https://github.com/raikel/mcrops

A small library for processing images of agricultural fields.
https://github.com/raikel/mcrops

agriculture image-processing opencv python

Last synced: 10 days ago
JSON representation

A small library for processing images of agricultural fields.

• Host: GitHub
• URL: https://github.com/raikel/mcrops
• Owner: raikel
• License: mit
• Created: 2019-11-04T01:19:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-13T16:25:54.000Z (about 5 years ago)
• Last Synced: 2024-09-28T09:09:17.739Z (about 2 months ago)
• Topics: agriculture, image-processing, opencv, python
• Language: Python
• Homepage:
• Size: 608 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
Mcrops

=======================

mcrops is small library for processing images of agricultural fields. 

The homepage of mcrops with user documentation is located on:

https://mcrops.readthedocs.io

.. image:: ./docs/_static/sample_output.jpg

Getting started

==========

You can use `pip` to install mcrops::

    pip install mcrops

Alternatively, you can download the latest source code using git::

    git clone git://github.com/raikel/mcrops.git

and run the setup command from the source directory::

    python setup.py install

from the source directory.

Example usages

============

Here is a basic example usages of `mcrops`.

.. code-block:: python

    import math    

    import cv2 as cv    

    from mcrops import veget, rows, utils

    

    # Path to crop field image

    image_path = 'path/to/image'

    # Image resolution in pixels/meter (it has not to be an exact value)

    res = 20

    

    # Load a crop field image

    image = cv.imread(image_path)

    h, w = image.shape[:2]

    image_draw = image.copy()

    # Segment vegetation

    veg_mask = veget.segment_vegetation(image)

    # Detect the crop field ROI area

    roi_poly = veget.detect_roi(

        veg_mask, row_sep=row_sep, resolution=res

    )

    # Draw the contours of the ROI area

    cv.drawContours(

        image=image_draw,

        contours=[roi_poly],

        contourIdx=-1,

        color=(0, 0, 255),

        thickness=8,

        lineType=cv.LINE_AA

    )

    # Detect the mean crop rows direction

    direction = rows.detect_direction(

        veg_mask=veg_mask,

        window_shape=(20, 30),

        resolution=res

    )

    

    # Draw an arrow indicating the direction of the crop rows

    pt1 = (int(w/2), int(h/2))

    length = res * 20

    pt2 = (

        pt1[0] + min(max(0, int(math.cos(direction) * length)), w - 1),

        pt1[1] + min(max(0, int(math.sin(direction) * length)), h - 1)

    )

    cv.arrowedLine(image_draw, pt1, pt2, color=(0, 255, 0), thickness=8)

    # Normalize the crop field image and the vegetation mask (trim them to ROI

    # area and rotate them such that crop rows are horizontal)

    image_rows, roi_poly_norm, _ = veget.norm_image(

        image=image,

        roi_poly=roi_poly,

        rows_direction=direction

    )

    veg_mask, roi_poly_norm, _ = veget.norm_image(

        image=veg_mask,

        roi_poly=roi_poly,

        rows_direction=direction,

        is_mask=True

    )

    # Build a mask image from the ROI polyline

    roi_mask = utils.poly_mask(roi_poly_norm, veg_mask.shape[:2])

    # Create a row-oriented vegetation density map from the vegetation mask

    density_map = veget.mask_density(

        veg_mask,

        roi_mask,

        resolution=res,

        cell_size=(8, 8)

    )

    # Convert the row-oriented vegetation density map to a color image

    density_image = utils.array_image(density_map, colormap=cv.COLORMAP_JET)

    # Detect the crop rows (ridges and furrows)

    row_ridges, row_furrows = rows.detect_rows(

        veg_mask,

        roi_mask,

        resolution=res,

        row_sep=row_sep,

        fusion_thr=0.4

    )

    # Draw the crop rows lines

    image_rows = utils.draw_rows(image_rows, row_ridges)

    cv.imshow('Crop field image', image_draw)

    cv.imshow('Vegetation mask', veg_mask)

    cv.imshow('Vegetation density map', density_image)

    cv.imshow('Detected crop rows', image_rows)

    cv.waitKey(0)

    cv.destroyAllWindows()

Workflow to contribute

======================

To contribute to mcrops, first create an account on `github

`_. Once this is done, fork the `mcrops repository

`_ to have your own repository,

clone it using 'git clone' on the computers where you want to work. Make

your changes in your clone, push them to your github account, test them

on several computers, and when you are happy with them, send a pull

request to the main repository.