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@ -1,45 +0,0 @@
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class Shape:
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def __init__(self, x, y):
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self.x = x
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self.y = y
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def move(self, delta_x, delta_y):
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self.x = self.x + delta_x
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self.y = self.y + delta_y
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class Square(Shape):
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def __init__(self, side=1, x=0, y=0):
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super().__init__(x, y)
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self.side = side
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class Circle(Shape):
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pi = 3.14159
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all_circles = []
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def __init__(self, radius=1, x=0, y=0):
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super().__init__(x, y)
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self.radius = radius
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self.all_circles.append(self)
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@property
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def radius(self):
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return self._radius
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@radius.setter
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def radius(self, value):
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if value < 0:
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raise ValueError("Radius cannot be negative")
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self._radius = value
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@classmethod
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def total_area(cls):
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area = 0
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for circle in cls.all_circles:
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area += cls.circle_area(circle.radius)
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return area
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@staticmethod
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def circle_area(radius):
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return __class__.pi * radius * radius
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@ -1,12 +0,0 @@
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def wrap_with_html(fn):
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def wrapper_func(*args):
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print('<html>')
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fn(*args)
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print('</html>')
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return wrapper_func
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@wrap_with_html
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def echo(param):
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print(param)
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echo('Hello')
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@ -1,33 +0,0 @@
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class HtmlElement:
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tag = "html"
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indent = " "
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def __init__(self, content=None, **kwargs):
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if content is None:
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self.contents = []
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else:
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self.contents = [content]
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self.attributes = kwargs
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def append(self, new_content):
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self.contents.append(new_content)
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def render(self, cur_ind=""):
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print(cur_ind + f"<{self.tag}>")
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for content in self.contents:
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try:
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content.render(cur_ind + self.indent)
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except AttributeError:
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print(cur_ind + self.indent + content)
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print(cur_ind + f"</{self.tag}>")
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class Body(HtmlElement):
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tag = "body"
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class Paragraph(HtmlElement):
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tag = "p"
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para = Paragraph("hello")
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body = Body(para)
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body.append("world")
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doc = HtmlElement(body)
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doc.render()
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11
etc/nasa.py
11
etc/nasa.py
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@ -1,11 +0,0 @@
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import requests
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import json
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api_key = "DEMO_KEY"
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def get_weather():
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url = f"https://api.nasa.gov/insight_weather/?api_key={api_key}&feedtype=json&ver=1.0"
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response = requests.get(url)
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return json.loads(response.text)
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print(get_weather())
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214
sat.py
214
sat.py
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@ -1,9 +1,6 @@
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from PIL import Image
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from PIL import Image
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import requests
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import requests
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from sat7_pointer import *
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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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# 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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# from the three bands.
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@ -16,17 +13,17 @@ from lxml import etree
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#
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#
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# The composite image is saved as a PNG file.
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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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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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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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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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composite = Image.merge('RGB', (band3, band2, band1))
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# Load corner coordinates of the image.
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# Load corner coordinates of the image.
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#
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#
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# The coordinates are stored in a MTL text file.
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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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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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# Fetch coordinates of a city.
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#
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#
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@ -37,18 +34,14 @@ mtl_data = load_metadata("LE07_L1TP_177025_20210723_20210818_02_T1_MTL.txt")
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#
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#
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# # City is Belgorod, Russia.
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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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url = 'http://nominatim.openstreetmap.org/search?q=Belgorod,+Russia&format=json'
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response = requests.get(url)
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response = requests.get(url)
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data = response.json()
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data = response.json()
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# Convert bounding box coordinates to image coordinates.
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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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x0, y1 = lat_lot_to_pixel(data[0]['boundingbox'][0], data[0]['boundingbox'][2], mtl_data)
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data[0]["boundingbox"][0], data[0]["boundingbox"][2], mtl_data
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x1, y0 = lat_lot_to_pixel(data[0]['boundingbox'][1], data[0]['boundingbox'][3], 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(x0, y0)
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print(x1, y1)
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print(x1, y1)
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@ -57,7 +50,7 @@ print(composite.size)
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cropped = composite.crop((x0, y0, x1, y1))
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cropped = composite.crop((x0, y0, x1, y1))
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cropped.save("cropped.png")
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cropped.save('cropped.png')
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###############################################################################
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###############################################################################
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# LAB 2
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# LAB 2
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@ -66,7 +59,7 @@ cropped.save("cropped.png")
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# Load landsat band 4 TIF image.
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# Load landsat band 4 TIF image.
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# Band 4 is near infrared.
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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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band4 = Image.open('LE07_L1TP_177025_20210723_20210818_02_T1_B4.TIF')
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# Claculate the NDVI.
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# Claculate the NDVI.
