Combine 2016-2017 and 2012-2014 datasets

[gsganden]

Using only the 2016-2017 data is very limiting because it is only from mid-summer. I wouldn't expect models trained on just this data to generalize to other times of year, and indeed we have seen substantial performance drops on images from other times of year. We have data from all seasons from 2012-2014. It is formatted differently but contains roughly the same information. Putting these datasets together and training on the result is the lowest-hanging fruit for providing more value with this project.

[DigitalPhilosopher]

Would it be possible to train models for each different part of the year? So a model for summer, a model for fall, etc.
With these models it would be possible to conclude a probability by either (1) taking the sum of the probabilities of each model or (2) detecting the season in which the photo was taken.

[gsganden]

It would be possible, but I doubt that it would be better. Do you think that it would be?

[DigitalPhilosopher]

I think it would be possible to get better results. Detecting objects (or in this case animals) is easier/more efficient in similar environments. I think, that this would factor in for this algorithm as well.

[gsganden]

Try it!

[datmar]

Hi all! Came across this project and it looks both interesting and in my wheelhouse. Interested in contributing if you are open to it and it is still an active project.

@DigitalPhilosopher are you working on this issue or is it up for grabs?

[gsganden]

Great! It is absolutely an active project in theory :-). Addressing this issue in particular would be a great help in moving it forward.

[datmar]

@gsganden Good to know ! I've made progress cleaning the 2012-14 labels in the CSV to align with 16-17, but I may need some extra information to restructure the image filenames to run through the build_dataset process. Particularly to recreate a process_raw.py for the 2012-14 images.

Is there any additional documentation regarding filename structure?

[gsganden]

I am not aware of any. @mfidino can you provide additional information?

[mfidino]

The filename structure should be something like:

./images_2016/DPT/D03-AMP1/ _CHIL - D03-AMP1-JU16_00037.JPG

This represents:

./{year sampled}/{transect sampled}/{site sampled}/{file name}

Transect is section of the city we are sampling (DPT is north west, RST is west, SCT is southwest, JNT is the heart of Chicago. What I think is likely the most important this is the site sampled part of the file name, which likely lines up with the 2012 - 2014 data?

You could also just parse the {file name} part to get the site info if you wanted.

[gsganden]

I'm seeing two issues with the 2012-2014 data:

1. The CSV says that the file paths start like this, with the season/year repeated in the third component: SP12/DPT/D02-HUP1-SP12/. However, the paths in the actual "FA" directories don't repeat the season in the third component, e.g. FA12/DPT/D02-HUP1/. @datmar is that what you are seeing?
2. labels.csv only covers 2012. @mfidino could we get labels for the 2013 and 2014 images?

[mfidino]

It's going to take me a bit to look into the labels for the 2013 and 2014 images for a couple reasons.

1. I'm taking a few weeks off of work as I just became a father.
2. Those data are on the zoo network, and when I am back to work I'll be working remotely due to covid-19.

Way back when we tagged those images we used to write the species tag into the photo metadata. I wrote a ruby script a long time ago to pull those tags (If I recall all the keywords are in the under subject in the exif data.

Ruby script here. Which may at least point out to where you can look to get the species tags.
https://github.com/mfidino/photo_pull/blob/master/parse_photo_exif.rb

[gsganden]

Thank you, and congrats on the new baby!

[datmar]

mfidino thank you, that's good to know!

@gsganden
Sorry haven't had much time to work on this, schedule's been a little upended due to covid-19 (I'm in Manhattan).
Will squeeze some time into getting this done in the next few days.

I'm seeing two issues with the 2012-2014 data:

1. The CSV says that the file paths start like this, with the season/year repeated in the third component: SP12/DPT/D02-HUP1-SP12/. However, the paths in the actual "FA" directories don't repeat the season in the third component, e.g. FA12/DPT/D02-HUP1/. @datmar is that what you are seeing?
2. labels.csv only covers 2012. @mfidino could we get labels for the 2013 and 2014 images?

1. I am seeing that as well, should be an easy fix?
2. Answered by mfidino. Will look into it.

[gsganden]

Sorry haven't had much time to work on this, schedule's been a little upended due to covid-19 (I'm in Manhattan).
Will squeeze some time into getting this done in the next few days.

