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• Developing MobyCAIRO

  26 May 2021, by Multimedia Mike — General

  I recently published a tool called MobyCAIRO. The ‘CAIRO’ part stands for Computer-Assisted Image ROtation, while the ‘Moby’ prefix refers to its role in helping process artifact image scans to submit to the MobyGames database. The tool is meant to provide an accelerated workflow for rotating and cropping image scans. It works on both Windows and Linux. Hopefully, it can solve similar workflow problems for other people.

  As of this writing, MobyCAIRO has not been tested on Mac OS X yet– I expect some issues there that should be easily solvable if someone cares to test it.

  The rest of this post describes my motivations and how I arrived at the solution.

  Background
  I have scanned well in excess of 2100 images for MobyGames and other purposes in the past 16 years or so. The workflow looks like this:

  
  

  

  Image workflow

  
  

  It should be noted that my original workflow featured me manually rotating the artifact on the scanner bed in order to ensure straightness, because I guess I thought that rotate functions in image editing programs constituted dark, unholy magic or something. So my workflow used to be even more arduous:

  
  

  

  I can’t believe I had the patience to do this for hundreds of scans

  
  

  Sometime last year, I was sitting down to perform some more scanning and found myself dreading the oncoming tedium of straightening and cropping the images. This prompted a pivotal question:

  
  Why can’t a computer do this for me?
  

  After all, I have always been a huge proponent of making computers handle the most tedious, repetitive, mind-numbing, and error-prone tasks. So I did some web searching to find if there were any solutions that dealt with this. I also consulted with some like-minded folks who have to cope with the same tedious workflow.

  I came up empty-handed. So I endeavored to develop my own solution.

  Problem Statement and Prior Work
  
  I want to develop a workflow that can automatically rotate an image so that it is straight, and also find the most likely crop rectangle, uniformly whitening the area outside of the crop area (in the case of circles).

  As mentioned, I checked to see if any other programs can handle this, starting with my usual workhorse, Photoshop Elements. But I can’t expect the trimmed down version to do everything. I tried to find out if its big brother could handle the task, but couldn’t find a definitive answer on that. Nor could I find any other tools that seem to take an interest in optimizing this particular workflow.

  When I brought this up to some peers, I received some suggestions, including an idea that the venerable GIMP had a feature like this, but I could not find any evidence. Further, I would get responses of “Program XYZ can do image rotation and cropping.” I had to tamp down on the snark to avoid saying “Wow! An image editor that can perform rotation AND cropping? What a game-changer!” Rotation and cropping features are table stakes for any halfway competent image editor for the last 25 or so years at least. I am hoping to find or create a program which can lend a bit of programmatic assistance to the task.

  Why can’t other programs handle this? The answer seems fairly obvious: Image editing tools are general tools and I want a highly customized workflow. It’s not reasonable to expect a turnkey solution to do this.

  Brainstorming An Approach
  I started with the happiest of happy cases— A disc that needed archiving (a marketing/press assets CD-ROM from a video game company, contents described here) which appeared to have some pretty clear straight lines:

  
  
  

  My idea was to try to find straight lines in the image and then rotate the image so that the image is parallel to the horizontal based on the longest single straight line detected.

  I just needed to figure out how to find a straight line inside of an image. Fortunately, I quickly learned that this is very much a solved problem thanks to something called the Hough transform. As a bonus, I read that this is also the tool I would want to use for finding circles, when I got to that part. The nice thing about knowing the formal algorithm to use is being able to find efficient, optimized libraries which already implement it.

  Early Prototype
  A little searching for how to perform a Hough transform in Python led me first to scikit. I was able to rapidly produce a prototype that did some basic image processing. However, running the Hough transform directly on the image and rotating according to the longest line segment discovered turned out not to yield expected results.

  
  
  

  It also took a very long time to chew on the 3300×3300 raw image– certainly longer than I care to wait for an accelerated workflow concept. The key, however, is that you are apparently not supposed to run the Hough transform on a raw image– you need to compute the edges first, and then attempt to determine which edges are ‘straight’. The recommended algorithm for this step is the Canny edge detector. After applying this, I get the expected rotation:

  
  
  

  The algorithm also completes in a few seconds. So this is a good early result and I was feeling pretty confident. But, again– happiest of happy cases. I should also mention at this point that I had originally envisioned a tool that I would simply run against a scanned image and it would automatically/magically make the image straight, followed by a perfect crop.

