deepsortv4.py

# -*- coding: utf-8 -*-
"""Deepsortv4.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1gKViQZSB-ECwkFQbF4VPIaO_kGf-rBge
"""

# Commented out IPython magic to ensure Python compatibility.
#mount drive
# %cd ..
from google.colab import drive
drive.mount('/content/gdrive')

# this creates a symbolic link so that now the path /content/gdrive/My\ Drive/ is equal to /mydrive
!ln -s /content/gdrive/My\ Drive/ /mydrive

# list the contents of /mydrive
!ls /mydrive

#Navigate to /mydrive/YOLOV4_Det
# %cd /mydrive/yolov4-deepsort

!git clone https://github.com/theAIGuysCode/yolov4-deepsort

## yolov4-tiny
!python save_model.py --weights ./data/yolov4-custom_best.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4

# run DeepSort with YOLOv4 Object Detections as backbone (enable --info flag to see info about tracked objects)
!python object_tracker.py --video ./data/video/crowded.mp4 --output ./outputs/tracker.avi --model yolov4 --dont_show --info

# define helper function to display videos
import io 
from IPython.display import HTML
from base64 import b64encode
def show_video(file_name, width=640):
  # show resulting deepsort video
  mp4 = open(file_name,'rb').read()
  data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
  return HTML("""
  <video width="{0}" controls>
        <source src="{1}" type="video/mp4">
  </video>
  """.format(width, data_url))

!pwd

# Commented out IPython magic to ensure Python compatibility.
# convert resulting video from avi to mp4 file format
import os
path_video = os.path.join("outputs","tracker.avi")
# %cd outputs/
!ffmpeg -y -loglevel panic -i tracker.avi output.mp4
# %cd ..

# output object tracking video
path_output = os.path.join("outputs","output.mp4")
show_video(path_output, width=960)

!pwd

!git clone https://github.com/JonathonLuiten/TrackEval.git

# Commented out IPython magic to ensure Python compatibility.
# %cd TrackEval/

!unzip data.zip

!python scripts/run_mot_challenge.py --BENCHMARK MOT20 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL MPNTrack --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1

!python scripts/run_mot_challenge.py --BENCHMARK MOT20 --TRACKERS_TO_EVAL LPC_MOT --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1