No worries, this is all voluntary so anything you can do is a bonus. I have time to work on this project today and this ticket is by far the top priority, so I'll be working on it as well.

1. Yes. Here is what I came up with:

def get_path(filename_build):
    if filename_build.startswith("FA"):
        filename_build = Path(filename_build)
        return (
            Path(os.getenv("AUTOFOCUS_DATA_DIR"))
            / "lpz_2012-2014"
            / "raw"
            / Path(*filename_build.parts[:2])
            / "-".join(filename_build.parts[2].split("-")[:2])
            / Path(*filename_build.parts[3:])
        )
    else:
        return (
            Path(os.getenv("AUTOFOCUS_DATA_DIR"))
            / "lpz_2012-2014"
            / "raw"
            / filename_build
        )

df.loc[:, "path"] = df.loc[:, "filename_build"].progress_apply(get_path)

[gsganden]

2. I might be doing it wrong, but FWIW I'm not getting anywhere with the metadata.

>> from PIL import Image
>>
>>
>> def get_exif(filename):
>>     image = Image.open(filename)
>>     image.verify()
>>     return image._getexif()
>>
>>
>> df.loc[:, "path"].progress_apply(get_exif).notna().mean()
0.0

>> import piexif
>>
>> piexif.load(str(df1.loc[0, "path"]))
{'0th': {}, 'Exif': {}, 'GPS': {}, 'Interop': {}, '1st': {}, 'thumbnail': None}

>> identify -verbose "data/lpz_2012-2014/raw/FA14/JNT/J01-LMP1/J01-LMP1-FA14 (11).JPG" 
Image: data/lpz_2012-2014/raw/FA14/JNT/J01-LMP1/J01-LMP1-FA14 (11).JPG
  Format: JPEG (Joint Photographic Experts Group JFIF format)
  Class: DirectClass
  Geometry: 227x227+0+0
  Resolution: 72x72
  Print size: 3.15278x3.15278
  Units: Undefined
  Type: TrueColor
  Endianess: Undefined
  Colorspace: sRGB
  Depth: 8-bit
  Channel depth:
    red: 8-bit
    green: 8-bit
    blue: 8-bit
  Channel statistics:
    Red:
      min: 0 (0)
      max: 255 (1)
      mean: 125.861 (0.493571)
      standard deviation: 47.4512 (0.186083)
      kurtosis: 0.0259633
      skewness: 0.116636
    Green:
      min: 0 (0)
      max: 255 (1)
      mean: 129.525 (0.507942)
      standard deviation: 47.0568 (0.184536)
      kurtosis: 0.046401
      skewness: -0.0850175
    Blue:
      min: 0 (0)
      max: 224 (0.878431)
      mean: 67.0616 (0.262987)
      standard deviation: 43.2846 (0.169743)
      kurtosis: -0.0088973
      skewness: 0.538302
  Image statistics:
    Overall:
      min: 0 (0)
      max: 255 (1)
      mean: 107.482 (0.4215)
      standard deviation: 45.9693 (0.180272)
      kurtosis: 1.94601
      skewness: 0.14245
  Rendering intent: Perceptual
  Gamma: 0.454545
  Chromaticity:
    red primary: (0.64,0.33)
    green primary: (0.3,0.6)
    blue primary: (0.15,0.06)
    white point: (0.3127,0.329)
  Interlace: None
  Background color: white
  Border color: srgb(223,223,223)
  Matte color: grey74
  Transparent color: black
  Compose: Over
  Page geometry: 227x227+0+0
  Dispose: Undefined
  Iterations: 0
  Compression: JPEG
  Quality: 75
  Orientation: Undefined
  Properties:
    date:create: 2020-03-17T15:09:12+00:00
    date:modify: 2016-09-02T07:01:19+00:00
    jpeg:colorspace: 2
    jpeg:sampling-factor: 2x2,1x1,1x1
    signature: b74a2e95ab2c5990d8da57c24705af34221be10b7db1d93d0153241429216bc6
  Artifacts:
    filename: data/lpz_2012-2014/raw/FA14/JNT/J01-LMP1/J01-LMP1-FA14 (11).JPG
    verbose: true
  Tainted: False
  Filesize: 24.6KB
  Number pixels: 51.5K
  Pixels per second: 0B
  User time: 0.000u
  Elapsed time: 0:01.000