{"id":24306556,"url":"https://github.com/tmsincomb/imagetocsv","last_synced_at":"2025-09-26T11:31:07.959Z","repository":{"id":62971838,"uuid":"563440461","full_name":"tmsincomb/ImageToCSV","owner":"tmsincomb","description":"Converts an image to a CSV. 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align=\"center\"\u003e\n  \u003cbr\u003e\n\n\u003c/h1\u003e\n\n\u003cdiv class=\"flex-container\" align=\"center\"\u003e\n    \u003ca href=\"https://github.com/jwillis0720/template-repo/commits/master\"\u003e\n    \u003ca href=\"https://img.shields.io/badge/Python-3.%7C3.8%7C3.9%7C3.10-blue\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Python-3.7%7C3.8%7C3.9%7C3.10%7C3.11-blue\"\n        alt=\"Python Version\"\u003e\n    \u003ca href=\"https://github.com/psf/black\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"\n        alt=\"Format Version\"\u003e\n    \u003ca href=\"https://codecov.io/gh/jwillis0720/template-repo\"\u003e\n    \u003ca href=\"https://github.com/pre-commit/pre-commit\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026logoColor=white\"\n        alt=\"pre commit\"\u003e\n    \u003c/br\u003e\n    \u003ca href=\"https://tesseract-ocr.github.io/tessdoc/Installation.html\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Tesseract-5.2.0-teal.svg\" alt=\"Tesseract\"\u003e\n    \u003ca href=\"https://poppler.freedesktop.org/\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Poppler-22.11.0-teal.svg\" alt=\"Tesseract\"\u003e\n    \u003c/br\u003e\n    \u003cimg src=\"https://img.shields.io/badge/mac%20os-000000?style=for-the-badge\u0026logo=macos\u0026logoColor=F0F0F0\"\n        alt=\"MacOS\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Linux-FCC624?style=for-the-badge\u0026logo=linux\u0026logoColor=black\"\n        alt=\"Linux\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Windows-0078D6?style=for-the-badge\u0026logo=windows\u0026logoColor=white\"\n        alt=\"Windows\"\u003e\n\u003c/div\u003e\n\n\u003cp align=\"center\" style=\"color:green\"\u003e\n  \u003ca href=\"#about\"\u003eAbout\u003c/a\u003e •\n  \u003ca href=\"#installation\"\u003eInstall\u003c/a\u003e •\n  \u003ca href=\"#terminal\"\u003eTerminal\u003c/a\u003e •\n  \u003ca href=\"#python\"\u003ePython\u003c/a\u003e •\n  \u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\n\u003c/p\u003e\n\n# About\n\nConverts an Image to a CSV. This exists because Chorus 3.0 is bat-shit and only shows images for vital metadata.\n\u003cimg src=\"https://raw.githubusercontent.com/tmsincomb/ImageToCSV/v0.2.0/docs/images/convert.png\" width=\"1025\"/\u003e\n\n# Prerequisites\n\n## Anaconda\n\nI am partial towards [miniforge](https://github.com/conda-forge/miniforge), but you can replace these commands with your favorite conda distribution.\n\n### Windows Miniforge Installation\n\nSpecial walkthrough for Windows users since Windows is awful.\n\n```bash\ncurl -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Windows-x86_64.exe\nstart /wait \"\" Miniforge3-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /D=%UserProfile%\\Miniforge3\n# Follow the prompts.\n```\n\n# Bugs\n\n## Linux using Python 3.7\n\nConda does not install all needed packages for Python 3.7 so you need to install the following to get ImageToCSV to work properly.