diff --git a/Particle_Run-in-space.mp4 b/Particle_Run-in-space.mp4 new file mode 100644 index 00000000..831abf93 Binary files /dev/null and b/Particle_Run-in-space.mp4 differ diff --git a/app.py b/app.py index 39f0b31b..b196f31f 100644 --- a/app.py +++ b/app.py @@ -4,6 +4,8 @@ import copy import argparse import itertools +import threading +import time from collections import Counter from collections import deque @@ -15,6 +17,10 @@ from model import KeyPointClassifier from model import PointHistoryClassifier +from eff_word_net.streams import SimpleMicStream +from eff_word_net.engine import HotwordDetector +from eff_word_net.audio_processing import First_Iteration_Siamese + def get_args(): parser = argparse.ArgumentParser() @@ -37,6 +43,70 @@ def get_args(): return args +# 音声認識の状態 +hotword_detected = False +hotword_confidence = 0.0 + #アニメーション表示のフラグ ################################################ +show_animation = False + +# 音声認識を行うスレッド関数 +def audio_recognition_thread(): + global hotword_detected,hotword_confidence + + base_model = First_Iteration_Siamese() + + # ホットワード検出器の初期化 - 閾値0.8 + ryouikitennkai_hw = HotwordDetector( + hotword="領域展開", + model=base_model, + reference_file="領域展開_ref.json", + threshold=0.9, + relaxation_time=0.8 + ) + + # モデルが期待するフレームサイズを確認 + expected_frames = ryouikitennkai_hw.model.window_frames + + # マイクストリームの初期化 + sample_rate = 16000 # デフォルトのサンプリングレート + window_length_secs = expected_frames / sample_rate # 適切な時間窓の計算 + + mic_stream = SimpleMicStream( + window_length_secs=window_length_secs, + sliding_window_secs=window_length_secs / 2, + ) + + mic_stream.start_stream() + + print("「領域展開」と言ってください") + try: + while True: + frame = mic_stream.getFrame() + + # フレームサイズの調整が必要か確認 + if frame.shape[0] != expected_frames: + if frame.shape[0] > expected_frames: + # 切り取り + frame = frame[:expected_frames] + else: + # パディング + padded_frame = np.zeros(expected_frames) + padded_frame[:frame.shape[0]] = frame + frame = padded_frame + + # スコアリング + result = ryouikitennkai_hw.scoreFrame(frame) + if result is None: + # 音声活動なし + continue + if result["match"]: + print(f"領域展開！ 信頼度: {result['confidence']:.4f}") + hotword_detected = True + hotword_confidence = result['confidence'] + + + finally: + mic_stream.stop_stream() def main(): # 引数解析 ################################################################# @@ -58,6 +128,8 @@ def main(): cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width) cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height) + # アニメーションビデオの準備 ############################################### + animation_video = cv.VideoCapture('Particle_Run-in-space.mp4') # モデルロード ############################################################# mp_hands = mp.solutions.hands hands = mp_hands.Hands( @@ -67,8 +139,9 @@ def main(): min_tracking_confidence=min_tracking_confidence, ) + mp_selfie_segmentation = mp.solutions.selfie_segmentation + selfie_segmentation = mp_selfie_segmentation.SelfieSegmentation(model_selection=1) keypoint_classifier = KeyPointClassifier() - point_history_classifier = PointHistoryClassifier() # ラベル読み込み ########################################################### @@ -96,17 +169,21 @@ def main(): # フィンガージェスチャー履歴 ################################################ finger_gesture_history = deque(maxlen=history_length) - # ######################################################################## - mode = 0 + # 音声認識スレッドを開始 ################################################### + audio_thread = threading.Thread(target=audio_recognition_thread, daemon=True) + audio_thread.start() + # # アニメーション表示のフラグ ################################################ + # show_animation = False + while True: fps = cvFpsCalc.get() - + global hotword_detected,show_animation # キー処理(ESC：終了) ################################################# key = cv.waitKey(10) if key == 27: # ESC break - number, mode = select_mode(key, mode) + number, mode = select_mode(key, mode=0) # カメラキャプチャ ##################################################### ret, image = cap.read() @@ -115,13 +192,47 @@ def main(): image = cv.flip(image, 1) # ミラー表示 debug_image = copy.deepcopy(image) - # 検出実施 ############################################################# - image = cv.cvtColor(image, cv.COLOR_BGR2RGB) - - image.flags.writeable = False - results = hands.process(image) - image.flags.writeable = True - + # ホットワードが検出された場合、アニメーション表示を切り替え ############### + if hotword_detected: + show_animation = True + print(f"アニメーション表示: {'ON' if show_animation else 'OFF'}") + hotword_detected = False + + + # 手のランドマーク検出 ################################################## + image_rgb = cv.cvtColor(image, cv.COLOR_BGR2RGB) + results = hands.process(image_rgb) + + # セグメンテーション処理 ############################################### + seg_results = selfie_segmentation.process(image_rgb) + segmentation_mask = seg_results.segmentation_mask + condition = segmentation_mask > 0.1 + # 出力画像の準備 ####################################################### + if show_animation: + # アニメーションフレームの取得 + ret_anim, anim_frame = animation_video.read() + if not ret_anim: + # ビデオの最後まで再生したら最初に戻る + animation_video.set(cv.CAP_PROP_POS_FRAMES, 0) + ret_anim, anim_frame = animation_video.read() + + # アニメーションフレームをリサイズ + anim_frame = cv.resize(anim_frame, (image.shape[1], image.shape[0])) + + # 人物とアニメーションを合成 + frame_with_alpha = cv.cvtColor(image, cv.COLOR_BGR2BGRA) + anim_frame_with_alpha = cv.cvtColor(anim_frame, cv.COLOR_BGR2BGRA) + + # アルファチャンネルに透明度を設定 + frame_with_alpha[:, :, 3] = np.where(condition, 255, 0) + + # 合成 + output_image = np.where(condition[:, :, np.newaxis], frame_with_alpha, anim_frame_with_alpha) + + # 4チャンネルから3チャンネルに変換（表示用） + output_image = cv.cvtColor(output_image, cv.COLOR_BGRA2BGR) + else: + output_image = debug_image # #################################################################### if results.multi_hand_landmarks is not None: for hand_landmarks, handedness in zip(results.multi_hand_landmarks, @@ -172,11 +283,17 @@ def main(): else: point_history.append([0, 0]) - debug_image = draw_point_history(debug_image, point_history) - debug_image = draw_info(debug_image, fps, mode, number) - + # 描画 ################################################################# + output_image = draw_point_history(output_image, point_history) + output_image = draw_info(output_image, fps, mode, number) + + # 画面反映 ############################################################# - cv.imshow('Hand Gesture Recognition', debug_image) + cv.imshow('Hand Gesture Or Voice Recognition', output_image) + + + + cap.release() cv.destroyAllWindows() diff --git "a/\351\240\230\345\237\237\345\261\225\351\226\213_ref.json" "b/\351\240\230\345\237\237\345\261\225\351\226\213_ref.json" new file mode 100644 index 00000000..8601a202 --- /dev/null +++ "b/\351\240\230\345\237\237\345\261\225\351\226\213_ref.json" @@ -0,0 +1,545 @@ +{ + "embeddings": [ + [ + -0.002565408358350396, 0.0005717967287637293, -6.735052011208609e-5, + 0.00046873107203282416, -0.0015215128660202026, 0.0002750725543592125, + 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