├── .gitignore ├── CHANGES.txt ├── LICENSE ├── PKG-INFO ├── README.md ├── WC1.las ├── forestlas ├── __init__.py ├── __init__.pyc ├── canopyComplexity.py ├── extractPlots.py ├── geometricTree.py ├── global_header.py ├── lasIO.py ├── lasIO_.py ├── lasIO_pool.py ├── lasStructure.py ├── lasStructure.pyc ├── recover_temp_files_.py ├── thinCloud.py ├── woodyAttribute.py ├── woodyAttribute_.py └── woodyAttribute_pool.py ├── forestlas_intro.ipynb └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | 107 | build/* 108 | -------------------------------------------------------------------------------- /CHANGES.txt: -------------------------------------------------------------------------------- 1 | v0.1.0, 1/10/2013 -- Initial release. -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 1.0 2 | Name: ForestLAS 3 | Version: 0.1.3.5 4 | Summary: Tools for generating woody attribution features 5 | Home-page: http://www.crcsi.com.au/Research/2-Feature-Extraction/2-07-Woody-Vegetation 6 | Author: Phil Wilkes 7 | Author-email: phil.wilkes@rmit.edu.au 8 | License: LICENSE.txt 9 | Description: UNKNOWN 10 | Platform: UNKNOWN 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # forestlas 2 | [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) 3 | 4 | ![LiDAR derived vertical profiles](http://www2.geog.ucl.ac.uk/~ucfaptv/3_PLOTS_geo_trees_bar_spectra_v5.png) 5 | Python code for generating metrics of forest vertical structure from airborne LiDAR data. This code was developed as 6 | part of my PhD (completed in 2016, can be viewed 7 | here) 8 | and was developed over the forests of Victoria, Australia. 9 | The aim was to develop a suite of metrics that are robust to forest type i.e. can be applied without prior information of 10 | forest structure. 11 | 12 | There are a number of methods available, check this 13 | Jupyter notebook for an introduction. 14 | Functions include reading `.las` files to numpy array, writing to `.las` as well as a number of methods to dice, slice and tile 15 | LiDAR data. 16 | The main set of functions found in `forestlas.canopyComplexity`. 17 | These allow you to derive metrics of vertical canopy structure such as Pgap and also estimate number of canopy layers. 18 | More information can be found in this paper Wilkes, P. et al. (2016). Using discrete-return airborne laser scanning to 19 | quantify number of canopy strata across diverse forest types. Methods in Ecology and Evolution, 7(6), 700–712. 20 | 21 | 22 | #### Funding 23 | This research was funded by the Australian Postgraduate Award, Cooperative Research Centre for Spatial Information 24 | under Project 2.07, TERN/AusCover and Commonwealth Scientific and IndustrialResearch Organisation (CSIRO) Postgraduate 25 | Scholarship. 26 | -------------------------------------------------------------------------------- /WC1.las: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/philwilkes/forestlas/5d0439adab918b51b00881186f1b1d670216b4cf/WC1.las -------------------------------------------------------------------------------- /forestlas/__init__.py: -------------------------------------------------------------------------------- 1 | # http://www.diveintopython3.net/case-study-porting-chardet-to-python-3.html#multifile-modules 2 | 3 | def detect(aBuf): 4 | from . import universaldetector 5 | u = universaldetector.UniversalDetector() 6 | u.reset() 7 | u.feed(aBuf) 8 | u.close() 9 | return u.result -------------------------------------------------------------------------------- /forestlas/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/philwilkes/forestlas/5d0439adab918b51b00881186f1b1d670216b4cf/forestlas/__init__.pyc -------------------------------------------------------------------------------- /forestlas/canopyComplexity.py: -------------------------------------------------------------------------------- 1 | # Wilkes et al. in review. Using discrete-return ALS for the assessment 2 | # of vertical canopy structure across diverse forest types 3 | # Methods in Ecology and Evolution 4 | 5 | from scipy.stats import * 6 | import numpy as np 7 | from scipy.interpolate import UnivariateSpline 8 | import glob 9 | import os 10 | import tempfile 11 | import multiprocessing 12 | import numpy.lib.recfunctions as rfn 13 | 14 | from forestlas.lasIO import * 15 | 16 | np.seterr(invalid="ignore") 17 | 18 | class CanopyComplexity: 19 | 20 | def __init__(self, verbose=False, mp=False): 21 | 22 | self.verbose = verbose 23 | 24 | if mp is not False: 25 | self.tempDir = mp 26 | else: 27 | self.tempDir = False 28 | 29 | def fromLAS(self, las, threshold=2.0, top_threshold=99, z_scale=False): 30 | 31 | self.threshold = threshold 32 | 33 | if isinstance(las, str): 34 | lasRAW = lasIO(las, tmpDir=self.tempDir, keepTemp=False).all().asArray() # reads las file 35 | 36 | if lasRAW['rtn_tot'].max() == 0: 37 | lasRAW['rtn_tot'][:] = 1 38 | 39 | self.las = lasRAW[lasRAW['rtn_tot'] > 0] # inds lines where rtn_tot == 0 and removes them 40 | self.z_scale = parseHeader(las)['zscale'] 41 | 42 | elif isinstance(las, np.ndarray): 43 | 44 | self.las = las 45 | if not z_scale: 46 | self.z_scale = .01 47 | 48 | else: 49 | raise Exception('input needs to path to LAS file or a LAS file array') 50 | 51 | self.z = self.las['z'] 52 | self.zw = self.las['rtn_tot'] 53 | self.lenLAS = len(self.las) 54 | 55 | rtn_weight = np.around(1. / self.las['rtn_tot'], decimals=2) 56 | self.total = np.sum(rtn_weight) # sum of weighted returns 57 | 58 | # removes ground and outliers 59 | idx = [(self.z > threshold) & (self.z < np.percentile(self.z, top_threshold))] 60 | self.z = self.z[idx] # ...from z 61 | self.zw = rtn_weight[idx] # ... and from weighted heights 62 | self._create_bins() 63 | 64 | return self 65 | 66 | def _create_bins(self): 67 | 68 | # create bin array 69 | #### required because of numpy float issue ### 70 | factor = 1 / self.z_scale 71 | z_min, z_max = self.threshold * factor, int((self.z.max() + (self.z_scale*1000)) * factor) 72 | ############################################## 73 | self.bins = int(z_max-(z_min-1)) # number of bins 74 | self.zxOrig = np.linspace(z_min, z_max, self.bins) / factor # "x-axis" 75 | 76 | return self 77 | 78 | def Pgap(self, frequency=False): 79 | 80 | if len(self.z) == 0: 81 | self.pgap = np.array([1]) 82 | else: 83 | #calculatePgap 84 | if not frequency: 85 | self.pgap = np.zeros(self.bins) # array for populating with pgap 86 | for (i, height_below_toc) in enumerate(self.zxOrig): 87 | idx = [self.z >= height_below_toc] # index all rows >= height z 88 | weight_of_returns_above_z = sum(self.zw[idx]) # calculates sum of weighted returns above height z 89 | self.pgap[i] = 1. - (weight_of_returns_above_z / self.total) # populates pgap with proportion of weight 90 | else: 91 | #calculateFreqPgap 92 | self.pgap = np.zeros(self.bins) # array for populating with pgap 93 | for (i, height_below_toc) in enumerate(sorted(self.zxOrig)): 94 | idx = [self.z >= height_below_toc] # index all rows >= height z 95 | num_of_returns_above_z = len(self.z[idx]) # calculates len of z array above height z 96 | self.pgap[i] = 1. - (num_of_returns_above_z / float(self.lenLAS)) # populates pgap with proportion of weight 97 | 98 | return self 99 | 100 | def CHP(self, method="sample", alpha=.3, frequency=False, normalise=False, noise=0.05): 101 | 102 | """ 103 | method is "model" for a log transformed and normalised CHP and "sample" for anything else! 104 | frequncy uses retrun frequency instead of weight 105 | """ 106 | 107 | if len(self.z) == 0: 108 | self.pgap = np.array([1]) 109 | self.fd = np.array([]) 110 | self.sd = np.array([]) 111 | self.spline = None 112 | self.ps = np.array([]) 113 | self.zx = np.array([1]) 114 | self.layerLocation = np.array([]) 115 | self.layerCount = 0 116 | self.crownBase = np.array([]) 117 | 118 | else: 119 | if method == 'sample': 120 | normalise = False 121 | self.alpha = alpha 122 | log_transform = False 123 | elif method == "model": 124 | normalise = True 125 | self.alpha = 0 126 | log_transform = True 127 | elif method == 'log_transform': 128 | normalise = False 129 | self.alpha = alpha 130 | log_transform = True 131 | else: 132 | raise Exception('method not recognised') 133 | 134 | self.Pgap() 135 | 136 | #smooth_pgap 137 | self.spline = UnivariateSpline(self.zxOrig, self.pgap, s=self.alpha) 138 | self.ps = self.spline(self.zxOrig) 139 | 140 | # clips ps and zx vectors to maximum height 141 | self.ps = self.ps[np.where(self.zxOrig < self.z.max() * 1)] 142 | self.pgap = self.pgap[np.where(self.zxOrig < self.z.max() * 1)] 143 | self.zx = self.zxOrig[np.where(self.zxOrig < self.z.max() * 1)] 144 | 145 | # log transformation ... or not 146 | if log_transform: 147 | self.cc = -np.log(self.ps) 148 | else: 149 | self.cc = 1 - self.ps 150 | 151 | # first_derivative 152 | # self.fd1 = np.hstack([np.diff(self.cc[::-1]) / self.z_scale, 0])[::-1] 153 | self.fd = -(np.gradient(self.cc) / self.z_scale) 154 | 155 | # removes any lingering negative values 156 | if self.fd.min() < 0: 157 | self.fd = self.fd + (-self.fd.min()) 158 | 159 | # normalise so sum of vector == 1, required for probability 160 | if normalise: 161 | self.fd = self.fd * ((1-self.pgap.min())/np.sum(self.fd)) 162 | 163 | #second_derivative 164 | self.sd = np.gradient(self.fd) / self.z_scale 165 | 166 | #number_of_modes 167 | signs = np.diff(self.sd/abs(self.sd)) # finds zero crossings 168 | idx = np.where(signs == -2)[0] 169 | potentialLayerLocation = self.zx[idx] # and their height 170 | layerAmplitude = self.fd[idx] # and the signal amplitude 171 | maxAmplitude = self.fd.max() # and the maximum amplitude for the CHP 172 | self.layerLocation = [layer for i, layer in enumerate(potentialLayerLocation) if layerAmplitude[i] > maxAmplitude * noise] # and filters noise 173 | self.layerCount = len(self.layerLocation) 174 | self.crownBase = self.zx[idx] # and their height 175 | 176 | return self 177 | 178 | def simulateCloud(self): 179 | 180 | """ 181 | This simulates a height vector and then assigns heights a weight 182 | """ 183 | 184 | self.zAll = np.hstack([0, self.zx]) # add ground to height bins 185 | 186 | # height weighting vector 187 | self.heightWeight = np.hstack([self.pgap.min(), self.fd]) # weights for each height bin and add ground weight 188 | #self.heightWeight = np.ma.masked_array(self.heightWeight, np.isnan(self.heightWeight)) 189 | 190 | # return weighting vector 191 | self.returnWeight = {} # dictionary to store self.returnWeight 192 | for h in np.unique(np.floor(self.zAll)): # rounds xs down to 1 metre bins 193 | # selects returns with heights in bin h 194 | self.returnWeight[h] = {} # creates dictionary within self.returnWeight to store count of NoR values 195 | idx = [(self.z > h) & (self.z <= h + 1)] 196 | NoR = self.zw[idx] # total number of returns for returns in range h to h+1 197 | for rtn in np.unique(NoR): 198 | self.returnWeight[h][rtn] = len(NoR[NoR == rtn]) # counts number of returns by rtn_tot 199 | sumNoR = np.sum(self.returnWeight[h].values()) # counts number of returns in bin 200 | for rtn in self.returnWeight[h].keys(): # return values in height bin 201 | # self.returnWeight[h][rtn] = self.returnWeight[h][rtn] / np.float(sumNoR) # calculates weight 202 | self.returnWeight[h][rtn] /= np.float(sumNoR) 203 | 204 | # Simulated height 205 | self.simHeightAll = np.random.choice(self.zAll, max(self.lenLAS, 100), p=self.heightWeight) 206 | #self.simHeightAll = np.random.choice(self.zAll, 100, p=self.heightWeight) 207 | 208 | # Simulated weights 209 | self.simRtnWeight = np.zeros(len(self.simHeightAll)) # array to store weights 210 | for i, z in enumerate(self.simHeightAll): 211 | hgt_bin = np.floor(z) 212 | for offset in [0, 1, -1, 2, -2, 3, -3]: # if weighting bin is empty selects a neigbour 213 | try: 214 | rtn_num = self.returnWeight[hgt_bin + offset].keys() # return numbers 215 | rtn_weights = self.returnWeight[hgt_bin + offset].values() # probability of return number 216 | """ return number randomly chosen (weighted by probability of return) """ 217 | self.simRtnWeight[i] = np.random.choice(rtn_num, 1, p=rtn_weights) # assignment of NoR value (divided by 1) 218 | break 219 | except: 220 | self.simRtnWeight[i] = 1 221 | 222 | return rfn.merge_arrays([np.array(self.simHeightAll, dtype=[('z', np.float)]), 223 | np.array(self.simRtnWeight, dtype=[('rtn_tot', np.float)])]) 224 | 225 | class bootstrapComplexity: 226 | 227 | import multiprocessing 228 | 229 | def __init__(self, las, verbose=False, processes=1, N=100): 230 | 231 | self.N = N 232 | self.verbose = verbose 233 | 234 | if type(las) is str: 235 | if os.path.isdir(las): 236 | self.l = glob.glob(os.path.join(las, "*.znr")) 237 | if len(self.l) == 0: self.l = glob.glob(os.path.join(las, "*.las")) 238 | elif os.path.isfile(las): self.l = [las] 239 | elif type(las) is list: 240 | self.l = las 241 | else: 242 | raise IOError("No .las or .znr files in {}".format(las)) 243 | 244 | self.mp(processes) 245 | 246 | def chp(self, las): 247 | 248 | if self.verbose: print 'processing:', las 249 | 250 | pid = multiprocessing.current_process()._identity[0] 251 | tempDirectoryName = "lidar.processing." + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + ".tmp" 252 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 253 | 254 | self.bsCHP = np.zeros(self.N) 255 | 256 | self.chp = CanopyComplexity(mp=tempDirectory).fromLAS(las).CHP("model") 257 | 258 | for i in range(self.N): 259 | 260 | z = self.chp.simulateCloud() 261 | 262 | if z['z'].max() < 2 or len(z) < 2 or 0 in z['rtn_tot']: 263 | self.bsCHP[i] = 0 264 | else: 265 | sample = CanopyComplexity().fromLAS(z).CHP() 266 | self.bsCHP[i] = sample.layerCount 267 | 268 | if type(las) is str: plotName = os.path.split(os.path.splitext(las)[0])[1] 269 | else: plotName = 'array' 270 | self.chp_dictionary[plotName] = self.bsCHP 271 | return self 272 | 273 | def mp(self, maxProcess): 274 | 275 | listI = 0 276 | manager = multiprocessing.Manager() 277 | self.chp_dictionary = manager.dict() 278 | 279 | for i in range((len(self.l) / maxProcess) + 1): 280 | 281 | jobs = [] 282 | 283 | if (maxProcess * listI) + maxProcess < len(self.l): 284 | processingList = self.l[maxProcess * listI: (maxProcess * listI) + maxProcess] 285 | else: processingList = self.l[maxProcess * listI:] 286 | 287 | for j, las in enumerate(processingList): # limits number of images run at once 288 | p = multiprocessing.Process(target=self.chp, args=(las, )) 289 | jobs.append(p) 290 | p.start() 291 | 292 | for proc in jobs: 293 | proc.join() 294 | 295 | listI += 1 296 | 297 | self.bsCHPmutiple = dict(self.chp_dictionary) 298 | 299 | if __name__ == '__main__': 300 | 301 | path = '/Users/phil/ALS/WC/spl/tile_20/WC1_5m_TILES/383475.0_5828910.0.las' 302 | las = lasIO(path).all().asArray() 303 | # plot = 'PE2744N2556' 304 | # las_path = os.path.join(path, plot + '.las') 305 | # las = CanopyComplexity().fromSample(las['z'], las['rtn_tot']).CHP('model') 306 | las = CanopyComplexity().fromLAS(las).CHP('model') 307 | chp = CanopyComplexity().fromLAS(las.simulateCloud()).CHP() 308 | print chp.zw 309 | 310 | 311 | -------------------------------------------------------------------------------- /forestlas/extractPlots.py: -------------------------------------------------------------------------------- 1 | """ 2 | Extract points for a given trap size around a given coordinate 3 | """ 4 | 5 | import os, sys, subprocess, csv, shutil, tempfile 6 | import MySQLdb as mdb 7 | import numpy as np 8 | from lasIO import * 9 | import multiprocessing 10 | import traceback 11 | 12 | class extract: 13 | 14 | def __init__(self, verbose=False, processes=4): 15 | 16 | self.verbose = verbose 17 | self.cwd = os.getcwd() 18 | self.maxProcess = int(processes) 19 | 20 | def query(self, points_raw, src="point", out=False, extent=24, 21 | round=True, flightlines=False, search=".", tmpDir=True, 22 | namePrefix=''): 23 | 24 | """ 25 | Is it a points query or an extent? 26 | """ 27 | try: 28 | self.fl = flightlines 29 | self.round = round 30 | self.extent = extent 31 | self.search = "%" + search + "%" 32 | self.tmpDir = tmpDir 33 | self.namePrefix = namePrefix 34 | 35 | if not out: 36 | parentDir, outDir = os.path.split(points_raw) 37 | if len(parentDir) == 0: parentDir = os.getcwd() 38 | self.outDir = os.path.join(parentDir, os.path.splitext(outDir)[0]) 39 | self.outLAS = False 40 | elif os.path.isdir(out): 41 | self.outDir = out 42 | self.outLAS = False 43 | elif out.endswith('.las'): 44 | self.outDir, self.outLAS = os.path.split(out) 45 | else: 46 | raise IOError('output destination not recognised') 47 | 48 | if os.path.isdir(self.outDir) is False: 49 | os.makedirs(self.outDir) 50 | 51 | if self.verbose == True: 52 | print "src: {}".format(src) 53 | print "extent: {}".format(self.extent) 54 | print "round: {}".format(round) 55 | print "flightlines selected: {}".format(self.fl) 56 | 57 | if src == "point": 58 | self.read_points(points_raw) 59 | else: 60 | plots = read_extent(points_raw) 61 | 62 | except Exception as err: 63 | print traceback.format_exc() 64 | finally: 65 | os.chdir(self.cwd) 66 | 67 | def read_points(self, points_raw): 68 | 69 | self.tiles_to_process = [] 70 | 71 | with open(points_raw) as pr: 72 | 73 | for i in pr.read().split('\n'): 74 | if i.find('#') != -1: 75 | continue 76 | elif i != "": 77 | i = str.split(i, ',') 78 | point = str(i[0]) 79 | x = float(i[1]) 80 | y = float(i[2]) 81 | else: continue 82 | 83 | #names point if not 84 | if point == None: 85 | point = str(x) + '_' + str(y) 86 | 87 | #print "processing point: {} x: {} y: {}".format(point, x, y) 88 | 89 | # calculates search self.extent 90 | xmin = float(x)-(self.extent/2.) 91 | ymin = float(y)-(self.extent/2.) 92 | xmax = float(x)+(self.extent/2.) 93 | ymax = float(y)+(self.extent/2.) 