\n\n```bash\nsudo apt install --yes build-essential libpoppler-cpp-dev pkg-config tesseract-ocr libtesseract-dev\n```\n\n# Installation MacOS\n\n```bash\nconda create -n imagetocsv pip python=3.11.6\nconda activate imagetocsv\nconda install -y -c conda-forge clang_osx-64 clangxx_osx-64 poppler==22.11.0 tesseract==5.2.0\npip install imagetocsv\n```\n\n# Installation Linux\n\n```bash\nconda create -n imagetocsv pip python=3.11.6\nconda activate imagetocsv\nconda install -y -c conda-forge gcc_linux-64 gxx_linux-64 poppler==22.11.0 tesseract==5.2.0\npip install imagetocsv\n```\n\n# Installation Windows 10/11\n\n### Note that this is untested and may not work if you do not have a C++ compiler installed.\n\n```bash\nconda create -n imagetocsv pip python=3.11.6\nconda activate imagetocsv\nconda install -y -c conda-forge poppler==22.11.0 tesseract==5.2.0\npip install imagetocsv\n```\n\n# Development\n\n## Pip install cloned repo\n\n```bash\npip install -e \".[dev]\"\n```\n\n## Testing\n\n```bash\ncd ImageToCSV\npytest -svvv tests\n```\n\n## Preconfiguring Index/Column Names\n\nIf you have a lot of images to convert, you can preconfigure the index and column names to save time in the hardcoded_options.py file [here](https://github.com/tmsincomb/ImageToCSV/blob/main/imagetocsv/hardcoded_options.py). Each option will need a format of ``dict[str, tuple[str, list[str], list[str]]]`` aka slug -\u003e (name, indexes, columns).\n\n```bash\nimagetocsv -p myslug myimage.png\n```\n\n# Usage\n\n## Terminal Help Message\n\n[CSV_PATH] means that it is a stdout (printed on the terminal) if output is not specified.\n\n```bash\nUsage: imagetocsv [OPTIONS] IMAGE_PATH [CSV_PATH]\n\n  Console script for imagetocsv.\n\nOptions:\n  --version                 Show the version and exit.\n  -v, --verbose             Vebosity level, ex. -vvvvv for debug level logging\n  -n, --index_name TEXT     Index Name for the CSV file\n  -i, --index TEXT          Index for the CSV file\n  -c, --column_header TEXT  Columns for the CSV file\n  -p, --preconfigured-options TEXT\n\n  --help                    Show this message and exit.\n```\n\n## Terminal\n\n```bash\n# Convert an image to a CSV\nimagetocsv myimage.png output.csv\n```\n\n```bash\n# Convert an image to a stdout (printed on the terminal) TSV\nimagetocsv myimage.png\n```\n\n|      |      0 | 1      | 2       | 3         | 4      | 5         | 6      |\n| ---: | -----: | :----- | :------ | :-------- | :----- | :-------- | :----- |\n|    0 | 598150 |        | 100.00% | 123428.50 | 57.53% | 130689.00 | 50.55% |\n|    1 | 237987 | 39.79% | 39.79%  | 134356.00 | 14.45% | 102556.00 | 30.89% |\n|    2 | 228000 | 95.80% | 38.12%  | 433804.00 | 13.96% | 100917.00 | 29.64% |\n|    3 | 222453 | 97.57% | 37.19%  | 133307.00 | 13.63% | 100091.00 | 29.09% |\n|    4 | 212474 | 95.51% | 35.52%  | 134238.00 | 12.97% | 9700.00   | 29.27% |\n|    5 |  55885 | 26.30% | 9.34%   | 131386.00 | 13.34% | 93086.00  | 27.69% |\n|    6 |  34745 | 56.80% | 5.31%   | 127549.00 | 10.25% | 88501.00  | 24.60% |\n|    7 |  22496 | 40.25% | 3.76%   | 14152450  | 15.79% | 102606.00 | 30.31% |\n|    8 |  17409 | 77.39% | 2.91%   | 144624.00 | 14.88% | 107966.00 | 28.93% |\n|    9 |   2663 | 15.30% | 0.45%   | 163750.00 | 11.93% | 130908.00 | 26.18% |\n|   10 |      5 | 0.03%  | 0.00%   | 166073.00 | 5.07%  | 160211.00 | 6.57%  |\n|   11 |  14736 | 84.65% | 2.46%   | 14126450  | 14.20% | 103995.00 | 28.13% |\n|   12 |      5 | 0.03%  | 0.00%   | 162803.00 | 6.04%  | 156540.00 | 9.02%  |\n|   13 |      0 | 0.00%  | 0.00%   |           |        |           |        |\n|   14 |   8888 | 39.51% | 1.49%   | 431473.00 | 15.37% | 90965.50  | 28.65% |\n|   15 |   1806 | 8.03%  | 0.30%   | 153347.00 | 12.19% | 121119.50 | 24.60% |\n|   16 |   4896 | 21.76% | 0.82%   | 141244.00 | 16.41% | 101527.00 | 30.63% |\n|   17 |   6906 | 30.70% | 1.15%   | 147753.00 | 12.13% | 113108.50 | 25.94% |\n\n```bash\n# Used for Chorus 3.0 to auto header and index names.