  Along came my MobyGames comrade Foxhack to disabuse me of the hope of ever developing a fully automated tool. Just try and find a usefully long straight line in this:

  
  

  Darn it, Foxhack…
  

  There are straight edges, to be sure. But my initial brainstorm of rotating according to the longest straight edge looks infeasible. Further, it’s at this point that we start brainstorming that perhaps we could match on ratings badges such as the standard ESRB badges omnipresent on U.S. video games. This gets into feature detection and complicates things.

  This Needs To Be Interactive
  At this point in the effort, I came to terms with the fact that the solution will need to have some element of interactivity. I will also need to get out of my safe Linux haven and figure out how to develop this on a Windows desktop, something I am not experienced with.

  I initially dreamed up an impressive beast of a program written in C++ that leverages Windows desktop GUI frameworks, OpenGL for display and real-time rotation, GPU acceleration for image analysis and processing tricks, and some novel input concepts. I thought GPU acceleration would be crucial since I have a fairly good GPU on my main Windows desktop and I hear that these things are pretty good at image processing.

  I created a list of prototyping tasks on a Trello board and made a decent amount of headway on prototyping all the various pieces that I would need to tie together in order to make this a reality. But it was ultimately slowgoing when you can only grab an hour or 2 here and there to try to get anything done.

  Settling On A Solution
  Recently, I was determined to get a set of old shareware discs archived. I ripped the data a year ago but I was blocked on the scanning task because I knew that would also involve tedious straightening and cropping. So I finally got all the scans done, which was reasonably quick. But I was determined to not manually post-process them.

  This was fairly recent, but I can’t quite recall how I managed to come across the OpenCV library and its Python bindings. OpenCV is an amazing library that provides a significant toolbox for performing image processing tasks. Not only that, it provides “just enough” UI primitives to be able to quickly create a basic GUI for your program, including image display via multiple windows, buttons, and keyboard/mouse input. Furthermore, OpenCV seems to be plenty fast enough to do everything I need in real time, just with (accelerated where appropriate) CPU processing.

  So I went to work porting the ideas from the simple standalone Python/scikit tool. I thought of a refinement to the straight line detector– instead of just finding the longest straight edge, it creates a histogram of 360 rotation angles, and builds a list of lines corresponding to each angle. Then it sorts the angles by cumulative line length and allows the user to iterate through this list, which will hopefully provide the most likely straightened angle up front. Further, the tool allows making fine adjustments by 1/10 of an angle via the keyboard, not the mouse. It does all this while highlighting in red the straight line segments that are parallel to the horizontal axis, per the current candidate angle.

  
  
  

  The tool draws a light-colored grid over the frame to aid the user in visually verifying the straightness of the image. Further, the program has a mode that allows the user to see the algorithm’s detected edges:

  
  
  

  For the cropping phase, the program uses the Hough circle transform in a similar manner, finding the most likely circles (if the image to be processed is supposed to be a circle) and allowing the user to cycle among them while making precise adjustments via the keyboard, again, rather than the mouse.

  
  
  

  Running the Hough circle transform is a significantly more intensive operation than the line transform. When I ran it on a full 3300×3300 image, it ran for a long time. I didn’t let it run longer than a minute before forcibly ending the program. Is this approach unworkable? Not quite– It turns out that the transform is just as effective when shrinking the image to 400×400, and completes in under 2 seconds on my Core i5 CPU.

  For rectangular cropping, I just settled on using OpenCV’s built-in region-of-interest (ROI) facility. I tried to intelligently find the best candidate rectangle and allow fine adjustments via the keyboard, but I wasn’t having much success, so I took a path of lesser resistance.

  Packaging and Residual Weirdness
  I realized that this tool would be more useful to a broader Windows-using base of digital preservationists if they didn’t have to install Python, establish a virtual environment, and install the prerequisite dependencies. Thus, I made the effort to figure out how to wrap the entire thing up into a monolithic Windows EXE binary. It is available from the project’s Github release page (another thing I figured out for the sake of this project!).

  The binary is pretty heavy, weighing in at a bit over 50 megabytes. You might advise using compression– it IS compressed! Before I figured out the --onefile command for pyinstaller.exe, the generated dist/ subdirectory was 150 MB. Among other things, there’s a 30 MB FORTRAN BLAS library packaged in!

  Conclusion and Future Directions
  Once I got it all working with a simple tkinter UI up front in order to select between circle and rectangle crop modes, I unleashed the tool on 60 or so scans in bulk, using the Windows forfiles command (another learning experience). I didn’t put a clock on the effort, but it felt faster. Of course, I was livid with proudness the whole time because I was using my own tool. I just wish I had thought of it sooner. But, really, with 2100+ scans under my belt, I’m just getting started– I literally have thousands more artifacts to scan for preservation.