94 | 95 | if xmin > ymin: 96 | xmin, ymin, xmax, ymax = ymin, xmin, ymax, xmax 97 | 98 | self.tiles_to_process.append({"xmin":xmin, "ymin":ymin, 99 | "xmax":xmax, "ymax":ymax, 100 | "point":point}) 101 | 102 | if self.maxProcess == 0: 103 | self.select_tiles(xmin, ymin, xmax, ymax, point) 104 | else: self.mp() 105 | 106 | def mp(self): 107 | 108 | listI = 0 109 | 110 | for i in range((len(self.tiles_to_process) / self.maxProcess) + 1): 111 | 112 | jobs = [] 113 | 114 | try: 115 | if (self.maxProcess * listI) + self.maxProcess < len(self.tiles_to_process): 116 | processingList = self.tiles_to_process[self.maxProcess * listI: (self.maxProcess * listI) + self.maxProcess] 117 | else: processingList = self.tiles_to_process[self.maxProcess * listI:] 118 | 119 | 120 | 121 | for j, t in enumerate(processingList): # limits number of images run at once 122 | p = multiprocessing.Process(target=self.select_tiles, args=(t["xmin"], 123 | t["ymin"], 124 | t["xmax"], 125 | t["ymax"], 126 | t["point"], )) 127 | jobs.append(p) 128 | p.start() 129 | 130 | for proc in jobs: 131 | proc.join() 132 | 133 | except: 134 | print self.tiles_to_process[0:1] 135 | print self.maxProcess, type(self.maxProcess) 136 | print listI, type(listI) 137 | raise NameError 138 | 139 | listI += 1 140 | 141 | def select_tiles(self, xmin, ymin, xmax, ymax, point): 142 | 143 | ## connects to db 144 | try: 145 | con = mdb.connect('127.0.0.1', 'seo', 'lidar', 'lidar') # server, user, pw, db 146 | cur = con.cursor() 147 | except Exception, err: 148 | raise Exception(err) 149 | 150 | ## selects either raw flightlines or processed .las files 151 | if self.fl == True: 152 | fl = '"%classed"' 153 | else: 154 | fl = '"%_height"' 155 | 156 | # queries db for available als tiles 157 | query = ('select concat(path, "/", tilename, ".", format) from tiles where \ 158 | (xmin > %(xmin)s and xmin < %(xmax)s and ymin > %(ymin)s and ymin < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 159 | (xmax > %(xmin)s and xmax < %(xmax)s and ymax > %(ymin)s and ymax < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 160 | (xmin > %(xmin)s and xmin < %(xmax)s and ymax > %(ymin)s and ymax < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 161 | (xmax > %(xmin)s and xmax < %(xmax)s and ymin > %(ymin)s and ymin < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 162 | (xmax > %(xmax)s and xmin < %(xmin)s and ymax > %(ymin)s and ymax < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 163 | (xmax > %(xmax)s and xmin < %(xmin)s and ymin > %(ymin)s and ymin < %(ymax)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 164 | (xmax < %(xmax)s and xmax > %(xmin)s and ymax > %(ymax)s and ymin < %(ymin)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 165 | (xmin < %(xmax)s and xmin > %(xmin)s and ymax > %(ymax)s and ymin < %(ymin)s and (path like "%(name)s" or tilename like "%(name)s")) or \ 166 | (xmax > %(xmax)s and xmin < %(xmin)s and ymax > %(ymax)s and ymin < %(ymin)s and (path like "%(name)s" or tilename like "%(name)s"))\ 167 | ' % {'xmin':xmin, 'xmax':xmax, 'ymin':ymin, 'ymax':ymax, 'name':self.search}) 168 | 169 | # if self.verbose is True: 170 | # print query 171 | 172 | # creates list of tiles from query 173 | cur.execute(query) 174 | ans = cur.fetchall() 175 | self.tile_list = [a[0] for a in ans] 176 | cur.close() # closes cursor 177 | con.close() # closes db connection 178 | 179 | if len(self.tile_list) == 0 and self.verbose == True: 180 | print query 181 | 182 | ## self.extent of tile 183 | print 'processing %s tile(s) for point %s' % (len(self.tile_list), point) 184 | if self.verbose: 185 | for tile in self.tile_list: 186 | print tile 187 | 188 | if len(self.tile_list) > 0: 189 | if not self.outLAS: 190 | self.outLAS = os.path.join(self.outDir, 191 | '{}_'.format(self.namePrefix) + point + '.las') 192 | self.clipLAS(xmin, ymin, xmax, ymax) 193 | else: 194 | print "outside of bounds: {}".format(point) 195 | with open(os.path.join(self.outDir, "log.csv"), "a") as log: 196 | log.write(",".join([point, "Out of bounds", str(np.mean([xmax, xmin])), 197 | str(np.mean([ymax, ymin])), "\n"])) 198 | 199 | def clipLAS(self, xmin, ymin, xmax, ymax): 200 | 201 | x = np.mean([xmax, xmin]) 202 | y = np.mean([ymax, ymin]) 203 | 204 | if not self.tmpDir or self.maxProcess == 0: 205 | tempDirectoryName = "lidar.processing." + str(np.random.randint(0, 9999999)) + ".tmp" 206 | else: 207 | pid = multiprocessing.current_process()._identity[0] 208 | tempDirectoryName = "lidar.processing." + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + ".tmp" 209 | self.tmpDir = os.path.join(tempfile.gettempdir(), tempDirectoryName) 210 | 211 | lasIO(self.tile_list, 212 | out=self.outLAS, 213 | verbose=self.verbose, 214 | znr=False, 215 | search=self.search, 216 | tmpDir=self.tmpDir, 217 | keepTemp=False).plot(x, y, 218 | extent=self.extent, 219 | round=self.round).exportLAS() 220 | 221 | if self.verbose: 222 | print ".las exported to: {}".format(self.outLAS) 223 | 224 | if not os.path.isfile(self.outLAS): 225 | with open(os.path.join(self.outDir, "log.csv"), "a") as log: 226 | log.write(",".join([point, ",".join(self.tile_list), str(x), str(y), "\n"])) 227 | 228 | if __name__=='__main__': 229 | extract(verbose=False).query(sys.argv[1]) 230 | -------------------------------------------------------------------------------- /forestlas/geometricTree.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | import math 4 | 5 | class crownDim: 6 | 7 | def __init__(self, verbose=False, plot=False): 8 | 9 | self.verbose = verbose 10 | self.plot = plot 11 | 12 | if plot is not False: 13 | self.a = plot 14 | 15 | def genus_params(self, allometric, dbh, height=None, genus=None, species=None, height_class=None, position="none"): 16 | 17 | self.genus = genus 18 | self.position = position 19 | 20 | if allometric == 'scanlan': 21 | ### Scanlan 2010 ### 22 | self.height = 15.868 + (0.411 * dbh) - 0.003 * (dbh - 53.346)**2 23 | self.base = 13.622 + (0.255 * dbh) - 0.002 * (dbh - 53.346)**2 24 | self.radius = 1.966 + (0.115 * dbh) 25 | 26 | elif allometric == 'rushworth': 27 | if species == "polyanthemos" and position == 1: 28 | self.height = 6.73589841952 + 0.199401565137 * dbh 29 | self.radius = 2.44438391898 + 0.0968236918828 * dbh 30 | self.base = 5.64863434095 + 0.0508669633267 * dbh 31 | elif species == "polyanthemos" and position == 2: 32 | self.height = 8.03659852077 + 0.0980603043708 * dbh 33 | self.radius = 1.53665512596 + 0.153832682204 * dbh 34 | self.base = 4.77895559438 + -0.0501664079081 * dbh 35 | elif species == "melliodora" and position == 1: 36 | self.height = 13.1074336283 + 0.181415929204 * dbh 37 | self.radius = -0.0258262268705 + 0.261383748994 * dbh 38 | self.base = -3.84514883347 + 0.421962992759 * dbh 39 | elif species == "melliodora" and position == 2: 40 | self.height = 4.62598991008 + 0.592333644974 * dbh 41 | self.radius = -2.06257925378 + 0.404778261046 * dbh 42 | self.base = 4.13866554657 + 0.0782895004268 * dbh 43 | elif species == "tricarpa" and position == 1: 44 | self.height = 11.2973396649 + 0.118405746654 * dbh 45 | self.radius = 1.90859934333 + 0.11935877297 * dbh 46 | self.base = 7.15742290658 + -0.0309056611952 * dbh 47 | elif species == "tricarpa" and position == 2: 48 | self.height = 5.05200102811 + 0.277243417286 * dbh 49 | self.radius = -0.306595001588 + 0.223036436419 * dbh 50 | self.base = 5.20539453592 + -0.00489743984257 * dbh 51 | elif species == "microcarpa": 52 | self.height = 6.14528846053 + 0.541168944217 * dbh 53 | self.radius = 0.937658231167 + 0.180962968401 * dbh 54 | self.base = 1.10902140512 + 0.3315224801 * dbh 55 | elif species == "macrorrhyncha": 56 | self.height = 3.56849545646 + 0.489722695707 * dbh 57 | self.radius = 0.192442248096 + 0.16049402261 * dbh 58 | self.base = 4.13546317166 + 0.10044532714 * dbh 59 | elif position == 1: 60 | self.height = 3.05768707007 + 3.75103882462 * np.log(dbh) 61 | self.radius = -8.08923000772 + 4.14669504717 * np.log(dbh) 62 | self.base = 7.26517208273 + -0.0301284879315 * dbh 63 | elif position == 2: 64 | self.height = 0.481343823925 + 3.74241504451 * np.log(dbh) 65 | self.radius = -6.32450180289 + 3.57228666209 * np.log(dbh) 66 | self.base = 5.34980523765 + -0.0219543192422 * dbh 67 | else: 68 | print species 69 | 70 | #self.base = 0.616759536617 + 3.07542949969 * np.log(self.height / 2.42) 71 | 72 | ### Rushworth field work 73 | #self.height = 5.59 * np.log(0.48 * dbh) 74 | #self.base = 4.91 * np.log(0.22 * dbh) 75 | #self.radius = 3.28 * np.log(0.15 * dbh) 76 | 77 | elif allometric == 'field': 78 | if "eucalypt" in genus.lower(): 79 | if height_class == 0 and position <= 1: # low height and dominant 80 | self.height = 1.86064881159 + 4.33064331125 * np.log(dbh) # n:306, rmse: 1.45, r2: 0.68 81 | self.radius = -4.40385183196 + 2.99180690117 * np.log(dbh) # n:196, rmse: 0.51, r2: 0.84 82 | self.base = 3.77967694134 + 0.293670137086 * self.height # n:169, rmse: 1.01, r2: 0.22 83 | if height_class == 1 and position <= 1: # med height and dominant 84 | self.height = -6.3614102534 + 11.5476586081 * np.log(dbh) # n:285, rmse: 3.98, r2: 0.40 85 | self.radius = -30.9357885227 + 9.7432187604 * np.log(dbh)# n:272, rmse: 1.40, r2: 0.75 86 | self.base = 11.0629490818 + 0.410104416904 * self.height # n:233, rmse: 3.08, r2: 0.39 87 | # if height_class == 2 and position == 1: # tall height and dominant (all trees) 88 | # self.height = -14.8638124803 + 16.0836040014 * np.log(dbh) # n:22, rmse: 5.78, r2: 0.50 89 | # self.radius = -30.9357885227 + 9.7432187604 * np.log(dbh) # combined with height class >0 as n = 2 90 | # self.base = 11.0629490818 + 0.410104416904 * self.height # ditto 91 | # if height_class == 2 and position == 1: # tall height and dominant (regnans only) 92 | # self.height = -51.6987300663 + 22.503790377 * np.log(dbh) # n:22, rmse: 5.78, r2: 0.50 93 | # self.radius = -17.7715368458 + 7.04971631466 * np.log(dbh) # combined with height class >0 as n = 2 94 | # self.base = 9.83446096282 + 0.285250808259 * self.height # ditto 95 | if height_class == 2 and position == 1: # tall height and dominant (regnans only) 96 | self.height = height # n:22, rmse: 5.78, r2: 0.50 97 | self.radius = -17.7715368458 + 7.04971631466 * np.log(dbh) # combined with height class >0 as n = 2 98 | self.base = 0.6 * height # ditto 99 | if height_class == 0 and position == 2: # low height and suppressed 100 | self.height = -2.20552357108 + 4.98820033612 * np.log(dbh) # n:246, rmse: 1.77, r2: 0.39 101 | self.radius = -6.02277337577 + 3.42232555079 * np.log(dbh) # n:132, rmse: 0.76, r2: 0.48 102 | self.base = 0.84309744471 + 0.39574379931 * self.height # n:132, rmse: 0.99, r2: 0.35 103 | if height_class > 0 and position == 2: # med height and suppressed 104 | self.height = -10.3155403958 + 9.37056330951 * np.log(dbh) # n:23, rmse: 2.51, r2: 0.70 105 | self.radius = -6.02277337577 + 3.42232555079 * np.log(dbh) # combined with height class = 0 as n = 2 106 | self.base = 0.84309744471 + 0.39574379931 * self.height # ditto 107 | else: # non-eucalypt 108 | if height_class == 0: # tall height and dominant 109 | self.height = -7.25044287906 + 6.92940716229 * np.log(dbh) # n:97, rmse: 2.07, r2: 0.55 110 | self.radius = -7.42177845525 + 3.955374589 * np.log(dbh) # n:77, rmse: 0.63, r2: 0.75 111 | self.base = 1.99951178234 + 0.459496058702 * self.height # n:77, rmse: 1.00, r2: 0.69 112 | if height_class == 1: # low height and suppressed 113 | self.height = -11.4257385832 + 8.43514568771 * np.log(dbh) # n:86, rmse: 1.58, r2: 0.73 114 | self.radius = -4.94874693431 + 3.05793566579 * np.log(dbh) # n:11, rmse: 0.56, r2: 0.69 115 | self.base = 2.02235653003 + 0.485452812202 * self.height # n:11, rmse: 0.77, r2: 0.54 116 | if height_class == 2: # med height and suppressed 117 | self.height = -13.944895656 + 9.01486400668 * np.log(dbh) # n:75, rmse: 1.53, r2: 0.82 118 | self.radius = -10.1429095331 + 5.06708994335 * np.log(dbh) # n:32, rmse: 0.54, r2: 0.72 119 | self.base = 2.37924762553 + 0.421325043146 * self.height # n:32, rmse: 0.49, r2: 0.75 120 | 121 | self.height = height 122 | 123 | if height > 50 or species == "regnans": 124 | self.base = (1-0.29) * height 125 | elif height > 10: 126 | self.base = 0.55 * height 127 | else: self.base = 0.7 * height 128 | #print dbh, self.height, self.radius, self.base, height_class, position 129 | 130 | else: raise NameError("allometric relationship keyword not understood") 131 | 132 | #if self.base > self.height: 133 | # self.height, self.base = self.base, self.height 134 | 135 | if self.verbose: 136 | print "dbh: {}, h: {}, r: {}, b: {}, a:{}".format(dbh, self.height, self.radius, self.base, xxx) 137 | 138 | return self 139 | 140 | def ellipsoid_volume(self): 141 | 142 | crown_depth = self.height - self.base 143 | crown_centre = self.height - (crown_depth / 2) 144 | a = crown_depth / 2 # semimajor self.ais 145 | b = self.radius / 2 # semiminor self.ais 146 | 147 | L = {} 148 | 149 | if self.plot is not False: 150 | for t in range(0, 360): 151 | x = a*math.sin(math.radians(t)) 152 | y = b*math.cos(math.radians(t)) 153 | self.a.scatter(y, x + crown_centre, s=1, c="g", edgecolor="none") 154 | #if "ucalypt" in self.genus: 155 | # x = (a * .75) * math.sin(math.radians(t)) 156 | # y = (b * .75) * math.cos(math.radians(t)) 157 | # self.a.scatter(y, x + (a / 1.375) + self.base, s=1, c="r", edgecolor="none") 158 | 159 | eccentricity = np.sqrt(b**2./a**2.) 160 | hyp = a * eccentricity 161 | bins = np.linspace(-np.floor(a), np.floor(a), np.floor(a) * 2 + 1) 162 | 163 | for i, opp in enumerate(bins): 164 | angle = math.asin((opp * eccentricity) / hyp) 165 | adj = math.cos(angle) * hyp 166 | area = np.pi * adj**2 167 | # calculates "hollow" eucalypt crowns 168 | if "ucalypt" in self.genus: 169 | j = float(i) / len(bins) 170 | if j <= 0.75: 171 | factor = self.exp(j, 0.26229889, -1.52996682) 172 | area = area * factor 173 | # calculates suppressed trees 174 | if self.position == 2: 175 | area *= 0.5 176 | if "othofagus" in self.genus: 177 | area *= 1.2 178 | opp += crown_centre 179 | L[opp] = area 180 | 181 | return L 182 | 183 | def exp(self, x, a, b): 184 | return a * np.exp(-b * x) -------------------------------------------------------------------------------- /forestlas/global_header.py: -------------------------------------------------------------------------------- 1 | __author__ = 'phil' 2 | 3 | def h(): 4 | return {'sysid': 'RECOVERED TEMP FILE ', 5 | 'gensoftware': 'CRC207 LiDAR analysis software ', 6 | 'vermajor': 1, 7 | 'verminor': 2, 8 | 'pointformat': 1, 9 | 'numvlrecords': 0, 10 | 'pointreclen': 28, 11 | 'fileyear': 3000, 12 | 'fileday': 226, 13 | 'headersize': 227, 14 | 'reserved': 0, 15 | 'xmin': 9999999., 16 | 'xmax': -9999999., 17 | 'ymin': 9999999., 18 | 'ymax': -9999999., 19 | 'zmin': 9999., 20 | 'zmax': -9999., 21 | 'filesourceid': 0, 22 | 'zoffset': 0.0, 23 | 'guid1': 0, 24 | 'guid2': 0, 25 | 'guid3': 0, 26 | 'guid4': (0, 0, 0, 0, 0, 0, 0, 0), 27 | 'xscale': 0.01, 28 | 'yscale': 0.01, 29 | 'zscale': 0.01, 30 | 'numptbyreturn': (0, 0, 0, 0, 0), 31 | 'infile': None, 32 | 'yoffset': 0.0, 33 | 'filesig': 'LASF', 34 | 'offset': 313-54, 35 | 'xoffset': 0.0, 36 | 'numptrecords': 0, 37 | } 38 | -------------------------------------------------------------------------------- /forestlas/lasIO.py: -------------------------------------------------------------------------------- 1 | import sys, os, struct, array, math, datetime, tempfile, shutil, subprocess 2 | import multiprocessing 3 | import numpy as np 4 | 5 | ### from forestlas.lasStructure import * 6 | from lasStructure import * 7 | 8 | class lasIO: 9 | 10 | """ 11 | This allows reading and writing of .las files (currently supports 1.1 and 1.2). 12 | .las files can be read as one file(s), gridded or a plot of data extracted. 13 | Output types include .las, a numpy array or as .znr file where only the height 14 | and the "number of returns" metadata remain. 15 | """ 16 | 17 | def __init__(self, path, out=False, znr=False, verbose=False, search=".", 18 | tmpDir=False, keepTemp=False): 19 | 20 | """ 21 | Functions creates holder for .las file and setups required dictionaries 22 | etc. 23 | 24 | Parameters 25 | ---------- 26 | path: File path or list 27 | file path to tile .las file or directory containing .las files, 28 | also excepts a list of file paths. 29 | 30 | out: Path to directory or path to save .las to, Default None 31 | If None then saves output to os.getcwd() 32 | 33 | znr: Boolean. Default True 34 | Save output as a .znr file where only the height and "number of 35 | returns metadata is retained. 36 | 37 | search: String. Default "." 38 | Can be used to filter .las files if a directory is supplied for 39 | "path" 40 | 41 | tmpDir: File path to temporary directory. Default False 42 | Multiprocessing is not available with this module but is by others 43 | that call lasIO. This specifies the temporary directory. 44 | 45 | keepTemp: Boolean. Default True 46 | When False all temporary files are kept. It is important to run 47 | removeTemp() to clean up before exiting 48 | 49 | Returns 50 | ------- 51 | out: self 52 | 53 | """ 54 | 55 | self.verbose = verbose 56 | self.keepTemp = keepTemp 57 | 58 | ### parse args and create file structure 59 | # is path a directory or file 60 | if isinstance(path, list) : 61 | self.tileList = path 62 | self.dir = os.path.split(path[0])[0] 63 | self.tile = None 64 | elif os.path.isfile(path) : 65 | self.dir = os.path.split(path)[0] 66 | if self.dir == '': self.dir = os.getcwd() 67 | self.tile = os.path.split(path)[1] 68 | self.tileList = [os.path.join(self.dir, self.tile)] 69 | elif os.path.isdir(path) : 70 | self.dir = path 71 | self.tile = None 72 | tileList = os.listdir(self.dir) 73 | self.tileList = [tile for tile in tileList if (tile.endswith(".las") 74 | or tile.endswith(".laz") 75 | or tile.endswith(".znr")) 76 | and tile.find(search) != -1] 77 | else: 78 | raise NameError ("path not recognised") 79 | 80 | if len(self.tileList) == 0: 81 | raise IOError("There are no .las, .laz or .znr tiles in {}".format(path)) 82 | 83 | # create temporary directory in %temp% 84 | if tmpDir: 85 | self.tempDirectory = tmpDir 86 | if os.path.isdir(self.tempDirectory) is False: 87 | os.makedirs(self.tempDirectory) 88 | else: 89 | tempDirectoryName = "lidar.processing." + str(np.random.randint(0, 9999999)) + ".tmp" 90 | self.tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 91 | os.makedirs(self.tempDirectory) 92 | 93 | #tempDirectoryName = "lidar.processing." + str(np.random.randint(0, 9999999)) + ".tmp" 94 | #self.tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 95 | #os.makedirs(self.tempDirectory) 96 | 97 | if self.verbose: print "temporary directory at: {}".format(self.tempDirectory) 98 | 99 | # create output directory 100 | if not out: 101 | self.savePath = self.dir 102 | self.saveLAS = None 103 | elif os.path.isdir(out) : 104 | self.savePath = out 105 | self.saveLAS = None 106 | else: 107 | self.savePath = os.path.split(out)[0] 108 | self.saveLAS = os.path.split(out)[1] 109 | 110 | # znr 111 | if znr: 112 | self.znr = True 113 | else: self.znr = False 114 | 115 | # global variables 116 | self.numberTiles = 1 117 | for i, las in enumerate(self.tileList): 118 | las = os.path.join(self.dir, las) 119 | if i == 0: 120 | self.globalHeader = parseHeader(las) 121 | # self.globalHeader["numptrecords"] -= 1 122 | if self.globalHeader['filesig'] == "ZNRF": 123 | self.ptFrmt, self.dt = znrStruct() 124 | self.znr = True 125 | else: 126 | self.ptFrmt, self.dt = getPtFrmt(self.globalHeader, verbose=True) 127 | self.vlr = getVLR(self.globalHeader["headersize"], las) 128 | self.h = parseHeader(las) 129 | else: 130 | lasHeader = parseHeader(os.path.join(path, las)) 131 | self.globalHeader["numptrecords"] += lasHeader["numptrecords"] 132 | if lasHeader["xmax"] > self.globalHeader["xmax"]: 133 | self.globalHeader["xmax"] = lasHeader["xmax"] 134 | if lasHeader["xmin"] < self.globalHeader["xmin"]: 135 | self.globalHeader["xmin"] = lasHeader["xmin"] 136 | if lasHeader["ymax"] > self.globalHeader["ymax"]: 137 | self.globalHeader["ymax"] = lasHeader["ymax"] 138 | if lasHeader["ymin"] < self.globalHeader["ymin"]: 139 | self.globalHeader["ymin"] = lasHeader["ymin"] 140 | self.numberTiles += 1 141 | 142 | self.x_centre = np.mean([self.globalHeader["xmax"], self.globalHeader["xmin"]]) 143 | self.y_centre = np.mean([self.globalHeader["ymax"], self.globalHeader["ymin"]]) 144 | 145 | if self.verbose: print "number of tiles to process: {}".format(self.numberTiles) 146 | self.area = (self.globalHeader["xmax"] - self.globalHeader["xmin"]) * (self.globalHeader["ymax"] - self.globalHeader["ymin"]) 147 | # self.pointDensity = self.globalHeader["numptrecords"] / self.area 148 | 149 | # admin! 