inital commit. basic heatmap generator. still have to make many options configurable.
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.gitignore
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*.png
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# Created by https://www.toptal.com/developers/gitignore/api/python
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# Edit at https://www.toptal.com/developers/gitignore?templates=python
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### Python ###
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# Byte-compiled / optimized / DLL files
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__pycache__/
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# profiling data
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# End of https://www.toptal.com/developers/gitignore/api/python
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111
outputHightMap.py
Executable file
111
outputHightMap.py
Executable file
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#! /usr/bin/env python3
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#command line arguments:
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# --help, -h, outputs usage of the program
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# -x, -y, outputs width and hight of the output image
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# --output, -o, name of output file. if there are multiple input files, there will be a number prepended to this.
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# after all comamnd line arguments, file or files(space seperated) to process.
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import numpy as np
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import sys, argparse, laspy, logging
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import seaborn as sns; sns.set_theme()
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import matplotlib.pyplot as plt
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from PIL import Image
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logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
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imgX=100
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imgY=100
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#TODO: make it iterate over multiple files.
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inFile = sys.argv[1]
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lasFile = laspy.file.File(inFile, mode = "r")
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#import each dimention scaled.
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x = lasFile.x
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y = lasFile.y
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z = lasFile.z
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maxes = np.array(lasFile.header.max)*np.array(lasFile.header.scale)
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mins = np.array(lasFile.header.min)*np.array(lasFile.header.scale)
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logging.debug(f'max values is {maxes}')
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logging.debug(f'min values is {mins}')
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intensity = lasFile.intensity
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#dimention that will be z(top down) dimention in final heatmap. TODO: auto detect this based on dimention with least variance.
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zDim=0
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xDim=1
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yDim=2
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points = np.stack((x,y,z,intensity), axis=-1)
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#points should now look like
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#[[x,y,z,intensity]
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# [x,y,z,intensity]
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# ...
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# [x,y,z,intensity]
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# [x,y,z,intensity]]
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logging.debug(f'points is\n{points}')
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xRange = maxes[xDim]-mins[xDim]
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yRange = maxes[yDim]-mins[yDim]
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zRange = maxes[zDim]-mins[zDim]
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def sort(array):
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#sort by zDim column, first to last.
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logging.debug(f'zDim sliced points is\n{array[:,zDim]}')
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#the [::-1] reverses the resulting array, so that sortedPoints will be from biggest to smallest.
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ind = np.argsort(array[:,zDim])[::-1]
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sortedPoints = array[ind]
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logging.debug(f'sortedPoints is\n{sortedPoints}')
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return sortedPoints
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sortedPoints = sort(points)
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imageArray = np.zeros((imgX, imgY))
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def scale(array, xRange, yRange, maxX, maxY):
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logging.debug(f'xRange is {xRange} and yRange is {yRange}')
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xScale = maxX/xRange
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yScale = maxY/yRange
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scaledArray = sortedPoints[:, 0:3]
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scaledArray[:,xDim]=scaledArray[:,xDim]-mins[xDim]
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scaledArray[:,xDim]=scaledArray[:,xDim]*xScale
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logging.debug(f'xmin in scaledArray is {scaledArray[:,xDim].min()}')
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logging.debug(f'xmin in scaledArray is {scaledArray[:,xDim].max()}')
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scaledArray[:,yDim]=scaledArray[:,yDim]-mins[yDim]
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scaledArray[:,yDim]=scaledArray[:,yDim]*yScale
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logging.debug(f'ymin in scaledArray is {scaledArray[:,yDim].min()}')
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logging.debug(f'ymin in scaledArray is {scaledArray[:,yDim].max()}')
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logging.debug(f'scaledArray is\n{scaledArray}')
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return scaledArray
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scaledArray = scale(points, xRange, yRange, imgX, imgY)
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def isInxyRange(xMin, xMax, yMin, yMax, xVal, yVal):
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return (xMin<=xVal) and (xVal<xMax) and (yMin<=yVal) and (yVal<yMax)
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for x in range(imgX):
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for y in range(imgY):
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if x==imgX:
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xMax=x+2
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else:
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xMax=x+1
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if y==imgY:
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yMax=y+2
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else:
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yMax=y+1
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zVal=0
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logging.debug(f'yMax is {yMax} and xMax is {xMax}')
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for i in range(scaledArray.shape[0]):
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if isInxyRange(x, xMax, y, yMax, scaledArray[i,xDim], scaledArray[i,yDim]):
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zVal = scaledArray[i,zDim]
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break;
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imageArray[x,y]=zVal
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logging.info(f'zVal at {x},{y} is {zVal}')
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logging.debug(f'imageArray is {imageArray}')
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heatMap = sns.heatmap(imageArray, center=((maxes[zDim]+mins[zDim])/2), square=True)
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heatMapFig = heatMap.get_figure()
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heatMapFig.savefig("output.png")
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