114 lines
4.4 KiB
Python
Executable file
114 lines
4.4 KiB
Python
Executable file
#! /usr/bin/env python3
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#command line arguments:
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# -x, -y, 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 os.path
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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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logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d-%H:%M:%S', level=logging.INFO)
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logging.basicConfig(format='%(asctime)s - %(levelname)s - %(funcName)s - %(message)s', datefmt='%Y-%m-%d-%H:%M:%S', level=logging.INFO)
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def parse_arguments():
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parser = argparse.ArgumentParser(description='create a top down hightmap from a LIDAR point file.')
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#TODO to structure this for multiple files, set action='append'. this will store a list of the files to process. Also set nargs='+'.
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parser.add_argument('file', help='LIDAR file to process.')
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parser.add_argument('-x', default=100, type=int, help='horizontal size (in cells) of the output image. Defaults to 100')
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parser.add_argument('-y', default=100, type=int, help='vertical size (in cells) if the output image. Defaults to 100')
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parser.add_argument('-o', '--output', metavar='file', help='name of output file. will default to [name of input file].png if not given.')
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args=parser.parse_args()
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inFile = os.path.realpath(args.file)
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if args.output==None:
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outFile = f'{os.path.dirname(inFile)}/{os.path.basename(inFile)}.png'
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else:
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outFile=args.output
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logging.info(f'outputing to {outFile}')
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return inFile, outFile, args.x, args.y
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def scale(array, desiredmaxX, desiredmaxY):
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logging.debug(f'xMax is {np.max(array[:,xDim])} and xMin is {np.min(array[:,xDim])}')
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logging.debug(f'yMax is {np.max(array[:,yDim])} and yMin is {np.min(array[:,yDim])}')
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ax=desiredmaxX/(np.max(array[:,xDim])-np.min(array[:,xDim]))
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bx=-ax*np.min(array[:,xDim])
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ay=desiredmaxY/(np.max(array[:,yDim])-np.min(array[:,yDim]))
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by=-ay*np.min(array[:,yDim])
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#slice indexes 0-2 from the second dimention
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array[:,xDim]=ax*array[:,xDim]+bx
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array[:,yDim]=ay*array[:,yDim]+by
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logging.debug(f'array is\n{array}')
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logging.debug(f'xMax is {np.max(array[:,xDim])} and xMin is {np.min(array[:,xDim])}')
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logging.debug(f'yMax is {np.max(array[:,yDim])} and yMin is {np.min(array[:,yDim])}')
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return array
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def process_LIDAR(inFile, imgX, imgY):
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#import each dimention scaled.
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lasFile=laspy.file.File(inFile, mode = 'r')
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z = lasFile.z
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x = lasFile.x
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y = lasFile.y
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intensity = lasFile.intensity
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points = np.stack((z,x,y), axis=-1)
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#points should now look like
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#[[z,x,y]
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# [z,x,y]
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# ...
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# [z,x,y]
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# [z,x,y]]
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logging.debug(f'points is\n{points}')
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length=points.shape[0]
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logging.info(f'{length} points in LIDAR file.')
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imageArray = np.zeros((imgX, imgY))
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points = scale(points, imgX, imgY)
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#sys.exit()
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#for each entry in points, figure out what pixel it will go into, and assign that pixel the zval, unless the zval already in that pixel is higher.
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for i in range(len(points)):
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logging.info(f'{i} points processed of {length} total points')
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#the if statements are reqired for edge cases relateing to the bottom row and the far right column, to make sure points dont get left out.
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xPixel=np.floor(points[i,xDim]).astype(int)
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if xPixel==imgX:
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xPixel-=1
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yPixel=np.floor(points[i,yDim]).astype(int)
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if yPixel==imgY:
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yPixel-=1
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imageArray[xPixel,yPixel]=np.maximum(imageArray[xPixel,yPixel], points[i,zDim])
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logging.debug(f'imageArray is {imageArray}')
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return imageArray
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def gen_heatmap(imageArray, outFile):
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heatMap = sns.heatmap(imageArray, center=(np.max(imageArray)+np.min(imageArray))/2, robust=True, square=True)
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heatMapFig = heatMap.get_figure()
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heatMapFig.savefig(outFile)
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#TODO: make it iterate over multiple files.
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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, while being overridable on the command line.
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zDim=1
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xDim=2
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yDim=0
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inFile, outFile, imgX, imgY = parse_arguments()
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imageArray=process_LIDAR(inFile, imgX, imgY)
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logging.info('processed all points. generating heatmap.')
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gen_heatmap(imageArray, outFile)
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