  The tool isn’t perfect, of course. Just tonight, I threw another scan at MobyCAIRO. Just go ahead and try to find straight lines in this specimen:

  
  
  

  I eventually had to use the text left and right of center to line up against the grid with the manual keyboard adjustments. Still, I’m impressed by how these computer vision algorithms can see patterns I can’t, highlighting lines I never would have guessed at.

  I’m eager to play with OpenCV some more, particularly the video processing functions, perhaps even some GPU-accelerated versions.

  The post Developing MobyCAIRO first appeared on Breaking Eggs And Making Omelettes.

• FFMPEG Issue: Video breaks a lot hence the real time video gets distorted and gets a little delayed while streaming on a webpage from drone

  26 March 2022, by ashiyaa nunhuck

  ****I am trying to detect a face from my drone camera in real time.The video streams successfully but it is delayed and breaks a lot. Is there any solution to this problem? How can i have a smooth video streaming with little delay and no video breaking in order to succeed in detecting a face? Your help will be much appreciated.
Also, this is printed while my code is running::

  


  


  INFO:werkzeug:127.0.0.1 - - [27/May/2021 15:16:14] "GET
/video/streaming HTTP/1.1" 200 -
INFO:drone_face_recognition_and_tracking.controllers.server:'action':
'command', 'cmd': 'takeOff'
INFO:drone_face_recognition_and_tracking.models.manage_drone:'action':
'send_command', 'command': 'takeoff' [h264 @ 0x55aa924a2e40] error
while decoding MB 45 38, bytestream -6 [h264 @ 0x55aa924a2e40]
concealing 424 DC, 424 AC, 424 MV errors in I frame [h264 @
0x55aa922a9a00] concealing 687 DC, 687 AC, 687 MV errors in P frame
[h264 @ 0x55aa923f79c0] left block unavailable for requested intra
mode [h264 @ 0x55aa923f79c0] error while decoding MB 0 34, bytestream
1347 [h264 @ 0x55aa923f79c0] concealing 709 DC, 709 AC, 709 MV errors
in P frame INFO:werkzeug:127.0.0.1 - - [27/May/2021 15:16:17] "POST
/api/command/ HTTP/1.1" 200 - pipe:0: corrupt decoded frame in stream
0
Last message repeated 2 times [h264 @ 0x55aa922a9a00] error while decoding MB 49 30, bytestream -6 [h264 @ 0x55aa922a9a00] concealing
900 DC, 900 AC, 900 MV errors in P frame pipe:0: corrupt decoded frame
in stream 0
INFO:drone_face_recognition_and_tracking.models.manage_drone:'action':
'receive_response', 'response': b'ok'
INFO:drone_face_recognition_and_tracking.controllers.server:'action':
'command', 'cmd': 'faceDetectAndTrack' INFO:werkzeug:127.0.0.1 - -
[27/May/2021 15:16:21] "POST /api/command/ HTTP/1.1" 200 - [h264 @
0x55aa924a2e40] left block unavailable for requested intra4x4 mode -1
[h264 @ 0x55aa924a2e40] error while decoding MB 0 30, bytestream 1624
[h264 @ 0x55aa924a2e40] concealing 949 DC, 949 AC, 949 MV errors in I
frame pipe:0: corrupt decoded frame in stream 0 [h264 @
0x55aa9244d400] left block unavailable for requested intra mode [h264
@ 0x55aa9244d400] error while decoding MB 0 12, bytestream 2936 [h264
@ 0x55aa9244d400] concealing 2029 DC, 2029 AC, 2029 MV errors in I
frame pipe:0: corrupt decoded frame in stream 0 [h264 @
0x55aa924bf700] concealing 1632 DC, 1632 AC, 1632 MV errors in P frame
pipe:0: corrupt decoded frame in stream 0 [h264 @ 0x55aa92414280]
concealing 1571 DC, 1571 AC, 1571 MV errors in P frame

  


  


  Here is my code:****

  


  import logging
import contextlib
import os
import socket
import subprocess
import threading
import time
import cv2 as cv
import numpy as np

from drone_face_recognition_and_tracking.models.base import Singleton

logger = logging.getLogger(__name__)

DEFAULT_DISTANCE = 0.30
DEFAULT_SPEED = 10
DEFAULT_DEGREE = 10

FRAME_X = int(320)  # try 640
FRAME_Y = int(240)  # try 480
FRAME_AREA = FRAME_X * FRAME_Y

