319 lines
10 KiB
Python
319 lines
10 KiB
Python
from PIL import Image
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import requests
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from sat7_pointer import *
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import numpy as np
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from tqdm import tqdm
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from lxml import etree
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# Load Landsat 7 band 1, 2, 3 TIF images and create a composite image
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# from the three bands.
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#
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# The composite image is a greyscale image with a red, green and blue
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# channel.
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#
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# The red channel is the red channel of band 3, the green channel is the
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# green channel of band 2 and the blue channel is the blue channel of band 1.
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#
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# The composite image is saved as a PNG file.
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band1 = Image.open("LE07_L1TP_177025_20210723_20210818_02_T1_B1.TIF")
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band2 = Image.open("LE07_L1TP_177025_20210723_20210818_02_T1_B2.TIF")
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band3 = Image.open("LE07_L1TP_177025_20210723_20210818_02_T1_B3.TIF")
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composite = Image.merge("RGB", (band3, band2, band1))
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# Load corner coordinates of the image.
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#
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# The coordinates are stored in a MTL text file.
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mtl_data = load_metadata("LE07_L1TP_177025_20210723_20210818_02_T1_MTL.txt")
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# Fetch coordinates of a city.
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#
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# The coordinates of a city are fetched from the OpenStreetMap API.
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#
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# The coordinates are used to calculate the corner coordinates of the
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# composite image.
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#
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# # City is Belgorod, Russia.
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url = "http://nominatim.openstreetmap.org/search?q=Belgorod,+Russia&format=json"
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response = requests.get(url)
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data = response.json()
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# Convert bounding box coordinates to image coordinates.
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x0, y1 = lat_lot_to_pixel(
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data[0]["boundingbox"][0], data[0]["boundingbox"][2], mtl_data
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)
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x1, y0 = lat_lot_to_pixel(
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data[0]["boundingbox"][1], data[0]["boundingbox"][3], mtl_data
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)
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print(x0, y0)
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print(x1, y1)
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print(composite.size)
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# Crop the image to the bounding box coordinates.
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cropped = composite.crop((x0, y0, x1, y1))
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cropped.save("cropped.png")
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###############################################################################
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# LAB 2
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###############################################################################
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# Load landsat band 4 TIF image.
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# Band 4 is near infrared.
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band4 = Image.open("LE07_L1TP_177025_20210723_20210818_02_T1_B4.TIF")
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# Claculate the NDVI.
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#
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# The NDVI is calculated by dividing
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# difference of the red and near infrared band 4 by the sum of the
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# near infrared and near infrared band 4.
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#
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# The NDVI is calculated for each pixel in the cropped image.
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#
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# The NDVI is saved as a PNG image.
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ndvi = band4.copy().crop((x0, y0, x1, y1))
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red = band3.copy().crop((x0, y0, x1, y1))
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result = cropped.copy()
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print(red.getpixel((0, 0)))
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print(ndvi.getpixel((0, 0)))
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# Colors for the NDVI.
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def get_color(value):
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if value < 0.033:
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return (255, 255, 255)
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elif value < 0.066:
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return (196, 184, 168)
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elif value < 0.1:
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return (180, 150, 108)
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elif value < 0.133:
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return (164, 130, 76)
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elif value < 0.166:
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return (148, 114, 60)
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elif value < 0.2:
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return (124, 158, 44)
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elif value < 0.25:
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return (148, 182, 20)
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elif value < 0.3:
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return (116, 170, 4)
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elif value < 0.35:
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return (100, 162, 4)
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elif value < 0.4:
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return (84, 150, 4)
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elif value < 0.45:
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return (60, 134, 4)
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elif value < 0.5:
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return (28, 114, 4)
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elif value < 0.6:
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return (4, 96, 4)
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elif value < 0.7:
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return (4, 74, 4)
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elif value < 0.8:
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return (4, 56, 4)
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elif value < 0.9:
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return (4, 40, 4)
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else:
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return (0, 0, 0)
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for x in tqdm(range(ndvi.size[0]), desc="NDVI"):
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for y in range(ndvi.size[1]):
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r = red.getpixel((x, y))
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nir = ndvi.getpixel((x, y))
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if nir + r == 0:
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result.putpixel((x, y), get_color(0))
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else:
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result.putpixel((x, y), get_color((nir - r) / (nir + r)))
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result.save("ndvi.png")
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# Calculate FAPAR (Fraction of Absorbed Photosynthetically Active Radiation)
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# for each pixel in the cropped image.
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#
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# Bands 1, 3, 4 are used.
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solar_zenith_angle = np.radians(float(mtl_data["SUN_ELEVATION"]))
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sensor_zenith_angle = np.radians(0)
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sun_sensor_relative_azimuth = np.radians(float(mtl_data["SUN_AZIMUTH"]))
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gain = [float(mtl_data["RADIANCE_MULT_BAND_" + str(i)]) for i in [1, 3, 4]]
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offset = [float(mtl_data["RADIANCE_ADD_BAND_" + str(i)]) for i in [1, 3, 4]]
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dsol = float(mtl_data["EARTH_SUN_DISTANCE"])
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pic = [0.643, 0.80760, 0.89472]
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k = [0.76611, 0.63931, 0.81037]
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theta = [-0.10055, -0.06156, -0.03924]
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k = [0.63931,0.81037, 0.76611]
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pic = [0.80760, 0.89472, 0.643]
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theta = [-0.06156, -0.03924, -0.10055]
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E0 = [1969, 1551, 1044]
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cosg = np.cos(solar_zenith_angle) * np.cos(sensor_zenith_angle) + np.sin(
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solar_zenith_angle
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) * np.sin(sensor_zenith_angle) * np.cos(sun_sensor_relative_azimuth)
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G = (
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np.tan(solar_zenith_angle) ** 2
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+ np.tan(sensor_zenith_angle) ** 2
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- 2
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* np.tan(solar_zenith_angle)
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* np.tan(sensor_zenith_angle)
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* np.cos(sun_sensor_relative_azimuth)
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) ** 0.5
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polynoms = np.array(
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[
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[0.27505, 0.35511, -0.004, -0.322, 0.299, -0.0131, 0, 0, 0, 0, 0],
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[-10.036, -0.019804, 0.55438, 0.14108, 12.494, 0, 0, 0, 0, 0, 1],
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[
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0.42720,
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0.069884,
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-0.33771,
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0.24690,
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-1.0821,
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-0.30401,
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-1.1024,
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-1.2596,
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-0.31949,
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-1.4864,
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0,
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],
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]
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)
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blue = band1.copy().crop((x0, y0, x1, y1))
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red = band3.copy().crop((x0, y0, x1, y1))
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nir = band4.copy().crop((x0, y0, x1, y1))
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result = cropped.copy()
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f1 = [
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((np.cos(solar_zenith_angle) * np.cos(sensor_zenith_angle)) ** (k[i] - 1))
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/ (np.cos(solar_zenith_angle) + np.cos(sensor_zenith_angle)) ** (1 - k[i])
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for i in range(3)
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]
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f2 = [
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(1 - theta[i] ** 2) / (1 + 2 * theta[i] * cosg + theta[i] ** 2) ** (3 / 2)
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for i in range(3)
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]
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f3 = [1 + (1 - pic[i]) / (1 + G) for i in range(3)]
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F = [f1[i] * f2[i] * f3[i] for i in range(3)]
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def get_color_fapar(value, rho):
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if (0 < rho[0] and rho[0] < 0.257752) \
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and (0 < rho[1] and rho[1] < 0.48407) \
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and (0 < rho[2] and rho[2] < 0.683928) \
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and (rho[0] <= rho[2]) \
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and (rho[2] >= 1.26826*rho[1]):
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return get_color(value)
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if (rho[0] <= 0) or (rho[1] <= 0) or (rho[2] <= 0):
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return (0, 0, 0)
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if (rho[0] >= 0.257752) or (rho[1] >= 0.48407) or (rho[2] >= 0.683928):
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return (255, 255, 255)
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if (0 < rho[0] and rho[0] < 0.257752) \
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and (0 < rho[1] and rho[1] < 0.48407) \
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and (0 < rho[2] and rho[2] < 0.683928) \
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and (rho[0] >= rho[2]):
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return (0, 0, 255)
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if (0 < rho[0] and rho[0] < 0.257752) \
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and (0 < rho[1] and rho[1] < 0.48407) \
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and (0 < rho[2] and rho[2] < 0.683928) \
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and (rho[0] <= rho[2]) \
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and (1.25*rho[1] > rho[2]):
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return (255, 150, 150)
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if (rho[1] < 0) or (rho[2] < 0):
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return (0, 0, 0)
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if value < 0 or value > 1:
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return (0, 0, 0)
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return (int(180.0 * (1 - value)), 255, 255)
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for x in tqdm(range(result.size[0]), desc="FAPAR"):
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for y in range(result.size[1]):
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bands = [blue.getpixel((x, y)), red.getpixel((x, y)), nir.getpixel((x, y))]
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rho_i = [
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(
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(np.pi * (gain[i] * bands[i] + offset[i]) * dsol ** 2)
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/ (E0[i] * np.cos(sensor_zenith_angle))
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)
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/ F[i]
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for i in range(3)
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]
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g1 = (
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(polynoms[1, 0] * (rho_i[0] + polynoms[1, 1]) ** 2)
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+ (polynoms[1, 2] * (rho_i[1] + polynoms[1, 3]) ** 2)
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+ polynoms[1, 4] * rho_i[0] * rho_i[1]
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) / (
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polynoms[1, 5] * (rho_i[0] + polynoms[1, 6]) ** 2
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+ polynoms[1, 7] * (rho_i[1] + polynoms[1, 8]) ** 2
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+ polynoms[1, 9] * rho_i[0] * rho_i[1]
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+ polynoms[1, 10]
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)
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g2 = (
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(polynoms[2, 0] * (rho_i[0] + polynoms[2, 1]) ** 2)
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+ (polynoms[2, 2] * (rho_i[2] + polynoms[2, 3]) ** 2)
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+ polynoms[2, 4] * rho_i[0] * rho_i[2]
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) / (
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polynoms[2, 5] * (rho_i[0] + polynoms[2, 6]) ** 2
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+ polynoms[2, 7] * (rho_i[2] + polynoms[2, 8]) ** 2
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+ polynoms[2, 9] * rho_i[0] * rho_i[2]
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+ polynoms[2, 10]
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)
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FAPAR = ((polynoms[0, 0] * g2) - polynoms[0, 1] * g1 - polynoms[0, 2]) / (
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(polynoms[0, 3] - g1) ** 2 + (polynoms[0, 4] - g2) ** 2 + polynoms[0, 5]
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)
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result.putpixel((x, y), get_color_fapar(FAPAR, rho_i))
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result.save('fapar.png')
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root = etree.Element("kml")
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doc = etree.SubElement(root, "Document")
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start_x = 550
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start_y = 900
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width = 30
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height = 30
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result = result.crop((start_x, start_y, start_x+width, start_y+height))
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for x in tqdm(range(result.size[0]), desc="KML"):
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for y in range(result.size[1]):
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color = result.getpixel((x, y))
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if color == (0, 0, 0):
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continue
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coord = pixel_to_lat_lot(x+x0+start_x, y+y0+start_y, mtl_data)
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neighbour_r = pixel_to_lat_lot(x+1+x0+start_x, y+y0+start_y, mtl_data)
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neighbour_d = pixel_to_lat_lot(x+x0+start_x, y+1+y0+start_y, mtl_data)
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neighbour_rd = pixel_to_lat_lot(x+1+x0+start_x, y+1+y0+start_y, mtl_data)
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placemark = etree.SubElement(doc, "Placemark")
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etree.SubElement(placemark, "name").text = f"{x}_{y}"
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etree.SubElement(placemark, "styleUrl").text = f"#style_{x}_{y}"
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polygon = etree.SubElement(placemark, "Polygon")
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outer = etree.SubElement(polygon, "outerBoundaryIs")
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linearring = etree.SubElement(outer, "LinearRing")
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coordinates = etree.SubElement(linearring, "coordinates")
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coordinates.text = f"{coord[0]},{coord[1]},0 {neighbour_r[0]},{neighbour_r[1]},0 {neighbour_rd[0]},{neighbour_rd[1]},0 {neighbour_d[0]},{neighbour_d[1]},0 {coord[0]},{coord[1]},0"
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color = result.getpixel((x, y))
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color = (color[2], color[1], color[0])
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color = '%02x%02x%02x' % color
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color = "64" + color
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style = etree.SubElement(doc, "Style")
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style.set("id", f"style_{x}_{y}")
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polystyle = etree.SubElement(style, "PolyStyle")
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etree.SubElement(polystyle, "color").text = color
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with open("fapar.kml", "wb") as f:
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f.write(etree.tostring(root, pretty_print=True)) |