150 | self.counter = self.globalHeader["numptrecords"] // 20 151 | if self.globalHeader["numptrecords"] > 100000000: 152 | self.counter = self.globalHeader["numptrecords"] // 100 153 | self.badPoints = 0 154 | self.filesOpen = 0 155 | 156 | def laz2las(self, tile): 157 | 158 | """ 159 | If input file is a .laz file then calls a LAStools to 160 | decompress to .las. Requires LAStools to be installed and in 161 | the PATH to be in the system environment variables 162 | """ 163 | 164 | print "converting to .las: {}".format(os.path.abspath(tile)) 165 | os.chdir(self.dir) 166 | tileName = os.path.splitext(os.path.split(tile)[1])[0] + ".las" 167 | tempFile = os.path.join(self.tempDirectory, tileName) 168 | cmd = ["las2las", "-i", tile, "-o", tempFile] 169 | subprocess.call(cmd) 170 | return tempFile 171 | 172 | def tiling(self, resolution, takeSample=False, buffer=0): 173 | 174 | """ 175 | Used to tile .las file 176 | 177 | Parameters 178 | ---------- 179 | resolution: int 180 | Tile resolution 181 | 182 | takeSample: int, Default False 183 | Samples data at specified factor 184 | 185 | Returns 186 | ------- 187 | out: self 188 | 189 | """ 190 | 191 | if self.globalHeader["filesig"] == "ZNRF": 192 | raise NameError("Can not tile a .znr file as there are no x y coordinates") 193 | 194 | self.totPoints = 0 195 | self.globalHeader["guid1"] = resolution 196 | 197 | self.xmax = (float(np.ceil(self.globalHeader["xmax"])) // resolution) * resolution # to make nice neat boxes! 198 | self.xmin = (float(np.floor(self.globalHeader["xmin"])) // resolution) * resolution 199 | self.ymax = (float(np.ceil(self.globalHeader["ymax"])) // resolution) * resolution 200 | self.ymin = (float(np.floor(self.globalHeader["ymin"])) // resolution) * resolution 201 | 202 | if self.tile == None: 203 | dirName = str(resolution) + "m_TILES" 204 | else: 205 | dirName = os.path.splitext(self.tile)[0] + "_" + str(resolution) + "m_TILES" 206 | self.tilePath = os.path.join(self.savePath, dirName) 207 | if os.path.isdir(self.tilePath) : 208 | shutil.rmtree(self.tilePath) 209 | os.makedirs(self.tilePath) 210 | 211 | # tile dictionary and other variables 212 | self.xy_dictionary = {} 213 | for i, x in enumerate(np.arange(self.xmin, self.xmax + resolution, resolution)): 214 | for j, y in enumerate(np.arange(self.ymin, self.ymax + resolution, resolution)): 215 | self.xy_dictionary[(x, y)] = {"xmin":x - buffer, 216 | "ymin":y - buffer, 217 | "zmin":999, 218 | "xmax":x + resolution + buffer, 219 | "ymax":y + resolution + buffer, 220 | "zmax": -999, 221 | "num_rtn": {1:0, 2:0, 3:0, 4:0, 5:0}, 222 | "i":0, 223 | "tempFile": os.path.abspath(os.path.join(self.tempDirectory, str(x) + "_" + str(y) + ".temp")), 224 | "outFile": os.path.abspath(os.path.join(self.tilePath, str(x) + "_" + str(y) + ".las")), 225 | "isOpen": False, 226 | "lastTouched": None 227 | } 228 | 229 | keys = np.array(self.xy_dictionary.keys(), dtype=[('x', int), ('y', int)]) 230 | 231 | if self.verbose: print "number of plots: {}".format(len(self.xy_dictionary)) 232 | 233 | for tile in self.tileList: 234 | 235 | tile = os.path.join(self.dir, tile) 236 | self.h = parseHeader(tile) 237 | 238 | if self.h["filesig"] == "LASF": 239 | if self.h["guid2"] == 1: 240 | tile = self.laz2las(tile) 241 | 242 | self.h["num_rtn"] = {} 243 | 244 | if takeSample: 245 | sample = self.generateSample(takeSample) 246 | if self.verbose: 247 | print "random sample produced: {}".format(len(sample)) 248 | else: 249 | sample = range(self.h['numptrecords']) 250 | 251 | with open(os.path.join(self.dir, tile), 'rb') as fh: 252 | 253 | fh.seek(self.h["offset"]) 254 | numPoints = 0 255 | 256 | for i in sample: # loops through all points 257 | 258 | if i%self.counter == 0 and self.verbose: 259 | print "{}% | {} of {} points selected | {}".format(np.round((np.float(self.totPoints)/self.globalHeader['numptrecords'])*100), numPoints, self.globalHeader['numptrecords'], datetime.datetime.now()) 260 | 261 | fh.seek(self.h["offset"] + (numPoints * self.h['pointreclen'])) # searches to beginning of point 262 | 263 | point_dictionary = extract_return(self, fh) 264 | X, Y = point_dictionary['x'], point_dictionary['y'] 265 | KEYS = keys[(X >= keys['x'] - buffer) & (X < keys['x'] + resolution + buffer) & 266 | (Y >= keys['y'] - buffer) & (Y < keys['y'] + resolution + buffer)] 267 | 268 | for key in KEYS: 269 | 270 | self.xy_dictionary[tuple(key)] = self.writePoint(self.xy_dictionary[tuple(key)], point_dictionary) 271 | 272 | if point_dictionary["rtn_num"] < 6 and point_dictionary["rtn_num"] > 0: 273 | self.xy_dictionary[tuple(key)]["num_rtn"][point_dictionary["rtn_num"]] += 1 274 | 275 | numPoints += 1 276 | self.totPoints += 1 277 | 278 | # closes file handles that have been unused for a while 279 | if self.filesOpen == 40: 280 | self.closeFiles() 281 | self.filesOpen = 20 282 | 283 | # deletes .las tiles that were converted from .laz 284 | if self.tempDirectory in tile: 285 | os.unlink(tile) 286 | 287 | for key in self.xy_dictionary.keys(): 288 | if self.xy_dictionary[key]["isOpen"] is not False: 289 | self.xy_dictionary[key]["isOpen"].close() 290 | 291 | if self.verbose : print "number of bad points = {}".format(self.badPoints) 292 | if self.znr : 293 | self.ptFrmt, self.dt = znrStruct() 294 | self.globalHeader["pointreclen"] = 5 295 | else: 296 | if resolution >= 1: self.h['guid1'] = resolution 297 | 298 | self.outTileCount = 0 299 | 300 | return self 301 | 302 | def all(self, takeSample=False): 303 | 304 | """ 305 | Returns complete .las file 306 | 307 | Parameters 308 | ---------- 309 | 310 | takeSample: int, Default False 311 | Samples data at specified factor 312 | 313 | Returns 314 | ------- 315 | out: self 316 | 317 | """ 318 | 319 | self.totPoints = 0 320 | self.globalHeader["guid1"] = 0 321 | 322 | x, y = self.globalHeader["xmin"], self.globalHeader["ymin"] 323 | if self.saveLAS == None: 324 | self.saveLAS = str(int(x)) + "_" + str(int(y)) + "_OUT.las" 325 | self.xy_dictionary = {} 326 | self.xy_dictionary['all'] = { "xmin":x, \ 327 | "ymin":y, \ 328 | "zmin":999, \ 329 | "xmax":self.globalHeader["xmax"], \ 330 | "ymax":self.globalHeader["ymax"], \ 331 | "zmax": -999, \ 332 | "num_rtn": {1:0, 2:0, 3:0, 4:0, 5:0}, \ 333 | "i":0, \ 334 | "outFile": os.path.abspath(os.path.join(self.savePath, self.saveLAS)), \ 335 | "tempFile": os.path.abspath(os.path.join(self.tempDirectory, str(x) + "_" + str(y) + ".temp")), \ 336 | "isOpen": False, \ 337 | "lastTouched": None 338 | } 339 | 340 | for tile in self.tileList: 341 | 342 | tile = os.path.join(self.dir, tile) 343 | self.h = parseHeader(tile) 344 | 345 | if self.h["filesig"] == "LASF": 346 | if self.h["guid2"] == 1: 347 | tile = self.laz2las(tile) 348 | 349 | self.h["num_rtn"] = {} 350 | 351 | if takeSample: 352 | sample = self.generateSample(takeSample) 353 | if self.verbose : 354 | print "random sample produced: {}".format(len(sample)) 355 | else: 356 | sample = range(self.h['numptrecords']) 357 | 358 | with open(os.path.join(self.dir, tile), 'rb') as fh: 359 | 360 | fh.seek(self.h["offset"]) 361 | numPoints = 0 362 | 363 | for i in sample: # loops through all points 364 | #for i in range(10): # number of rows 365 | 366 | try: 367 | if i%self.counter == 0 and self.verbose: 368 | self.printProgress() 369 | except: pass 370 | 371 | fh.seek(self.h["offset"] + (numPoints * self.h['pointreclen'])) # searches to beginning of point 372 | 373 | try: 374 | point_dictionary = extract_return(self, fh) 375 | 376 | if self.znr is False: 377 | self.xy_dictionary["all"] = self.writePoint(self.xy_dictionary["all"], point_dictionary) 378 | # populates number of returns 379 | if point_dictionary["rtn_num"] < 6 and point_dictionary["rtn_num"] > 0: 380 | self.xy_dictionary["all"]["num_rtn"][point_dictionary["rtn_num"]] += 1 381 | 382 | else: 383 | self.xy_dictionary["all"] = self.writeZNR(self.xy_dictionary["all"], point_dictionary) 384 | 385 | numPoints += 1 386 | self.totPoints += 1 387 | except: 388 | self.badPoints += 1 389 | continue 390 | 391 | for key in self.xy_dictionary.keys(): 392 | if self.xy_dictionary[key]["isOpen"] is not False: 393 | self.xy_dictionary[key]["isOpen"].close() 394 | 395 | if self.verbose : print "number of bad points: {}".format(self.badPoints) 396 | if self.znr : 397 | self.ptFrmt, self.dt = znrStruct() 398 | self.globalHeader["pointreclen"] = 5 399 | 400 | return self 401 | 402 | def plot(self, centre_x, centre_y, extent=24, round=False): 403 | """ 404 | 405 | Returns a plotwise array of points from the tile defined with 406 | lasIO with plot centre at centre_x and centre_y and area equal 407 | to (radius*2)**2 if round=False and pi*radius**2 if round=True. 408 | 409 | The radius defaults to 10 metres. Plots are square by default 410 | but can be circular with round=True. 411 | 412 | Returns self which returns a numpy array if asArray is cploted, 413 | or can saved as .las, .txt or .xyz by cploting exportLAS, 414 | exportTXT or exportXYZ respectively. 415 | 416 | Paramaters 417 | ---------- 418 | centre_x, centre_y : int or float 419 | Cartesian coordinates of plot centre (in the same coordinate 420 | system as data) 421 | 422 | extent : int, float or tuple with length of 2 423 | Diameter of round plot or extent of square plot. Will except 424 | a tuple of two ints or floats. 425 | 426 | round : Boolean, defualt False 427 | If False a square plot is returned, if True a round plot is 428 | returned 429 | 430 | Returns 431 | ------- 432 | out : self 433 | 434 | """ 435 | 436 | if isinstance(extent, tuple): 437 | extent_x = extent[0] / 2. 438 | extent_y = extent[1] / 2. 439 | else: 440 | extent_x, extent_y = extent / 2., extent / 2. 441 | 442 | xmin = centre_x - extent_x 443 | xmax = centre_x + extent_x 444 | ymin = centre_y - extent_y 445 | ymax = centre_y + extent_y 446 | 447 | self.totPoints = 0 448 | self.globalHeader["guid1"] = 0 449 | 450 | x, y = self.globalHeader["xmin"], self.globalHeader["ymin"] 451 | if self.saveLAS == None: 452 | self.saveLAS = str(int(x)) + "_" + str(int(y)) + "_OUT.las" 453 | self.xy_dictionary = {} 454 | self.xy_dictionary['plot'] = {"xmin":xmin, 455 | "ymin":ymin, 456 | "zmin":999, 457 | "xmax":xmax, 458 | "ymax":ymax, 459 | "zmax": -999, 460 | "num_rtn": {1:0, 2:0, 3:0, 4:0, 5:0}, 461 | "i":0, 462 | "outFile": os.path.abspath(os.path.join(self.savePath, self.saveLAS)), 463 | "tempFile": os.path.abspath(os.path.join(self.tempDirectory, str(x) + "_" + str(y) + ".PLOT.temp")), 464 | "isOpen": False, 465 | "lastTouched": None 466 | } 467 | 468 | for tile in self.tileList: 469 | 470 | tile = os.path.join(self.dir, tile) 471 | self.h = parseHeader(tile) 472 | self.tile = tile 473 | 474 | if self.h["xmax"] < xmin or self.h["xmin"] > xmax or self.h["ymax"] < ymin or self.h["ymin"] > ymax: 475 | #print "skipping {}: out of bounds".format(tile) 476 | continue 477 | 478 | if self.h["filesig"] == "LASF": 479 | if self.h["guid2"] == 1: 480 | tile = self.laz2las(tile) 481 | 482 | self.h["num_rtn"] = {} 483 | 484 | with open(tile, 'rb') as fh: 485 | 486 | fh.seek(self.h["offset"]) 487 | 488 | for i in range(self.h['numptrecords']): # loops through plot points 489 | 490 | if i%self.counter == 0 and self.verbose : 491 | self.printProgress() 492 | 493 | fh.seek(self.h["offset"] + (i * self.h['pointreclen'])) # searches to beginning of point 494 | 495 | try: 496 | 497 | # test x point first ... 498 | fh.seek(self.h['offset'] + (i * self.h['pointreclen'])) 499 | x = fh.read(4) 500 | x = struct.unpack('=' + 'L', x)[0] 501 | x = (x * self.h['xscale'] ) + self.h['xoffset'] 502 | if x < xmin or x > xmax: 503 | continue 504 | 505 | # test y point next ... 506 | fh.seek(self.h['offset'] + (i * self.h['pointreclen'] + 4)) 507 | y = fh.read(4) 508 | y = struct.unpack('=' + 'L', y)[0] 509 | y = (y * self.h['yscale']) + self.h['yoffset'] 510 | if y < ymin or y > ymax: 511 | continue 512 | 513 | # extract round plot 514 | if round and round_plot((x, y), centre_x, centre_y, xmax, xmin, ymax, ymin, extent_x) == 0: 515 | continue 516 | 517 | fh.seek(self.h["offset"] + (i * self.h['pointreclen'])) # searches to beginning of point 518 | 519 | if not self.znr: 520 | point_dictionary = extract_return(self, fh) 521 | 522 | self.xy_dictionary["plot"] = self.writePoint(self.xy_dictionary["plot"], point_dictionary) 523 | 524 | # populates number of returns 525 | if point_dictionary["rtn_num"] < 6 and point_dictionary["rtn_num"] > 0: 526 | self.xy_dictionary["plot"]["num_rtn"][point_dictionary["rtn_num"]] += 1 527 | 528 | else: 529 | point_dictionary = extract_return(self, fh) 530 | self.xy_dictionary["plot"] = self.writeZNR(self.xy_dictionary["plot"], point_dictionary) 531 | 532 | self.totPoints += 1 533 | 534 | except: 535 | self.badPoints += 1 536 | continue 537 | 538 | if self.xy_dictionary["plot"]["isOpen"]: 539 | self.xy_dictionary["plot"]["isOpen"].close() 540 | 541 | if self.verbose : print "number of bad points = {}".format(self.badPoints) 542 | if self.znr : 543 | self.ptFrmt, self.dt = znrStruct() 544 | self.globalHeader["pointreclen"] = 5 545 | 546 | return self 547 | 548 | def fromGrid(self, csv, resolution=None): 549 | """ 550 | 551 | Returns a plotwise array of points from the tile defined with 552 | lasIO with plot centre at centre_x and centre_y and area equal 553 | to (radius*2)**2 if round=False and pi*radius**2 if round=True. 554 | 555 | The radius defaults to 10 metres. Plots are square by default 556 | but can be circular with round=True. 557 | 558 | Returns self which returns a numpy array if asArray is cploted, 559 | or can saved as .las, .txt or .xyz by cploting exportLAS, 560 | exportTXT or exportXYZ respectively. 561 | 562 | Paramaters 563 | ---------- 564 | centre_x, centre_y : int or float 565 | Cartesian coordinates of plot centre (in the same coordinate 566 | system as data) 567 | 568 | extent : int, float or tuple with length of 2 569 | Diameter of round plot or extent of square plot. Will except 570 | a tuple of two ints or floats. 571 | 572 | round : Boolean, defualt False 573 | If False a square plot is returned, if True a round plot is 574 | returned 575 | 576 | Returns 577 | ------- 578 | out : self 579 | 580 | """ 581 | 582 | self.totPoints = 0 583 | self.globalHeader["guid1"] = 0 584 | 585 | grid = np.loadtxt(csv, skiprows=1, delimiter=',', dtype=([('x', np.float), ('y', np.float)])) 586 | grid['x'] = grid['x'].astype(int) 587 | grid['y'] = grid['y'].astype(int) 588 | 589 | if not resolution: 590 | resolution = grid['x'][1] - grid['x'][0] 591 | 592 | xmin = grid['x'].min() - (resolution / 2.) 593 | xmax = grid['x'].max() + (resolution / 2.) 594 | ymin = grid['y'].min() - (resolution / 2.) 595 | ymax = grid['y'].max() + (resolution / 2.) 596 | 597 | if self.verbose: 598 | print 'grid resolution:', resolution 599 | print 'aiming to produce {} tiles'.format(len(grid)) 600 | 601 | self.xy_dictionary = {} 602 | for x, y in grid: 603 | self.xy_dictionary[(x, y)] = {"xmin":x - (resolution / 2.), 604 | "ymin":y - (resolution / 2.), 605 | "zmin":999, 606 | "xmax":x + (resolution / 2.), 607 | "ymax":y + (resolution / 2.), 608 | "zmax": -999, 609 | "num_rtn": {1:0, 2:0, 3:0, 4:0, 5:0}, 610 | "i":0, 611 | "outFile": os.path.abspath(os.path.join(self.savePath, "{}_{}.PLOT.las".format(x, y))), 612 | "tempFile": os.path.abspath(os.path.join(self.tempDirectory, "{}_{}.PLOT.temp".format(x, y))), 613 | "isOpen": False, 614 | "lastTouched": None 615 | } 616 | 617 | keys = np.array(self.xy_dictionary.keys(), dtype=[('x', int), ('y', int)]) 618 | 619 | for tile in self.tileList: 620 | 621 | tile = os.path.join(self.dir, tile) 622 | self.h = parseHeader(tile) 623 | self.tile = tile 624 | 625 | if self.h["xmax"] < xmin or self.h["xmin"] > xmax or self.h["ymax"] < ymin or self.h["ymin"] > ymax: 626 | continue 627 | 628 | if self.h["filesig"] == "LASF": 629 | if self.h["guid2"] == 1: 630 | tile = self.laz2las(tile) 631 | 632 | self.h["num_rtn"] = {} 633 | 634 | with open(tile, 'rb') as fh: 635 | 636 | fh.seek(self.h["offset"]) 637 | 638 | for i in range(self.h['numptrecords']): # loops through plot points 639 | 640 | if i%self.counter == 0 and self.verbose : 641 | self.printProgress() 642 | 643 | fh.seek(self.h["offset"] + (i * self.h['pointreclen'])) # searches to beginning of point 644 | 645 | # test x point first ... 646 | fh.seek(self.h['offset'] + (i * self.h['pointreclen'])) 647 | x = fh.read(4) 648 | x = struct.unpack('=' + 'L', x)[0] 649 | x = (x * self.h['xscale'] ) + self.h['xoffset'] 650 | if not xmin < x < xmax: 651 | continue 652 | 653 | # test y point next ... 