updated video reader

vision_agent/utils/video.py

@@ -1,19 +1,15 @@
 import base64
 import logging
-import math
-import os
 import tempfile
-from concurrent.futures import ProcessPoolExecutor, as_completed
-from typing import List, Tuple, cast
+from functools import lru_cache
+from typing import List, Tuple
 
 import cv2
 import numpy as np
-from moviepy.video.io.VideoFileClip import VideoFileClip
-from tqdm import tqdm
+from decord import VideoReader
 
 _LOGGER = logging.getLogger(__name__)
 # The maximum length of the clip to extract frames from, in seconds
-_CLIP_LENGTH = 30.0
 
 
 def play_video(video_base64: str) -> None:
@@ -47,169 +43,28 @@ def play_video(video_base64: str) -> None:
  cv2.destroyAllWindows()
 
 
+@lru_cache(maxsize=8)
 def extract_frames_from_video(
- video_uri: str, fps: float = 0.5, motion_detection_threshold: float = 0.0
+ video_uri: str, fps: float = 1.0
 ) -> List[Tuple[np.ndarray, float]]:
  """Extract frames from a video
 
  Parameters:
- video_uri: the path to the video file or a video file url
- fps: the frame rate per second to extract the frames
- motion_detection_threshold: The threshold to detect motion between
- changes/frames. A value between 0-1, which represents the percentage change
- required for the frames to be considered in motion. For example, a lower
- value means more frames will be extracted. A non-positive value will disable
- motion detection and extract all frames.
+ video_uri (str): the path to the video file or a video file url
+ fps (float): the frame rate per second to extract the frames
 
