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algorithm_rgb.py
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algorithm_rgb.py
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"""Greenness Transformer
"""
# Importing modules.
import numpy as np
# Definitions
VERSION = '1.0'
# Information on the creator of this algorithm
ALGORITHM_AUTHOR = 'Chris Schnaufer, Clairessa Brown, David Lebauer'
ALGORITHM_CONTRIBUTORS = ["Jacob van der Leeuw"]
ALGORITHM_NAME = 'Greenness Transformer'
ALGORITHM_DESCRIPTION = 'This algorithm performs a variety of calculations using RGB pixels from images in order' \
'to assess plant and crop health and growth'
# Citation information for publication
CITATION_AUTHOR = 'Clairessa Brown'
CITATION_TITLE = 'Woebbecke, D.M. et al'
CITATION_YEAR = '2020'
# The name of one or more variables returned by the algorithm, separated by commas
VARIABLE_NAMES = 'excess greenness index, green leaf index, cive, normalized difference index, excess red, ' \
'exgr, combined indices 1, combined indices 2, vegetative index, normalized green-red difference,' \
' percent green'
# Variable units matching the order of VARIABLE_NAMES, also comma-separated.
VARIABLE_UNITS = '[-510:510], [-1:1], [-255:255], [-127:129], [-255:255], [-255:332], ' \
'[-1000:1000], [-1000:1000], [-255:255], [-255:255], [0:100]'
# Variable labels matching the order of VARIABLE_NAMES, also comma-separated.
VARIABLE_LABELS = 'excess_greenness_index, green_leaf_index, cive, normalized_difference_index(pxarray), ' \
'excess_red, exgr, combined_indices_1, combined_indices_2, vegetative_index, ngrdi, percent_green'
# Entry point for plot-level RBG algorithm
def excess_greenness_index(pxarray: np.ndarray) -> float:
"""
Minimizes variation between different illuminations and enhances detection
of plants
"""
red, green, blue = get_red_green_blue_averages(pxarray)
return round(2 * green - (red + blue), 2)
def green_leaf_index(pxarray: np.ndarray) -> float:
"""
Calculates the green leaf index of an image
"""
red, green, blue = get_red_green_blue_averages(pxarray)
return round((2 * green - red - blue) / (2 * green + red + blue), 2)
def cive(pxarray: np.ndarray) -> float:
"""
Can measure crop growth status
"""
red, green, blue = get_red_green_blue_averages(pxarray)
return round(0.441 * red - 0.811 * green + 0.385 * blue + 18.78745, 2)
def normalized_difference_index(pxarray: np.ndarray) -> float:
"""
Calculates the normalized difference index of an image
"""
red, green, _ = get_red_green_blue_averages(pxarray)
return round(128 * ((green - red) / (green + red)) + 1, 2)
def excess_red(pxarray: np.ndarray) -> float:
"""
Finds potential illumination issues that make it difficult to
tease apart redness from crops/leaves from soil or camera
artifacts
"""
red, green, _ = get_red_green_blue_averages(pxarray)
return round(1.3 * red - green, 2)
def exgr(pxarray: np.ndarray) -> float:
"""
Minimizes the variation between different illuminations
"""
return round(excess_greenness_index(pxarray) - excess_red(pxarray), 2)
def combined_indices_1(pxarray: np.ndarray) -> float:
"""
Combined indices calculation 1
"""
return round(excess_greenness_index(pxarray) + cive(pxarray), 2)
def combined_indices_2(pxarray: np.ndarray) -> float:
"""
Combined indices calculation 2
"""
return round(0.36 * excess_greenness_index(pxarray) + 0.47 * cive(pxarray) + 0.17 * vegetative_index(pxarray), 2)
def vegetative_index(pxarray: np.ndarray) -> float:
"""
Minimize illumination differences between images
"""
red, green, blue = get_red_green_blue_averages(pxarray)
return round(green / ((red ** 0.667) * (blue ** .333)), 2)
def ngrdi(pxarray: np.ndarray) -> float:
"""
Can measure crop growth status
"""
red, green, _ = get_red_green_blue_averages(pxarray)
return round((green - red) / (green + red), 2)
def percent_green(pxarray: np.ndarray) -> float:
"""
Returns the percentage of an image that is green, which can be used
to identify plant cover
"""
if pxarray.shape[2] < 4:
# Get redness, greenness, and blueness values
red = np.sum(pxarray[:, :, 0])
green = np.sum(pxarray[:, :, 1])
blue = np.sum(pxarray[:, :, 2])
else:
# Handle Alpha channel masking
# Get redness, greenness, and blueness values
alpha_mask = np.where(pxarray[:, :, 3] == 0, 1, 0) # Convert alpha channel to numpy.ma acceptable format
channel_masked = np.ma.array(pxarray[:, :, 0], mask=alpha_mask)
red = np.ma.sum(channel_masked)
channel_masked = np.ma.array(pxarray[:, :, 1], mask=alpha_mask)
green = np.ma.sum(channel_masked)
channel_masked = np.ma.array(pxarray[:, :, 2], mask=alpha_mask)
blue = np.ma.sum(channel_masked)
del channel_masked
return round(green / (red + green + blue), 2)
def get_red_green_blue_averages(pxarray: np.ndarray) -> tuple:
"""
Returns the average red, green, and blue values in a pxarray object
"""
if pxarray.shape[2] < 4:
# Get redness, greenness, and blueness values
red = np.average(pxarray[:, :, 0])
green = np.average(pxarray[:, :, 1])
blue = np.average(pxarray[:, :, 2])
else:
# Handle Alpha channel masking
# Get redness, greenness, and blueness values
alpha_mask = np.where(pxarray[:, :, 3] == 0, 1, 0) # Convert alpha channel to numpy.ma acceptable format
channel_masked = np.ma.array(pxarray[:, :, 0], mask=alpha_mask)
red = np.ma.average(channel_masked)
channel_masked = np.ma.array(pxarray[:, :, 1], mask=alpha_mask)
green = np.ma.average(channel_masked)
channel_masked = np.ma.array(pxarray[:, :, 2], mask=alpha_mask)
blue = np.ma.average(channel_masked)
del channel_masked
return red, green, blue
def calculate(pxarray: np.ndarray) -> list:
"""Calculates one or more values from plot-level RGB data
Arguments:
pxarray: Array of RGB data for a single plot
Return:
Returns a list of the calculated values from the
"""
return_list = [excess_greenness_index(pxarray), green_leaf_index(pxarray), cive(pxarray),
normalized_difference_index(pxarray), excess_red(pxarray), exgr(pxarray),
combined_indices_1(pxarray), combined_indices_2(pxarray), vegetative_index(pxarray),
ngrdi(pxarray), percent_green(pxarray)]
return return_list