cleanup read_data.py

src/pupil_labs/automate_custom_events/read_data.py

@@ -189,110 +189,4 @@ def create_gaze_overlay_video(merged_video, video_path, world_timestamps_df, out
             extra={"markup": True},
         )
         cv2.destroyAllWindows()
-
-
-def read_timestamps(path: Union[str, Path]) -> np.ndarray:
-    """Read gaze timestamps from a binary file.gaze psX.time"""
-    return np.fromfile(str(path), dtype="<u8")
-
-
-def read_raw(path: Union[str, Path]) -> np.ndarray:
-    """Read gaze raw data from a binary file.gaze psX.raw"""
-    raw_data = np.fromfile(str(path), "<f4")
-    raw_data_dtype = raw_data.dtype
-    raw_data.shape = (-1, 2)
-    return np.asarray(raw_data, dtype=raw_data_dtype)
-
-
-# def create_gaze_overlay_video(video_path, recpath, merged_dataset, events_df):
-#     container = av.open(video_path)
-#     video_stream = container.streams.video[0]
-#     video_duration = float(video_stream.duration * video_stream.time_base)
-#     print(f"The video duration is {video_duration}")
-
-#     # Read first frame
-#     with av.open(video_path) as vid_container:
-#         logging.info("Reading first frame")
-#         vid_frame = next(vid_container.decode(video=0))
-
-#     num_processed_frames = 0
-#     # Create a new folder to save the gaze overlay video
-#     new_path = os.path.join(recpath, "gaze_module_video")
-
-#     # Check if the directory already exists
-#     if not os.path.exists(new_path):
-#         # Create the directory
-#         os.makedirs(new_path)
-#         print(f"Directory created at {new_path}")
-#     else:
-#         print(f"Directory already exists at {new_path}")
-
-#     output_file = os.path.join(new_path, "gaze_overlay_video.mp4")
-#     print(f"Output file: {output_file}")
-#     logging.info(
-#         "[white bold on #0d122a]◎ Gaze Module starts![/]",
-#         extra={"markup": True},
-#     )
-#     with av.open(video_path) as video:
-#         logging.info("Ready to process video")
-#         # Prepare the output video
-#         with av.open(output_file, "w") as out_container:
-#             out_video = out_container.add_stream(
-#                 "libx264", rate=30, options={"crf": "18"}
-#             )
-#             out_video.width = video.streams.video[0].width
-#             out_video.height = video.streams.video[0].height
-#             out_video.pix_fmt = "yuv420p"
-#             out_video.codec_context.time_base = Fraction(1, 30)
-
-#             lpts = -1
-
-#             # For every frame in the video
-#             # For every frame in the video
-#             with Progress() as progress_bar:
-#                 video_task = progress_bar.add_task(
-#                     "📹 Processing video", total=merged_dataset.shape[0]
-#                 )
-#                 progress_bar.add_task("Create gaze overlay..")
-#                 while num_processed_frames < merged_dataset.shape[0]:
-#                     row = merged_dataset.iloc[num_processed_frames]
-#                     # Get the frame
-#                     vid_frame, lpts = get_frame(video, int(row["pts"]), lpts, vid_frame)
-#                     if vid_frame is None:
-#                         break
-#                     img_original = vid_frame.to_ndarray(format="rgb24")
-#                     # Prepare the frame
-#                     frame = cv2.cvtColor(img_original, cv2.COLOR_RGB2BGR)
-#                     frame = np.asarray(frame, dtype=np.float32)
-#                     frame = frame[:, :, :]
-#                     xy = row[["gaze x [px]", "gaze y [px]"]].to_numpy(dtype=np.int32)
-#                     gaze_circle_size = 30
-
-#                     # make a aoi_circle on the gaze
-#                     if not np.isnan(xy).any():
-#                         cv2.circle(frame, xy, gaze_circle_size, (0, 0, 255), 10)
-
-#                     # Finally get the frame ready.
-#                     out_ = cv2.normalize(
-#                         frame, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U
-#                     )
-#                     cv2.cvtColor(out_, cv2.COLOR_BGR2RGB, out_)
-#                     np.expand_dims(out_, axis=2)
-#                     out_frame = av.VideoFrame.from_ndarray(out_, format="rgb24")
-#                     for packet in out_video.encode(out_frame):
-#                         out_container.mux(packet)
-#                     if num_processed_frames % 100 == 0:
-#                         logging.info(
-#                             f"Processed {num_processed_frames} frames out of {merged_dataset.shape[0]}"
-#                         )
-#                     progress_bar.advance(video_task)
-#                     progress_bar.refresh()
-#                     num_processed_frames += 1
-#                 out_container.close()
-#             logging.info(
-#                     "[white bold on #0d122a]◎ Gaze overlay rendering completed! ⚡️[/]",
-#                     extra={"markup": True},
-#                 )
-#             # save the csvs
-#         merged_dataset.to_csv(Path(recpath, "render_output.csv"), index=False)
-#         return new_path
+