FRAME_SIZE = FRAME_AREA * 3
FRAME_CENTER_X = FRAME_X / 2
FRAME_CENTER_Y = FRAME_Y / 2

CMD_FFMPEG = (f'ffmpeg -probesize 32 -hwaccel auto -hwaccel_device opencl -i pipe:0 '
              f'-pix_fmt bgr24 -s {FRAME_X}x{FRAME_Y} -f rawvideo pipe:1')

FACE_DETECT_XML_FILE = './drone_face_recognition_and_tracking/models/haarcascade_frontalface_default.xml'


def receive_video(stop_event, pipe_in, host_ip, video_port):
    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as sock_video:
        sock_video.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
        sock_video.settimeout(.5)
        sock_video.bind((host_ip, video_port))
        data = bytearray(2048)
        while not stop_event.is_set():
            try:
                size, addr = sock_video.recvfrom_into(data)
                # logger.info({'action': 'receive_video', 'data': data})
            except socket.timeout as ex:
                logger.warning({'action': 'receive_video', 'ex': ex})
                time.sleep(0.5)
                continue
            except socket.error as ex:
                logger.error({'action': 'receive_video', 'ex': ex})
                break

            try:
                pipe_in.write(data[:size])
                pipe_in.flush()
            except Exception as ex:
                logger.error({'action': 'receive_video', 'ex': ex})
                break


class Tello_Drone(metaclass=Singleton):
    def __init__(self, host_ip='192.168.10.2', host_port=8889,
                 drone_ip='192.168.10.1', drone_port=8889,
                 is_imperial=False, speed=DEFAULT_SPEED):
        self.host_ip = host_ip
        self.host_port = host_port
        self.drone_ip = drone_ip
        self.drone_port = drone_port
        self.drone_address = (drone_ip, drone_port)
        self.is_imperial = is_imperial
        self.speed = speed
        self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        self.socket.bind((self.host_ip, self.host_port))

        self.response = None
        self.stop_event = threading.Event()
        self._response_thread = threading.Thread(target=self.receive_response, args=(self.stop_event, ))
        self._response_thread.start()

        self.proc = subprocess.Popen(CMD_FFMPEG.split(' '),
                                     stdin=subprocess.PIPE,
                                     stdout=subprocess.PIPE)
        self.proc_stdin = self.proc.stdin
        self.proc_stdout = self.proc.stdout

        self.video_port = 11111

        self._receive_video_thread = threading.Thread(
            target=receive_video,
            args=(self.stop_event, self.proc_stdin,
                  self.host_ip, self.video_port,))
        self._receive_video_thread.start()

        self.face_cascade = cv.CascadeClassifier(FACE_DETECT_XML_FILE)
        self._is_enable_face_detect = False

        self.send_command('command')
        self.send_command('streamon')
        self.set_speed(self.speed)

    def receive_response(self, stop_event):
        while not stop_event.is_set():
            try:
                self.response, ip = self.socket.recvfrom(3000)
                logger.info({'action': 'receive_response',
                             'response': self.response})
            except socket.error as ex:
                logger.error({'action': 'receive_response',
                             'ex': ex})
                break

    def __dell__(self):
        self.stop()

    def stop(self):
        self.stop_event.set()
        retry = 0
        while self._response_thread.is_alive():
            time.sleep(0.3)
            if retry > 30:
                break
            retry += 1
        self.socket.close()
        os.kill(self.proc.pid, 9)

    def send_command(self, command):
        logger.info({'action': 'send_command', 'command': command})
        self.socket.sendto(command.encode('utf-8'), self.drone_address)

        retry = 0
        while self.response is None:
            time.sleep(0.3)
            if retry > 3:
                break
            retry += 1

        if self.response is None:
            response = None
        else:
            response = self.response.decode('utf-8')
        self.response = None
        return response

    def takeoff(self):
        return self.send_command('takeoff')

    def land(self):
        return self.send_command('land')

    def move(self, direction, distance):
        distance = float(distance)
        if self.is_imperial:
            distance = int(round(distance * 30.48))
        else:
            distance = int(round(distance * 100))
        return self.send_command(f'{direction} {distance}')

    def up(self, distance=DEFAULT_DISTANCE):
        return self.move('up', distance)

    def down(self, distance=DEFAULT_DISTANCE):
        return self.move('down', distance)

    def left(self, distance=DEFAULT_DISTANCE):
        return self.move('left', distance)

    def right(self, distance=DEFAULT_DISTANCE):
        return self.move('right', distance)

    def forward(self, distance=DEFAULT_DISTANCE):
        return self.move('forward', distance)

