaw, pitch):
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ret = ''
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if yaw > YAW_THRESHOLD:
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ret += 'Left '
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elif yaw > -YAW_THRESHOLD:
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ret += 'Center '
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else:
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ret += 'Right '
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if pitch > PITCH_THRESHOLD:
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ret += 'Lower'
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elif pitch > -PITCH_THRESHOLD:
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ret += 'Center'
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else:
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ret += 'Upper'
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return ret
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areas = ['left upper', 'center', 'right upper', 'left lower', 'right lower']
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# 1. クラスのセットアップ (FaceLandmarkerに変更)
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BaseOptions = python.BaseOptions
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FaceLandmarker = vision.FaceLandmarker
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FaceLandmarkerOptions = vision.FaceLandmarkerOptions
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VisionRunningMode = vision.RunningMode
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# 2. 顔ランドマーカーの設定
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# 変換行列（顔の向きデータ）を出力するようにフラグを立てます
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options = FaceLandmarkerOptions(
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base_options=BaseOptions(model_asset_path='face_landmarker.task'),
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running_mode=VisionRunningMode.VIDEO,
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output_facial_transformation_matrixes=True, # 顔の向き取得に必須
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num_faces=1 # 検出する顔の最大数
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)
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cap = cv2.VideoCapture(0)
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# FaceLandmarkerのインスタンスを生成
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with FaceLandmarker.create_from_options(options) as landmarker:
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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break
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rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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frame_timestamp_ms = int(cap.get(cv2.CAP_PROP_POS_MSEC))
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# 稀にタイムスタンプが0や重複になるのを防ぐ安全策
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if frame_timestamp_ms == 0:
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frame_timestamp_ms = 1
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mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
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# 3. 検出の実行
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result = landmarker.detect_for_video(mp_image, frame_timestamp_ms)
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# 4. 変換行列から顔の向き（オイラー角）を計算
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if result.facial_transformation_matrixes:
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for matrix in result.facial_transformation_matrixes:
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# 4x4の変換行列から、左上の3x3の回転行列のみを抽出
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rot_mat = matrix[:3, :3]
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# OpenCVを使って回転行列をオイラー角（度数法）に分解
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angles, _, _, _, _, _ = cv2.RQDecomp3x3(rot_mat)
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# ピッチ(上下), ヨー(左右), ロール(傾き)の取得
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# ※カメラや座標系によって軸の順番は変わる場合があります
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pitch = angles[0]
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yaw = angles[1]
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roll = angles[2]
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# オイラー角からAOIを推定
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estimate_aoi = get_aoi(yaw, pitch)
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text_aoi = f"Estimated AOI: {estimate_aoi}"
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# 画面上に角度を描画
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text_pitch = f"Pitch (Up/Down): {pitch:.1f}"
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text_yaw = f"Yaw (Left/Right): {yaw:.1f}"
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text_roll = f"Roll (Tilt): {roll:.1f}"
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cv2.putText(frame, text_aoi, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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cv2.putText(frame, text_pitch, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
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cv2.putText(frame, text_yaw, (20, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
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cv2.putText(frame, text_roll, (20, 130), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
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cv2.imshow('Area of Interest Estimation', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows() |