このサイトを参考にして、YOLOv3による物体検出を学習する。このサイトの場合、通りを行き交う歩行者の検出を行っている。最近は、通りを行き交う人々の顔認識なんかも実験的に行われおり、AIの進歩の目まぐるしさに驚愕させられる今日この頃だ。

必要なデータのダウンロード¶

以下のセルでは、モジュールのインポートと必要なデータのダウンロード、ダウロードしたデータのロード等が行われている。

import os
from os.path import exists, join, basename

project_name = "deep_sort_pytorch"
if not exists(project_name):
  # クローンしてインストール
  !git clone -q --recursive https://github.com/ZQPei/deep_sort_pytorch.git
  
import sys
sys.path.append(project_name)
sys.path.append(join(project_name, 'YOLOv3'))

import IPython
from IPython.display import clear_output

if not exists('yolov3.weights'):
  !wget -q https://pjreddie.com/media/files/yolov3.weights
    
if not exists('ckpt.t7'):
  file_id = '1_qwTWdzT9dWNudpusgKavj_4elGgbkUN'
  !curl -Lb ./cookie "https://drive.google.com/uc?export=download&id={file_id}" -o ckpt.t7

import cv2
import time

from YOLOv3 import YOLOv3
from deep_sort import DeepSort
from util import draw_bboxes

yolo3 = YOLOv3("deep_sort_pytorch/YOLOv3/cfg/yolo_v3.cfg","yolov3.weights","deep_sort_pytorch/YOLOv3/cfg/coco.names", is_xywh=True)
deepsort = DeepSort("ckpt.t7")

Loading weights from yolov3.weights... Done!
Loading weights from ckpt.t7... Done!

VIDEO_URL = 'http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/Datasets/TownCentreXVID.avi'
DURATION_S = 20  # 最初の20秒間のみ抽出

video_file_name = 'video.mp4'
if not exists(video_file_name):
  !wget -q $VIDEO_URL
  dowloaded_file_name = basename(VIDEO_URL)
  # aviをmp4に変換する。
  !ffmpeg -y -loglevel info -t $DURATION_S -i $dowloaded_file_name $video_file_name

def show_local_mp4_video(file_name, width=640, height=480):
  import io
  import base64
  from IPython.display import HTML
  video_encoded = base64.b64encode(io.open(file_name, 'rb').read())
  return HTML(data='''<video width="{0}" height="{1}" alt="test" controls>
                        <source src="data:video/mp4;base64,{2}" type="video/mp4" />
                      </video>'''.format(width, height, video_encoded.decode('ascii')))
 
clear_output()
video = show_local_mp4_video('video.mp4')

from IPython.display import Video

Video("https://eigo.rumisunheart.com/wp-content/uploads/2019/09/video.mp4", width=800, height=600)

この通りを行き交う人々を認識することになる。

yolo3による物体認識(動画編)¶

video_capture = cv2.VideoCapture()
if video_capture.open('video.mp4'):
  width, height = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
  fps = video_capture.get(cv2.CAP_PROP_FPS)
  !rm -f output.mp4 output.avi
  # mp4でセーブできないのでaviでセーブする。
  video_writer = cv2.VideoWriter("output.avi", cv2.VideoWriter_fourcc(*'MJPG'), fps, (width, height))
  while video_capture.isOpened():
    ret, frame = video_capture.read()
    if not ret:
      break
      
    start = time.time()
    xmin, ymin, xmax, ymax = 0, 0, width, height
    im = frame[ymin:ymax, xmin:xmax, (2,1,0)]
    bbox_xywh, cls_conf, cls_ids = yolo3(im)
    if bbox_xywh is not None:
        mask = cls_ids==0
        bbox_xywh = bbox_xywh[mask]
        bbox_xywh[:,3] *= 1.2
        cls_conf = cls_conf[mask]
        outputs = deepsort.update(bbox_xywh, cls_conf, im)
        if len(outputs) > 0:
            bbox_xyxy = outputs[:,:4]
            identities = outputs[:,-1]
            frame = draw_bboxes(frame, bbox_xyxy, identities, offset=(xmin,ymin))

    end = time.time()
    print("time: {}s, fps: {}".format(end-start, 1/(end-start)))
            
    video_writer.write(frame)
  video_capture.release()
  video_writer.release()
  
  # aviをmp4に変換する。
  !ffmpeg -y -loglevel info -i output.avi output.mp4
else:
  print("can't open the given input video file!")

