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• How to obtain time markers for video splitting using python/OpenCV

  30 mars 2016, par Bleddyn Raw-Rees

  Hi I’m new to the world of programming and computer vision so please bare with me.

  I’m working on my MSc project which is researching automated deletion of low value content in digital file stores. I’m specifically looking at the sort of long shots that often occur in natural history filming whereby a static camera is left rolling in order to capture the rare snow leopard or whatever. These shots may only have some 60s of useful content with perhaps several hours of worthless content either side.

  As a first step I have a simple motion detection program from Adrian Rosebrock’s tutorial [http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/#comment-393376]. Next I intend to use FFMPEG to split the video.

  What I would like help with is how to get in and out points based on the first and last points that motion is detected in the video.

  Here is the code should you wish to see it...

  # import the necessary packages
  
  import argparse
  
  import datetime
  
  import imutils
  
  import time
  
  import cv2
  
  
  
  # construct the argument parser and parse the arguments
  
  ap = argparse.ArgumentParser()
  
  ap.add_argument("-v", "--video", help="path to the video file")
  
  ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
  
  args = vars(ap.parse_args())
  
  
  
  # if the video argument is None, then we are reading from webcam
  
  if args.get("video", None) is None:
  
  camera = cv2.VideoCapture(0)
  
  time.sleep(0.25)
  
  
  
  # otherwise, we are reading from a video file
  
  else:
  
      camera = cv2.VideoCapture(args["video"])
  
  
  
  # initialize the first frame in the video stream
  
  firstFrame = None
  
  
  
  # loop over the frames of the video
  
  while True:
  
      # grab the current frame and initialize the occupied/unoccupied
  
      # text
  
      (grabbed, frame) = camera.read()
  
      text = "Unoccupied"
  
  
  
      # if the frame could not be grabbed, then we have reached the end
  
      # of the video
  
      if not grabbed:
  
          break
  
  
  
      # resize the frame, convert it to grayscale, and blur it
  
      frame = imutils.resize(frame, width=500)
  
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  
      gray = cv2.GaussianBlur(gray, (21, 21), 0)
  
  
  
      # if the first frame is None, initialize it
  
      if firstFrame is None:
  
          firstFrame = gray
  
          continue
  
  
  
      # compute the absolute difference between the current frame and
  
      # first frame
  
      frameDelta = cv2.absdiff(firstFrame, gray)
  
      thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
  
  
  
      # dilate the thresholded image to fill in holes, then find contours
  
      # on thresholded image
  
      thresh = cv2.dilate(thresh, None, iterations=2)
  
      (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  
  
  
      # loop over the contours
  
      for c in cnts:
  
          # if the contour is too small, ignore it
  
          if cv2.contourArea(c) < args["min_area"]:
  
              continue
  
  
  
          # compute the bounding box for the contour, draw it on the frame,
  
          # and update the text
  
          (x, y, w, h) = cv2.boundingRect(c)
  
          cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
  
          text = "Occupied"
  
  
  
      # draw the text and timestamp on the frame
  
      cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
  
          cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
  
      cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
  
          (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
  
  
  
      # show the frame and record if the user presses a key
  
      cv2.imshow("Security Feed", frame)
  
      cv2.imshow("Thresh", thresh)
  
      cv2.imshow("Frame Delta", frameDelta)
  
      key = cv2.waitKey(1) & 0xFF
  
  
  
      # if the `q` key is pressed, break from the lop
  
      if key == ord("q"):
  
          break
  
  
  
  # cleanup the camera and close any open windows
  
  camera.release()
  
  cv2.destroyAllWindows()

  Thanks !

• How to obtain time markers for video splitting using python/OpenCV

  10 novembre 2018, par Bleddyn Raw-Rees

  I’m working on my MSc project which is researching automated deletion of low value content in digital file stores. I’m specifically looking at the sort of long shots that often occur in natural history filming whereby a static camera is left rolling in order to capture the rare snow leopard or whatever. These shots may only have some 60s of useful content with perhaps several hours of worthless content either side.

  As a first step I have a simple motion detection program from Adrian Rosebrock’s tutorial [http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/#comment-393376]. Next I intend to use FFMPEG to split the video.

  What I would like help with is how to get in and out points based on the first and last points that motion is detected in the video.

  Here is the code should you wish to see it...

  # import the necessary packages
  
  import argparse
  
  import datetime
  
  import imutils
  
  import time
  
  import cv2
  
  
  
  # construct the argument parser and parse the arguments
  
  ap = argparse.ArgumentParser()
  
  ap.add_argument("-v", "--video", help="path to the video file")
  
  ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
  
  args = vars(ap.parse_args())
  
  
  
  # if the video argument is None, then we are reading from webcam
  
  if args.get("video", None) is None:
  
  camera = cv2.VideoCapture(0)
  
  time.sleep(0.25)
  
  
  
  # otherwise, we are reading from a video file
  
  else:
  
      camera = cv2.VideoCapture(args["video"])
  
  
  
  # initialize the first frame in the video stream
  
  firstFrame = None
  
  
  
  # loop over the frames of the video
  
  while True:
  
      # grab the current frame and initialize the occupied/unoccupied
  
      # text
  
      (grabbed, frame) = camera.read()
  
      text = "Unoccupied"
  
  
  
      # if the frame could not be grabbed, then we have reached the end
  
      # of the video
  
      if not grabbed:
  
          break
  
  
  
      # resize the frame, convert it to grayscale, and blur it
  
      frame = imutils.resize(frame, width=500)
  
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  
      gray = cv2.GaussianBlur(gray, (21, 21), 0)
  
  
  
      # if the first frame is None, initialize it
  
      if firstFrame is None:
  
          firstFrame = gray
  
          continue
  
  
  
      # compute the absolute difference between the current frame and
  
      # first frame
  
      frameDelta = cv2.absdiff(firstFrame, gray)
  
      thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
  
  
  
      # dilate the thresholded image to fill in holes, then find contours
  
      # on thresholded image
  
      thresh = cv2.dilate(thresh, None, iterations=2)
  
      (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  
  
  
      # loop over the contours
  
      for c in cnts:
  
          # if the contour is too small, ignore it
  
          if cv2.contourArea(c) < args["min_area"]:
  
              continue
  
  
  
          # compute the bounding box for the contour, draw it on the frame,
  
          # and update the text
  
          (x, y, w, h) = cv2.boundingRect(c)
  
          cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
  
          text = "Occupied"
  
  
  
      # draw the text and timestamp on the frame
  
      cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
  
          cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
  
      cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
  
          (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
  
  
  
      # show the frame and record if the user presses a key
  
      cv2.imshow("Security Feed", frame)
  
      cv2.imshow("Thresh", thresh)
  
      cv2.imshow("Frame Delta", frameDelta)
  
      key = cv2.waitKey(1) & 0xFF
  
  
  
      # if the `q` key is pressed, break from the lop
  
      if key == ord("q"):
  
          break
  
  
  
  # cleanup the camera and close any open windows
  
  camera.release()
  
  cv2.destroyAllWindows()

• Unable to decode media issue on chrome browser

  20 octobre 2018, par Shrikant D

  I am experiencing an issue while playing media on my application. I am using html5 player which works fine on another browser but the media not worked on Google Chrome. Below is the ffmpeg output.

  ffmpeg version 3.4.1 Copyright (c) 2000-2017 the FFmpeg developers
  
  
  
    built with Apple LLVM version 8.0.0 (clang-800.0.42.1)
  
  
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/3.4.1 --enable-shared --enable-pthreads --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-gpl --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --enable-videotoolbox --disable-lzma
  
  
  
    libavutil      55. 78.100 / 55. 78.100
  
  
  
    libavcodec     57.107.100 / 57.107.100
  
  
  
    libavformat    57. 83.100 / 57. 83.100
  
  
  
    libavdevice    57. 10.100 / 57. 10.100
  
  
  
    libavfilter     6.107.100 /  6.107.100
  
  
  
    libavresample   3.  7.  0 /  3.  7.  0
  
  
  
    libswscale      4.  8.100 /  4.  8.100
  
  
  
    libswresample   2.  9.100 /  2.  9.100
  
  
  
    libpostproc    54.  7.100 / 54.  7.100
  
  
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'sources-5a6b388fb093f.mp4':
  
  
  
    Metadata:
  
  
  
      major_brand     : mp42
  
  
  
      minor_version   : 0
  
  
  
      compatible_brands: mp42mp41
  
  
  
      creation_time   : 2017-10-31T01:45:10.000000Z
  
  
  
    Duration: 00:04:55.72, start: 0.000000, bitrate: 10300 kb/s
  
  
  
      Stream #0:0(eng): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(tv, bt709), 1920x1080, 9975 kb/s, 59.94 fps, 59.94 tbr, 60k tbn, 119.88 tbc (default)
  
  
  
      Metadata:
  
  
  
        creation_time   : 2017-10-31T01:45:10.000000Z
  
  
  
        handler_name    : Alias Data Handler
  
  
  
        encoder         : AVC Coding
  
  
  
      Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 317 kb/s (default)
  
  
  
      Metadata:
  
  
  
        creation_time   : 2017-10-31T01:45:10.000000Z
  
  
  
        handler_name    : Alias Data Handler

  The issue is replicable on Windows, Linux/Ubuntu and Mac on Google Chrome browser.

  UPDATED :

  render_id: 56
  
  player_id: 0
  
  origin_url: https://example.com/
  
  frame_url: https://example.com/node/83810
  
  frame_title: 
  
  url: https://example.com/sites/default/files/sources-5a6b388fb093f.mp4
  
  total_bytes: 380754023
  
  streaming: false
  
  single_origin: true
  
  passed_cors_access_check: false
  
  range_header_supported: true
  
  pipeline_state: kStopped
  
  info: Selected FFmpegVideoDecoder for video decoding, config: codec: h264 format: 1 profile: h264 main coded size: [1920,1080] visible rect: [0,0,1920,1080] natural size: [1920,1080] has extra data? true encryption scheme: Unencrypted rotation: 0°
  
  audio_channels_count: 2
  
  audio_codec_name: aac
  
  audio_sample_format: Float 32-bit planar
  
  audio_samples_per_second: 48000
  
  bitrate: 10300299
  
  found_audio_stream: true
  
  found_video_stream: true
  
  height: 1080
  
  max_duration: 295.722667
  
  start_time: 0
  
  time_base: 1/60000
  
  video_codec_name: h264
  
  video_format: PIXEL_FORMAT_I420
  
  video_is_encrypted: false
  
  width: 1920
  
  audio_dds: false
  
  audio_decoder: FFmpegAudioDecoder
  
  video_dds: false
  
  video_decoder: FFmpegVideoDecoder
  
  audio_buffering_state: BUFFERING_HAVE_ENOUGH
  
  video_buffering_state: BUFFERING_HAVE_ENOUGH
  
  for_suspended_start: false
  
  pipeline_buffering_state: BUFFERING_HAVE_ENOUGH
  
  duration: 295.722667
  
  event: PAUSE
  
  error: video decode error
  
  pipeline_error: PIPELINE_ERROR_DECODE

  Added chrome Player Properties for specific media.