kopia lustrzana https://github.com/OpenDroneMap/WebODM
112 wiersze
3.8 KiB
Python
112 wiersze
3.8 KiB
Python
# Algos from https://github.com/dirceup/tiled-vegetation-indices/blob/master/app/lib/vegetation_index.rb
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# Functions can use all of the supported functions and operators from
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# https://numexpr.readthedocs.io/en/latest/user_guide.html#supported-operators
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import re
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from functools import lru_cache
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algos = {
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'VARI': {
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'expr': '(G - R) / (G + R - B)',
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'help': 'Visual Atmospheric Resistance Index shows the areas of vegetation.'
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},
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'NDVI': {
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'expr': '(N - R) / (N + R)',
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'help': 'Normalized Difference Vegetation Index shows the amount of green vegetation.'
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},
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'BAI': {
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'expr': '1.0 / (((0.1 - R) ** 2) + ((0.06 - N) ** 2))',
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'help': 'Burn Area Index hightlights burned land in the red to near-infrared spectrum.'
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},
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'GLI': {
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'expr': '((G * 2) - R - B) / ((G * 2) + R + B)',
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'help': 'Green Leaf Index shows greens leaves and stems.'
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},
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'GNDVI':{
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'expr': '(N - G) / (N + G)',
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'help': 'Green Normalized Difference Vegetation Index is similar to NDVI, but measures the green spectrum instead of red.'
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},
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'GRVI':{
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'expr': 'N / G',
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'help': 'Green Ratio Vegetation Index is sensitive to photosynthetic rates in forests.'
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},
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'SAVI':{
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'expr': '(1.5 * (N - R)) / (N + R + 0.5)',
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'help': 'Soil Adjusted Vegetation Index is similar to NDVI but attempts to remove the effects of soil areas using an adjustment factor (0.5).'
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},
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'MNLI':{
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'expr': '((N ** 2 - R) * 1.5) / (N ** 2 + R + 0.5)',
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'help': 'Modified Non-Linear Index improves the Non-Linear Index algorithm to account for soil areas.'
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},
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'MSR': {
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'expr': '((N / R) - 1) / (sqrt(N / R) + 1)',
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'help': 'Modified Simple Ratio is an improvement of the Simple Ratio (SR) index to be more sensitive to vegetation.'
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},
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'RDVI': {
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'expr': '(N - R) / sqrt(N + R)',
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'help': 'Renormalized Difference Vegetation Index uses the difference between near-IR and red, plus NDVI to show areas of healthy vegetation.'
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},
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'TDVI': {
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'expr': '1.5 * ((N - R) / sqrt(N ** 2 + R + 0.5))',
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'help': 'Transformed Difference Vegetation Index highlights vegetation cover in urban environments.'
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},
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'OSAVI': {
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'expr': '(N - R) / (N + R + 0.16)',
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'help': 'Optimized Soil Adjusted Vegetation Index is based on SAVI, but tends to work better in areas with little vegetation where soil is visible.'
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},
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'LAI': {
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'expr': '3.618 * (2.5 * (N - R) / (N + 6*R - 7.5*B + 1)) * 0.118',
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'help': 'Leaf Area Index estimates foliage areas and predicts crop yields.'
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},
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'EVI': {
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'expr': '2.5 * (N - R) / (N + 6*R - 7.5*B + 1)',
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'help': 'Enhanced Vegetation Index is useful in areas where NDVI might saturate, by using blue wavelengths to correct soil signals.'
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},
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# more?
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'_TESTRB': {
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'expr': 'R + B'
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},
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'_TESTFUNC': {
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'expr': 'R + (sqrt(B) )'
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}
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}
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camera_filters = [
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'RGB',
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'NRG',
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'NGB',
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'RGN',
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# more?
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]
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@lru_cache(maxsize=20)
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def lookup_formula(algo, band_order = 'RGB'):
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if algo is None:
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return None
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if band_order is None:
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band_order = 'RGB'
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if algo not in algos:
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raise ValueError("Cannot find algorithm " + algo)
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input_bands = tuple(band_order)
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def repl(matches):
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b = matches.group(1)
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try:
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return 'b' + str(input_bands.index(b) + 1)
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except ValueError:
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raise ValueError("Cannot find band \"" + b + "\" from \"" + band_order + "\". Choose a proper band order.")
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return re.sub("([A-Z]+?[a-z]*)", repl, re.sub("\s+", "", algos[algo]['expr']))
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def get_algorithm_list():
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return [{'id': k, **algos[k]} for k in algos if not k.startswith("_")]
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def get_camera_filters_list():
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return camera_filters
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