cleaned up the code to put a bunch of things inside functions to make it

easier to process multiple files in the future.

outputHightMap.py

@@ -10,28 +10,28 @@ import sys, argparse, laspy, logging
 import seaborn as sns; sns.set_theme()
 import matplotlib.pyplot as plt
 
-logging.basicConfig(format='%(asctime)s:%(message)s', level=logging.INFO)
+logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d-%H:%M:%S', level=logging.INFO)
-#logging.basicConfig(format='%(asctime)s:%(message)s', level=logging.DEBUG)
+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.')
-    parser.add_argument('file', help='.las file to process.')
+    #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', help='name of output file. will default to [name of input file].png if not given.')
+    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()
 
-    imgX=args.x
+    inFile = os.path.realpath(args.file)
-    imgY=args.y
-
-    inFile=args.file
 
     if args.output==None:
         outFile = f'{os.path.dirname(inFile)}/{os.path.basename(inFile)}.png'
     else:
         outFile=args.output
     
-    print(f'outputing to {outFile}')
+    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])}')
@@ -52,42 +52,35 @@ def scale(array, desiredmaxX, desiredmaxY):
 
     return array
 
-#TODO: make it iterate over multiple files.
+def process_LIDAR(inFile, imgX, imgY):
-parse_arguments()
+    #import each dimention scaled.
+    lasFile=laspy.file.File(inFile, mode = 'r')
+    z = lasFile.z
+    x = lasFile.x
+    y = lasFile.y
+    intensity = lasFile.intensity
 
-#import each dimention scaled.
+    points = np.stack((z,x,y), axis=-1)
-lasFile=laspy.file.File(inFile, mode = 'r')
-z = lasFile.z
-x = lasFile.x
-y = lasFile.y
-intensity = lasFile.intensity
 
-points = np.stack((z,x,y), axis=-1)
+    #points should now look like
+    #[[z,x,y]
+    # [z,x,y]
+    # ...
+    # [z,x,y]
+    # [z,x,y]]
 
-#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.
+    logging.debug(f'points is\n{points}')
-zDim=1
+    length=points.shape[0]
-xDim=2
+    logging.info(f'{length} points in LIDAR file.')
-yDim=0
 
-#points should now look like
+    imageArray = np.zeros((imgX, imgY))
-#[[z,x,y]
-# [z,x,y]
-# ...
-# [z,x,y]
-# [z,x,y]]
 
-logging.debug(f'points is\n{points}')
+    points = scale(points, imgX, imgY)
-length=points.shape[0]
-print(f'{length} points in LIDAR file.')
 
-imageArray = np.zeros((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.
-points = scale(points, imgX, imgY)
+    for i in range(len(points)):
-
+        logging.info(f'{i} points processed of {length} total points')
-#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')
         #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:
@@ -97,9 +90,24 @@ for i in range(len(points)):
             yPixel-=1
         imageArray[xPixel,yPixel]=np.maximum(imageArray[xPixel,yPixel], points[i,zDim])
 
-logging.debug(f'imageArray is {imageArray}')
+    logging.debug(f'imageArray is {imageArray}')
+    return imageArray
+
+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)
 
-print('processed all points. generating heatmap.')
-heatMap = sns.heatmap(imageArray, center=(np.max(imageArray)+np.min(imageArray))/2, robust=True, square=True)
-heatMapFig = heatMap.get_figure()
-heatMapFig.savefig(outFile)