\nimagetocsv -p chorus myimage.png\n```\n\n| Population      | Events  | % Parent | % Total | FSC-A Median | FSC-A %rCV | SSC-A Median | SSC-A %rCV |\n| :-------------- | :------ | :------- | :------ | :----------- | :--------- | :----------- | :--------- |\n| All Events      | 598,150 |          | 100.00% | 123428.50    | 57.53%     | 130689.00    | 50.55%     |\n| Lymphocytes     | 237,987 | 39.79%   | 39.79%  | 134356.00    | 14.45%     | 102556.00    | 30.89%     |\n| Single cells... | 228,000 | 95.80%   | 38.12%  | 433804.00    | 13.96%     | 100917.00    | 29.64%     |\n| Single cells... | 222,453 | 97.57%   | 37.19%  | 133307.00    | 13.63%     | 100091.00    | 29.09%     |\n| Live/Dead       | 212,474 | 95.51%   | 35.52%  | 134238.00    | 12.97%     | 9700.00      | 29.27%     |\n| CD19+ Dump-     | 55,885  | 26.30%   | 9.34%   | 131386.00    | 13.34%     | 93086.00     | 27.69%     |\n| Naive gD+       | 34,745  | 56.80%   | 5.31%   | 127549.00    | 10.25%     | 88501.00     | 24.60%     |\n| Memory IgD-     | 22,496  | 40.25%   | 3.76%   | 14152450     | 15.79%     | 102606.00    | 30.31%     |\n| IgD- KO-        | 17,409  | 77.39%   | 2.91%   | 144624.00    | 14.88%     | 107966.00    | 28.93%     |\n| P15-1           | 2,663   | 15.30%   | 0.45%   | 163750.00    | 11.93%     | 130908.00    | 26.18%     |\n| P15-2           | 5       | 0.03%    | 0.00%   | 166073.00    | 5.07%      | 160211.00    | 6.57%      |\n| P15-3           | 14,736  | 84.65%   | 2.46%   | 14126450     | 14.20%     | 103995.00    | 28.13%     |\n| P15-4           | 5       | 0.03%    | 0.00%   | 162803.00    | 6.04%      | 156540.00    | 9.02%      |\n| MARIO WT++      | 0       | 0.00%    | 0.00%   |              |            |              |            |\n| P14-1           | 8,888   | 39.51%   | 1.49%   | 431473.00    | 15.37%     | 90965.50     | 28.65%     |\n| P14-2           | 1,806   | 8.03%    | 0.30%   | 153347.00    | 12.19%     | 121119.50    | 24.60%     |\n| P14-3           | 4896    | 21.76%   | 0.82%   | 141244.00    | 16.41%     | 101527.00    | 30.63%     |\n| P14-4           | 6,906   | 30.70%   | 1.15%   | 147753.00    | 12.13%     | 113108.50    | 25.94%     |\n\n## Terminal Advanced\n\nAdding Index Name, Index, and Column Header. They need to match the deminsions of the matrix! This may be more trouble than its worth so, this is just to show you can do it. No pressure.\n\n```bash\nimagetocsv myimage.jpg \\\n  --index_name \"Population\" \\\n  --index \"All Events,Lymphocytes,Single cells...,Single cells...,Live/Dead,CD19+ Dump-,Naive gD+,Memory IgD-,IgD- KO-,P15-1,P15-2,P15-3,P15-4,MARIO WT++,P14-1,P14-2,P14-3,P14-4\" \\\n  --column_header \"Events,%Parent,%Total,FSC-A Median,FSC-A %rCV,SSC-A Median,SSC-A %rCV\"\n```\n\n| Population      | Events  | % Parent | % Total | FSC-A Median | FSC-A %rCV | SSC-A Median | SSC-A %rCV |\n| :-------------- | :------ | :------- | :------ | :----------- | :--------- | :----------- | :--------- |\n| All Events      | 598,150 |          | 100.00% | 123428.50    | 57.53%     | 130689.00    | 50.55%     |\n| Lymphocytes     | 237,987 | 39.79%   | 39.79%  | 134356.00    | 14.45%     | 102556.00    | 30.89%     |\n| Single cells... | 