Metadata-Version: 2.4
Name: pcr_framework
Version: 2026.10.20
Summary: A modular computer vision framework developed by PCR-UPM for image processing tasks.
Home-page: https://github.com/pcr-upm/pcr_framework
Author: Roberto Valle
Author-email: roberto.valle@upm.es
Keywords: computer vision
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: opencv-python
Requires-Dist: opencv-contrib-python
Requires-Dist: rasterio
Requires-Dist: pillow
Requires-Dist: pascal-voc-writer
Provides-Extra: gdal
Requires-Dist: gdal; extra == "gdal"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: provides-extra
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#### Description

Modular computer vision library developed by the [PCR-UPM](https://pcr-upm.github.io/) group for high-performance image processing tasks. 
Designed with research and engineering in mind, the framework provides reusable components for image preprocessing, and model evaluation. 
It is built on top of widely adopted libraries such as OpenCV, NumPy, and Rasterio.

#### System Requirements

This package requires GDAL. It is recommended to install it using Conda:

```
conda install -c conda-forge gdal
```

#### Requisites
- pcr_framework https://github.com/pcr-upm/pcr_framework
- tqdm
- scikit-learn (training)
- natsort (evaluation)

#### Installation
This repository must be located before the following directory:
```
images_experiments
    └── pcr_framework
```
#### Usage
```
usage: images_experiments.py [-h] --mode {train,test} --part
                             {detection,segmentation,recognition,all}
                             --anns-file ANNS_FILE [--show-viewer]
                             [--save-file] [--save-image] --detection
                             {retinanet17_detection,scrdet19_detection} 
                             --segmentation
                             {unet15_segmentation,ocr20_segmentation}
                             --recognition
                             {resnet15_recognition}
```

* Use the -h option to show the help message and exit.

* Use the --mode option to choose the mode (train, test).

* Use the --part option to choose the tasks (detection, segmentation, recognition, all).

* Use the --anns-file option to define the annotations input file.

* Use the --show-viewer option to show results visually.

* Use the --save-file option to save experiments in a text file.

* Use the --save-image option to save the processed images.

* Use the --detection option to select the detection module.

* Use the --segmentation option to select the segmentation module.

* Use the --recognition option to select the recognition module.
```
> python test/images_experiments.py -m train -p detection -a database/detection/15cm/cowc/cowc_ann_train_train.txt -d scrdet19_detection --database COWC --gpu 0 --gpu 1
```

```
> python test/images_experiments.py -m test -p detection -a database/detection/15cm/cowc/cowc_ann_train_test.txt -d scrdet19_detection --database COWC --gpu 0
```

```
> python test/images_experiments.py -m test -p all -a database/detection/15cm/cowc/cowc_ann_train_test.txt -d scrdet19_detection -s unet15_segmentation -r resnet15_recognition --database COWC --gpu 0
```