  Returns:
  a list of tuples containing the extracted frame and the timestamp in seconds.
  E.g. [(frame1, 0.0), (frame2, 0.5), ...]. The timestamp is the time in seconds
  from the start of the video. E.g. 12.125 means 12.125 seconds from the start of
  the video. The frames are sorted by the timestamp in ascending order.
  """
- with VideoFileClip(video_uri) as video:
- video_duration: float = video.duration
- num_workers = os.cpu_count()
- clip_length: float = min(video_duration, _CLIP_LENGTH)
- start_times = list(range(0, math.ceil(video_duration), math.ceil(clip_length)))
- assert start_times, f"No frames to extract from the input video: {video_uri}"
- segment_args = [
- {
- "video_uri": video_uri,
- "start": start,
- "end": (
- start + clip_length if i < len(start_times) - 1 else video_duration
- ),
- "fps": fps,
- "motion_detection_threshold": motion_detection_threshold,
- }
- for i, start in enumerate(start_times)
- ]
- if (
- cast(float, segment_args[-1]["end"])
- - cast(float, segment_args[-1]["start"])
- < 1
- ):
- # If the last segment is less than 1s, merge it with the previous segment
- # This is to avoid the failure of the last segment extraction
- assert (
- len(segment_args) > 1
- ), "Development bug - Expect at least 2 segments."
- segment_args[-2]["end"] = video_duration
- segment_args.pop(-1)
- _LOGGER.info(
- f"""Created {len(segment_args)} segments from the input video {video_uri} of length {video.duration}s, with clip size: {clip_length}s and {num_workers} workers.
- Segments: {segment_args}
- """
- )
- frames = []
- with tqdm(total=len(segment_args)) as pbar:
- with ProcessPoolExecutor(max_workers=num_workers) as executor:
- futures = [
- executor.submit(_extract_frames_by_clip, **kwargs) # type: ignore
- for kwargs in segment_args
- ]
- for future in as_completed(futures):
- result = future.result()
- frames.extend(result)
- pbar.update(1)
- frames.sort(key=lambda x: x[1])
- _LOGGER.info(f"Extracted {len(frames)} frames from video {video_uri}")
- return frames
-
-
-def _extract_frames_by_clip(
- video_uri: str,
- start: int = 0,
- end: float = -1,
- fps: int = 2,
- motion_detection_threshold: float = 0.06,
-) -> List[Tuple[np.ndarray, float]]:
- """Extract frames from a video clip with start and end time in seconds.
-
- Parameters:
- video_uri: the path to the video file or a video file url
- start: the start time (in seconds) of the clip to extract
- end: the end time (in seconds, up to millisecond level precision) of the clip to extract, if -1, extract the whole video
- fps: the frame rate to extract the frames
- motion_detection_threshold: the threshold to detect the motion between frames
- """
- with VideoFileClip(video_uri) as video:
- source_fps = video.fps
- if end <= 0:
- end = video.duration
- _LOGGER.info(
- f"Extracting frames from video {video_uri} ({video.duration}s) with start={start}s and end={end}s"
- )
- clip = video.subclip(start, end)
- processable_frames = int(clip.duration * fps)
- _LOGGER.info(
- f"Extracting frames from video clip of length {clip.duration}s with FPS={fps} and start_time={start}s. Total number of frames in clip: {processable_frames}"
- )
- frames = []
- total_count, skipped_count = 0, 0
- prev_processed_frame = None
- pbar = tqdm(
- total=processable_frames, desc=f"Extracting frames from clip {start}-{end}"
- )
- for i, frame in enumerate(clip.iter_frames(fps=fps, dtype="uint8")):
- total_count += 1
- pbar.update(1)
- if motion_detection_threshold > 0:
- curr_processed_frame = _preprocess_frame(frame)
- # Skip the frame if it is similar to the previous one
- if prev_processed_frame is not None and _similar_frame(
- prev_processed_frame,
- curr_processed_frame,
- threshold=motion_detection_threshold,
- ):
- skipped_count += 1
- continue
- prev_processed_frame = curr_processed_frame
- ts = round(clip.reader.pos / source_fps, 3)
- frames.append((frame, ts))
-
- _LOGGER.info(
- f"""Finished!
- Frames extracted: {len(frames)}
- Extracted frame timestamp: {[f[1] for f in frames]}
- Total processed frames: {total_count}
- Skipped frames: {skipped_count}
- Scan FPS: {fps}
- Clip start time: {start}s, {clip.pos}
- Clip end time: {end}s
- Clip duration: {clip.duration}s
- Clip total frames: {clip.duration * source_fps}
- Video duration: {video.duration}s
- Video FPS: {video.fps}
- Video total frames: {video.reader.nframes}"""
- )
- return frames
-
-
-def _preprocess_frame(frame: np.ndarray) -> np.ndarray:
- # Convert to grayscale
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
- frame = cv2.GaussianBlur(src=frame, ksize=(5, 5), sigmaX=0)
- return frame
-
-
-def _similar_frame(
- prev_frame: np.ndarray, curr_frame: np.ndarray, threshold: float
-) -> bool:
- """Detect two frames are similar or not
-
- Parameters:
- threshold: similarity threshold, a value between 0-1, the percentage change that is considered a different frame.
- """
- # calculate difference and update previous frame TODO: don't assume the processed image is cached
- diff_frame = cv2.absdiff(src1=prev_frame, src2=curr_frame)
- # Only take different areas that are different enough (>20 / 255)
- thresh_frame = cv2.threshold(
- src=diff_frame, thresh=20, maxval=255, type=cv2.THRESH_BINARY
- )[1]
- change_percentage = cv2.countNonZero(thresh_frame) / (
- curr_frame.shape[0] * curr_frame.shape[1]
- )
- _LOGGER.debug(f"Image diff: {change_percentage}")
- return change_percentage < threshold
+ vr = VideoReader(video_uri)
+ orig_fps = vr.get_avg_fps()
+ if fps > orig_fps:
+ fps = orig_fps
+
+ s = orig_fps / fps
+ samples = [(int(i * s), int(i * s) / orig_fps) for i in range(int(len(vr) / s))]
+ frames = vr.get_batch([s[0] for s in samples]).asnumpy()
+ return [(frames[i, :, :, :], samples[i][1]) for i in range(len(samples))]