    def back(self, distance=DEFAULT_DISTANCE):
        return self.move('back', distance)

    def set_speed(self, speed):
        return self.send_command(f'speed {speed}')

    def clockwise(self, degree=DEFAULT_DEGREE):
        return self.send_command(f'cw {degree}')

    def counter_clockwise(self, degree=DEFAULT_DEGREE):
        return self.send_command(f'ccw {degree}')

    def video_binary_generator(self):
        while True:
            try:
                frame = self.proc_stdout.read(FRAME_SIZE)
            except Exception as ex:
                logger.error({'action': 'video_binary_generator', 'ex': ex})
                continue

            if not frame:
                continue

            frame = np.fromstring(frame, np.uint8).reshape(FRAME_Y, FRAME_X, 3)
            yield frame

    def enable_face_detect(self):
        self._is_enable_face_detect = True

    def disable_face_detect(self):
        self._is_enable_face_detect = False

    def video_jpeg_generator(self):
        for frame in self.video_binary_generator():
            if self._is_enable_face_detect:
                gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
                faces = self.face_cascade.detectMultiScale(gray, 1.2, 4)
                for (x, y, w, h) in faces:
                    cv.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
                    break

            _, jpeg = cv.imencode('.jpg', frame)
            jpeg_binary = jpeg.tobytes()
            yield jpeg_binary


  


• ffmpeg converting .vob to .mp4 --- colors are washed out

  28 May 2021, by Blattla

  I am very new to ffmpeg. Currently I am trying to convert old family movies from vob to mp4.
After getting some help I used this command:

  


  ffmpeg -i 1998.vob -codec:v libx264 -crf 17 -pix_fmt yuv420p -codec:a aac -movflags +faststart 1998.mp4


  


  Two things that still bug me. The output is almost 20% larger than the input and the colors are washed out. Although the movies are old and of low quality (grainy, rather flat colors, ...) the colors in the output movie are less intense than in the original one. Larger file size would be ok for me but do you have any idea which part of my command might cause the color issue?

  


