Difference between revisions of "Eyesis4Pi 393 Downloading footage"

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Line 22: Line 22:
 
  '''result tree:'''
 
  '''result tree:'''
 
  <font size='2' color='darkblue'>/data/footage/test1
 
  <font size='2' color='darkblue'>/data/footage/test1
  └── Crucial_CT2050MX300SSD1_163315B50899-part2 - each drive has a unique identifier, the script creates a corresponding directory
+
  └── Crucial_CT2050MX300SSD1_163315B50899-part2 <font color='black'>- each drive has a unique identifier, the script creates a corresponding directory</font>
 
      ├── file_2.img
 
      ├── file_2.img
 
      ├── file_3.img
 
      ├── file_3.img

Revision as of 16:20, 22 April 2017

Install

git clone https://git.elphel.com/Elphel/elphel-tools-x393.git

Scripts

  • ext_ssd_download.py - download footage from external SSD (connected to PC, camera off)
$./ext_ssd_download.py -h - print help
  • int_ssd_download.py - download footage from Eyesis4Pi-393 internal SSD (camera's eSATA cables allow to download data from internal SSDs)
$./int_ssd_download.py -h - print help

Download footage example

1. Connect SSD to PC

2. Identify the device (using dmesg, for example). Example: /dev/sdc. The second partition contains footage = /dev/sdc2

3. cd to the dir

$ cd elphel-tools-x393

4. Download footage in 10GB chunks (the script has 20MB overlapping to prevent corrupted images in the preceding chunk)

  • Download 30GB but skip the first 20GB (download range = 20-50GB on the SSD), to /data/footage/test1 on PC:
$./ext_ssd_download.py -p /dev/sdb2 -n 3 -s 2 /data/footage/test1

result tree:
/data/footage/test1
└── Crucial_CT2050MX300SSD1_163315B50899-part2 - each drive has a unique identifier, the script creates a corresponding directory
    ├── file_2.img
    ├── file_3.img
    └── file_4.img
  • Download whole partition, to /data/footage/test1 on PC:
$./ext_ssd_download.py -p /dev/sdb2 /data/footage/test2

5. Repeat steps 1.-4. for the next SSD (if PC fits only one).

Note: The 3rd subcamera SSD's footage is 6 times less than the 1st and the 2nd as it contains images from 2 sensors, others - from 12 each