Merge branch 'cliOptions'

I may or may not have forgotten about this branch for several months...
This commit is contained in:
gabe 2021-07-14 14:16:55 -05:00
commit 32f54cc211

View file

@ -1,7 +1,6 @@
#! /usr/bin/env python3
#command line arguments:
# --help, -h, outputs usage of the program
# -x, -y, outputs width and hight of the output image
# -x, -y, width and hight of the output image
# --output, -o, name of output file. if there are multiple input files, there will be a number prepended to this.
# after all comamnd line arguments, file or files(space seperated) to process.
@ -10,10 +9,29 @@ import numpy as np
import sys, argparse, laspy, logging
import seaborn as sns; sns.set_theme()
import matplotlib.pyplot as plt
from PIL import Image
logging.basicConfig(format='%(asctime)s:%(message)s', level=logging.INFO)
#logging.basicConfig(format='%(asctime)s:%(message)s', level=logging.DEBUG)
logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d-%H:%M:%S', level=logging.INFO)
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(funcName)s - %(message)s', datefmt='%Y-%m-%d-%H:%M:%S', level=logging.INFO)
def parse_arguments():
parser = argparse.ArgumentParser(description='create a top down hightmap from a LIDAR point file.')
#TODO to structure this for multiple files, set action='append'. this will store a list of the files to process. Also set nargs='+'.
parser.add_argument('file', help='LIDAR file to process.')
parser.add_argument('-x', default=100, type=int, help='horizontal size (in cells) of the output image. Defaults to 100')
parser.add_argument('-y', default=100, type=int, help='vertical size (in cells) if the output image. Defaults to 100')
parser.add_argument('-o', '--output', metavar='file', help='name of output file. will default to [name of input file].png if not given.')
args=parser.parse_args()
inFile = os.path.realpath(args.file)
if args.output==None:
outFile = f'{os.path.dirname(inFile)}/{os.path.basename(inFile)}.png'
else:
outFile=args.output
logging.info(f'outputing to {outFile}')
return inFile, outFile, args.x, args.y
def scale(array, desiredmaxX, desiredmaxY):
logging.debug(f'xMax is {np.max(array[:,xDim])} and xMin is {np.min(array[:,xDim])}')
@ -34,18 +52,9 @@ def scale(array, desiredmaxX, desiredmaxY):
return array
imgX=500
imgY=500
#TODO: make it iterate over multiple files.
inFile = os.path.realpath(sys.argv[1])
lasFile = laspy.file.File(inFile, mode = 'r')
outFile = f'{os.path.dirname(inFile)}/{imgX}*{imgY}{os.path.basename(inFile)}.png'
print(f'outputing to {outFile}')
def process_LIDAR(inFile, imgX, imgY):
#import each dimention scaled.
lasFile=laspy.file.File(inFile, mode = 'r')
z = lasFile.z
x = lasFile.x
y = lasFile.y
@ -53,11 +62,6 @@ intensity = lasFile.intensity
points = np.stack((z,x,y), axis=-1)
#dimention that will be z(top down) dimention in final heatmap. TODO: auto detect this based on dimention with least variance.
zDim=1
xDim=2
yDim=0
#points should now look like
#[[z,x,y]
# [z,x,y]
@ -67,7 +71,7 @@ yDim=0
logging.debug(f'points is\n{points}')
length=points.shape[0]
print(f'{length} points in LIDAR file.')
logging.info(f'{length} points in LIDAR file.')
imageArray = np.zeros((imgX, imgY))
@ -76,7 +80,7 @@ points = scale(points, imgX, imgY)
#sys.exit()
#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.
for i in range(len(points)):
print(f'{i} points processed of {length} total points')
logging.info(f'{i} points processed of {length} total points')
#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.
xPixel=np.floor(points[i,xDim]).astype(int)
if xPixel==imgX:
@ -87,8 +91,23 @@ for i in range(len(points)):
imageArray[xPixel,yPixel]=np.maximum(imageArray[xPixel,yPixel], points[i,zDim])
logging.debug(f'imageArray is {imageArray}')
return imageArray
print('processed all points. generating heatmap.')
def gen_heatmap(imageArray, outFile):
heatMap = sns.heatmap(imageArray, center=(np.max(imageArray)+np.min(imageArray))/2, robust=True, square=True)
heatMapFig = heatMap.get_figure()
heatMapFig.savefig(outFile)
#TODO: make it iterate over multiple files.
#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.
zDim=1
xDim=2
yDim=0
inFile, outFile, imgX, imgY = parse_arguments()
imageArray=process_LIDAR(inFile, imgX, imgY)
logging.info('processed all points. generating heatmap.')
gen_heatmap(imageArray, outFile)