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

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.

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.