654 | fh.seek(self.h['offset'] + (i * self.h['pointreclen'] + 4)) 655 | y = fh.read(4) 656 | y = struct.unpack('=' + 'L', y)[0] 657 | y = (y * self.h['yscale']) + self.h['yoffset'] 658 | if not ymin < y < ymax: 659 | continue 660 | 661 | # extract round plot 662 | inGrid = False 663 | idx = [(grid['x'] > self.h["xmin"]) | (grid['x'] <= self.h["xmax"]) | 664 | (grid['y'] > self.h["ymin"]) | (grid['y'] <= self.h["ymax"])] 665 | for row in grid[idx]: 666 | if ((row[0] - (resolution / 2.)) < x < (row[0] + (resolution / 2.)) and 667 | (row[1] - (resolution / 2.)) < y < (row[1] + (resolution / 2.))): 668 | inGrid = True 669 | break 670 | 671 | if not inGrid: continue 672 | 673 | fh.seek(self.h["offset"] + (i * self.h['pointreclen'])) # searches to beginning of point 674 | 675 | # try: 676 | point_dictionary = extract_return(self, fh) 677 | X, Y = point_dictionary['x'], point_dictionary['y'] 678 | KEYS = keys[(X >= keys['x'] - (resolution / 2.)) & (X <= keys['x'] + (resolution / 2.)) & 679 | (Y >= keys['y'] - (resolution / 2.)) & (Y <= keys['y'] + (resolution / 2.))] 680 | 681 | for key in KEYS: 682 | 683 | self.xy_dictionary[tuple(key)] = self.writePoint(self.xy_dictionary[tuple(key)], point_dictionary) 684 | 685 | if point_dictionary["rtn_num"] < 6 and point_dictionary["rtn_num"] > 0: 686 | self.xy_dictionary[tuple(key)]["num_rtn"][point_dictionary["rtn_num"]] += 1 687 | 688 | self.totPoints += 1 689 | 690 | # except: 691 | # self.badPoints += 1 692 | # continue 693 | 694 | # closes file handles that have been unused for a while 695 | if self.filesOpen == 40: 696 | self.closeFiles() 697 | self.filesOpen = 20 698 | 699 | # deletes .las tiles that were converted from .laz 700 | if self.tempDirectory in tile: 701 | os.unlink(tile) 702 | 703 | for key in self.xy_dictionary.keys(): 704 | if self.xy_dictionary[key]["isOpen"] is not False: 705 | self.xy_dictionary[key]["isOpen"].close() 706 | 707 | if self.verbose : print "number of bad points = {}".format(self.badPoints) 708 | if self.znr : 709 | self.ptFrmt, self.dt = znrStruct() 710 | self.globalHeader["pointreclen"] = 5 711 | else: 712 | if resolution >= 1: self.h['guid1'] = resolution 713 | 714 | self.outTileCount = 0 715 | 716 | return self 717 | 718 | def writePoint(self, tile, d): 719 | 720 | # opens file for writing points to 721 | 722 | if not type(tile["isOpen"]) == 'file': 723 | tile["isOpen"] = open(tile["tempFile"], "ab") 724 | self.filesOpen += 1 725 | 726 | # writes point 727 | for i in self.ptFrmt: 728 | if i[0] == 'return_grp': 729 | byte = ((d['scan_edge'] & 1) << 7) | ((d['scan_dir'] & 1) << 6) | ((d['rtn_tot'] & 7) << 3) | (d['rtn_num'] & 7) 730 | elif i[0] == 'x': 731 | byte = (d['x'] - self.h['xoffset']) / self.h['xscale'] 732 | elif i[0] == 'y': 733 | byte = (d['y'] - self.h['yoffset']) / self.h['yscale'] 734 | elif i[0] == 'z': 735 | byte = (d['z'] - self.h['zoffset']) / self.h['zscale'] 736 | else: 737 | byte = d[i[0]] 738 | 739 | tile["isOpen"].write(struct.pack('=' + i[2], byte)) 740 | 741 | # updates header information 742 | if d['z'] > tile["zmax"]: 743 | tile["zmax"] = d['z'] 744 | if d['z'] < tile["zmin"]: 745 | tile["zmin"] = d['z'] 746 | tile["i"] += 1 747 | 748 | tile["lastTouched"] = datetime.datetime.now() 749 | 750 | return tile 751 | 752 | def writeZNR(self, tile, d): 753 | 754 | # opens file for writing points to 755 | if tile["isOpen"] == False: 756 | tile["isOpen"] = open(tile["tempFile"], "ab") 757 | self.filesOpen += 1 758 | 759 | # writes point 760 | for i in znrStruct()[0]: # retrieves struct from lasStructure 761 | if i[0] == 'z': 762 | byte = (d['z'] / .01 ) 763 | else: 764 | byte = d[i[0]] 765 | tile["isOpen"].write(struct.pack('=' + i[2], byte)) 766 | 767 | # updates header information 768 | if d['z'] > tile["zmax"]: 769 | tile["zmax"] = d['z'] 770 | if d['z'] < tile["zmin"]: 771 | tile["zmin"] = d['z'] 772 | tile["i"] += 1 773 | 774 | tile["lastTouched"] = datetime.datetime.now() 775 | 776 | return tile 777 | 778 | def asDic(self): 779 | 780 | """ 781 | Returns array as a dictionary where the keys are 782 | the central coordinates and the values are a list of 783 | tuples (height, number of returns). This is useful 784 | for plotting or calculating continuous variables across 785 | a plot. 786 | """ 787 | 788 | arr = {} 789 | for i, key in enumerate(self.xy_dictionary.keys()): 790 | tile = self.xy_dictionary[key] 791 | if tile["i"] == 0: 792 | del tile 793 | continue 794 | a = np.zeros(tile["i"], dtype = self.dt) 795 | with open(tile["tempFile"], "rb") as fh: 796 | for j, line in enumerate(range(tile["i"])): 797 | fh.seek(j * self.globalHeader['pointreclen']) 798 | d = extract_return(self, fh) 799 | for field in d: 800 | a[j][field] = d[field] 801 | arr[(tile["xmin"], tile["ymin"])] = a 802 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 803 | 804 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 805 | 806 | return arr 807 | 808 | def asArray(self): 809 | 810 | """ 811 | Returns all points in a single array or as a list of 812 | arrays if there is more than one tile. If there is 813 | more than one tile then it may be preferable to use 814 | asDic command. 815 | """ 816 | 817 | arr = [] 818 | for i, key in enumerate(self.xy_dictionary.keys()): 819 | tile = self.xy_dictionary[key] 820 | if tile["i"] == 0: 821 | del tile 822 | continue 823 | if self.znr: 824 | a = np.zeros(tile["i"], dtype = znrStruct()[1]) 825 | else: 826 | a = np.zeros(tile["i"], dtype = self.dt) 827 | with open(tile["tempFile"], "rb") as fh: 828 | for j, line in enumerate(range(tile["i"])): 829 | fh.seek(j * self.globalHeader['pointreclen']) 830 | d = extract_return(self, fh) 831 | for field in d: 832 | a[j][field] = d[field] 833 | arr.append(a) 834 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 835 | 836 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 837 | 838 | if len(arr) == 1: # if list arr has only one array... 839 | arr = arr[0] # ...return only the array 840 | 841 | return arr 842 | 843 | def exportLAS(self, out=False): 844 | 845 | """ 846 | Writes tile(s) to .las format. Requires znr=False or 847 | will throw an exception 848 | """ 849 | 850 | if self.znr : raise NameError("Processed as ZNR, can not export to .LAS") 851 | if len(self.xy_dictionary) > 10: 852 | nTileCounter = len(self.xy_dictionary) // 10 853 | else: nTileCounter = len(self.xy_dictionary) 854 | 855 | tileCount = 0 856 | 857 | for i, key in enumerate(self.xy_dictionary.keys()): 858 | if self.xy_dictionary[key]["i"] == 0: 859 | del self.xy_dictionary[key] 860 | continue 861 | else: 862 | tileCount += 1 863 | 864 | if i%nTileCounter == 0 and i > 0 and self.verbose: 865 | print "{:.0f}% | {} of {} tiles exported | {}".format( np.float(i) / len(self.xy_dictionary) * 100, i, len(self.xy_dictionary), datetime.datetime.now()) 866 | 867 | tile = self.xy_dictionary[key] 868 | self.h['gensoftware'] = 'CRC207 LiDAR analysis software ' 869 | self.h['sysid'] = 'CRC207 LiDAR analysis software ' 870 | self.h['xmin'] = tile['xmin'] 871 | self.h['xmax'] = tile['xmax'] 872 | self.h['ymin'] = tile['ymin'] 873 | self.h['ymax'] = tile['ymax'] 874 | self.h['zmin'] = tile['zmin'] 875 | self.h['zmax'] = tile['zmax'] 876 | self.h['numptbyreturn'] = tuple([tile["num_rtn"][i] for i in range(1, 6)]) # sorting out the rtn_num tuple 877 | self.h['numptrecords'] = tile["i"] 878 | self.h['guid2'] = 0 879 | if self.h['numvlrecords'] > 0: 880 | self.h['offset'] = 313 881 | self.h['numvlrecords'] = 1 882 | 883 | if out: 884 | if out.endswith('.las') and len(self.xy_dictionary) > 1: 885 | outFile = out[:-4] + '.' + str(self.outTileCount) + '.las' 886 | elif out.endswith('.las') and os.path.isdir(os.path.split(out)[0]): 887 | outFile = out 888 | elif os.path.isdir(out): 889 | outFile = os.path.join(out, os.path.split(tile["outFile"])[1]) 890 | else: 891 | raise IOError('out path not recognised') 892 | else: 893 | outFile = tile["outFile"] 894 | self.savePath = os.path.split(outFile)[0] 895 | 896 | with open(outFile, "wb") as outOpen: 897 | for j in headerstruct(): 898 | if j[2] == 'c': 899 | outOpen.write(self.h[j[0]]) 900 | elif j[3] > 1: 901 | outOpen.write(struct.pack('=' + str(j[3]) + j[2], *self.h[j[0]])) 902 | else: 903 | outOpen.write(struct.pack('=' + j[2] , self.h[j[0]])) 904 | 905 | ## write VLR 906 | if self.h['numvlrecords'] > 0: 907 | # keeps only the first VLR e.g. the projection data 908 | outOpen.write(self.vlr[:86]) 909 | 910 | ## write points 911 | outOpen.seek(self.h['offset']) 912 | with open(tile["tempFile"], "rb") as o: 913 | points = o.read() 914 | outOpen.write(points) 915 | 916 | tile["isOpen"] = "written_to" 917 | 918 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 919 | 920 | if tileCount > 0: 921 | print "100% | {} of {} tiles exported | {}".format(len(self.xy_dictionary), 922 | len(self.xy_dictionary), 923 | datetime.datetime.now()) 924 | 925 | if len(self.xy_dictionary) == 1: 926 | print ".las file written to {}".format(outFile) 927 | else: 928 | print ".las file(s) written to {}".format(os.path.split(outFile)[0]) 929 | else: 930 | print "! no tiles to export !" 931 | 932 | if not self.keepTemp: shutil.rmtree(self.tempDirectory) 933 | 934 | return self 935 | 936 | def np2ZNR(self, arr): 937 | 938 | """ 939 | Can be used to export a numpy array in .znr format 940 | """ 941 | 942 | if self.znr == False: raise NameError ("Data processed as .LAS and therefore wcan not be saved as .ZNR") 943 | 944 | self.ptFrmt, self.dt = znrStruct() 945 | 946 | if len(self.xy_dictionary) > 10: 947 | nTileCounter = len(self.xy_dictionary) // 10 948 | else: nTileCounter = len(self.xy_dictionary) 949 | 950 | for i, key in enumerate(self.xy_dictionary.keys()): 951 | if self.xy_dictionary[key]["i"] == 0: 952 | del self.xy_dictionary[key] 953 | continue 954 | 955 | if i%nTileCounter == 0: 956 | print "{}% | {} of {} tiles exported | {}".format(np.round((i/np.float(len(self.xy_dictionary)))*100), i, len(self.xy_dictionary), datetime.datetime.now()) 957 | 958 | tile = self.xy_dictionary[key] 959 | self.h['filesig'] = "ZNRF" 960 | self.h['gensoftware'] = 'CRC207 LiDAR analysis software ' 961 | self.h['sysid'] = 'CRC207 LiDAR analysis software ' 962 | self.h['xmin'] = tile['xmin'] 963 | self.h['xmax'] = tile['xmax'] 964 | self.h['ymin'] = tile['ymin'] 965 | self.h['ymax'] = tile['ymax'] 966 | self.h['zmin'] = tile['zmin'] 967 | self.h['zmax'] = tile['zmax'] 968 | self.h['numptrecords'] = tile["i"] 969 | self.h['resolution'] = self.globalHeader['guid1'] 970 | self.h['pointreclen'] = 5 971 | self.h['headersize'] = 92 972 | self.h['offset'] = 178 973 | self.h['zscale'] = 0.01 974 | self.h['zoffset'] = 0 975 | 976 | outFile = os.path.splitext(tile["outFile"])[0] + ".znr" 977 | with open(outFile, "wb") as out: 978 | for i in znrHeaderStruct(): 979 | if i[2] == 'c': 980 | out.write(self.h[i[0]]) 981 | else: 982 | out.write(struct.pack('=' + i[2] , self.h[i[0]])) 983 | 984 | ## write VLR 985 | out.write(self.vlr) 986 | 987 | ## write points 988 | for line in arr: 989 | for i in znrStruct()[0]: # retrieves struct from lasStructure 990 | if i[0] == 'z': 991 | byte = line[0] / .01 992 | else: 993 | byte = line[1] 994 | out.write(struct.pack('=' + i[2], byte)) 995 | 996 | tile["isOpen"] = "written_to" 997 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 998 | 999 | if self.verbose : print ".znr file written to {}".format(os.path.split(outFile)[0]) 1000 | 1001 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1002 | 1003 | def np2LAS(self, arr, out=False): 1004 | 1005 | """ 1006 | Can be used to export a numpy array in .las format 1007 | """ 1008 | 1009 | if self.znr: raise NameError ("Data processed as .ZNR and therefore wcan not be saved as .LAS") 1010 | 1011 | self.h['gensoftware'] = 'CRC207 LiDAR analysis software ' 1012 | self.h['sysid'] = 'CRC207 LiDAR analysis software ' 1013 | self.h['xmin'] = arr['x'].min() 1014 | self.h['xmax'] = arr['x'].max() 1015 | self.h['ymin'] = arr['y'].min() 1016 | self.h['ymax'] = arr['y'].max() 1017 | self.h['zmin'] = arr['z'].min() 1018 | self.h['zmax'] = arr['z'].max() 1019 | ### sorting out the rtn_num tuple 1020 | rtn = np.zeros(5).astype(int) 1021 | for row in arr: 1022 | rtn_num = row['rtn_num'] - 1 1023 | if rtn_num < 5: 1024 | rtn[rtn_num] += 1 1025 | self.h['numptbyreturn'] = tuple(rtn) 1026 | self.h['numptrecords'] = len(arr) 1027 | self.h['guid2'] = 0 1028 | 1029 | if out: 1030 | if out.endswith('.las'): 1031 | outFile = out 1032 | elif os.path.isdir(out): 1033 | outFile = os.path.join(out, os.path.split(tile["outFile"])[1]) 1034 | else: 1035 | raise IOError('out path not recognised') 1036 | else: 1037 | x = str(np.mean(arr['x']).astype(int)) 1038 | y = str(np.mean(arr['y']).astype(int)) 1039 | outFile = os.path.join(self.savePath, "{}_{}.las".format(x, y)) 1040 | 1041 | with open(outFile, "wb") as out: 1042 | for j in headerstruct(): 1043 | if j[2] == 'c': 1044 | out.write(self.h[j[0]]) 1045 | elif j[3] > 1: 1046 | out.write(struct.pack('=' + str(j[3]) + j[2], *self.h[j[0]])) 1047 | else: 1048 | out.write(struct.pack('=' + j[2] , self.h[j[0]])) 1049 | 1050 | ## write VLR 1051 | if self.h['numvlrecords'] > 0: 1052 | out.write(self.vlr) 1053 | 1054 | ## write points 1055 | out.seek(self.h['offset']) 1056 | for d in arr: 1057 | for i in self.ptFrmt: 1058 | if i[0] == 'return_grp': 1059 | byte = ((d['scan_edge'] & 1) << 7) | ((d['scan_dir'] & 1) << 6) | ((d['rtn_tot'] & 7) << 3) | (d['rtn_num'] & 7) 1060 | elif i[0] == 'x': 1061 | byte = (d['x'] - self.h['xoffset']) / self.h['xscale'] 1062 | elif i[0] == 'y': 1063 | byte = (d['y'] - self.h['yoffset']) / self.h['yscale'] 1064 | elif i[0] == 'z': 1065 | byte = (d['z'] - self.h['zoffset']) / self.h['zscale'] 1066 | else: 1067 | byte = d[i[0]] 1068 | out.write(struct.pack('=' + i[2], byte)) 1069 | 1070 | if self.verbose: print ".las file written to {}".format(outFile) 1071 | 1072 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1073 | 1074 | return outFile 1075 | 1076 | def exportZNR(self): 1077 | 1078 | if self.znr == False: raise NameError ("Data processed as .LAS and therefore cannot be saved as .ZNR") 1079 | 1080 | self.ptFrmt, self.dt = znrStruct() 1081 | self.globalHeader["pointreclen"] = 5 1082 | if len(self.xy_dictionary) > 10: 1083 | nTileCounter = len(self.xy_dictionary) // 10 1084 | else: nTileCounter = len(self.xy_dictionary) 1085 | 1086 | for i, key in enumerate(self.xy_dictionary.keys()): 1087 | if self.xy_dictionary[key]["i"] == 0: 1088 | del self.xy_dictionary[key] 1089 | continue 1090 | 1091 | print len(self.xy_dictionary) 1092 | if i%nTileCounter == 0: 1093 | print "{}% | {} of {} tiles exported | {}".format(np.round((i/np.float(len(self.xy_dictionary)))*100), i, len(self.xy_dictionary), datetime.datetime.now()) 1094 | 1095 | tile = self.xy_dictionary[key] 1096 | self.h['filesig'] = "ZNRF" 1097 | self.h['gensoftware'] = 'CRC207 LiDAR analysis software ' 1098 | self.h['sysid'] = 'CRC207 LiDAR analysis software ' 1099 | self.h['xmin'] = tile['xmin'] 1100 | self.h['xmax'] = tile['xmax'] 1101 | self.h['ymin'] = tile['ymin'] 1102 | self.h['ymax'] = tile['ymax'] 1103 | self.h['zmin'] = tile['zmin'] 1104 | self.h['zmax'] = tile['zmax'] 1105 | self.h['numptrecords'] = tile["i"] 1106 | self.h['resolution'] = self.globalHeader['guid1'] 1107 | self.h['pointreclen'] = 5 1108 | self.h['headersize'] = 92 1109 | self.h['offset'] = 178 1110 | self.h['zscale'] = 0.01 1111 | self.h['zoffset'] = 0 1112 | 1113 | outFile = os.path.splitext(tile["outFile"])[0] + ".znr" 1114 | with open(outFile, "wb") as out: 1115 | for i in znrHeaderStruct(): 1116 | if i[2] == 'c': 1117 | out.write(self.h[i[0]]) 1118 | else: 1119 | out.write(struct.pack('=' + i[2] , self.h[i[0]])) 1120 | 1121 | ## write VLR 1122 | out.write(self.vlr) 1123 | 1124 | ## write points 1125 | with open(tile["tempFile"], "rb") as o: 1126 | points = o.read() 1127 | out.write(points) 1128 | 1129 | tile["isOpen"] = "written_to" 1130 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 1131 | 1132 | if len(self.xy_dictionary) == 0: 1133 | print "No tiles written" 1134 | elif len(self.xy_dictionary) == 1: 1135 | print ".znr file written to {}".format(tile["outFile"]) 1136 | else: 1137 | if self.verbose : print ".znr files written to {}".format(os.path.split(outFile)[0]) 1138 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1139 | 1140 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1141 | 1142 | def LAS2txt(self, enum=False, outFile=False): 1143 | 1144 | for i, key in enumerate(self.xy_dictionary.keys()): 1145 | tile = self.xy_dictionary[key] 1146 | if tile["i"] == 0: 1147 | del tile 1148 | continue 1149 | if enum: 1150 | savePath = os.path.join(os.path.split(tile["outFile"])[0], "{}.txt".format(i)) 1151 | elif outFile: 1152 | if os.path.isdir(outFile): 1153 | savePath = os.path.join(outFile, "{}.txt".format(i)) 1154 | else: 1155 | savePath = outFile 1156 | else: savePath = os.path.splitext(tile["outFile"])[0] + ".txt" 1157 | a = np.zeros(tile["i"], dtype = self.dt) 1158 | with open(tile["tempFile"], "rb") as fh: 1159 | for j, line in enumerate(range(tile["i"])): 1160 | fh.seek(j * self.globalHeader['pointreclen']) 1161 | d = extract_return(self, fh) 1162 | for field in d: 1163 | a[j][field] = d[field] 1164 | np.savetxt(savePath, np.transpose([a['x'], \ 1165 | a['y'], \ 1166 | a['z'], \ 1167 | a['class'], \ 1168 | a['i'], \ 1169 | a['scan_ang'], \ 1170 | a['rtn_num'], \ 1171 | a['rtn_tot']]), \ 1172 | fmt='%.1f', delimiter=',') 1173 | if self.verbose: print '.txt saved to:', savePath 1174 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 1175 | 1176 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1177 | 1178 | def exportXYZ(self): 1179 | 1180 | for i, key in enumerate(self.xy_dictionary.keys()): 1181 | tile = self.xy_dictionary[key] 1182 | if tile["i"] == 0: 1183 | del tile 1184 | continue 1185 | self.savePath = os.path.splitext(tile["outFile"])[0] + ".txt" 1186 | a = np.zeros(tile["i"], dtype = self.dt) 1187 | with open(tile["tempFile"], "rb") as fh: 1188 | for j, line in enumerate(range(tile["i"])): 1189 | fh.seek(j * self.globalHeader['pointreclen']) 1190 | d = extract_return(self, fh) 1191 | for field in d: 1192 | a[j][field] = d[field] 1193 | np.savetxt(self.savePath, np.transpose([a['x'], a['y'], a['z']]), fmt='%.1f', delimiter=',') 1194 | 1195 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 1196 | 1197 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 1198 | 1199 | if self.verbose: print 'XYZ saved to:', self.savePath 1200 | 1201 | return self 1202 | 1203 | def closeFiles(self): 1204 | 1205 | l = {} 1206 | for key in self.xy_dictionary.keys(): 1207 | if self.xy_dictionary[key]["lastTouched"] is not None: 1208 | l[key] = self.xy_dictionary[key]["lastTouched"] 1209 | for i, (key, val) in enumerate(zip(l.keys(), sorted(l.values()))): 1210 | if self.xy_dictionary[key]["isOpen"] != False: 1211 | self.xy_dictionary[key]["isOpen"].close() 1212 | self.xy_dictionary[key]["isOpen"] = False 1213 | if i == 20: 1214 | return 1215 | 1216 | def printProgress(self): 1217 | 1218 | print "{}% | {} of {} points processed | {}".format(np.round((np.float(self.totPoints)/self.globalHeader['numptrecords'])*100), self.totPoints, self.globalHeader['numptrecords'], datetime.datetime.now()) 1219 | 1220 | def generateSample(self, sample): 1221 | 1222 | return np.random.choice(self.h['numptrecords'], 1223 | size=int(self.h['numptrecords'] / sample), 1224 | replace=False) 1225 | 1226 | def removeTemp(self): 1227 | 1228 | if os.path.isdir(self.tempDirectory): 1229 | for file in os.listdir(self.tempDirectory): 1230 | os.unlink(os.path.join(self.tempDirectory, file)) 1231 | 1232 | shutil.rmtree(self.tempDirectory) 1233 | if self.verbose: print "{} has been deleted".format(self.tempDirectory) 1234 | 1235 | 1236 | def parseHeader(filename): 1237 | 1238 | """ 1239 | returns header information as a dictionary. 