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#
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#
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@ -122,8 +115,7 @@ def get_color(value):
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else:
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else:
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return (0, 0, 0)
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return (0, 0, 0)
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for x in range(ndvi.size[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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for y in range(ndvi.size[1]):
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r = red.getpixel((x, y))
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r = red.getpixel((x, y))
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nir = ndvi.getpixel((x, y))
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nir = ndvi.getpixel((x, y))
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@ -132,188 +124,6 @@ for x in tqdm(range(ndvi.size[0]), desc="NDVI"):
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else:
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else:
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result.putpixel((x, y), get_color((nir - r) / (nir + r)))
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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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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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|
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(polynoms[1, 0] * (rho_i[0] + polynoms[1, 1]) ** 2)
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|
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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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|
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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))
|
|
||||||
|
|
||||||
result.save('fapar.png')
|
|
||||||
|
|
||||||
|
|
||||||
root = etree.Element("kml")
|
|
||||||
doc = etree.SubElement(root, "Document")
|
|
||||||
|
|
||||||
start_x = 550
|
|
||||||
start_y = 900
|
|
||||||
width = 30
|
|
||||||
height = 30
|
|
||||||
result = result.crop((start_x, start_y, start_x+width, start_y+height))
|
|
||||||
|
|
||||||
for x in tqdm(range(result.size[0]), desc="KML"):
|
|
||||||
for y in range(result.size[1]):
|
|
||||||
color = result.getpixel((x, y))
|
|
||||||
if color == (0, 0, 0):
|
|
||||||
continue
|
|
||||||
coord = pixel_to_lat_lot(x+x0+start_x, y+y0+start_y, mtl_data)
|
|
||||||
neighbour_r = pixel_to_lat_lot(x+1+x0+start_x, y+y0+start_y, mtl_data)
|
|
||||||
neighbour_d = pixel_to_lat_lot(x+x0+start_x, y+1+y0+start_y, mtl_data)
|
|
||||||
neighbour_rd = pixel_to_lat_lot(x+1+x0+start_x, y+1+y0+start_y, mtl_data)
|
|
||||||
placemark = etree.SubElement(doc, "Placemark")
|
|
||||||
etree.SubElement(placemark, "name").text = f"{x}_{y}"
|
|
||||||
etree.SubElement(placemark, "styleUrl").text = f"#style_{x}_{y}"
|
|
||||||
polygon = etree.SubElement(placemark, "Polygon")
|
|
||||||
outer = etree.SubElement(polygon, "outerBoundaryIs")
|
|
||||||
linearring = etree.SubElement(outer, "LinearRing")
|
|
||||||
coordinates = etree.SubElement(linearring, "coordinates")
|
|
||||||
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"
|
|
||||||
color = result.getpixel((x, y))
|
|
||||||
color = (color[2], color[1], color[0])
|
|
||||||
color = '%02x%02x%02x' % color
|
|
||||||
color = "64" + color
|
|
||||||
style = etree.SubElement(doc, "Style")
|
|
||||||
style.set("id", f"style_{x}_{y}")
|
|
||||||
polystyle = etree.SubElement(style, "PolyStyle")
|
|
||||||
etree.SubElement(polystyle, "color").text = color
|
|
||||||
|
|
||||||
with open("fapar.kml", "wb") as f:
|
|
||||||
f.write(etree.tostring(root, pretty_print=True))
|
|
||||||
|
|
@ -51,20 +51,6 @@ def load_metadata(mtl_file):
|
||||||
|
|
||||||
return mtl_data
|
return mtl_data
|
||||||
|
|
||||||
def pixel_to_lat_lot(x, y, mtl_data):
|
|
||||||
k = (x - 0.5) / mtl_data['width']
|
|
||||||
r = (y - 0.5) / mtl_data['height']
|
|
||||||
|
|
||||||
y = k*(mtl_data['vector_a'] + mtl_data['mu'] * (mtl_data['vector_a'] - mtl_data['vector_s'])) \
|
|
||||||
+ r * (mtl_data['vector_c'] + mtl_data['nu'] * (mtl_data['vector_c'] - mtl_data['vector_s']))
|
|
||||||
B = np.array([mtl_data['vector_a'], mtl_data['vector_c'], mtl_data['vector_s']-y]).T
|
|
||||||
ags = np.linalg.solve(B, y.T)
|
|
||||||
al = ags[0]
|
|
||||||
gm = ags[1]
|
|
||||||
ecef = mtl_data['ecef_corners'][0, :] + al*mtl_data['vector_a'] + gm*mtl_data['vector_c']
|
|
||||||
lat, lon, alt = pyproj.transform(mtl_data['ecef'], mtl_data['lla'], ecef[0], ecef[1], ecef[2], radians=False)
|
|
||||||
return [lat, lon]
|
|
||||||
|
|
||||||
def lat_lot_to_pixel(lat, lon, mtl_data):
|
def lat_lot_to_pixel(lat, lon, mtl_data):
|
||||||
target_vector = np.array([lat, lon, 0])
|
target_vector = np.array([lat, lon, 0])
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue