Mothra analyzes images of lepidopterans — mainly butterflies and moths — and measures their wing lengths. Using binarization techniques and calculating the resolution of ruler ticks, we read in images of lepidopterans and output the millimeter lengths of their wings.

Usage

$ python pipeline.py -p -i [input directory or image path] -o [output directory] -csv [csv output file path]

The pipeline script combines four modules to analyze an image: ruler detection, binarization, tracing, and final measurement. These modules are located in /mothra. Python module requirements are listed in requirements.txt.

Run the pipeline.py file with the arguments to read input images, and output result images and a .csv file with the measurements.

The results are cached in cachedir so that if the same methods are re-run with the same inputs, the computation will simply be retrieved from memory instead of being recomputed. Delete cachedir to remove the cache and to recompute all results. If the source files for any part of the pipeline are tweaked, then results will be recomputed automatically.

Parameters

The following parameters can be used as input arguments for pipeline.py:

• -p, --plot : This flag is used to generate output images. When this option is ommited the pipeline do not plot images, thus improving runtime and saving space.
• -pp, --detailed_plot : Outputs detailed plots to help debugging. Included in the detailed plot are the various points of interest of the image marked in seperate plots, as well as the method we are using to measure the pixels per millimeter on the ruler. This option can also be ommitted to improve runtime and save space. An example of the -pp option follows:

• -i, --input : A single image input or a directory of images to be analyzed. (Default is input_images).
• -o, --output_folder : The output directory in which the result images will be outputted. (Default is outputs).
• -s, --stage : The stage which to run the pipeline until. Options are 'ruler_detection', 'binarization', and 'measurements'. Default is measurement (running to completion). Running the pipeline and stopping at an earlier stage can be useful for debugging.
• -ar, --auto_rotate : Enable automatic rotation of input images, according to the information in the EXIF tag.
• -csv, --path_csv : Path of .csv file for the measurement results. (Default is results.csv).
• -dpi : Optional argument to specify resolution of the output image. (Default is 300.)

Measurement results

The file with measurement results (default: results.csv), returned by this pipeline, contains the following measurements:

• image_id, the filename of the processed image.
• left_wing (mm), distance from the tip of the left wing to the left shoulder.
• right_wing (mm), distance from the tip of the right wing to the right shoulder.
• left_wing_center (mm), distance from the left wing to the center.
• right_wing_center (mm), distance from the right wing to the center.
• wing_span (mm), distance from the tip of the left wing to the tip of the right wing.
• wing_shoulder (mm), distance from the left shoulder to the right shoulder.
• position, position of the lepidopteran being right-side_up or upside_down.
• gender, gender of the lepidopteran being female or male, returned if position is right-side_up.
• prob_upside_down, the probability of the prediction returned by the network to be upside_down.
• prob_female, the probability of the prediction returned by the network to be female.
• prob_male, the probability of the prediction returned by the network to be male.

Example

Example data can be found at github.com/machine-shop/mothra-data. For this example, clone the repository alongside the folder mothra.

git clone https://github.com/machine-shop/mothra.git git clone https://github.com/machine-shop/mothra-data.git

Resulting files:

/mothra ... pipeline.py ... /mothra-data image1.jpg image2.jpg ...

Running the command

$ python pipeline.py -p -i ../mothra-data -o ../test_output -csv ../test_output/results.csv

in ./mothra will run the pipeline on the example data in the folder /mothra-data. The file locations should look like this:

/mothra ... pipeline.py ... /mothra-data image1.jpg image2.jpg ... /test_output image1.jpg image2.jpg ... results.csv

/test_output contains the resulting images, i.e. /test_output/image1.jpg contains the results of /mothra-data/image1.jpg, /test_output/image2.jpg has the results of /mothra-data/image2.jpg, and so on.

For a larger repository of test data (~15 GB), please check Wilson et al. (2020).

Miscellaneous

The testing suite can be run with PYTHONPATH=. pytest from /mothra.

Result Plotting

result_plotting.py is a script that generates a histogram of differences between actual measurements and predicted measurements. This is useful for debugging and evaluating accuracy. This can be used in isolation from the main pipeline, and simply takes in the predicted results.csv from the pipeline and either an .xlsx file or .csv file with actual measurements.

Usage

python result_plotting.py -a "h_comma_wing_lengths.xlsx" -n "full name" -l "Left" -r "Right"

Apart from outputting a plot of the differences, it can also output a comparison.csv with all differences between predicted and actual measurements, and/or a outliers.csv with only measurement differences from outliers. It can also copy outlier images to a outliers/ folder, for easier debugging by rerunning the pipeline on these outlier images.

Parameters

The following parameters can be used as input arguments for result_plotting.py:

• -a, --actual : File path of either Excel .xlsx file or .csv file containing the actual measurements.
• -n, --name : Name of column in actual file that contains the image names for each measurement.
• -l, --left : Name of column in actual file that contains the name of left wing measurements.
• -r, --right : Name of column in actual file that contains the name of right wing measurements.
• -p, --predicted : File path of .csv predictions outputted by this pipeline, results.csv by default.
• -c, --comparison : If specified, will output a comparison.csv file containing all measurements and the differences.
• -o, --outliers : If specified, will output a outliers.csv file containing only measurements that are deemed outliers.
• -sd, --sd : By default, the standard deviation to determine an outlier is ± 2 standard deviations away from the average measurement. If specified, you can use something else.
• -co, --copy_outliers : Specify a folder where the outlier images are from, and copy any outlier images to a outliers/ folder in the current directory.

Example

Example files:

/"Measured_images_Data_H.comma" BMNHE_1354218.JPG ... ... /mothra result_plotting.py h_comma_wing_lengths.xlsx results.csv

h_comma_wing_lengths.xlsx has these columns:

| full name | ... | Right | Left | | | ----------------- | --- | ----- | ------ | --- | | BMNHE_1354218.JPG | ... | 15.11 | 15.288 | | | ... | ... | ... | ... | | | | | | | |

Thus, we will specify -n "full name", -l "Left", -r "Right".

We know what results.csv looks like (since it is output by the pipeline).

We also want to output both the comparison.csv and outliers.csv table, so we will specify -c and -o, and we also want to copy outliers from the images folder Measured_images_Data_H.comma, so we will do -co "../Measured_images_Data_H.comma". We also want to define an outlier as a measurement ± 1.75 standard deviations from the actual measurement, so we well specify -sd 1.75. This way, the usage is:

python result_plotting.py -a "h_comma_wing_lengths.xlsx" -n "full name" -l "Left" -r "Right" -c -o -co "../Measured_images_Data_H.comma" -sd 1.75

File results:

/mothra result_plotting.py h_comma_wing_lengths.xlsx results.csv ... result_plot.png comparison.csv outliers.csv /outliers BMNHE_1354218.JPG (an outlier image) ...

Citing mothra

If you find mothra useful, please cite:

Wilson, R. J. et al. (2022). Applying computer vision to digitised natural history collections for climate change research: Temperature-size responses in British butterflies. Methods in Ecology and Evolution, 1–13.

Mothra's logo was designed by Rogério Maroja, and is available under the CC-BY license.