  ffmpeg version 4.4 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple clang version 12.0.0 (clang-1200.0.32.29)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.4_1 --enable-shared --enable-pthreads --enable-version3 --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox
  libavutil      56. 70.100 / 56. 70.100
  libavcodec     58.134.100 / 58.134.100
  libavformat    58. 76.100 / 58. 76.100
  libavdevice    58. 13.100 / 58. 13.100
  libavfilter     7.110.100 /  7.110.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  9.100 /  5.  9.100
  libswresample   3.  9.100 /  3.  9.100
  libpostproc    55.  9.100 / 55.  9.100
Input #0, mpeg, from '1995.vob':
  Duration: 00:00:14.64, start: 0.232178, bitrate: 1676252 kb/s
  Stream #0:0[0x1bf]: Data: dvd_nav_packet
  Stream #0:1[0x1e0]: Video: mpeg2video (Main), yuv420p(tv, bt470bg, top first), 704x576 [SAR 12:11 DAR 4:3], 25 fps, 25 tbr, 90k tbn, 50 tbc
    Side data:
      cpb: bitrate max/min/avg: 9548800/0/0 buffer size: 1835008 vbv_delay: N/A
  Stream #0:2[0x80]: Audio: ac3, 48000 Hz, stereo, fltp, 256 kb/s
File '999.mp4' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:1 -> #0:0 (mpeg2video (native) -> h264 (libx264))
  Stream #0:2 -> #0:1 (ac3 (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x7ff666814400] using SAR=12/11
[libx264 @ 0x7ff666814400] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7ff666814400] profile High, level 3.0, 4:2:0, 8-bit
[libx264 @ 0x7ff666814400] 264 - core 161 r3049 55d517b - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=17.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to '999.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt470bg, top coded first (swapped)), 704x576 [SAR 12:11 DAR 4:3], q=2-31, 25 fps, 12800 tbn
    Metadata:
      encoder         : Lavc58.134.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s
    Metadata:
      encoder         : Lavc58.134.100 aac
frame=    1 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=   59 fps=0.0 q=22.0 size=     256kB time=00:00:02.04 bitrate=1024.2kbits/frame=   75 fps= 72 q=22.0 size=    1024kB time=00:00:02.68 bitrate=3120.9kbits/frame=   95 fps= 61 q=22.0 size=    2048kB time=00:00:03.45 bitrate=4854.6kbits/frame=  112 fps= 54 q=22.0 size=    2816kB time=00:00:04.18 bitrate=5517.2kbits/frame=  131 fps= 51 q=22.0 size=    4096kB time=00:00:04.92 bitrate=6809.0kbits/frame=  149 fps= 48 q=22.0 size=    4864kB time=00:00:05.65 bitrate=7048.3kbits/frame=  169 fps= 47 q=22.0 size=    6144kB time=00:00:06.42 bitrate=7838.3kbits/frame=  183 fps= 44 q=22.0 size=    6912kB time=00:00:07.04 bitrate=8043.1kbits/frame=  203 fps= 43 q=22.0 size=    7936kB time=00:00:07.80 bitrate=8326.3kbits/frame=  221 fps= 42 q=22.0 size=    8960kB time=00:00:08.51 bitrate=8623.2kbits/frame=  240 fps= 42 q=22.0 size=    9984kB time=00:00:09.28 bitrate=8813.5kbits/frame=  261 fps= 41 q=22.0 size=   11264kB time=00:00:10.06 bitrate=9164.0kbits/frame=  278 fps= 40 q=22.0 size=   12288kB time=00:00:10.81 bitrate=9306.9kbits/frame=  294 fps= 40 q=22.0 size=   13568kB time=00:00:11.45 bitrate=9702.3kbits/frame=  311 fps= 39 q=22.0 size=   14592kB time=00:00:12.09 bitrate=9882.4kbits/frame=  329 fps= 39 q=22.0 size=   15616kB time=00:00:12.86 bitrate=9944.5kbits/frame=  347 fps= 39 q=22.0 size=   16640kB time=00:00:13.56 bitrate=10046.8kbitsframe=  366 fps= 38 q=22.0 size=   17920kB time=00:00:14.33 bitrate=10240.0kbitsframe=  385 fps= 38 q=22.0 size=   19200kB time=00:00:15.12 bitrate=10398.9kbitsframe=  400 fps= 38 q=22.0 size=   20224kB time=00:00:15.63 bitrate=10594.9kbitsframe=  418 fps= 38 q=22.0 size=   21504kB time=00:00:16.34 bitrate=10780.1kbitsframe=  431 fps= 37 q=22.0 size=   22528kB time=00:00:16.89 bitrate=10922.7kbitsframe=  447 fps= 37 q=22.0 size=   