1240 | """ 1241 | 1242 | with open(filename,'rb') as fh: 1243 | header = {'infile':filename} 1244 | 1245 | if fh.read(4) == "ZNRF": 1246 | headerStructType = znrHeaderStruct() 1247 | else: headerStructType = headerstruct() 1248 | 1249 | fh.seek(0) 1250 | 1251 | for i in headerStructType: 1252 | if i[2] == 'c': 1253 | value = fh.read(i[1]) 1254 | elif i[3] > 1: 1255 | value = struct.unpack( '=' + str(i[3]) + i[2] , fh.read(i[1]) ) 1256 | else: 1257 | value = struct.unpack( '=' + i[2] , fh.read(i[1]) )[0] 1258 | header[i[0]] = value 1259 | 1260 | if headerStructType == headerstruct(): 1261 | if header["pointformat"] > 127: # it is a .laz file 1262 | header["pointformat"] -= 128 1263 | header["numvlrecords"] -= 1 1264 | header["offset"] -= 100 1265 | header["guid2"] = 1 1266 | 1267 | return header 1268 | 1269 | 1270 | def extract_return(self, fh): 1271 | 1272 | if self.znr: 1273 | ptFrmt = znrStruct()[0] 1274 | else: ptFrmt = self.ptFrmt 1275 | 1276 | point_dictionary = {} # dictionary for storing values in for each point 1277 | 1278 | for ent in ptFrmt: 1279 | byte = fh.read(ent[1]) 1280 | val = struct.unpack('=' + ent[2] , byte)[0] 1281 | if ent[0] == 'x': 1282 | val = (val * self.h['xscale'] ) + self.h['xoffset'] 1283 | if ent[0] == 'y': 1284 | val = (val * self.h['yscale'] ) + self.h['yoffset'] 1285 | if ent[0] == 'z': 1286 | val = (val * self.h['zscale'] ) + self.h['zoffset'] 1287 | if ent[0] == 'return_grp': 1288 | point_dictionary['rtn_num'] = val & 7 1289 | #if point_dictionary['rtn_num'] == 0: 1290 | # raise Exception 1291 | point_dictionary['rtn_tot'] = (val >> 3) & 7 1292 | point_dictionary['scan_dir'] = (val >> 6) & 1 1293 | point_dictionary['scan_edge'] = (val >> 7) 1294 | continue # required so that 'return_grp' is not added to dictionary 1295 | 1296 | point_dictionary[ent[0]] = val 1297 | 1298 | if point_dictionary["z"] > 1000000: 1299 | raise NameError("z very high: {}".format(point_dictionary["z"])) 1300 | 1301 | return point_dictionary 1302 | 1303 | 1304 | def round_plot(point, plot_x, plot_y, xmax, xmin, ymax, ymin, r): 1305 | 1306 | x, y = point 1307 | 1308 | #NW 1309 | if plot_x <= x and plot_y >= y: 1310 | adj = x - plot_x 1311 | opp = plot_y - y 1312 | hyp = math.sqrt(math.pow(adj, 2) + math.pow(opp, 2)) 1313 | 1314 | #SW 1315 | elif plot_x <= x and plot_y <= y: 1316 | adj = x - plot_x 1317 | opp = y - plot_y 1318 | hyp = math.sqrt(math.pow(adj, 2) + math.pow(opp, 2)) 1319 | 1320 | #SE 1321 | elif plot_x >= x and plot_y <= y: 1322 | adj = plot_x - x 1323 | opp = y - plot_y 1324 | hyp = math.sqrt(math.pow(adj, 2) + math.pow(opp, 2)) 1325 | 1326 | #NE 1327 | elif plot_x >= x and plot_y >= y: 1328 | adj = plot_x - x 1329 | opp = plot_y - y 1330 | hyp = math.sqrt(math.pow(adj, 2) + math.pow(opp, 2)) 1331 | 1332 | if hyp > r: return 0 1333 | 1334 | 1335 | def getPtFrmt(globalHeader, verbose=False): 1336 | 1337 | # return structure 1338 | if "txt2las" in globalHeader["gensoftware"]: 1339 | ptFrmt, dt = point_fmtLTstruct() 1340 | elif globalHeader["pointformat"] == 0: 1341 | ptFrmt, dt = point_fmt0struct() 1342 | elif globalHeader["pointformat"] == 1: 1343 | ptFrmt, dt = point_fmt1struct() 1344 | elif globalHeader["pointformat"] == 3: 1345 | ptFrmt, dt = point_fmt3struct() 1346 | 1347 | return ptFrmt, dt 1348 | 1349 | 1350 | def getVLR(headerSize, las): 1351 | fh = open(os.path.join(las)) 1352 | fh.seek(headerSize) 1353 | vlr = fh.read(86) 1354 | fh.close() 1355 | return vlr 1356 | 1357 | 1358 | if __name__ == '__main__': 1359 | 1360 | # import glob 1361 | # 1362 | # A = [] 1363 | # for las in glob.glob('/Users/phil/ALS/RiverHealth/Las/originalLas/*.las'): 1364 | # name = os.path.split(las)[1] 1365 | # x, y = int(name[1:4]), int(name[5:9]) 1366 | # # plt.scatter(x, y) 1367 | # if y > 5950: A.append(las) 1368 | 1369 | # T = lasIO('/Users/phil/ALS/WC/spl/tile_20/WC1_10m_TILES', verbose=True).tiling(5).exportLAS('/Users/phil/ALS/WC/spl/tile_20/WC1_5m_TILES') 1370 | T = lasIO('/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile/WC45_SUB.las', verbose=True, keepTemp=True).tiling(20).exportLAS() 1371 | # T = lasIO('/Users/phil/ALS/WC/spl/tile_20/WC1_test', verbose=True).all().exportLAS('/Users/phil/ALS/WC/spl/tile_20/WC1_test/all.las') 1372 | # T = lasIO('/Users/phil/ALS/RiverHealth/Las/originalLas', verbose=True).fromGrid('/Users/phil/Dropbox/regular_points_3_30_test.csv').exportLAS('/Users/phil/ALS/WC/spl/tile_20/WC1_grid/works') 1373 | 1374 | # lasIO('/Users/phil/ALS/WC/spl/tile_20/WC1.las', verbose=True, keepTemp=True).all().exportLAS() -------------------------------------------------------------------------------- /forestlas/lasIO_pool.py: -------------------------------------------------------------------------------- 1 | import os 2 | import struct 3 | import math 4 | import datetime 5 | import tempfile 6 | import shutil 7 | import subprocess 8 | import multiprocessing 9 | import random 10 | import numpy as np 11 | from lasStructure import * 12 | 13 | class lasIO: 14 | 15 | """ 16 | This allows reading and writing of .las files (currently supports 1.1 and 1.2). 17 | .las files can be read as one file(s), gridded or a plot of data extracted. 18 | Output types include .las, a numpy array or as .znr file where only the height 19 | and the "number of returns" metadata remain. 20 | """ 21 | 22 | def __init__(self, path, out=False, verbose=False, search=".", 23 | tmpDir=False, keepTemp=False, number_of_processes=1, 24 | copy=False, create_temp=True): 25 | 26 | """ 27 | Functions creates holder for .las file and setups required dictionaries 28 | etc. 29 | 30 | Parameters 31 | ---------- 32 | path: File path or list 33 | file path to tile .las file or directory containing .las files, 34 | also excepts a list of file paths. 35 | 36 | out: Path to directory or path to save .las to, Default None 37 | If None then saves output to os.getcwd() 38 | 39 | search: String. Default "." 40 | Can be used to filter .las files if a directory is supplied for 41 | "path" 42 | 43 | tmpDir: File path to temporary directory. Default False 44 | Multiprocessing is not available with this module but is by others 45 | that call lasIO. This specifies the temporary directory. 46 | 47 | keepTemp: Boolean. Default True 48 | When False all temporary files are kept. It is important to run 49 | removeTemp() to clean up before exiting 50 | 51 | number_of_processes: Int, Default 1 52 | If processing a number of tiles or using a regular grid, Python's 53 | multiprocessing canbe envoked. This variable sets the number of 54 | cores that are utilised. 55 | 56 | copy: Boolean, Default False 57 | Wether to copy files to the temp directory before processing. This 58 | can speed thinks up if reading a number of large files simultaneously 59 | from a remote drive. 60 | 61 | Returns 62 | ------- 63 | out: self 64 | 65 | """ 66 | 67 | self.verbose = verbose 68 | self.keepTemp = keepTemp 69 | self.number_of_processes = number_of_processes 70 | 71 | ### parse args and create file structure 72 | # is path a directory or file 73 | if isinstance(path, list) : 74 | self.tile_list = path 75 | self.dir = os.path.split(path[0])[0] 76 | self.tile = None 77 | elif os.path.isfile(path) : 78 | self.dir = os.path.split(path)[0] 79 | if self.dir == '': self.dir = os.getcwd() 80 | self.tile = os.path.split(path)[1] 81 | self.tile_list = [os.path.join(self.dir, self.tile)] 82 | elif os.path.isdir(path) : 83 | self.dir = path 84 | self.tile = None 85 | tile_list = os.listdir(self.dir) 86 | self.tile_list = [tile for tile in tile_list if (tile.endswith(".las") 87 | or tile.endswith(".laz")) 88 | and tile.find(search) != -1] 89 | else: 90 | raise NameError ("path not recognised") 91 | 92 | if len(self.tile_list) == 0: 93 | raise IOError("There are no .las or .laz tiles in {}".format(path)) 94 | 95 | # create temporary directory in %temp% 96 | if create_temp: 97 | if tmpDir: 98 | self.tempDirectory = tmpDir 99 | if os.path.isdir(self.tempDirectory) is False: 100 | os.makedirs(self.tempDirectory) 101 | else: 102 | tempDirectoryName = "lidar.processing." + str(np.random.randint(0, 9999999)) + ".tmp" 103 | self.tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 104 | os.makedirs(self.tempDirectory) 105 | 106 | if self.verbose: print "temporary directory at: {}".format(self.tempDirectory) 107 | 108 | # create output directory 109 | if not out: 110 | self.savePath = self.dir 111 | self.saveLAS = None 112 | elif os.path.isdir(out): 113 | self.savePath = out 114 | self.saveLAS = None 115 | else: 116 | self.savePath = os.path.split(out)[0] 117 | self.saveLAS = os.path.split(out)[1] 118 | 119 | # global variables 120 | self.numberTiles = 1 121 | for i, las in enumerate(self.tile_list): 122 | las = os.path.join(self.dir, las) 123 | if i == 0: 124 | self.globalHeader = parseHeader(las) 125 | # self.globalHeader["numptrecords"] = lasHeader["numptrecords"] 126 | if self.globalHeader['filesig'] == "ZNRF": 127 | raise Exception('ZNR is deprecated, use an older version of lasIO') 128 | else: 129 | self.ptFrmt, self.dt = getPtFrmt(self.globalHeader, verbose=True) 130 | self.vlr = getVLR(self.globalHeader["headersize"], las) 131 | self.h = parseHeader(las) 132 | else: 133 | lasHeader = parseHeader(os.path.join(path, las)) 134 | self.globalHeader["numptrecords"] += lasHeader["numptrecords"] 135 | if lasHeader["xmax"] > self.globalHeader["xmax"]: 136 | self.globalHeader["xmax"] = lasHeader["xmax"] 137 | if lasHeader["xmin"] < self.globalHeader["xmin"]: 138 | self.globalHeader["xmin"] = lasHeader["xmin"] 139 | if lasHeader["ymax"] > self.globalHeader["ymax"]: 140 | self.globalHeader["ymax"] = lasHeader["ymax"] 141 | if lasHeader["ymin"] < self.globalHeader["ymin"]: 142 | self.globalHeader["ymin"] = lasHeader["ymin"] 143 | self.numberTiles += 1 144 | 145 | self.x_centre = np.mean([self.globalHeader["xmax"], self.globalHeader["xmin"]]) 146 | self.y_centre = np.mean([self.globalHeader["ymax"], self.globalHeader["ymin"]]) 147 | 148 | if self.verbose: print "number of tiles to process:", self.numberTiles 149 | if self.numberTiles > 1 and self.verbose: print "processing tiles from:", self.dir 150 | # self.area = (self.globalHeader["xmax"] - self.globalHeader["xmin"]) * (self.globalHeader["ymax"] - self.globalHeader["ymin"]) 151 | # self.pointDensity = self.globalHeader["numptrecords"] / self.area 152 | 153 | # admin! 154 | self.counter = self.globalHeader["numptrecords"] // 20 155 | if self.globalHeader["numptrecords"] > 1e7: 156 | self.counter = self.globalHeader["numptrecords"] // 100 157 | self.badPoints = 0 158 | self.copy = copy 159 | if self.copy and self.verbose: 160 | print 'tiles are copied to temp' 161 | self.resolution = None 162 | 163 | def grid(self, csv, resolution=None, take_sample=False): 164 | 165 | """ 166 | 167 | Returns a plotwise array of points from the tile defined with 168 | lasIO with plot centre at centre_x and centre_y and area equal 169 | to (radius*2)**2 if round=False and pi*radius**2 if round=True. 170 | 171 | The radius defaults to 10 metres. Plots are square by default 172 | but can be circular with round=True. 173 | 174 | Returns self which returns a numpy array if asArray is cploted, 175 | or can saved as .las, .txt or .xyz by cploting exportLAS, 176 | exportTXT or exportXYZ respectively. 177 | 178 | Paramaters 179 | ---------- 180 | centre_x, centre_y : int or float 181 | Cartesian coordinates of plot centre (in the same coordinate 182 | system as data) 183 | 184 | extent : int, float or tuple with length of 2 185 | Diameter of round plot or extent of square plot. Will except 186 | a tuple of two ints or floats. 187 | 188 | round : Boolean, defualt False 189 | If False a square plot is returned, if True a round plot is 190 | returned 191 | 192 | Returns 193 | ------- 194 | out : self 195 | 196 | """ 197 | 198 | self.take_sample = take_sample 199 | 200 | self.Global_totPoints = 0 201 | self.Global_files_open = 0 202 | self.globalHeader["guid1"] = 0 203 | 204 | self.grid = np.loadtxt(csv, skiprows=1, delimiter=',', dtype=([('x', np.float), ('y', np.float)])) 205 | self.grid['x'] = self.grid['x'].astype(int) 206 | self.grid['y'] = self.grid['y'].astype(int) 207 | 208 | if not resolution: 209 | self.resolution = self.grid['x'][1] - self.grid['x'][0] 210 | else: 211 | self.resolution = resolution 212 | 213 | self.xmin = self.grid['x'].min() - (self.resolution / 2.) 214 | self.xmax = self.grid['x'].max() + (self.resolution / 2.) 215 | self.ymin = self.grid['y'].min() - (self.resolution / 2.) 216 | self.ymax = self.grid['y'].max() + (self.resolution / 2.) 217 | 218 | if self.tile == None: 219 | dirName = str(self.resolution) + "m_GRID" 220 | else: 221 | dirName = os.path.splitext(self.tile)[0] + "_" + str(self.resolution) + "m_GRID" 222 | self.savePath = os.path.join(self.savePath, dirName) 223 | if os.path.isdir(self.savePath): 224 | shutil.rmtree(self.savePath) 225 | 226 | if self.verbose: 227 | print 'grid resolution:', self.resolution 228 | print 'aiming to produce {} tiles'.format(len(self.grid)) 229 | 230 | self.xy_dictionary = dict() 231 | for x, y in self.grid: 232 | self.xy_dictionary[(x, y)] = {"xmin":x - (self.resolution / 2.), 233 | "ymin":y - (self.resolution / 2.), 234 | "zmin":999, 235 | "xmax":x + (self.resolution / 2.), 236 | "ymax":y + (self.resolution / 2.), 237 | "zmax": -999, 238 | "num_rtn": {1:0, 2:0, 3:0, 4:0, 5:0}, 239 | "i":0, 240 | "outFile": os.path.abspath(os.path.join(self.savePath, "{}_{}.PLOT.las".format(x, y))), 241 | "tempFile": os.path.abspath(os.path.join(self.tempDirectory, "{}_{}.PLOT.temp".format(x, y))), 242 | "isOpen": False, 243 | "lastTouched": None 244 | 245 | } 246 | 247 | self.keys = np.array(self.xy_dictionary.keys(), dtype=[('x', int), ('y', int)]) 248 | 249 | for tile in self.tile_list: 250 | 251 | if len(os.path.split(tile)) > 1: 252 | tile = os.path.join(self.dir, tile) 253 | 254 | if self.copy: 255 | shutil.copyfile(tile, os.path.join(self.tempDirectory, os.path.split(tile)[1])) 256 | tile = os.path.join(self.tempDirectory, os.path.split(tile)[1]) 257 | h = parseHeader(tile) 258 | 259 | if h["filesig"] == "LASF": 260 | if h["guid2"] == 1: 261 | tile = self.laz2las(tile) 262 | 263 | if self.take_sample: 264 | sample = self.generateSample(self.take_sample) 265 | if self.verbose: 266 | print "random sample produced: {}".format(len(sample)) 267 | else: 268 | sample = range(h['numptrecords']) 269 | 270 | grid = self.grid[(self.grid['x'] >= h['xmin']) & (self.grid['x'] <= h['xmax']) & 271 | (self.grid['y'] >= h['ymin']) & (self.grid['y'] <= h['ymax'])] 272 | 273 | with open(os.path.join(self.dir, tile), 'rb') as fh: 274 | 275 | fh.seek(h["offset"]) 276 | 277 | for i in sample: # loops through all points 278 | 279 | if self.Global_totPoints%self.counter == 0 and self.verbose: 280 | print "{:.0f}% | {} of {} new tiles created | {} | {}".format((self.Global_totPoints / float(self.globalHeader['numptrecords'])) * 100, 281 | len(os.listdir(self.tempDirectory)), len(self.grid), 282 | os.path.split(tile)[1], 283 | datetime.datetime.now()) 284 | self.Global_totPoints += 1 285 | 286 | try: 287 | # test x point first ... 288 | fh.seek(h['offset'] + (i * h['pointreclen'])) 289 | x = struct.unpack('=' + 'L', fh.read(4))[0] 290 | x = (x * h['xscale'] ) + h['xoffset'] 291 | if not self.xmin < x < self.xmax: 292 | continue 293 | 294 | # test y point next ... 295 | fh.seek(h['offset'] + (i * h['pointreclen'] + 4)) 296 | y = struct.unpack('=' + 'L', fh.read(4))[0] 297 | y = (y * h['yscale']) + h['yoffset'] 298 | if not self.ymin < y < self.ymax: 299 | continue 300 | 301 | # extract round plot 302 | inGrid = False 303 | idx = [(grid['x'] > h["xmin"]) | (grid['x'] <= h["xmax"]) | 304 | (grid['y'] > h["ymin"]) | (grid['y'] <= h["ymax"])] 305 | for row in grid[idx]: 306 | if ((row[0] - (self.resolution / 2.)) < x < (row[0] + (self.resolution / 2.)) and 307 | (row[1] - (self.resolution / 2.)) < y < (row[1] + (self.resolution / 2.))): 308 | inGrid = True 309 | break 310 | 311 | if not inGrid: continue 312 | 313 | fh.seek(h["offset"] + (i * h['pointreclen'])) # searches to beginning of point 314 | 315 | point_dictionary = extract_return(self, fh) 316 | X, Y = point_dictionary['x'], point_dictionary['y'] 317 | KEYS = self.keys[(X >= self.keys['x'] - (self.resolution / 2.)) & (X < self.keys['x'] + (self.resolution / 2.)) & 318 | (Y >= self.keys['y'] - (self.resolution / 2.)) & (Y < self.keys['y'] + (self.resolution / 2.))] 319 | 320 | for key in KEYS: 321 | self.xy_dictionary[tuple(key)] = self.writePoint(self.xy_dictionary[tuple(key)], point_dictionary, h) 322 | 323 | except: 324 | self.badPoints += 1 325 | 326 | # deletes .las tiles that were converted from .laz or copies 327 | if self.tempDirectory in tile: os.unlink(tile) 328 | 329 | if self.resolution >= 1: h['guid1'] = self.resolution 330 | 331 | return self 332 | 333 | def writePoint(self, tile, d, h): 334 | 335 | pointString = '' 336 | for i in self.ptFrmt: 337 | if i[0] == 'return_grp': 338 | byte = ((d['scan_edge'] & 1) << 7) | ((d['scan_dir'] & 1) << 6) | \ 339 | ((d['rtn_tot'] & 7) << 3) | (d['rtn_num'] & 7) 340 | elif i[0] == 'x': 341 | byte = (d['x'] - h['xoffset']) / h['xscale'] 342 | elif i[0] == 'y': 343 | byte = (d['y'] - h['yoffset']) / h['yscale'] 344 | elif i[0] == 'z': 345 | byte = (d['z'] - h['zoffset']) / h['zscale'] 346 | else: 347 | byte = d[i[0]] 348 | pointString += struct.pack('=' + i[2], byte) 349 | 350 | with open(tile["tempFile"], "ab") as o: 351 | o.write(pointString) 352 | 353 | # updates header information 354 | if d['z'] > tile["zmax"]: 355 | tile["zmax"] = d['z'] 356 | if d['z'] < tile["zmin"]: 357 | tile["zmin"] = d['z'] 358 | if 0 < d["rtn_num"] < 6: 359 | tile["num_rtn"][d["rtn_num"]] += 1 360 | tile["i"] += 1 361 | 362 | return tile 363 | 364 | def asArray(self): 365 | 366 | """ 367 | Returns all points in a single array or as a list of 368 | arrays if there is more than one tile. If there is 369 | more than one tile then it may be preferable to use 370 | asDic command. 371 | """ 372 | 373 | arr = [] 374 | for i, key in enumerate(self.xy_dictionary.keys()): 375 | tile = self.xy_dictionary[key] 376 | if tile["i"] == 0: 377 | del tile 378 | continue 379 | a = np.zeros(tile["i"], dtype = self.dt) 380 | with open(tile["tempFile"], "rb") as fh: 381 | for j, line in enumerate(range(tile["i"])): 382 | fh.seek(j * self.globalHeader['pointreclen']) 383 | d = extract_return(self, fh) 384 | for field in d: 385 | a[j][field] = d[field] 386 | arr.append(a) 387 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 388 | 389 | if self.keepTemp is False: shutil.rmtree(self.tempDirectory) 390 | 391 | if len(arr) == 1: # if list arr has only one array... 392 | arr = arr[0] # ...return only the array 393 | 394 | return arr 395 | 396 | def exportLAS(self, out=False): 397 | 398 | """ 399 | Writes tile(s) to .las format. 400 | """ 401 | 402 | if len(self.xy_dictionary) > 10: 403 | nTileCounter = len(self.xy_dictionary) // 10 404 | else: nTileCounter = len(self.xy_dictionary) 405 | 406 | self.xy_dictionary = {key:values for key, values in self.xy_dictionary.items() if values['i'] > 0} 407 | 408 | if len(self.xy_dictionary) > 0: 409 | for i, key in enumerate(self.xy_dictionary.keys()): 410 | 411 | if i%nTileCounter == 0 and i > 0 and self.verbose: 412 | print "{:.0f}% | {} of {} tiles exported | {}".format( np.float(i) / len(self.xy_dictionary) * 100, i, len(self.xy_dictionary), datetime.datetime.now()) 413 | 414 | tile = self.xy_dictionary[key] 415 | self.h['gensoftware'] = 'CRC207 LiDAR analysis software ' 416 | self.h['sysid'] = 'CRC207 LiDAR analysis software ' 417 | self.h['xmin'] = tile['xmin'] 418 | self.h['xmax'] = tile['xmax'] 419 | self.h['ymin'] = tile['ymin'] 420 | self.h['ymax'] = tile['ymax'] 421 | self.h['zmin'] = tile['zmin'] 422 | self.h['zmax'] = tile['zmax'] 423 | self.h['numptbyreturn'] = tuple([tile["num_rtn"][i] for i in range(1, 6)]) # sorting out the rtn_num tuple 424 | self.h['numptrecords'] = tile["i"] 425 | self.h['guid2'] = 0 426 | if self.h['numvlrecords'] > 0: 427 | self.h['offset'] = 313 428 | self.h['numvlrecords'] = 1 429 | if self.resolution != None: 430 | self.h['guid1'] = self.resolution 431 | 432 | if out: 433 | if out.endswith('.las') and len(self.xy_dictionary) > 1: 434 | outFile = out[:-4] + '.' + str(self.outTileCount) + '.las' 435 | elif out.endswith('.las') and os.path.isdir(os.path.split(out)[0]): 436 | outFile = out 437 | elif os.path.isdir(out): 438 | outFile = os.path.join(out, os.path.split(tile["outFile"])[1]) 439 | else: 440 | raise IOError('out path not recognised') 441 | else: 442 | if not os.path.isdir(self.savePath): 443 | os.makedirs(self.savePath) 444 | outFile = tile["outFile"] 445 | self.savePath = os.path.split(outFile)[0] 446 | 447 | with open(outFile, "wb") as outOpen: 448 | for j in headerstruct(): 449 | if j[2] == 'c': 450 | outOpen.write(self.h[j[0]]) 451 | elif j[3] > 1: 452 | outOpen.write(struct.pack('=' + str(j[3]) + j[2], *self.h[j[0]])) 453 | else: 454 | outOpen.write(struct.pack('=' + j[2] , self.h[j[0]])) 455 | 456 | ## write VLR 457 | if self.h['numvlrecords'] > 0: 458 | # keeps only the first VLR e.g. the projection data 459 | outOpen.write(self.vlr[:86]) 460 | 461 | ## write points 462 | outOpen.seek(self.h['offset']) 463 | with open(tile["tempFile"], "rb") as o: 464 | points = o.read() 465 | outOpen.write(points) 466 | 467 | tile["isOpen"] = "written_to" 468 | 469 | if self.keepTemp is False: os.unlink(tile["tempFile"]) 470 | 471 | print "100% | {} of {} tiles exported | {}".format(len(self.xy_dictionary), 472 | len(self.xy_dictionary), 473 | datetime.datetime.now()) 474 | 475 | if len(self.xy_dictionary) == 1: 476 | print ".las file written to {}".format(outFile) 477 | else: 478 | print ".las file(s) written to {}".format(os.path.split(outFile)[0]) 479 | else: 480 | print "! no tiles to export !" 481 | 482 | if not self.keepTemp: shutil.rmtree(self.tempDirectory) 483 | 484 | return self 485 | 486 | def removeTemp(self): 487 | 488 | if os.path.isdir(self.tempDirectory): 489 | for file in os.listdir(self.tempDirectory): 490 | os.unlink(os.path.join(self.tempDirectory, file)) 491 | 492 | shutil.rmtree(self.tempDirectory) 493 | if self.verbose: print "{} has been deleted".format(self.tempDirectory) 494 | 495 | def parseHeader(filename): 496 | 497 | """ 498 | returns header information as a dictionary. 499 | """ 500 | 501 | with open(filename,'rb') as fh: 502 | header = {'infile':filename} 503 | 504 | if fh.read(4) == "ZNRF": 505 | raise Exception('ZNR is deprecated - use an older version of lasIO') 506 | else: headerStructType = headerstruct() 507 | 508 | fh.seek(0) 509 | 510 | for i in headerStructType: 511 | if i[2] == 'c': 512 | value = fh.read(i[1]) 513 | elif i[3] > 1: 514 | value = struct.unpack( '=' + str(i[3]) + i[2] , fh.read(i[1]) ) 515 | else: 516 | value = struct.unpack( '=' + i[2] , fh.read(i[1]) )[0] 517 | header[i[0]] = value 518 | 519 | if headerStructType == headerstruct(): 520 | if header["pointformat"] > 127: # it is a .laz file 521 | header["pointformat"] -= 128 522 | header["numvlrecords"] -= 1 523 | header["offset"] -= 100 524 | header["guid2"] = 1 525 | 526 | return header 527 | 528 | 529 | def extract_return(self, fh): 530 | 531 | ptFrmt = self.ptFrmt 532 | 533 | point_dictionary = {} # dictionary for storing values in for each point 534 | 535 | for ent in ptFrmt: 536 | byte = fh.read(ent[1]) 537 | val = struct.unpack('=' + ent[2] , byte)[0] 538 | if ent[0] == 'x': 539 | val = (val * self.h['xscale'] ) + self.h['xoffset'] 540 | if ent[0] == 'y': 541 | val = (val * self.h['yscale'] ) + self.h['yoffset'] 542 | if ent[0] == 'z': 543 | val = (val * self.h['zscale'] ) + self.h['zoffset'] 544 | if ent[0] == 'return_grp': 545 | point_dictionary['rtn_num'] = val & 7 546 | #if point_dictionary['rtn_num'] == 0: 547 | # raise Exception 548 | point_dictionary['rtn_tot'] = (val >> 3) & 7 549 | point_dictionary['scan_dir'] = (val >> 6) & 1 550 | point_dictionary['scan_edge'] = (val >> 7) 551 | continue # required so that 'return_grp' is not added to dictionary 552 | 553 | point_dictionary[ent[0]] = val 554 | 555 | if point_dictionary["z"] > 1000000: 556 | raise NameError("z very high: {}".format(point_dictionary["z"])) 557 | 558 | return point_dictionary 559 | 560 | 561 | def getPtFrmt(globalHeader, verbose=False): 562 | 563 | # return structure 564 | if "txt2las" in globalHeader["gensoftware"]: 565 | ptFrmt, dt = point_fmtLTstruct() 566 | elif globalHeader["pointformat"] == 0: 567 | ptFrmt, dt = point_fmt0struct() 568 | elif globalHeader["pointformat"] == 1: 569 | ptFrmt, dt = point_fmt1struct() 570 | elif globalHeader["pointformat"] == 3: 571 | ptFrmt, dt = point_fmt3struct() 572 | 573 | return ptFrmt, dt 574 | 575 | 576 | def getVLR(headerSize, las): 577 | fh = open(os.path.join(las)) 578 | fh.seek(headerSize) 579 | vlr = fh.read(86) 580 | fh.close() 581 | return vlr 582 | # 583 | # 584 | # if __name__ == '__main__': 585 | # 586 | # import shutil 587 | # path = '/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile' 588 | # shutil.rmtree(os.path.join(path, 'WC45_SUB_20m_TILES')) 589 | # os.makedirs(os.path.join(path, 'WC45_SUB_20m_TILES')) 590 | # start = datetime.datetime.now() 591 | # # lasIO(os.path.join(path, 'WC45_SUB_5m_SUBSET'), verbose=True, number_of_processes=4).all().exportLAS() 592 | # lasIO(os.path.join(path, 'WC45_SUB.las'), verbose=True, number_of_processes=1).grid(os.path.join(path, 'coords_10.csv'), resolution=10).exportLAS() 593 | # # lasIO(os.path.join(path, 'WC45_SUB_5m_SUBSET'), verbose=True, number_of_processes=8).tiling(1000).exportLAS(os.path.join(path, 'WC45_SUB_20m_TILES')) 594 | # print datetime.datetime.now() - start -------------------------------------------------------------------------------- /forestlas/lasStructure.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def headerstruct(): 4 | return (('filesig', 4,'c',4), 5 | ('filesourceid', 2,'H',1), 6 | ('reserved' , 2,'H',1), 7 | ('guid1' , 4,'L',1), 8 | ('guid2' , 2,'H',1), 9 | ('guid3' , 2,'H',1), 10 | ('guid4' , 8,'B',8), 11 | ('vermajor' , 1,'B',1), 12 | ('verminor' , 1,'B',1), 13 | ('sysid' , 32,'c',32), 14 | ('gensoftware' , 32,'c',32), 15 | ('fileday' , 2,'H',1), 16 | ('fileyear' , 2,'H',1), 17 | ('headersize' , 2,'H',1), 18 | ('offset' , 4,'L',1), 19 | ('numvlrecords', 4,'L',1), 20 | ('pointformat' , 1,'B',1), 21 | ('pointreclen' , 2,'H',1), 22 | ('numptrecords', 4,'L',1), 23 | ('numptbyreturn', 20,'L',5), 24 | ('xscale' , 8,'d',1), 25 | ('yscale' , 8,'d',1), 26 | ('zscale' , 8,'d',1), 27 | ('xoffset' , 8,'d',1), 28 | ('yoffset' , 8,'d',1), 29 | ('zoffset' , 8,'d',1), 30 | ('xmax' , 8,'d',1), 31 | ('xmin' , 8,'d',1), 32 | ('ymax' , 8,'d',1), 33 | ('ymin' , 8,'d',1), 34 | ('zmax' , 8,'d',1), 35 | ('zmin' , 8,'d',1) ) 36 | 37 | def point_fmt0struct(): 38 | return ((('x', 4,'l',1) , 39 | ('y', 4,'l',1) , 40 | ('z', 4,'l',1) , 41 | ('i', 2,'H',1) , 42 | ('return_grp', 1,'B',1) , 43 | ('class', 1,'B',1) , 44 | ('scan_ang', 1,'b',1) , 45 | ('user_data', 1,'B',1) , 46 | ('point_src_id', 2,'H',1)), 47 | 48 | np.dtype([('x', np.float), 49 | ('y', np.float), 50 | ('z', np.float), 51 | ('i', np.int), 52 | ('rtn_num', np.int), 53 | ('rtn_tot', np.int), 54 | ('scan_dir', np.int), 55 | ('scan_edge', np.int), 56 | ('class', np.int), 57 | ('scan_ang', np.int), 58 | ('user_data', np.int), 59 | ('point_src_id', np.int)])) 60 | 61 | def point_fmt1struct(): 62 | return ((('x', 4,'l',1), 63 | ('y', 4,'l',1), 64 | ('z', 4,'l',1), 65 | ('i', 2,'H',1), 66 | ('return_grp', 1,'B',1), 67 | ('class', 1,'B',1), 68 | ('scan_ang', 1,'b',1), 69 | ('user_data', 1,'B',1), 70 | ('point_src_id', 2,'H',1), 71 | ('gps_time', 8,'d',1)), 72 | 73 | np.dtype([('x', np.float), 74 | ('y', np.float), 75 | ('z', np.float), 76 | ('i', np.int), 77 | ('rtn_num', np.int), 78 | ('rtn_tot', np.int), 79 | ('scan_dir', np.int), 80 | ('scan_edge', np.int), 81 | ('class', np.int), 82 | ('scan_ang', np.int), 83 | ('user_data', np.int), 84 | ('point_src_id', np.int), 85 | ('gps_time', np.float)])) 86 | 87 | def point_fmt3struct(): 88 | return ((('x', 4,'l',1), 89 | ('y', 4,'l',1), 90 | ('z', 4,'l',1), 91 | ('i', 2,'H',1), 92 | ('return_grp', 1,'B',1), 93 | ('class', 1,'B',1), 94 | ('scan_ang', 1,'b',1), 95 | ('user_data', 1,'B',1), 96 | ('point_src_id', 2,'H',1), 97 | ('gps_time', 8,'d',1), 98 | ('r', 2,'H',1), 99 | ('g', 2,'H',1), 100 | ('b', 2,'H',1)), 101 | 102 | np.dtype([('x', np.float), 103 | ('y', np.float), 104 | ('z', np.float), 105 | ('i', np.int), 106 | ('rtn_num', np.int), 107 | ('rtn_tot', np.int), 108 | ('scan_dir', np.int), 109 | ('scan_edge', np.int), 110 | ('class', np.int), 111 | ('scan_ang', np.int), 112 | ('user_data', np.int), 113 | ('point_src_id', np.int), 114 | ('gps_time', np.float), 115 | ('r', np.int), 116 | ('g', np.int), 117 | ('b', np.int)])) 118 | 119 | def znrHeaderStruct(): 120 | return (('filesig', 4,'c',4), 121 | ('filesourceid', 2,'H',1), 122 | ('reserved' , 2,'H',1), 123 | ('resolution' , 4,'L',1), 124 | ('headersize' , 2,'H',1), 125 | ('offset' , 4,'L',1), 126 | ('numvlrecords', 4,'L',1), 127 | ('pointreclen' , 2,'H',1), 128 | ('numptrecords', 4,'L',1), 129 | ('zscale' , 8,'d',1), 130 | ('zoffset' , 8,'d',1), 131 | ('xmax' , 8,'d',1), 132 | ('xmin' , 8,'d',1), 133 | ('ymax' , 8,'d',1), 134 | ('ymin' , 8,'d',1), 135 | ('zmax' , 8,'d',1), 136 | ('zmin' , 8,'d',1)) 137 | 138 | def znrStruct(): 139 | return ((('z', 4, 'l', 1, np.float), 140 | ('rtn_tot', 1, 'B', 1, np.int)), 141 | 142 | np.dtype([('z', np.float), 143 | ('rtn_tot', np.int)])) 144 | 145 | def point_fmtLTstruct(): 146 | return ((('x', 4,'l',1), 147 | ('y', 4,'l',1), 148 | ('z', 4,'l',1), 149 | ('i', 2,'H',1), 150 | ('return_grp', 1,'B',1), 151 | ('class', 1,'B',1), 152 | ('scan_ang', 1,'b',1), 153 | ('user_data', 1,'B',1), 154 | ('point_src_id', 2,'H',1), 155 | ('gps_time', 8,'d',1)), 156 | 157 | np.dtype([('x', np.float), 158 | ('y', np.float), 159 | ('z', np.float), 160 | ('i', np.int), 161 | ('rtn_num', np.int), 162 | ('rtn_tot', np.int), 163 | ('scan_dir', np.int), 164 | ('scan_edge', np.int), 165 | ('class', np.int), 166 | ('scan_ang', np.int), 167 | ('user_data', np.int), 168 | ('point_src_id', np.int), 169 | ('gps_time', np.float)]) ) -------------------------------------------------------------------------------- /forestlas/lasStructure.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/philwilkes/forestlas/5d0439adab918b51b00881186f1b1d670216b4cf/forestlas/lasStructure.pyc -------------------------------------------------------------------------------- /forestlas/recover_temp_files_.py: -------------------------------------------------------------------------------- 1 | __author__ = 'phil' 2 | import os 3 | import struct 4 | import numpy as np 5 | from forestlas import lasIO_, lasStructure 6 | import global_header 7 | 8 | class recover_temp_files: 9 | 10 | def __init__(self, temp_directory, out_dir=False, ptFrmt=1, ): 11 | 12 | self.temp_directory = temp_directory 13 | if not out_dir: 14 | out_dir = self.temp_directory 15 | else: 16 | out_dir = out_dir 17 | 18 | if ptFrmt == 1: 19 | ptFrmt = lasStructure.point_fmt1struct()[0] 20 | dt = lasStructure.point_fmt1struct()[1] 21 | 22 | for las_temp in os.listdir(self.temp_directory): 23 | 24 | h = global_header.h() 25 | num_records = os.path.getsize(os.path.join(self.temp_directory, las_temp)) / h['pointreclen'] 26 | rtn_num = [0, 0, 0, 0, 0] 27 | a = np.zeros(num_records, dtype=dt) 28 | 29 | with open(os.path.join(self.temp_directory, las_temp), 'rb') as fh: 30 | for j, line in enumerate(range(num_records)): 31 | fh.seek(j * h['pointreclen']) 32 | d = extract_return(ptFrmt, fh, h) 33 | for field in d: 34 | # print field 35 | a[j][field] = d[field] 36 | 37 | if 0 < d['rtn_num'] < 6: 38 | rtn_num[d['rtn_num']-1] += 1 39 | 40 | # break 41 | 42 | h['numptbyreturn'] = tuple(rtn_num) 43 | np2LAS(a, h, ptFrmt, outFile=os.path.join(out_dir, '{}.recovered.las'.format(las_temp[:-6]))) 44 | 45 | class recover_temp_file: 46 | 47 | def __init__(self, las_temp, out_dir=False, verbose=False): 48 | 49 | # self.temp_directory = temp_directory 50 | if not out_dir: 51 | out_dir, tile = os.path.split(las_temp) 52 | else: 53 | tile = os.path.split(las_temp)[1] 54 | 55 | ptFrmt = lasStructure.point_fmt1struct()[0] 56 | dt = lasStructure.point_fmt1struct()[1] 57 | h = global_header.h() 58 | num_records = os.path.getsize(las_temp) / h['pointreclen'] 59 | rtn_num = [0, 0, 0, 0, 0] 60 | a = np.zeros(num_records, dtype=dt) 61 | 62 | with open(las_temp, 'rb') as fh: 63 | for j, line in enumerate(range(num_records)): 64 | fh.seek(j * h['pointreclen']) 65 | d = extract_return(ptFrmt, fh, h) 66 | for field in d: 67 | # print field 68 | a[j][field] = d[field] 69 | 70 | if 0 < d['rtn_num'] < 6: 71 | rtn_num[d['rtn_num']-1] += 1 72 | 73 | # break 74 | 75 | h['numptbyreturn'] = tuple(rtn_num) 76 | np2LAS(a, h, ptFrmt, 77 | outFile=os.path.join(out_dir, '{}.recovered.las'.format(tile[:-5])), 78 | verbose=verbose) 79 | 80 | def np2LAS(arr, h, ptFrmt, outFile=False, verbose=False): 81 | 82 | """ 83 | Can be used to export a numpy array in .las format 84 | """ 85 | 86 | h['gensoftware'] = 'CRC207 LiDAR analysis software ' 87 | h['sysid'] = 'CRC207 LiDAR analysis software ' 88 | h['xmin'] = arr['x'].min() 89 | h['xmax'] = arr['x'].max() 90 | h['ymin'] = arr['y'].min() 91 | h['ymax'] = arr['y'].max() 92 | h['zmin'] = arr['z'].min() 93 | h['zmax'] = arr['z'].max() 94 | ### sorting out the rtn_num tuple 95 | rtn = np.zeros(5).astype(int) 96 | for row in arr: 97 | rtn_num = row['rtn_num'] - 1 98 | if rtn_num < 5: 99 | rtn[rtn_num] += 1 100 | h['numptbyreturn'] = tuple(rtn) 101 | h['numptrecords'] = len(arr) 102 | h['guid2'] = 0 103 | 104 | with open(outFile, "wb") as out: 105 | for j in lasStructure.headerstruct(): 106 | if j[2] == 'c': 107 | out.write(h[j[0]]) 108 | elif j[3] > 1: 109 | out.write(struct.pack('=' + str(j[3]) + j[2], *h[j[0]])) 110 | else: 111 | out.write(struct.pack('=' + j[2] , h[j[0]])) 112 | 113 | ## write points 114 | out.seek(h['offset']) 115 | for d in arr: 116 | for i in ptFrmt: 117 | if i[0] == 'return_grp': 118 | byte = ((d['scan_edge'] & 1) << 7) | ((d['scan_dir'] & 1) << 6) | ((d['rtn_tot'] & 7) << 3) | (d['rtn_num'] & 7) 119 | elif i[0] == 'x': 120 | byte = (d['x'] - h['xoffset']) / h['xscale'] 121 | elif i[0] == 'y': 122 | byte = (d['y'] - h['yoffset']) / h['yscale'] 123 | elif i[0] == 'z': 124 | byte = (d['z'] - h['zoffset']) / h['zscale'] 125 | else: 126 | byte = d[i[0]] 127 | out.write(struct.pack('=' + i[2], byte)) 128 | 129 | if verbose: print ".las file recoered to {}".format(outFile) 130 | 131 | def extract_return(ptFrmt, fh, h): 132 | 133 | point_dictionary = {} # dictionary for storing values in for each point 134 | 135 | for ent in ptFrmt: 136 | byte = fh.read(ent[1]) 137 | val = struct.unpack('=' + ent[2] , byte)[0] 138 | if ent[0] == 'x': 139 | val = (val * h['xscale'] ) + h['xoffset'] 140 | if ent[0] == 'y': 141 | val = (val * h['yscale'] ) + h['yoffset'] 142 | if ent[0] == 'z': 143 | val = (val * h['zscale'] ) + h['zoffset'] 144 | if ent[0] == 'return_grp': 145 | point_dictionary['rtn_num'] = val & 7 146 | #if point_dictionary['rtn_num'] == 0: 147 | # raise Exception 148 | point_dictionary['rtn_tot'] = (val >> 3) & 7 149 | point_dictionary['scan_dir'] = (val >> 6) & 1 150 | point_dictionary['scan_edge'] = (val >> 7) 151 | continue # required so that 'return_grp' is not added to dictionary 152 | 153 | point_dictionary[ent[0]] = val 154 | 155 | if point_dictionary["z"] > 1000000: 156 | raise NameError("z very high: {}".format(point_dictionary["z"])) 157 | 158 | return point_dictionary 159 | 160 | if __name__ == '__main__': 161 | 162 | import multiprocessing 163 | import glob 164 | 165 | F = glob.glob('/var/folders/3g/x_8fg5zj7lvghkk1q3xz9d380000gp/T/lidar.processing.1402121.tmp/*.temp') 166 | # F = ((f, {'verbose':False}) for f in F) 167 | kw = {'verbose':True, 'out_dir':'/User/phil/ALS/SCRATCH'} 168 | 169 | # p = multiprocessing.Pool(4) 170 | # [p.apply(recover_temp_file, (f,), kw) for f in F] 171 | 172 | p = multiprocessing.Pool(4) 173 | p.map(recover_temp_file, ()) 174 | for f in F: 175 | recover_temp_file(f[0], **f[1]) 176 | # recover_temp_file(*arg, **kargs) -------------------------------------------------------------------------------- /forestlas/thinCloud.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | from forestlas.lasIO import * 4 | 5 | # Wilkes et al. 2015. Understanding the effects of ALS pulse density 6 | # for metric retrieval across diverse forest types. PE&RS 7 | 8 | class thinCloud: 9 | 10 | def __init__(self, las, ppsm): 11 | 12 | if isinstance(las, str): 13 | las = lasIO(las).all().asArray() 14 | 15 | self.ppsm = ppsm 16 | self.las = las 17 | self.xy = 1. / np.sqrt(ppsm) 18 | 19 | self.xmin = np.round(self.las['x'].min()) 20 | self.xmax = np.round(self.las['x'].max()) 21 | self.ymin = np.round(self.las['y'].min()) 22 | self.ymax = np.round(self.las['y'].max()) 23 | 24 | 25 | def thinned(self, Ex=0, Ey=0, fsw=1): 26 | 27 | """ 28 | fsw = first return search window, it is best to set this to the number of 29 | iterations but sometimes this is to small. 30 | 31 | RETURNS self.thinnedCloud POINT CLOUD 32 | """ 33 | 34 | fsw = (self.xy / 2 ) / fsw 35 | 36 | self.thinnedCloud = np.array([], dtype=self.las.dtype) 37 | first = self.las[self.las['rtn_num'] == 1] 38 | other = self.las[self.las['rtn_num'] != 1] 39 | self.sw = (self.las['z'].max() - self.las['z'].min()) * np.tan(np.deg2rad(5)) 40 | 41 | if self.sw == 0: self.sw = 1 42 | 43 | for ii, i in enumerate(np.linspace(self.xmin, self.xmax, 44 | (self.xmax - self.xmin) / self.xy + 1) + Ex): 45 | for jj, j in enumerate(np.linspace(self.ymin, self.ymax, 46 | (self.ymax - self.ymin) / self.xy + 1) + Ey): 47 | 48 | f = first[(first['x'] > (i - fsw)) & 49 | (first['x'] <= (i + fsw)) & 50 | (first['y'] > (j - fsw)) & 51 | (first['y'] <= (j + fsw))] 52 | o = other[(other['x'] > (i - self.sw)) & (other['x'] <= (i + self.sw)) & 53 | (other['y'] > (j - self.sw)) & (other['y'] <= (j + self.sw))] 54 | 55 | if len(f) > 0: 56 | fx = f['x'] - i 57 | fy = f['y'] - j 58 | distance_m = np.hypot(fx, fy) 59 | idx = [distance_m == distance_m.min()] 60 | f = f[idx][0] 61 | rtn_tot = f["rtn_tot"] 62 | self.thinnedCloud = np.hstack([self.thinnedCloud, f]) 63 | if rtn_tot > 1: 64 | for rtn_num in range(2, rtn_tot + 1): 65 | rn = o[o["rtn_num"] == rtn_num] 66 | if len(rn) > 0: 67 | rx = rn['x'] - f['x'] 68 | ry = rn['y'] - f['y'] 69 | distance_m = np.hypot(rx, ry) 70 | idx = [distance_m == distance_m.min()] 71 | rn = rn[idx][0] 72 | self.thinnedCloud = np.hstack([self.thinnedCloud, rn]) 73 | 74 | return self.thinnedCloud 75 | -------------------------------------------------------------------------------- /forestlas/woodyAttribute.py: -------------------------------------------------------------------------------- 1 | import sys, os, glob, multiprocessing, datetime, shutil, tempfile 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from osgeo import osr, gdal 5 | 6 | from forestlas.lasIO import * 7 | from forestlas.canopyComplexity import * 8 | 9 | class woodyAttribute: 10 | 11 | """ 12 | Input is a directory containing tiled .las files and generates 13 | a raster of desired metrics, the defult is canopy height 14 | 15 | TO DO: 16 | 1: Get projection information from .las 17 | """ 18 | 19 | def __init__(self, verbose=False, maxProcess=4): 20 | self.verbose = verbose 21 | self.maxProcess = maxProcess 22 | 23 | def canopyHeight(self, directory, resolution=None, height=95): 24 | 25 | self.metric = 'height' 26 | self.height = height 27 | 28 | self.processSetup(directory, resolution) 29 | return self 30 | 31 | def canopyComplexity(self, directory, alpha=.3, N=20, resolution=None): 32 | 33 | self.metric = 'complexity' 34 | self.alpha = alpha 35 | self.N = N 36 | 37 | self.processSetup(directory, resolution) 38 | return self 39 | 40 | def Pgap(self, directory, N=20, resolution=None, ): 41 | 42 | self.metric = 'Pgap' 43 | self.N = N 44 | 45 | self.processSetup(directory, resolution) 46 | return self 47 | 48 | def fhd(self, directory, resolution=None, ): 49 | 50 | self.metric = 'fhd' 51 | 52 | self.processSetup(directory, resolution) 53 | return self 54 | 55 | def Cv(self, directory, resolution=None, ): 56 | 57 | self.metric = 'Cv' 58 | 59 | self.processSetup(directory, resolution) 60 | return self 61 | 62 | def fracCover(self, directory, resolution=None, cellSize=1, threshold=.3): 63 | 64 | self.metric = 'FC' 65 | self.cellSize = cellSize 66 | self.threshold = threshold 67 | 68 | self.processSetup(directory, resolution) 69 | return self 70 | 71 | def processSetup(self, directory, resolution): 72 | 73 | self.dir = directory 74 | self.resolution = resolution 75 | 76 | self.LASlist = sorted(glob.glob(os.path.join(self.dir, '*.znr'))) 77 | if len(self.LASlist) == 0: 78 | self.LASlist = sorted(glob.glob(os.path.join(self.dir, '*.las'))) 79 | self.counter = len(self.LASlist) // 100 80 | 81 | if len(self.LASlist) == 0: raise Exception('No .las files in {}'.format(self.dir)) 82 | 83 | self.createGrid() 84 | self.mp(self.LASlist) 85 | self.v, self.X, self.Y = self.populateGrid(self.plot_dictionary) 86 | return self 87 | 88 | def calculateComplexity(self, las): 89 | 90 | ### Generate plot profile ### 91 | 92 | # calculate plot centre 93 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 94 | pid = multiprocessing.current_process()._identity[0] 95 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 96 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 97 | model = CanopyComplexity(mp=tempDirectory).fromLAS(las).CHP(method='model') 98 | 99 | if model.z.max() < 2: 100 | self.plot_dictionary[x, y] = 0 101 | 102 | else: 103 | results = np.zeros(self.N) 104 | 105 | for i in range(self.N): 106 | z, zw = model.simulateCloud 107 | chp = CanopyComplexity().fromSample(z, zw).CHP(alpha=self.alpha) 108 | results[i] = chp.layerCount 109 | 110 | if len(results) > 0: 111 | self.plot_dictionary[x, y] = results.mean() 112 | else: self.plot_dictionary[x, y] = 0 113 | 114 | def calculatePgap(self, las): 115 | 116 | ### Generate plot profile ### 117 | 118 | # calculate plot centre 119 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 120 | pid = multiprocessing.current_process()._identity[0] 121 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 122 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 123 | 124 | pgap = CanopyComplexity(mp=tempDirectory).fromLAS(las, threshold = 1).Pgap() 125 | 126 | self.plot_dictionary[x, y] = pgap.min() 127 | 128 | def calculateFC(self, las): 129 | LAS = lasIO.lasIO(las) 130 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 131 | tiles = LAS.tiling(self.cellSize).asDic() 132 | o = np.zeros(len(tiles)) 133 | for i, tile in enumerate(tiles.keys()): 134 | T = tiles[tile] 135 | if len(T) > 0 and T['z'].max() > 1.7: 136 | pgap = CanopyComplexity().fromSample(T['z'], T['rtn_tot'], threshold=0).Pgap() 137 | o[i] = pgap.pgap[np.where(pgap.z > 1.7)][0] 138 | else: 139 | o[i] = 0 140 | self.plot_dictionary[x, y] = len(o[o > self.threshold]) / float(len(o)) 141 | 142 | def calculateHeight(self, las): 143 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 144 | pid = multiprocessing.current_process()._identity[0] 145 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 146 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 147 | las = lasIO.lasIO(las, tmpDir=tempDirectory, keepTemp=False).all().asArray() 148 | self.plot_dictionary[x, y] = np.percentile(las['z'], self.height) 149 | 150 | def calculateCv(self, las): 151 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 152 | pid = multiprocessing.current_process()._identity[0] 153 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 154 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 155 | las = lasIO(las, tmpDir=tempDirectory, keepTemp=False).all().asArray() 156 | self.plot_dictionary[x, y] = las['z'].std() / las['z'].mean() 157 | 158 | def calculateFHD(self, las): 159 | x, y = self.tileCentre(parseHeader(las)) # calculate plot centre 160 | pid = multiprocessing.current_process()._identity[0] 161 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 162 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 163 | chp = CanopyComplexity(mp=tempDirectory, keepTemp=False).fromLAS(las) 164 | fhd = [] 165 | for h in np.linspace(2, chp.z.max() // 2 * 2, chp.z.max() // 2): 166 | pi = sum(chp.zw[(chp.z > h) & (chp.z < h + 2)]) / chp.zw.sum() 167 | if pi == 0: 168 | continue 169 | fhdh = pi * np.log(pi) 170 | if np.isnan(fhdh): fhdh = 0 171 | fhd.append(fhdh) 172 | self.plot_dictionary[x, y] = -sum(fhd) 173 | 174 | def mp(self, listItems): 175 | 176 | ''' Carries out the multiprocessing grunt work ''' 177 | 178 | manager = multiprocessing.Manager() 179 | self.plot_dictionary = manager.dict() 180 | self.global_x = manager.list() 181 | self.global_y = manager.list() 182 | 183 | listI = 0 184 | 185 | for i in range((len(listItems) / self.maxProcess) + 1): 186 | 187 | if self.verbose: 188 | print '{:.2f}% | processing job {} of {} | {}'.format((float(i) / ((len(listItems) / self.maxProcess) + 1)) * 100, \ 189 | i, (len(listItems) / self.maxProcess) + 1, \ 190 | datetime.datetime.now()) 191 | jobs = [] 192 | 193 | if (self.maxProcess * listI) + self.maxProcess < len(listItems): 194 | processingList = listItems[self.maxProcess * listI: (self.maxProcess * listI) + self.maxProcess] 195 | else: processingList = listItems[self.maxProcess * listI:] 196 | 197 | for j, las in enumerate(processingList): # limits number of lass run at once 198 | 199 | p = False 200 | processMap = {'height':self.calculateHeight, 'complexity':self.calculateComplexity, 201 | 'fhd':self.calculateFHD, 'Cv':self.calculateCv, 'Pgap':self.calculatePgap, 202 | 'FC':self.calculateFC} 203 | 204 | p = multiprocessing.Process(target=processMap[self.metric], args=(las, )) 205 | 206 | if p: 207 | jobs.append(p) 208 | p.start() 209 | 210 | for proc in jobs: 211 | proc.join() 212 | 213 | listI += 1 214 | 215 | def createGrid(self): 216 | 217 | # generate parmaeters from .las header 218 | header = parseHeader(self.LASlist[0]) # .las header to dictionary 219 | 220 | # search for predefined resolution if not there calculate 221 | # tile resolution from data 222 | if self.resolution != None: 223 | pass 224 | elif 'guid1' in header.keys() and header['guid1'] > 0: 225 | self.resolution = header['guid1'] 226 | elif 'resolution' in header.keys(): 227 | self.resolution = header['resolution'] 228 | else: 229 | self.resolution = header['xmax'] - header['xmin'] # a wild guess! 230 | 231 | if self.verbose == True: print 'grid resolution: {}'.format(self.resolution) 232 | 233 | # grabself.vlr info 234 | # will use this at a later date to grab projection info 235 | self.vlr = getVLR(header['headersize'], self.LASlist[0]) 236 | 237 | return self 238 | 239 | def populateGrid(self, plot_dictionary): 240 | 241 | self.v, self.X, self.Y = np.meshgrid(0., np.unique(self.global_x), np.unique(self.global_y)) 242 | 243 | for key in plot_dictionary.keys(): 244 | idx = [(self.X == key[0]) & (self.Y == key[1])] 245 | self.v[idx] = np.float(plot_dictionary[key]) 246 | 247 | self.v = np.rot90(self.v.reshape(np.shape(self.v)[0], np.shape(self.v)[2])) 248 | 249 | print self.X 250 | return self.v, self.X, self.Y 251 | 252 | def tileCentre(self, header): 253 | 254 | x_range = header['xmax'] - header['xmin'] 255 | x_centre = (header['xmax'] - (x_range / 2.)) 256 | x_min = x_centre - (self.resolution / 2.) 257 | x = x_min // self.resolution 258 | self.global_x.append(x) 259 | 260 | y_range = header['ymax'] - header['ymin'] 261 | y_centre = (header['ymax'] - (y_range / 2.)) 262 | y_max = y_centre + (self.resolution / 2.) 263 | y = y_max // self.resolution 264 | self.global_y.append(y) 265 | 266 | return x, y 267 | 268 | def exportTiff(self, saveTo=False): 269 | 270 | print 'writing to tiff' 271 | 272 | if saveTo: 273 | if os.path.isfile(saveTo): 274 | ans = raw_input('Image already exists, overwrite? (Y|N): ').lower() 275 | if ans == 'y': 276 | shutil.rmtree(path) 277 | else: 278 | raise NameError('Change save filepath') 279 | elif os.path.isdir(saveTo): 280 | saveTo = os.path.join(saveTo, '{}_{:.2f}.tif'.format(self.metric, self.resolution)) 281 | else: 282 | saveTo = os.path.join(self.dir, '{}_{:.2f}.tif'.format(self.metric, self.resolution)) 283 | 284 | driver = gdal.GetDriverByName('GTiff') 285 | xDim = int((self.X.max() - self.X.min()) + 1) 286 | yDim = int((self.Y.max() - self.Y.min()) + 1) 287 | dataset = driver.Create(saveTo, xDim, yDim, 1, gdal.GDT_Float32) 288 | 289 | # set projection 290 | srs = osr.SpatialReference() 291 | srs.ImportFromEPSG(28355) 292 | dataset.SetProjection(srs.ExportToWkt()) 293 | 294 | # set transformation 295 | dataset.SetGeoTransform([self.X.min() * self.resolution, self.resolution, 0, self.Y.max() * self.resolution, 0, 0 - self.resolution]) 296 | 297 | # write raster and close 298 | dataset.GetRasterBand(1).WriteArray(self.v.astype(float)) 299 | dataset = None # closes .tif 300 | 301 | print '{} tif saved at {}'.format(self.metric, saveTo) 302 | 303 | def asArray(self): 304 | return self.v 305 | 306 | if __name__ == '__main__': 307 | 308 | G = woodyAttribute(verbose=True, maxProcess=4).canopyHeight('/Users/phil/ALS/WC/spl/tile_20/WC1_5m_TILES').exportTiff(saveTo='/Users/phil/ALS/WC/spl/tile_20/WC1_5m_TILES/height_5.00.old.tif') -------------------------------------------------------------------------------- /forestlas/woodyAttribute_.py: -------------------------------------------------------------------------------- 1 | import os 2 | import glob 3 | import multiprocessing 4 | import datetime 5 | import tempfile 6 | import numpy as np 7 | 8 | from forestlas.lasIO import * 9 | 10 | class canopyStructure: 11 | 12 | """ 13 | Input is a directory containing tiled .las files and generates 14 | a raster of desired metrics, the defult is canopy height 15 | 16 | TO DO: 17 | 1: Get projection information from .las 18 | """ 19 | 20 | def __init__(self, las_files, alpha=.3, N=10, resolution=None, 21 | layers=False, canopyDensity=False, underDensity=False, 22 | height=False, pgap=False, baseHeight=False, 23 | canopyPgap=False, all=False, point_only=False, 24 | verbose=False, number_of_processes=4, threshold=2, points_per_voxel=2): 25 | 26 | self.verbose = verbose 27 | self.number_of_processes = number_of_processes 28 | 29 | if not layers \ 30 | and not canopyDensity \ 31 | and not underDensity \ 32 | and not height \ 33 | and not pgap \ 34 | and not baseHeight \ 35 | and not canopyPgap \ 36 | and not all \ 37 | and not point_only: 38 | raise Exception('no method chosen') 39 | 40 | if all: 41 | layers = True 42 | canopyDensity = True 43 | underDensity = True 44 | height = True 45 | pgap = True 46 | baseHeight = True 47 | canopyPgap = True 48 | 49 | self.alpha = alpha 50 | self.N = N 51 | self.threshold = threshold 52 | self.points_per_voxel = points_per_voxel 53 | 54 | self.metrics = {'layers':False, 55 | 'canopyDensity':False, 56 | 'underDensity':False, 57 | 'height': False, 58 | 'baseHeight': False, 59 | 'pgap': False, 60 | 'canopyPgap':False} 61 | 62 | if layers: 63 | self.metrics['layers'] = True 64 | if canopyDensity: 65 | self.metrics['canopyDensity'] = True 66 | if underDensity: 67 | self.metrics['underDensity'] = True 68 | if height: 69 | self.metrics['height'] = True 70 | if pgap: 71 | self.metrics['pgap'] = True 72 | if baseHeight: 73 | self.metrics['baseHeight'] = True 74 | if canopyPgap: 75 | self.metrics['canopyPgap'] = True 76 | if point_only: 77 | self.metrics['point_only'] = True 78 | 79 | if isinstance(las_files, list): 80 | self.dir = os.path.split(las_files[0])[0] 81 | if not os.path.isdir(self.dir): 82 | if os.path.isfile(os.path.join(os.getcwd(), las_files[0])): 83 | self.dir = os.getcwd() 84 | self.LASlist = las_files 85 | else: 86 | raise Exception('las files not in cwd and no directory supplied (suggest using glob)') 87 | else: 88 | self.LASlist = las_files 89 | elif os.path.isdir(las_files): 90 | self.dir = las_files 91 | self.LASlist = sorted(glob.glob(os.path.join(self.dir, '*.las'))) 92 | else: 93 | raise Exception('input needs to be a directory or list of .las files') 94 | 95 | self.resolution = resolution 96 | self.counter = len(self.LASlist) // 100 97 | 98 | if len(self.LASlist) == 0: raise Exception('No .las files in {}'.format(self.dir)) 99 | if self.verbose: print 'number of tiles to process:', len(self.LASlist) 100 | 101 | self.createGrid() 102 | self.mp(self.LASlist) 103 | self.populateGrid() 104 | 105 | def base(chp): 106 | return chp.threshold if len(chp.crownBase) == 0 else chp.crownBase[-1] 107 | 108 | def calculateStructure(self, las, x, y, lasF): 109 | 110 | from forestlas.canopyComplexity import CanopyComplexity 111 | 112 | ### Generate plot profile ### 113 | 114 | try: 115 | if las['z'].max() < self.threshold or len(las[las['z'] >= self.threshold]) < self.points_per_voxel: 116 | for metric in self.metrics.keys(): 117 | if metric != 'height': self.metrics[metric][x, y] = np.nan 118 | 119 | else: 120 | model = CanopyComplexity().fromLAS(las).CHP(method='model') 121 | results = {metric:np.zeros(self.N) for metric in self.metrics.keys()} 122 | for i in range(self.N): 123 | sim = model.simulateCloud() 124 | if sim['z'].max() < self.threshold or len(sim[sim['z'] >= self.threshold]) < self.points_per_voxel: 125 | results['layers'][i] = 0 126 | results['canopyDensity'][i] = 0 127 | results['underDensity'][i] = 1 128 | results['baseHeight'][i] = np.nan 129 | results['pgap'] = 1 130 | results['canopyPgap'] = np.nan 131 | else: 132 | chp = CanopyComplexity().fromLAS(sim, top_threshold=100).CHP(method='sample', alpha=self.alpha) 133 | for metric in results.keys(): 134 | if metric == 'layers': 135 | results[metric][i] = chp.layerCount 136 | if metric == 'canopyDensity': 137 | results[metric][i] = chp.fd[chp.zx >= self.base(chp)].sum() / float(chp.fd.sum()) 138 | if metric == 'underDensity': 139 | results[metric][i] = chp.fd[chp.zx < self.base(chp)].sum() / float(chp.fd.sum()) 140 | if metric == 'baseHeight': 141 | results[metric][i] = np.nan if len(chp.crownBase) == 0 else chp.crownBase[-1] 142 | if metric == 'pgap': 143 | results[metric][i] = chp.pgap.min() 144 | if metric == 'canopyPgap': 145 | results[metric][i] = chp.pgap[chp.zx == (chp.threshold if len(chp.crownBase) == 0 146 | else chp.crownBase[-1])][0] 147 | 148 | for metric in self.metrics.keys(): 149 | if metric != 'height': self.metrics[metric][x, y] = results[metric].mean() 150 | 151 | except Exception as err: 152 | print '!!!', lasF, err, '!!!' 