Pflow

Install

pip install -e '.[dev]'

Setup

cp .env.default .env

Run

pflows doc/examples/birds-grouped-categories.json

In this example, we defined a workflow to download the dataset from Roboflow as we can see in the image below:

Birds Dataset

The workflow is defined in a JSON file, where we define the steps to be executed:

[
  {
    "task": "roboflow_tools.download_dataset",
    "target_dir": "{{BASE_FOLDER}}/datasets/downloaded/cub200_parts-50",
    "url": "https://universe.roboflow.com/explainableai-lavbv/cub200_parts/dataset/50"
  },
  {
    "task": "yolo_v8.load_dataset",
    "folder_path": "{{BASE_FOLDER}}/datasets/downloaded/cub200_parts-50"
  },
  {
    "task": "base.count_images"
  },
  {
    "task": "base.count_categories"
  },
  {
    "task": "categories.group_categories",
    "groups": {
      "upper": [["eye", "bill", "head", "nape", "throat"]],
      "lower": [["belly", "feet", "tail"]],
      "middle": [["Wing", "breast", "back"]],
      "Wing": [["Wing"]],
      "back": [["back"]],
      "belly": [["belly"]],
      "bill": [["bill"]],
      "eye": [["eye"]],
      "feet": [["feet"]],
      "head": [["head"]],
      "nape": [["nape"]],
      "tail": [["tail"]],
      "throat": [["throat"]]
    },
    "condition": "any"
  },
  {
    "task": "categories.keep",
    "categories": ["upper", "lower", "middle"]
  },
  {
    "task": "base.count_images"
  },
  {
    "task": "base.count_categories"
  },
  {
    "task": "base.show_categories"
  },
  {
    "task": "yolo_v8.write",
    "target_dir": "{{BASE_FOLDER}}/datasets/processed/birds-grouped-categories-cub200_parts-50"
  }
]

The workflow is composed of the following steps:

Download the dataset from Roboflow
Load the dataset
Count the number of images
Count the number of categories
Group the categories, to create new categories based on the existing ones (upper, lower, middle)
Keep only the categories that are in the groups "upper", "lower" and "middle"
Count the number of images
Count the number of categories
Show the categories
Write the dataset to disk

As we can see, we can use the {{BASE_FOLDER}} variable to refer to the base folder of the project. These variables are defined in the .env file, which is used to configure the project.

We can see the input images and output images below:

Example Bird 1:

Bird 1 before

Example Bird 2:

Bird 2 before

Release files for pflows 0.1.23

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pflows 0.1.23
File	Size	Uploaded
pflows-0.1.23.tar.gz	5.4 MB	Mar 13, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for pflows 0.1.23
File	Interpreter	ABI	Platform	Reset
pflows-0.1.23-py3-none-any.whl 5.4 MB Mar 13, 2025	Python 3	none	any	Details

Total release size: 10.8 MB

Release files / pflows-0.1.23.tar.gz

Download URL	pflows-0.1.23.tar.gz
Size	5.4 MB
Tags	Source
SHA-256 checksum How to use checksums	`4e297dea0588a97d3ddbd00109404bcf0e1ac3b61598b9497e9bbf6416e9b6e0`
BLAKE2b-256 checksum How to use checksums	`59d10e0b0b5e40fec8e5f785a3b33abe4f13de97a3eeb676c7803a123e47a94d`
Upload date	Mar 13, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.0 CPython/3.11.4`

Release files / pflows-0.1.23-py3-none-any.whl

Download URL	pflows-0.1.23-py3-none-any.whl
Size	5.4 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`3ec43b1749d04dde024933ea38a17911eedb096631c23a69762a11e98a896d55`
BLAKE2b-256 checksum How to use checksums	`3cb2cc260cf1535219b71ab31ad79a7465971e4f479b4ecd05064869911cb7a3`
Upload date	Mar 13, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.0 CPython/3.11.4`