23808kB time=00:00:17.53 bitrate=11122.0kbitsframe=  466 fps= 37 q=22.0 size=   25088kB time=00:00:18.26 bitrate=11254.5kbitsframe=  479 fps= 36 q=22.0 size=   25856kB time=00:00:18.81 bitrate=11257.1kbitsframe=  497 fps= 36 q=22.0 size=   27136kB time=00:00:19.54 bitrate=11375.8kbitsframe=  513 fps= 36 q=22.0 size=   28416kB time=00:00:20.16 bitrate=11546.8kbitsframe=  530 fps= 36 q=22.0 size=   29440kB time=00:00:20.92 bitrate=11523.9kbitsframe=  546 fps= 36 q=22.0 size=   30464kB time=00:00:21.56 bitrate=11570.9kbitsframe=  564 fps= 36 q=22.0 size=   31744kB time=00:00:22.10 bitrate=11766.1kbitsframe=  580 fps= 36 q=19.0 size=   32512kB time=00:00:22.80 bitrate=11678.8kbitsframe=  598 fps= 36 q=22.0 size=   33792kB time=00:00:23.57 bitrate=11743.1kbitsframe=  614 fps= 35 q=22.0 size=   34560kB time=00:00:24.25 bitrate=11672.0kbitsframe=  630 fps= 35 q=22.0 size=   35328kB time=00:00:24.85 bitrate=11644.6kbitsframe=  646 fps= 35 q=22.0 size=   36096kB time=00:00:25.42 bitrate=11628.3kbitsframe=  662 fps= 35 q=22.0 size=   37376kB time=00:00:26.17 bitrate=11697.1kbitsframe=  682 fps= 35 q=22.0 size=   38400kB time=00:00:26.90 bitrate=11693.6kbitsframe=  699 fps= 35 q=22.0 size=   39424kB time=00:00:27.66 bitrate=11672.2kbitsframe=  719 fps= 35 q=22.0 size=   40704kB time=00:00:28.41 bitrate=11734.5kbitsframe=  736 fps= 35 q=22.0 size=   41728kB time=00:00:29.14 bitrate=11730.3kbitsframe=  753 fps= 35 q=22.0 size=   42752kB time=00:00:29.71 bitrate=11785.2kbitsframe=  764 fps= 35 q=22.0 size=   43520kB time=00:00:30.20 bitrate=11802.0kbitsframe=  776 fps= 35 q=22.0 size=   44544kB time=00:00:30.72 bitrate=11878.4kbitsframe=  786 fps= 34 q=22.0 size=   45312kB time=00:00:31.06 bitrate=11950.4kbitsframe=  799 fps= 34 q=22.0 size=   46080kB time=00:00:31.57 bitrate=11955.9kbitsframe=  815 fps= 34 q=22.0 size=   47104kB time=00:00:32.14 bitrate=12002.6kbitsframe=  830 fps= 34 q=22.0 size=   48128kB time=00:00:32.78 bitrate=12024.2kbitsframe=  839 fps= 33 q=22.0 size=   48640kB time=00:00:33.15 bitrate=12019.2kbitsframe=  847 fps= 33 q=22.0 size=   49152kB time=00:00:33.49 bitrate=12021.9kbitsframe=  858 fps= 33 q=22.0 size=   49920kB time=00:00:33.98 bitrate=12033.5kbitsframe=  867 fps= 32 q=22.0 size=   50688kB time=00:00:34.30 bitrate=12104.6kbitsframe=  880 fps= 32 q=22.0 size=   51456kB time=00:00:34.88 bitrate=12085.1kbitsframe=  898 fps= 32 q=22.0 size=   52480kB time=00:00:35.54 bitrate=12096.2kbitsframe=  913 fps= 32 q=22.0 size=   53504kB time=00:00:36.18 bitrate=12114.1kbitsframe=  923 fps= 32 q=22.0 size=   54016kB time=00:00:36.54 bitrate=12108.7kbitsframe=  940 fps= 32 q=22.0 size=   55040kB time=00:00:37.20 bitrate=12118.9kbitsframe=  958 fps= 32 q=22.0 size=   55808kB time=00:00:37.90 bitrate=12059.8kbitsframe=  975 fps= 32 q=22.0 size=   56832kB time=00:00:38.61 bitrate=12057.2kbitsframe=  994 fps= 32 q=22.0 size=   58112kB time=00:00:39.38 bitrate=12088.3kbitsframe= 1012 fps= 32 q=22.0 size=   59136kB time=00:00:40.02 bitrate=12104.6kbitsframe= 1026 fps= 32 q=22.0 size=   59904kB time=00:00:40.64 bitrate=12075.1kbitsframe= 1038 fps= 31 q=22.0 size=   60928kB time=00:00:41.21 bitrate=12109.9kbitsframe= 1046 fps= 31 q=22.0 size=   61440kB time=00:00:41.53 bitrate=12117.6kbitsframe= 1057 fps= 31 q=22.0 size=   61952kB time=00:00:41.87 bitrate=12119.0kbitsframe= 1071 fps= 31 q=22.0 size=   62976kB time=00:00:42.45 bitrate=12152.2kbitsframe= 1083 fps= 31 q=22.0 size=   63488kB time=00:00:42.90 bitrate=12123.0kbitsframe= 1096 fps= 31 q=22.0 size=   64256kB time=00:00:43.41 bitrate=12125.0kbitsframe= 1110 fps= 31 q=22.0 size=   65280kB time=00:00:44.09 bitrate=12127.5kbitsframe= 1126 fps= 31 q=22.0 size=   66048kB time=00:00:44.62 bitrate=12123.5kbitsframe= 1131 fps= 30 q=19.0 size=   66560kB time=00:00:44.86 