central-coast-aerial-survey/las_manipulation.py

import os
import sys
import argparse
import subprocess
from tqdm import tqdm
import numpy as np
from scipy import interpolate
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, Polygon

from survey_tools import call_lastools


def remove_problems(x_list, y_list, z_list, x_now, y_now, check_value):

    z_ave=nine_pt_moving_average(z_list)
    deriv_ave, chainage=forward_derivative(x_list, y_list, z_ave)
    deriv_real, chainage=two_point_derivative(x_list, y_list, z_list)

    #first find the reference contour on the beach
    #index_contour, x_now, y_now, distance=find_beach_reference_contour_choose_closest(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave, check_value)
    index_contour, x_now, y_now, distance=find_beach_reference_contour(chainage, z_ave, x_list, y_list, x_now, y_now,deriv_ave,deriv_real,check_value)
    if index_contour<len(chainage): #other wise keep everthing
        #find the beach slope, get the interpolated line (beach line) and the index of the reference contour +1
        beach_slope, beach_line, index_high=find_beach_slope(chainage, z_ave,index_contour, check_value)
        #find the natural deviation of the lower beach
        nat_dev=get_natural_deviation(chainage, z_list, index_contour, index_high, beach_line)

        for i in range(index_contour, len(z_list)):
            if abs(z_list[i]-float(beach_line(chainage[i])))>nat_dev:
                break
    else:
        i=index_contour

    z_return=z_list[0:i]
    chainage_return=chainage[0:i]
    return z_return, chainage_return, x_now, y_now, distance

def two_point_derivative(x_list, y_list, z_list):
    chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))]
    deriv=[(z_list[i+1]-z_list[i-1])/(chain[i+1]-chain[i-1]) for i in range(1, len(z_list)-1)]

    deriv.insert(0,0)
    deriv.append(0)

    return deriv, chain

def forward_derivative(x_list, y_list, z_list):
    chain=[((x_list[0]-x_list[i])**2+(y_list[0]-y_list[i])**2)**0.5 for i in range(0,len(x_list))]
    deriv=[(z_list[i]-z_list[i-1])/(chain[i]-chain[i-1]) for i in range(0, len(z_list)-1)]

    deriv.insert(0,0)

    return deriv, chain

def find_first_over_reference(z_list, value):
    i=len(z_list)-1

    while i>0 and z_list[i]<value:
        i=i-1

    return i

def nine_pt_moving_average(z_list):
    i=0
    move_ave=[]
    while i<len(z_list):
        if i<5:
            ave=np.mean([z_list[j] for j in range(0,i+5)])
        elif i>len(z_list)-5:
            ave=np.mean([z_list[j] for j in range(i-4,len(z_list))])
        else:
            ave=np.mean([z_list[j] for j in range(i-4,i+5)])

        move_ave.append(ave)
        i=i+1
    return move_ave

def find_neg_derivative(z_list, deriv_list):
    i=len(z_list)-5
    while z_list[i]>=0 and z_list[i+1]>=0 and z_list[i+2]>=0 and z_list[i+3]>=0 and z_list[i+4]>=0:
        i=i-1

    return i

def find_beach_reference_contour_choose_closest(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list, check_value):
    #note that z_list should be the 9 point moving average
    #assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD
    i=0
    choice_list=[]
    distance_list=[]
    if z_ave_list[i]>check_value:
        state_now='over'
    else:
        state_now='under'

    while i<len(z_ave_list):
        if state_now=='under' and z_ave_list[i]>check_value: #only keep if it is downward sloping
            state_now='over'
        elif state_now=='over' and z_ave_list[i]<check_value:
            choice_list.append(i)
            state_now='under'
            if x_last!=None:
                distance_list.append(((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5)
        i=i+1

    if len(choice_list)>0 and x_last==None: #choose the first time for the first point
        i=choice_list[0]
        distance=0
    elif len(choice_list)>0 and x_last!=None:
        assert(len(choice_list)==len(distance_list))
        i=choice_list[distance_list.index(min(distance_list))]
        distance=min(distance_list)


    if i>=len(x_list):
        i=len(x_list)-1
        if x_last!=None:
            distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
        else:
            distance=0

    x=x_list[i]
    y=y_list[i]

    return i, x, y, distance

def find_beach_reference_contour(chain_list, z_ave_list, x_list, y_list, x_last, y_last, deriv_ave_list,deriv_real_list, check_value):
    #note that z_list should be the 9 point moving average
    #assumes that beaches are shallow (|derivative|<0.3), sloping and between 0 - 4 m AHD

    i=len(z_ave_list)-1
    while i>=0 and (z_ave_list[i]>check_value+2 or z_ave_list[i]<check_value-2  or deriv_ave_list[i]>0 or max([abs(i) for i in deriv_real_list[max(0,i-7):i]]+[0])>0.3):#beaches are shallow sloping, low
        i=i-1