time: 1.4075300693511963s, fps: 0.7104643955925943
time: 0.34830641746520996s, fps: 2.8710352432707715
time: 0.38646364212036133s, fps: 2.587565532719782
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time: 0.27571940422058105s, fps: 3.6268756739368984
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ffmpeg version 4.0 Copyright (c) 2000-2018 the FFmpeg developers
  built with gcc 7.2.0 (crosstool-NG fa8859cb)
  configuration: --prefix=/root/.pyenv/versions/miniconda3-latest/envs/py373 --cc=/opt/conda/conda-bld/ffmpeg_1531088893642/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --enable-shared --enable-static --enable-zlib --enable-pic --enable-gpl --enable-version3 --disable-nonfree --enable-hardcoded-tables --enable-avresample --enable-libfreetype --disable-openssl --disable-gnutls --enable-libvpx --enable-pthreads --enable-libopus --enable-postproc --disable-libx264
  libavutil      56. 14.100 / 56. 14.100
  libavcodec     58. 18.100 / 58. 18.100
  libavformat    58. 12.100 / 58. 12.100
  libavdevice    58.  3.100 / 58.  3.100
  libavfilter     7. 16.100 /  7. 16.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  1.100 /  5.  1.100
  libswresample   3.  1.100 /  3.  1.100
  libpostproc    55.  1.100 / 55.  1.100
Input #0, avi, from 'output.avi':
  Metadata:
    encoder         : Lavf58.31.101
  Duration: 00:00:20.00, start: 0.000000, bitrate: 39103 kb/s
    Stream #0:0: Video: mjpeg (MJPG / 0x47504A4D), yuvj420p(pc, bt470bg/unknown/unknown), 1920x1080, 39167 kb/s, 25 fps, 25 tbr, 25 tbn, 25 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (mjpeg (native) -> mpeg4 (native))
Press [q] to stop, [?] for help
[swscaler @ 0x55db47af7780] deprecated pixel format used, make sure you did set range correctly
Output #0, mp4, to 'output.mp4':
  Metadata:
    encoder         : Lavf58.12.100
    Stream #0:0: Video: mpeg4 (mp4v / 0x7634706D), yuv420p, 1920x1080, q=2-31, 200 kb/s, 25 fps, 12800 tbn, 25 tbc
    Metadata:
      encoder         : Lavc58.18.100 mpeg4
    Side data:
      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1
frame=  500 fps= 90 q=31.0 Lsize=    3879kB time=00:00:19.96 bitrate=1591.9kbits/s speed=3.58x    
video:3876kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.077428%

#show_local_mp4_video('output.mp4', width=960, height=720)

from IPython.display import Video

Video("https://eigo.rumisunheart.com/wp-content/uploads/2019/09/output.mp4", width=800, height=600)

既存のコードを転用することで、非常に簡単に人物認識が可能なので驚かされる。

yolo3による物体認識(画像編)¶

import os
from os.path import exists, join, basename, splitext

git_repo_url = 'https://github.com/ayooshkathuria/pytorch-yolo-v3.git'
project_name = splitext(basename(git_repo_url))[0]
if not exists(project_name):
  # クローンして依存関係をインストール
  !git clone -q $git_repo_url
  #!cd $project_name && pip install -q -r requirement.txt
  
import sys
sys.path.append(project_name)
import time
import matplotlib
import matplotlib.pylab as plt
plt.rcParams["axes.grid"] = False

if not exists('yolov3.weights'):
  !wget -q https://drive.google.com/file/d/1I92QB0ifKoEOZcUq6gAfay-o4Tjw1xTU

IMAGE_URL = 'https://i.postimg.cc/02pvdYwM/ancient-architecture-buildings-1583704.jpg'

image_file = basename(IMAGE_URL)
!wget -q -O $image_file $IMAGE_URL
plt.figure(figsize=(15, 15))
plt.imshow(matplotlib.image.imread(image_file))

<matplotlib.image.AxesImage at 0x7efebac2e490>

!cd pytorch-yolo-v3 && python detect.py --weights ../yolov3.weights --images ../$image_file --det ..

plt.figure(figsize=(15, 15))
plt.imshow(matplotlib.image.imread('det_%s' % image_file))

Loading network.....
Network successfully loaded
ancient-architecture-buildings-1583704.jpg predicted in  0.321 seconds
Objects Detected:    person person person person person person person person person person person person person person car car truck clock
----------------------------------------------------------

SUMMARY
----------------------------------------------------------
Task                     : Time Taken (in seconds)

Reading addresses        : 0.044
Loading batch            : 0.361
Detection (1 images)     : 0.371
Output Processing        : 0.000
Drawing Boxes            : 0.303
Average time_per_img     : 1.055
----------------------------------------------------------

<matplotlib.image.AxesImage at 0x7efeba795390>