bitrate=12153.6kbitsframe= 1147 fps= 30 q=22.0 size=   67584kB time=00:00:45.46 bitrate=12178.4kbitsframe= 1155 fps= 30 q=22.0 size=   68096kB time=00:00:45.88 bitrate=12156.6kbitsframe= 1167 fps= 30 q=22.0 size=   68864kB time=00:00:46.33 bitrate=12174.9kbitsframe= 1175 fps= 30 q=22.0 size=   69376kB time=00:00:46.59 bitrate=12198.0kbitsframe= 1184 fps= 30 q=22.0 size=   70144kB time=00:00:46.93 bitrate=12243.3kbitsframe= 1196 fps= 30 q=22.0 size=   70912kB time=00:00:47.44 bitrate=12243.8kbitsframe= 1211 fps= 30 q=22.0 size=   71936kB time=00:00:48.06 bitrate=12260.7kbitsframe= 1226 fps= 30 q=22.0 size=   73216kB time=00:00:48.66 bitrate=12325.7kbitsframe= 1240 fps= 29 q=22.0 size=   73984kB time=00:00:49.17 bitrate=12325.3kbitsframe= 1256 fps= 29 q=22.0 size=   75264kB time=00:00:49.85 bitrate=12366.9kbitsframe= 1271 fps= 29 q=22.0 size=   76032kB time=00:00:50.43 bitrate=12350.4kbitsframe= 1280 fps= 29 q=22.0 size=   76800kB time=00:00:50.81 bitrate=12380.9kbitsframe= 1288 fps= 29 q=22.0 size=   77312kB time=00:00:51.13 bitrate=12385.4kbitsframe= 1295 fps= 29 q=22.0 size=   77824kB time=00:00:51.39 bitrate=12405.3kbitsframe= 1303 fps= 29 q=22.0 size=   78080kB time=00:00:51.73 bitrate=12364.0kbitsframe= 1316 fps= 29 q=22.0 size=   78848kB time=00:00:52.28 bitrate=12353.2kbitsframe= 1331 fps= 29 q=22.0 size=   79872kB time=00:00:52.86 bitrate=12377.3kbitsframe= 1349 fps= 29 q=22.0 size=   80896kB time=00:00:53.56 bitrate=12371.2kbitsframe= 1364 fps= 29 q=22.0 size=   81920kB time=00:00:54.14 bitrate=12394.5kbitsframe= 1379 fps= 29 q=22.0 size=   82944kB time=00:00:54.74 bitrate=12412.5kbitsframe= 1396 fps= 29 q=22.0 size=   83968kB time=00:00:55.38 bitrate=12420.5kbitsframe= 1417 fps= 29 q=22.0 size=   84992kB time=00:00:56.27 bitrate=12371.9kbitsframe= 1438 fps= 29 q=22.0 size=   86016kB time=00:00:57.10 bitrate=12338.5kbitsframe= 1458 fps= 29 q=22.0 size=   87040kB time=00:00:57.92 bitrate=12310.6kbitsframe= 1478 fps= 29 q=22.0 size=   88064kB time=00:00:58.64 bitrate=12301.4kbitsframe= 1498 fps= 29 q=22.0 size=   89088kB time=00:00:59.47 bitrate=12270.4kbits[mp4 @ 0x7ff666813200] Starting second pass: moving the moov atom to the beginning of the file
frame= 1500 fps= 28 q=-1.0 Lsize=   91853kB time=00:00:59.98 bitrate=12543.2kbits/s speed=1.13x    
video:90866kB audio:941kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.049858%
[libx264 @ 0x7ff666814400] frame I:12    Avg QP:18.57  size:116766
[libx264 @ 0x7ff666814400] frame P:426   Avg QP:20.52  size: 87768
[libx264 @ 0x7ff666814400] frame B:1062  Avg QP:23.56  size: 51088
[libx264 @ 0x7ff666814400] consecutive B-frames:  2.5%  6.4%  9.0% 82.1%
[libx264 @ 0x7ff666814400] mb I  I16..4:  1.0% 87.7% 11.3%
[libx264 @ 0x7ff666814400] mb P  I16..4:  0.5% 23.0%  4.2%  P16..4: 25.2% 30.8% 16.1%  0.0%  0.0%    skip: 0.1%
[libx264 @ 0x7ff666814400] mb B  I16..4:  0.1%  1.3%  0.6%  B16..8: 37.5% 21.2%  9.4%  direct:28.6%  skip: 1.4%  L0:31.5% L1:22.8% BI:45.8%
[libx264 @ 0x7ff666814400] 8x8 transform intra:80.8% inter:75.0%
[libx264 @ 0x7ff666814400] coded y,uvDC,uvAC intra: 97.5% 95.0% 63.3% inter: 90.0% 62.4% 12.3%
[libx264 @ 0x7ff666814400] i16 v,h,dc,p: 36% 46%  6% 12%
[libx264 @ 0x7ff666814400] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 16% 25%  5%  6%  7%  7%  8% 12%
[libx264 @ 0x7ff666814400] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 10% 29% 12%  6%  7%  6% 10%  7% 13%
[libx264 @ 0x7ff666814400] i8c dc,h,v,p: 36% 30% 20% 14%
[libx264 @ 0x7ff666814400] Weighted P-Frames: Y:29.3% UV:14.1%
[libx264 @ 0x7ff666814400] ref P L0: 39.8% 15.4% 21.2% 19.9%  3.8%
[libx264 @ 0x7ff666814400] ref B L0: 76.4% 19.1%  4.5%
[libx264 @ 0x7ff666814400] ref B L1: 90.7%  9.3%
[libx264 @ 0x7ff666814400] kb/s:12406.19
[aac @ 0x7ff666815c00] Qavg: 181.322