pfoeの実装#226
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Kasaiatsuki
reviewed
Jun 7, 2026
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| self.zed_camera.retrieve_image(self.zed_image, sl.VIEW.LEFT) | ||
| image = self.zed_image.get_data() | ||
| resized_image = cv2.resize(image, (224, 224)) |
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224x224で画像を使うなら800x600を圧縮するのではなく真ん中でクロップした方が画像が横に潰れなくて良いのではないでしょうか
Kasaiatsuki
reviewed
Jun 7, 2026
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| left_stick = msg.axes[0] | ||
| right_stick = msg.axes[4] |
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左スティックが前後の値、右スティックが左右の値をとることを想定しているなら、
left_stick = msg.axes[1]
right_stick = msg.axes[2]
が正しいと思います。
Kasaiatsuki
reviewed
Jun 7, 2026
| left_stick = msg.axes[0] | ||
| right_stick = msg.axes[4] | ||
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| self.joy_value = np.arctan2(right_stick, left_stick) |
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left_stick(並進速度)は記録し始めてからは基本倒しっぱなしで情報があまりないと思うので
self.joy_value = right_stick
のようにして１つにするのはどうでしょうか
left_stickが使わなそうでしたら上のとこも消してください。
Kasaiatsuki
reviewed
Jun 7, 2026
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| def timer_callback(self)->None: | ||
| if self.is_paused: | ||
| return | ||
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| current_time = time.time() | ||
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| if self.sdk_flag_: | ||
| image = self._capture_data_from_zed() | ||
| if image is None or self.joy_value is None: | ||
| return | ||
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| if self.last_sample_time is None or current_time - self.last_sample_time >= SAMPLE_INTERVAL: | ||
| get_image_tensor = self.feature_extractor.extract(image).astype(np.float32) | ||
| row = np.concatenate([ | ||
| get_image_tensor, | ||
| np.array([self.joy_value], dtype=np.float32) | ||
| ]) | ||
| self.bin_file.write(row.tobytes()) | ||
| self.log_writer.writerow([self.row_count, self.joy_value]) | ||
| self.row_count += 1 | ||
| self.last_sample_time = current_time | ||
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| else: | ||
| if self.latest_image is None or self.joy_value is None: | ||
| return | ||
| if self.last_sample_time is None or current_time - self.last_sample_time >= SAMPLE_INTERVAL: | ||
| cv_image = self.bridge.imgmsg_to_cv2(self.latest_image, desired_encoding='rgb8') | ||
| get_image_tensor = self.feature_extractor.extract(cv_image).astype(np.float32) | ||
| row = np.concatenate([ | ||
| get_image_tensor, | ||
| np.array([self.joy_value], dtype=np.float32) | ||
| ]) | ||
| self.bin_file.write(row.tobytes()) | ||
| self.log_writer.writerow([self.row_count, self.joy_value]) | ||
| self.row_count += 1 | ||
| self.last_sample_time = current_time |
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timer_callbackとサンプルタイムが同じだと画像を10hzで撮ったあとの特徴量の抽出までが間に合わないと思います。
特徴量の抽出がどれくらいかかるのか分かりませんがサンプルタイムを大きくするか特徴量の抽出はデータを取った後にするのがいいと思います。
Kasaiatsuki
reviewed
Jun 7, 2026
| param.requires_grad = False | ||
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| self.transform = transforms.Compose([ | ||
| transforms.Resize((224, 224)), |
Kasaiatsuki
reviewed
Jun 7, 2026
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| weights = models.EfficientNet_B0_Weights.IMAGENET1K_V1 | ||
| backbone = models.efficientnet_b0(weights=weights) |
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ネットで一度インストールするよりかは事前に重みファイルをディレクトリの中に置いておいてそれを使った方が安心安全だと思います。
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教師走行で記録した「映像＋操作の時系列（エピソード）」を覚えておき、本番走行中に「今カメラに映ってる景色は、記録のどのコマに一番似てるか」をパーティクルフィルタで推定して、そのコマの操作（joy値）を出力する。
学習（ニューラルネットの重み更新）はしない。記録との照合だけで動く方式。