    #find the first time it gets to check_value after this
    while i>=0 and z_ave_list[i]<check_value:
        i=i-1

    if i==0:
        i=len(z_ave_list)-1 # the whole this is above the beach

    if x_last!=None:
        distance=((x_last - x_list[i])**2+(y_last - y_list[i])**2)**0.5
    else:
        distance=0
    x=x_list[i]
    y=y_list[i]

    return i, x, y, distance


def find_beach_slope(chain_list, z_ave_list, ref_index, check_value):
    #ref index is the index of the check value
    #find the beach slope between this point and 1 m above this point

    i=ref_index
    while i>0 and z_ave_list[i]<check_value+1:
        i=i-1

    # Hide numpy floating point arithmetic warnings
    np.seterr(all='ignore')

    slope=(z_ave_list[i]-z_ave_list[ref_index])/(chain_list[i]-chain_list[ref_index])

    # Show numpy floating point arithmetic warnings
    np.seterr(all=None)


    beach_ave=interpolate.interp1d([min(chain_list),max(chain_list)], [(min(chain_list)-chain_list[ref_index])*slope+z_ave_list[ref_index], (z_ave_list[ref_index]-(chain_list[ref_index]-max(chain_list))*slope)])

    return slope, beach_ave, i

def get_natural_deviation(chain_list, z_list, ref_index, ref_high, beach_ave):
    #for the points considered to be on the beach (reference contour to reference contour +1), find the average natural deviation
    deviation=[]
    for i in range(ref_high, ref_index+1):
        dev_tmp=abs(z_list[i] - float(beach_ave(chain_list[i])))
        deviation.append(dev_tmp)

    natural_deviation=min(np.max(deviation),0.4) #THIS MAY BE TOO CONSERVATIVE

    return natural_deviation

def distance_point_to_poly(x_list, y_list, x_now, y_now):
    #make a line from the mid of x_list, y_list
    end=Point(x_list[-1], y_list[-1])

    point=Point(x_now, y_now)


    dist=point.distance(end)


    return dist

def polygon_wave_runup(xyz_1m, direction, shp_name, set_check_value, distance_check, zone):
    #print('starting processing of wave runup')

    all_data=pd.read_csv(xyz_1m, header=None, names=['X','Y','Z'])

    if direction=='north_south':
        all_data_sorted=all_data.sort_values(by=['X', 'Y'], ascending=[1,0])
    elif direction=='west_east':
        all_data_sorted=all_data.sort_values(by=['Y', 'X'], ascending=[0,1])

    fixed_now=0
    a=0
    X_tmp=[]
    processed_data = pd.DataFrame(columns=['X','Y','Z'])
    list_to_print=[10,20,30,40,50,60,70,80,90]
    crop_line=[]
    top_line=[]
    tmp_x_last=None
    tmp_y_last=None
    exceed_list=[]

    # Create progress bar
    pbar = tqdm(all_data_sorted.iterrows(), total=all_data_sorted.shape[0])
    for index, line in pbar:
        a=a+1
        percent_done=round(a/len(all_data_sorted)*100,1)
        if percent_done in list_to_print:
            #print("Finished %s%% of the processing" % percent_done)
            list_to_print=list_to_print[1:len(list_to_print)]
        if direction=='north_south':
            check_this=line['X']
        elif direction=='west_east':
            check_this=line['Y']
        if check_this==fixed_now:
            X_tmp.append(line['X'])
            Y_tmp.append(line['Y'])
            Z_tmp.append(line['Z'])
        else:
            if len(X_tmp)!=0:
                #try: ########may need to change!~!
                if len(X_tmp)>10:
                    Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value)
                #except:
                else:
                    Z_set=Z_tmp
                    temp_x=X_tmp[len(Z_set)-1]
                    temp_y=Y_tmp[len(Z_set)-1]
                    distance=0

                distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
                if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer)
                    tmp_x_last=temp_x
                    tmp_y_last=temp_y
                    crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
                    top_line.append([X_tmp[0], Y_tmp[0]])

                    #otherwise crop by the distance_check
                else:
                    exceed_list.append(1)
                    try:
                        tmp_x_last=X_tmp[len(X_tmp)-distance_check] #beacuse this is a 1m DSM, this works
                        tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]

                        crop_line.append([tmp_x_last, tmp_y_last])
                        top_line.append([X_tmp[0], Y_tmp[0]])
                    except:
                        print('problem with the last crop point, keeping whole line')
                        crop_line.append([X_tmp[-1], Y_tmp[-1]])
                        top_line.append([X_tmp[0], Y_tmp[0]])

                if direction=='north_south':
                    fixed_now=line['X']
                elif direction=='west_east':
                    fixed_now=line['Y']
                X_tmp=[line['X']]
                Y_tmp=[line['Y']]
                Z_tmp=[line['Z']]

            else:
                if direction=='north_south':
                    fixed_now=line['X']
                elif direction=='west_east':
                    fixed_now=line['Y']
                X_tmp=[line['X']]
                Y_tmp=[line['Y']]
                Z_tmp=[line['Z']]