228,000 | 95.80%   | 38.12%  | 433804.00    | 13.96%     | 100917.00    | 29.64%     |\n| Single cells... | 222,453 | 97.57%   | 37.19%  | 133307.00    | 13.63%     | 100091.00    | 29.09%     |\n| Live/Dead       | 212,474 | 95.51%   | 35.52%  | 134238.00    | 12.97%     | 9700.00      | 29.27%     |\n| CD19+ Dump-     | 55,885  | 26.30%   | 9.34%   | 131386.00    | 13.34%     | 93086.00     | 27.69%     |\n| Naive gD+       | 34,745  | 56.80%   | 5.31%   | 127549.00    | 10.25%     | 88501.00     | 24.60%     |\n| Memory IgD-     | 22,496  | 40.25%   | 3.76%   | 14152450     | 15.79%     | 102606.00    | 30.31%     |\n| IgD- KO-        | 17,409  | 77.39%   | 2.91%   | 144624.00    | 14.88%     | 107966.00    | 28.93%     |\n| P15-1           | 2,663   | 15.30%   | 0.45%   | 163750.00    | 11.93%     | 130908.00    | 26.18%     |\n| P15-2           | 5       | 0.03%    | 0.00%   | 166073.00    | 5.07%      | 160211.00    | 6.57%      |\n| P15-3           | 14,736  | 84.65%   | 2.46%   | 14126450     | 14.20%     | 103995.00    | 28.13%     |\n| P15-4           | 5       | 0.03%    | 0.00%   | 162803.00    | 6.04%      | 156540.00    | 9.02%      |\n| MARIO WT++      | 0       | 0.00%    | 0.00%   |              |            |              |            |\n| P14-1           | 8,888   | 39.51%   | 1.49%   | 431473.00    | 15.37%     | 90965.50     | 28.65%     |\n| P14-2           | 1,806   | 8.03%    | 0.30%   | 153347.00    | 12.19%     | 121119.50    | 24.60%     |\n| P14-3           | 4896    | 21.76%   | 0.82%   | 141244.00    | 16.41%     | 101527.00    | 30.63%     |\n| P14-4           | 6,906   | 30.70%   | 1.15%   | 147753.00    | 12.13%     | 113108.50    | 25.94%     |\n\n## Python\n\n```python\nfrom imagetocsv import imagetocsv\nfrom imagetocsv.examples import no_grid_example\n\n\ndf = imagetocsv(no_grid_example)\nprint(df.to_markdown())\n\n```\n\n|      |      0 | 1      | 2       | 3         | 4      | 5         | 6      |\n| ---: | -----: | :----- | :------ | :-------- | :----- | :-------- | :----- |\n|    0 | 598150 |        | 100.00% | 123428.50 | 57.53% | 130689.00 | 50.55% |\n|    1 | 237987 | 39.79% | 39.79%  | 134356.00 | 14.45% | 102556.00 | 30.89% |\n|    2 | 228000 | 95.80% | 38.12%  | 433804.00 | 13.96% | 100917.00 | 29.64% |\n|    3 | 222453 | 97.57% | 37.19%  | 133307.00 | 13.63% | 100091.00 | 29.09% |\n|    4 | 212474 | 95.51% | 35.52%  | 134238.00 | 12.97% | 9700.00   | 29.27% |\n|    5 |  55885 | 26.30% | 9.34%   | 131386.00 | 13.34% | 93086.00  | 27.69% |\n|    6 |  34745 | 56.80% | 5.31%   | 127549.00 | 10.25% | 88501.00  | 24.60% |\n|    7 |  22496 | 40.25% | 3.76%   | 14152450  | 15.79% | 102606.00 | 30.31% |\n|    8 |  17409 | 77.39% | 2.91%   | 144624.00 | 14.88% | 107966.00 | 28.93% |\n|    9 |   2663 | 15.30% | 0.45%   | 163750.00 | 11.93% | 130908.00 | 26.18% |\n|   10 |      5 | 0.03%  | 0.00%   | 166073.00 | 5.07%  | 160211.00 | 6.57%  |\n|   11 |  14736 | 84.65% | 2.46%   | 14126450  | 14.20% | 103995.00 | 28.13% |\n|   12 |      5 | 0.03%  | 0.00%   | 162803.00 | 6.04%  | 156540.00 | 9.02%  |\n|   13 |      0 | 0.00%  | 0.00%   |           |        |           |        |\n|   14 |   8888 | 39.51% | 1.49%   | 431473.00 | 15.37% | 90965.50  | 28.65% |\n|   15 |   1806 | 8.03%  | 0.30%   | 153347.00 | 12.19% | 121119.50 | 24.60% |\n|   16 |   4896 | 21.76% | 0.82%   | 141244.00 | 16.41% | 101527.00 | 30.63% |\n|   17 |   6906 | 30.70% | 1.15%   | 147753.00 | 12.13% | 113108.50 | 25.94% |\n\n## Python Advanced\n\n```python\nfrom imagetocsv import imagetocsv\nfrom imagetocsv.examples import no_grid_example\n\n\ndf = imagetocsv(\n        no_grid_example,\n        index_name=\"Population\",\n        index=[\n                \"All Events\",\n                \"Lymphocytes\",\n                \"Single cells...