153 | 154 | return self 155 | 156 | def mp(self, listItems): 157 | 158 | ''' Carries out the multiprocessing grunt work ''' 159 | 160 | manager = multiprocessing.Manager() 161 | for metric, do in self.metrics.items(): 162 | if self.metrics[metric]: 163 | self.metrics[metric] = manager.dict() 164 | else: 165 | del self.metrics[metric] 166 | 167 | # self.plot_dictionary = manager.dict() 168 | self.global_x = manager.list() 169 | self.global_y = manager.list() 170 | 171 | listI = 0 172 | 173 | for i in range((len(listItems) / self.number_of_processes) + 1): 174 | 175 | if self.counter > 0 and i%self.counter == 0 and self.verbose: 176 | print '{:.2f}% | processing job {} of {} | {}'.format((float(i) / ((len(listItems) / self.number_of_processes) + 1)) * 100, \ 177 | i, (len(listItems) / self.number_of_processes) + 1, \ 178 | datetime.datetime.now()) 179 | jobs = [] 180 | 181 | if (self.number_of_processes * listI) + self.number_of_processes < len(listItems): 182 | processingList = listItems[self.number_of_processes * listI: (self.number_of_processes * listI) + self.number_of_processes] 183 | else: processingList = listItems[self.number_of_processes * listI:] 184 | 185 | for j, las in enumerate(processingList): # limits number of lass run at once 186 | 187 | p = multiprocessing.Process(target=self.readLAS, args=(las, )) 188 | 189 | if p: 190 | jobs.append(p) 191 | p.start() 192 | 193 | for proc in jobs: 194 | proc.join() 195 | 196 | listI += 1 197 | 198 | def readLAS(self, lasF): 199 | 200 | # read las file and send to different processes 201 | pid = multiprocessing.current_process()._identity[0] 202 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 203 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 204 | 205 | las = lasIO(lasF, tmpDir=tempDirectory) 206 | X, Y = self.tileCentre(las) 207 | 208 | if 'point_only' in self.metrics.keys(): 209 | self.metrics['point_only'][X, Y] = 1 210 | 211 | else: 212 | las = las.all().asArray() 213 | 214 | if 'height' in self.metrics.keys(): 215 | self.metrics['height'][X, Y] = np.percentile(las['z'], 95) 216 | 217 | if len(set(self.metrics.keys()).intersection(['layers', 'canopyDensity', 218 | 'underDensity', 'pgap', 219 | 'baseHeight'])) > 0: 220 | self.calculateStructure(las, X, Y, lasF) 221 | 222 | def createGrid(self): 223 | 224 | # generate parmaeters from .las header 225 | header = parseHeader(self.LASlist[0]) # .las header to dictionary 226 | 227 | # search for predefined resolution if not there calculate 228 | # tile resolution from data 229 | if self.resolution != None: 230 | pass 231 | elif 'guid1' in header.keys() and header['guid1'] > 0: 232 | self.resolution = header['guid1'] 233 | elif 'resolution' in header.keys(): 234 | self.resolution = header['resolution'] 235 | else: 236 | self.resolution = header['xmax'] - header['xmin'] # a wild guess! 237 | 238 | if self.verbose == True: print 'grid resolution: {}'.format(self.resolution) 239 | 240 | # grabself.vlr info 241 | # will use this at a later date to grab projection info 242 | self.vlr = getVLR(header['headersize'], self.LASlist[0]) 243 | 244 | return self 245 | 246 | def populateGrid(self): 247 | 248 | self.metricGrids = {key:None for key in self.metrics.keys()} 249 | 250 | for metric in self.metrics.keys(): 251 | 252 | # array, self.X, self.Y = np.meshgrid(np.nan, np.unique(self.global_x), np.unique(self.global_y)) 253 | array, self.X, self.Y = np.meshgrid(np.nan, 254 | np.arange(min(self.global_x), max(self.global_x) + 1, self.resolution), 255 | np.arange(min(self.global_y), max(self.global_y) + 1, self.resolution)) 256 | 257 | for key in self.metrics[metric].keys(): 258 | idx = [(self.X == key[0]) & (self.Y == key[1])] 259 | array[idx] = np.float(self.metrics[metric][key]) 260 | 261 | self.metricGrids[metric] = np.rot90(array.reshape(np.shape(array)[0], np.shape(array)[2])) 262 | 263 | return self 264 | 265 | def tileCentre(self, las): 266 | 267 | 268 | x = np.floor(las.x_centre) 269 | self.global_x.append(x) 270 | 271 | y = np.round(las.y_centre) 272 | self.global_y.append(y) 273 | 274 | return x, y 275 | 276 | def exportTiff(self, saveTo=False): 277 | 278 | from osgeo import osr, gdal 279 | 280 | for metric in self.metrics.keys(): 281 | if saveTo: 282 | if os.path.isfile(saveTo): 283 | ans = raw_input('Image already exists, overwrite? (Y|N): ').lower() 284 | if ans == 'y': 285 | os.unlink(saveTo) 286 | else: 287 | raise NameError('Change save filepath') 288 | savePath = saveTo 289 | elif os.path.isdir(saveTo): 290 | savePath = os.path.join(saveTo, '{}_{:.2f}.tif'.format(metric, self.resolution)) 291 | elif saveTo.endswith('.tif'): 292 | dir = os.path.split(saveTo)[0] 293 | if os.path.isdir(dir): 294 | savePath = saveTo 295 | else: 296 | raise Exception('{} is not a directory'.format(dir)) 297 | else: 298 | savePath = os.path.join(self.dir, '{}_{:.2f}.tif'.format(metric, self.resolution)) 299 | 300 | driver = gdal.GetDriverByName('GTiff') 301 | xDim = len(np.unique(self.X)) 302 | yDim = len(np.unique(self.Y)) 303 | dataset = driver.Create(savePath, xDim, yDim, 1, gdal.GDT_Float32) 304 | 305 | # set projection 306 | srs = osr.SpatialReference() 307 | srs.ImportFromEPSG(28355) 308 | dataset.SetProjection(srs.ExportToWkt()) 309 | 310 | dataset.SetGeoTransform([self.X.min() - float(self.resolution) / 2., 311 | self.resolution, 312 | 0, 313 | self.Y.max() + float(self.resolution) / 2., 314 | 0, 315 | 0 - self.resolution]) 316 | 317 | # set no data value 318 | dataset.GetRasterBand(1).SetNoDataValue(-1) 319 | 320 | # write raster and close 321 | dataset.GetRasterBand(1).WriteArray(self.metricGrids[metric]) 322 | del dataset # closes .tif 323 | 324 | print '{} tif saved at {}'.format(metric, savePath) 325 | 326 | def asArray(self): 327 | return self.metricGrids 328 | 329 | if __name__ == '__main__': 330 | 331 | import glob 332 | # path = '/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile' 333 | path = '/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile/WC45_SUB_20m_TILES' 334 | L = glob.glob(os.path.join(path, '*.las')) 335 | # L = ['/Users/phil/Google_Drive/RiverHealth/DATA/AREA_A/10/309231.0_6004106.0.PLOT.las'] 336 | # canopyStructure(L, point_only=True, resolution=10, verbose=True) 337 | start = datetime.datetime.now() 338 | canopyStructure(L, height=True, verbose=True, number_of_processes=4).exportTiff() 339 | print datetime.datetime.now() - start 340 | # G = canopyStructure(glob.glob('/Users/phil/Google_Drive/RiverHealth/DATA/AREA_C/100/*.las'), height=True, verbose=True, number_of_processes=4, resolution=100)# -------------------------------------------------------------------------------- /forestlas/woodyAttribute_pool.py: -------------------------------------------------------------------------------- 1 | import os 2 | import glob 3 | import multiprocessing 4 | import datetime 5 | import tempfile 6 | import numpy as np 7 | 8 | from forestlas.lasIO import * 9 | 10 | def base(chp): 11 | return chp.threshold if len(chp.crownBase) == 0 else chp.crownBase[-1] 12 | 13 | class canopyStructure: 14 | 15 | """ 16 | Input is a directory containing tiled .las files and generates 17 | a raster of desired metrics, the defult is canopy height 18 | 19 | TO DO: 20 | 1: Get projection information from .las 21 | """ 22 | 23 | def __init__(self, las_files, alpha=.3, N=10, resolution=None, 24 | layers=False, canopyDensity=False, underDensity=False, 25 | height=False, pgap=False, baseHeight=False, 26 | canopyPgap=False, all=False, point_only=False, 27 | verbose=False, number_of_processes=4, threshold=2, points_per_voxel=2): 28 | 29 | self.verbose = verbose 30 | self.number_of_processes = number_of_processes 31 | 32 | if not layers \ 33 | and not canopyDensity \ 34 | and not underDensity \ 35 | and not height \ 36 | and not pgap \ 37 | and not baseHeight \ 38 | and not canopyPgap \ 39 | and not all \ 40 | and not point_only: 41 | raise Exception('no method chosen') 42 | 43 | if all: 44 | layers = True 45 | canopyDensity = True 46 | underDensity = True 47 | height = True 48 | pgap = True 49 | baseHeight = True 50 | canopyPgap = True 51 | 52 | self.alpha = alpha 53 | self.N = N 54 | self.threshold = threshold 55 | self.points_per_voxel = points_per_voxel 56 | 57 | self.metrics = {'layers':False, 58 | 'canopyDensity':False, 59 | 'underDensity':False, 60 | 'height': False, 61 | 'baseHeight': False, 62 | 'pgap': False, 63 | 'canopyPgap':False} 64 | 65 | if layers: 66 | self.metrics['layers'] = True 67 | if canopyDensity: 68 | self.metrics['canopyDensity'] = True 69 | if underDensity: 70 | self.metrics['underDensity'] = True 71 | if height: 72 | self.metrics['height'] = True 73 | if pgap: 74 | self.metrics['pgap'] = True 75 | if baseHeight: 76 | self.metrics['baseHeight'] = True 77 | if canopyPgap: 78 | self.metrics['canopyPgap'] = True 79 | if point_only: 80 | self.metrics['point_only'] = True 81 | 82 | if isinstance(las_files, list): 83 | self.dir = os.path.split(las_files[0])[0] 84 | if not os.path.isdir(self.dir): 85 | if os.path.isfile(os.path.join(os.getcwd(), las_files[0])): 86 | self.dir = os.getcwd() 87 | self.LASlist = las_files 88 | else: 89 | raise Exception('las files not in cwd and no directory supplied (suggest using glob)') 90 | else: 91 | self.LASlist = las_files 92 | elif os.path.isdir(las_files): 93 | self.dir = las_files 94 | self.LASlist = sorted(glob.glob(os.path.join(self.dir, '*.las'))) 95 | else: 96 | raise Exception('input needs to be a directory or list of .las files') 97 | 98 | self.resolution = resolution 99 | self.counter = len(self.LASlist) // 100 100 | 101 | if len(self.LASlist) == 0: raise Exception('No .las files in {}'.format(self.dir)) 102 | if self.verbose: 103 | print 'number of tiles to process:', len(self.LASlist) 104 | print 'number of CPUs:', self.number_of_processes 105 | 106 | self.createGrid() 107 | self.mp(self.LASlist) 108 | self.populateGrid() 109 | 110 | def calculateStructure(self, las, x, y, lasF): 111 | 112 | from forestlas.canopyComplexity import CanopyComplexity 113 | 114 | ### Generate plot profile ### 115 | 116 | try: 117 | if las['z'].max() < self.threshold or len(las[las['z'] >= self.threshold]) < self.points_per_voxel: 118 | for metric in self.metrics.keys(): 119 | if metric != 'height': self.metrics[metric][x, y] = np.nan 120 | 121 | else: 122 | model = CanopyComplexity().fromLAS(las).CHP(method='model') 123 | results = {metric:np.zeros(self.N) for metric in self.metrics.keys()} 124 | for i in range(self.N): 125 | sim = model.simulateCloud() 126 | if sim['z'].max() < self.threshold or len(sim[sim['z'] >= self.threshold]) < self.points_per_voxel: 127 | results['layers'][i] = 0 128 | results['canopyDensity'][i] = 0 129 | results['underDensity'][i] = 1 130 | results['baseHeight'][i] = np.nan 131 | results['pgap'] = 1 132 | results['canopyPgap'] = np.nan 133 | else: 134 | chp = CanopyComplexity().fromLAS(sim, top_threshold=100).CHP(method='sample', alpha=self.alpha) 135 | for metric in results.keys(): 136 | if metric == 'layers': 137 | results[metric][i] = chp.layerCount 138 | if metric == 'canopyDensity': 139 | results[metric][i] = chp.fd[chp.zx >= self.base(chp)].sum() / float(chp.fd.sum()) 140 | if metric == 'underDensity': 141 | results[metric][i] = chp.fd[chp.zx < self.base(chp)].sum() / float(chp.fd.sum()) 142 | if metric == 'baseHeight': 143 | results[metric][i] = np.nan if len(chp.crownBase) == 0 else chp.crownBase[-1] 144 | if metric == 'pgap': 145 | results[metric][i] = chp.pgap.min() 146 | if metric == 'canopyPgap': 147 | results[metric][i] = chp.pgap[chp.zx == (chp.threshold if len(chp.crownBase) == 0 148 | else chp.crownBase[-1])][0] 149 | 150 | for metric in self.metrics.keys(): 151 | if metric != 'height': self.metrics[metric][x, y] = results[metric].mean() 152 | 153 | except Exception as err: 154 | print '!!!', lasF, err, '!!!' 155 | 156 | return self 157 | 158 | def mp(self, listItems): 159 | 160 | ''' Carries out the multiprocessing grunt work ''' 161 | 162 | manager = multiprocessing.Manager() 163 | for metric, do in self.metrics.items(): 164 | if self.metrics[metric]: 165 | self.metrics[metric] = manager.dict() 166 | else: 167 | del self.metrics[metric] 168 | 169 | # self.plot_dictionary = manager.dict() 170 | self.global_x = manager.list() 171 | self.global_y = manager.list() 172 | 173 | p = multiprocessing.Pool(self.number_of_processes) 174 | # [p.apply(recover_temp_file, (f,), kw) for f in F] 175 | [p.apply(readLAS, (self, f)) for f in listItems] 176 | p.close() 177 | p.join() 178 | 179 | return self 180 | 181 | def createGrid(self): 182 | 183 | # generate parmaeters from .las header 184 | header = parseHeader(self.LASlist[0]) # .las header to dictionary 185 | 186 | # search for predefined resolution if not there calculate 187 | # tile resolution from data 188 | if self.resolution != None: 189 | pass 190 | elif 'guid1' in header.keys() and header['guid1'] > 0: 191 | self.resolution = header['guid1'] 192 | elif 'resolution' in header.keys(): 193 | self.resolution = header['resolution'] 194 | else: 195 | self.resolution = header['xmax'] - header['xmin'] # a wild guess! 196 | 197 | if self.verbose == True: print 'grid resolution: {}'.format(self.resolution) 198 | 199 | # grabself.vlr info 200 | # will use this at a later date to grab projection info 201 | self.vlr = getVLR(header['headersize'], self.LASlist[0]) 202 | 203 | return self 204 | 205 | def populateGrid(self): 206 | 207 | self.metricGrids = {key:None for key in self.metrics.keys()} 208 | 209 | for metric in self.metrics.keys(): 210 | 211 | # array, self.X, self.Y = np.meshgrid(np.nan, np.unique(self.global_x), np.unique(self.global_y)) 212 | array, self.X, self.Y = np.meshgrid(np.nan, 213 | np.arange(min(self.global_x), max(self.global_x) + 1, self.resolution), 214 | np.arange(min(self.global_y), max(self.global_y) + 1, self.resolution)) 215 | 216 | for key in self.metrics[metric].keys(): 217 | idx = [(self.X == key[0]) & (self.Y == key[1])] 218 | array[idx] = np.float(self.metrics[metric][key]) 219 | 220 | self.metricGrids[metric] = np.rot90(array.reshape(np.shape(array)[0], np.shape(array)[2])) 221 | 222 | return self 223 | 224 | def tileCentre(self, las): 225 | 226 | x = np.round(las.x_centre) 227 | self.global_x.append(x) 228 | 229 | y = np.round(las.y_centre) 230 | self.global_y.append(y) 231 | 232 | return x, y 233 | 234 | def exportTiff(self, saveTo=False): 235 | 236 | from osgeo import osr, gdal 237 | 238 | for metric in self.metrics.keys(): 239 | if saveTo: 240 | if os.path.isfile(saveTo): 241 | ans = raw_input('Image already exists, overwrite? (Y|N): ').lower() 242 | if ans == 'y': 243 | os.unlink(saveTo) 244 | else: 245 | raise NameError('Change save filepath') 246 | savePath = saveTo 247 | elif os.path.isdir(saveTo): 248 | savePath = os.path.join(saveTo, '{}_{:.2f}.tif'.format(metric, self.resolution)) 249 | elif saveTo.endswith('.tif'): 250 | dir = os.path.split(saveTo)[0] 251 | if os.path.isdir(dir): 252 | savePath = saveTo 253 | else: 254 | raise Exception('{} is not a directory'.format(dir)) 255 | else: 256 | savePath = os.path.join(self.dir, '{}_{:.2f}.tif'.format(metric, self.resolution)) 257 | 258 | driver = gdal.GetDriverByName('GTiff') 259 | xDim = len(np.unique(self.X)) 260 | yDim = len(np.unique(self.Y)) 261 | dataset = driver.Create(savePath, xDim, yDim, 1, gdal.GDT_Float32) 262 | 263 | # set projection 264 | srs = osr.SpatialReference() 265 | srs.ImportFromEPSG(28355) 266 | dataset.SetProjection(srs.ExportToWkt()) 267 | 268 | dataset.SetGeoTransform([self.X.min() - float(self.resolution) / 2., 269 | self.resolution, 270 | 0, 271 | self.Y.max() + float(self.resolution) / 2., 272 | 0, 273 | 0 - self.resolution]) 274 | 275 | # set no data value 276 | dataset.GetRasterBand(1).SetNoDataValue(-1) 277 | 278 | # write raster and close 279 | dataset.GetRasterBand(1).WriteArray(self.metricGrids[metric]) 280 | del dataset # closes .tif 281 | 282 | print '{} tif saved at {}'.format(metric, savePath) 283 | 284 | def asArray(self): 285 | return self.metricGrids 286 | 287 | def X(lasF): 288 | print lasF 289 | 290 | def readLAS(self, lasF): 291 | 292 | # read las file and send to different processes 293 | pid = multiprocessing.current_process()._identity[0] 294 | tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp' 295 | tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName) 296 | 297 | las = lasIO(lasF, tmpDir=tempDirectory) 298 | X, Y = self.tileCentre(las) 299 | 300 | if 'point_only' in self.metrics.keys(): 301 | self.metrics['point_only'][X, Y] = 1 302 | 303 | else: 304 | try: 305 | las = las.all().asArray() 306 | 307 | if 'height' in self.metrics.keys(): 308 | self.metrics['height'][X, Y] = np.percentile(las['z'], 95) 309 | 310 | if len(set(self.metrics.keys()).intersection(['layers', 'canopyDensity', 311 | 'underDensity', 'pgap', 312 | 'baseHeight'])) > 0: 313 | self.calculateStructure(las, X, Y, lasF) 314 | except: 315 | print lasF 316 | 317 | return self 318 | 319 | if __name__ == '__main__': 320 | 321 | import glob 322 | # path = '/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile' 323 | path = '/Users/phil/ALS/WC/spl/tile_20/ForestLAS_tutorial/LAS/large_tile/WC45_SUB_20m_TILES' 324 | L = glob.glob(os.path.join(path, '*.las')) 325 | # L = ['/Users/phil/Google_Drive/RiverHealth/DATA/AREA_A/10/309231.0_6004106.0.PLOT.las'] 326 | # canopyStructure(L, point_only=True, resolution=10, verbose=True) 327 | start = datetime.datetime.now() 328 | canopyStructure(L, pgap=True, verbose=True, number_of_processes=4, resolution=5).exportTiff(path) 329 | print datetime.datetime.now() - start 330 | # G = canopyStructure(glob.glob('/Users/phil/Google_Drive/RiverHealth/DATA/AREA_C/100/*.las'), height=True, verbose=True, number_of_processes=4, resolution=100)# -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from distutils.core import setup 2 | 3 | setup( 4 | name='ForestLAS', 5 | version='0.1.3.5', 6 | author='Phil Wilkes', 7 | author_email='phil.wilkes@rmit.edu.au', 8 | packages=['forestlas'], 9 | url='http://www.crcsi.com.au/Research/2-Feature-Extraction/2-07-Woody-Vegetation', 10 | license='LICENSE.txt', 11 | description='Tools for generating woody attribution features', 12 | long_description=open('README.md').read(), 13 | ) 14 | --------------------------------------------------------------------------------