    #for the last line
    derivative, chainage=forward_derivative(X_tmp, Y_tmp, Z_tmp)
    if len(X_tmp)>10:
        Z_set, chainage_tmp, temp_x, temp_y, distance=remove_problems(X_tmp, Y_tmp, Z_tmp,tmp_x_last, tmp_y_last, set_check_value)
    #except:
    else:
        Z_set=Z_tmp
        temp_x=X_tmp[len(Z_set)-1]
        temp_y=Y_tmp[len(Z_set)-1]
        distance=0
    X_set=X_tmp[0:len(Z_set)]
    Y_set=Y_tmp[0:len(Z_set)]


    #write to new data frame
    #if len(Z_set)>0:
    #    for i in range(0, len(Z_set)):
    #        processed_data =processed_data.append({'X':X_set[i],'Y':Y_set[i],'Z':Z_set[i],'r':r_set[i],'g':g_set[i],'b':b_set[i]}, ignore_index=True)
    #add to crop line
    distance_2_old=distance_point_to_poly(X_tmp, Y_tmp, temp_x, temp_y)
    if distance_2_old<distance_check: # find a way to change so it is checking the distance from the first crop polyogn, concave_now.buffer(buffer)
        tmp_x_last=temp_x
        tmp_y_last=temp_y
        crop_line.append([X_tmp[len(Z_set)-1], Y_tmp[len(Z_set)-1]])
        top_line.append([X_tmp[0], Y_tmp[0]])
        #otherwise crop by the distance_check
    else:
        exceed_list.append(1)
        tmp_x_last=X_tmp[len(X_tmp)-distance_check]
        tmp_y_last=Y_tmp[len(Y_tmp)-distance_check]
        crop_line.append(tmp_x_last, tmp_y_last)
        top_line.append([X_tmp[0], Y_tmp[0]])

        #otherwise dont add.  straight line is better
    if direction=='north_south':
        y_filtered=nine_pt_moving_average([i[1] for i in crop_line])
        crop_new=[[crop_line[i][0],y_filtered[i]] for i in range(0, len(crop_line))]
    elif direction=='west_east':
        x_filtered=nine_pt_moving_average([i[0] for i in crop_line])
        crop_new=[[x_filtered[i],crop_line[i][1]] for i in range(0, len(crop_line))]

    for_shape=crop_new+top_line[::-1]
    for_shape.append(crop_new[0])
    #print('exceeded the manual distance_check %s%% of the time. manually cropping undertaken' % (round(len(exceed_list)/a,2)*100))
    #making the cropping shapefile
    #print('making the crop polygon')

    # Export polygon as shapefile
    df = gpd.GeoDataFrame(geometry=[Polygon(for_shape)])
    df.crs = {'init': 'epsg:283{}'.format(zone), 'no_defs': True}
    df.to_file(shp_name, driver='ESRI Shapefile')

    return None

def remove_temp_files(directory):
    for f in os.listdir(directory):
        os.unlink(os.path.join(directory, f))

    return None

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        'input_file',
        metavar='PARAMS_FILE',
        help='name of parameter file',
        default=None)

    # Print usage if no arguments are provided
    if len(sys.argv) == 1:
        parser.print_help(sys.stderr)
        sys.exit(1)

    args = parser.parse_args()

    # read the parameters file and scroll through it
    input_file = args.input_file
    input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
    params_file=pd.read_excel(input_file, sheet_name="PARAMS")

    for i, row in params_file.iterrows():
        print("Starting to process %s" % row['Beach'])
        input_las = row['INPUT LAS']
        initial_crop_poly = row['INITIAL CROP POLY']
        lasground_step  = row['LASGROUND STEP']
        zone_MGA = row['ZONE MGA']
        check_value = row['CHECK VALUE']
        direct = row['DIRECTION']
        check_distance = row['CHECK DISTANCE']
        las_dir  = row['LAS CLASSIFIED FOLDER']
        shp_dir  = row['SHP SWASH FOLDER']
        tmp_dir = row['TMP FOLDER']

        # Get base name of input las
        las_basename = os.path.splitext(os.path.basename(input_las))[0]

        # Crop to beach boundary
        print('Clipping...')
        las_data = call_lastools('lasclip', input=input_las, output='-stdout',
                                 args=['-poly', initial_crop_poly], verbose=False)

        # Classify ground points
        print('Classifying ground...')
        las_data = call_lastools('lasground_new', input=las_data, output='-stdout',
                                 args=['-step', lasground_step], verbose=False)

        # Save classified point cloud
        las_name = os.path.join(las_dir, las_basename + '.las')
        with open (las_name, 'wb') as f:
            f.write(las_data)

        # Interpolate point cloud onto a grid
        print('Interpolating to grid...')
        xyz_name = os.path.join(tmp_dir, las_basename + '.xyz')
        call_lastools('las2dem', input=las_data, output=xyz_name,
                      args=['-step', 1], verbose=False)

        # Make runup clipping mask from gridded point cloud
        print('Calculating runup clipping mask...')
        shp_name = os.path.join(shp_dir, las_basename + '.shp')
        polygon_wave_runup(xyz_name, direct, shp_name, check_value, check_distance, zone_MGA)
        #NOTE THAT YOU NEED TO CHECK THE OUTPUT SHP FILE AND ADJUST AS REQUIRED

        #delete the temp files
        remove_temp_files(tmp_dir)


if __name__ == '__main__':
    main()