\",\n                \"Single cells...\",\n                \"Live/Dead\",\n                \"CD19+ Dump-\",\n                \"Naive gD+\",\n                \"Memory IgD-\",\n                \"IgD- KO-\",\n                \"P15-1\",\n                \"P15-2\",\n                \"P15-3\",\n                \"P15-4\",\n                \"MARIO WT++\",\n                \"P14-1\",\n                \"P14-2\",\n                \"P14-3\",\n                \"P14-4\",\n        ],\n        column_header=[\"Events\", \"% Parent\", \"% Total\", \"FSC-A Median\", \"FSC-A %rCV\", \"SSC-A Median\", \"SSC-A %rCV\"],\n)\nprint(df.to_markdown())\n```\n\n| Population      | Events  | % Parent | % Total | FSC-A Median | FSC-A %rCV | SSC-A Median | SSC-A %rCV |\n| :-------------- | :------ | :------- | :------ | :----------- | :--------- | :----------- | :--------- |\n| All Events      | 598,150 |          | 100.00% | 123428.50    | 57.53%     | 130689.00    | 50.55%     |\n| Lymphocytes     | 237,987 | 39.79%   | 39.79%  | 134356.00    | 14.45%     | 102556.00    | 30.89%     |\n| Single cells... | 228,000 | 95.80%   | 38.12%  | 433804.00    | 13.96%     | 100917.00    | 29.64%     |\n| Single cells... | 222,453 | 97.57%   | 37.19%  | 133307.00    | 13.63%     | 100091.00    | 29.09%     |\n| Live/Dead       | 212,474 | 95.51%   | 35.52%  | 134238.00    | 12.97%     | 9700.00      | 29.27%     |\n| CD19+ Dump-     | 55,885  | 26.30%   | 9.34%   | 131386.00    | 13.34%     | 93086.00     | 27.69%     |\n| Naive gD+       | 34,745  | 56.80%   | 5.31%   | 127549.00    | 10.25%     | 88501.00     | 24.60%     |\n| Memory IgD-     | 22,496  | 40.25%   | 3.76%   | 14152450     | 15.79%     | 102606.00    | 30.31%     |\n| IgD- KO-        | 17,409  | 77.39%   | 2.91%   | 144624.00    | 14.88%     | 107966.00    | 28.93%     |\n| P15-1           | 2,663   | 15.30%   | 0.45%   | 163750.00    | 11.93%     | 130908.00    | 26.18%     |\n| P15-2           | 5       | 0.03%    | 0.00%   | 166073.00    | 5.07%      | 160211.00    | 6.57%      |\n| P15-3           | 14,736  | 84.65%   | 2.46%   | 14126450     | 14.20%     | 103995.00    | 28.13%     |\n| P15-4           | 5       | 0.03%    | 0.00%   | 162803.00    | 6.04%      | 156540.00    | 9.02%      |\n| MARIO WT++      | 0       | 0.00%    | 0.00%   |              |            |              |            |\n| P14-1           | 8,888   | 39.51%   | 1.49%   | 431473.00    | 15.37%     | 90965.50     | 28.65%     |\n| P14-2           | 1,806   | 8.03%    | 0.30%   | 153347.00    | 12.19%     | 121119.50    | 24.60%     |\n| P14-3           | 4896    | 21.76%   | 0.82%   | 141244.00    | 16.41%     | 101527.00    | 30.63%     |\n| P14-4           | 6,906   | 30.70%   | 1.15%   | 147753.00    | 12.13%     | 113108.50    | 25.94%     |\n\n## License\n\n[![License](https://img.shields.io/github/license/tmsincomb/ImageToCSV)](https://opensource.org/licenses/MIT)\n\n- Copyright © Troy M. Sincomb\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsincomb%2Fimagetocsv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftmsincomb%2Fimagetocsv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsincomb%2Fimagetocsv/lists"}