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python/envi.py 20 KB
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  # -*- coding: utf-8 -*-
  """
  Created on Fri Jul 21 20:18:01 2017
  
  @author: david
  """
  
  import os
  import numpy
  import scipy
  import matplotlib.pyplot as plt
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  import sys
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  from math import floor
  import progressbar
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  class envi_header:
      def __init__(self, filename = ""):
          if filename != "":
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              self.initialize()
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              self.load(filename)
          else:
              self.initialize()
          
      #initialization function
      def initialize(self):
          self.samples = int(0)
          self.lines = int(0)
          self.bands = int(0)
          self.header_offset = int(0)
          self.data_type = int(4)
          self.interleave = "bsq"
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          self.sensor_type = "Unknown"
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          self.byte_order = int(0)
          self.x_start = int(0)
          self.y_start = int(0)
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          self.z_plot_titles = "Unknown, Unknown"
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          self.pixel_size = [float(0), float(0)]
          self.pixel_size_units = "Meters"
          self.wavelength_units = "Wavenumber"
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          self.description = "no description"
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          self.band_names = []
          self.wavelength = []
          
      #convert an ENVI data_type value to a numpy data type        
      def get_numpy_type(self, val):
          if val == 1:
              return numpy.byte
          elif val == 2:
              return numpy.int16
          elif val == 3:
              return numpy.int32
          elif val == 4:
              return numpy.float32
          elif val == 5:
              return numpy.float64
          elif val == 6:
              return numpy.complex64
          elif val == 9:
              return numpy.complex128
          elif val == 12:
              return numpy.uint16
          elif val == 13:
              return numpy.uint32
          elif val == 14:
              return numpy.int64
          elif val == 15:
              return numpy.uint64
      
      def get_envi_type(self, val):
          if val == numpy.byte:
              return 1
          elif val == numpy.int16:
              return 2
          elif val == numpy.int32:
              return 3
          elif val == numpy.float32:
              return 4
          elif val == numpy.float64:
              return 5
          elif val == numpy.complex64:
              return 6
          elif val == numpy.complex128:
              return 9
          elif val == numpy.uint16:
              return 12
          elif val == numpy.uint32:
              return 13
          elif val == numpy.int64:
              return 14
          elif val == numpy.uint64:
              return 15
              
      def load(self, fname):
          f = open(fname)
          l = f.readlines()
          if l[0].strip() != "ENVI":
              print("ERROR: not an ENVI file")
              return
          li = 1
          while li < len(l):
              #t = l[li].split()               #split the line into tokens
              #t = map(str.strip, t)               #strip all of the tokens in the token list
              
              #handle the simple conditions
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              #if l[li].startswith("file type"):
              #    if not l[li].strip().endswith("ENVI Standard"):
              #        print("ERROR: unsupported ENVI file format: " + l[li].strip())
              #        return
              if l[li].startswith("samples"):
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                  self.samples = int(l[li].split()[-1])
              elif l[li].startswith("lines"):
                  self.lines = int(l[li].split()[-1])
              elif l[li].startswith("bands"):
                  self.bands = int(l[li].split()[-1])
              elif l[li].startswith("header offset"):
                  self.header_offset = int(l[li].split()[-1])
              elif l[li].startswith("data type"):
                  self.data_type = self.get_numpy_type(int(l[li].split()[-1]))
              elif l[li].startswith("interleave"):
                  self.interleave = l[li].split()[-1].strip()
              elif l[li].startswith("sensor type"):
                  self.sensor_type = l[li].split()[-1].strip()
              elif l[li].startswith("byte order"):
                  self.byte_order = int(l[li].split()[-1])
              elif l[li].startswith("x start"):
                  self.x_start = int(l[li].split()[-1])
              elif l[li].startswith("y start"):
                  self.y_start = int(l[li].split()[-1])
              elif l[li].startswith("z plot titles"):
                  i0 = l[li].rindex('{')
                  i1 = l[li].rindex('}')
                  self.z_plot_titles = l[li][i0 + 1 : i1]
              elif l[li].startswith("pixel size"):
                  i0 = l[li].rindex('{')
                  i1 = l[li].rindex('}')
                  s = l[li][i0 + 1 : i1].split(',')
                  self.pixel_size = [float(s[0]), float(s[1])]
                  self.pixel_size_units = s[2][s[2].rindex('=') + 1:].strip()
              elif l[li].startswith("wavelength units"):
                  self.wavelength_units = l[li].split()[-1].strip()                
              
              #handle the complicated conditions
              elif l[li].startswith("description"):
                  desc = [l[li]]
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                  ''' 
                  while l[li].strip()[-1] != '}': #will fail if l[li].strip() is empty
                      li += 1
                      desc.append(l[li])
                  '''
                  while True:
                      if l[li].strip():
                         if  l[li].strip()[-1] == '}':
                             break
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                      li += 1
                      desc.append(l[li])
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                  desc = ''.join(list(map(str.strip, desc)))           #strip all white space from the string list
                  i0 = desc.rindex('{')
                  i1 = desc.rindex('}')
                  self.description = desc[i0 + 1 : i1]
                  
              elif l[li].startswith("band names"):
                  names = [l[li]]
                  while l[li].strip()[-1] != '}':
                      li += 1
                      names.append(l[li])
                  names = ''.join(list(map(str.strip, names)))           #strip all white space from the string list
                  i0 = names.rindex('{')
                  i1 = names.rindex('}')
                  names = names[i0 + 1 : i1]
                  self.band_names = list(map(str.strip, names.split(',')))
              elif l[li].startswith("wavelength"):
                  waves = [l[li]]
                  while l[li].strip()[-1] != '}':
                      li += 1
                      waves.append(l[li])
                  waves = ''.join(list(map(str.strip, waves)))           #strip all white space from the string list
                  i0 = waves.rindex('{')
                  i1 = waves.rindex('}')
                  waves = waves[i0 + 1 : i1]
                  self.wavelength = list(map(float, waves.split(',')))
  
              li += 1          
          
          f.close()
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      #save an ENVI header
      def save(self, fname):
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          f = open(fname, "w")
          f.write("ENVI\n")
          f.write("description = {" + self.description + "}" + "\n")
          f.write("samples = " + str(self.samples) + "\n")
          f.write("lines = " + str(self.lines) + "\n")
          f.write("bands = " + str(self.bands) + "\n")
          f.write("header offset = " + str(self.header_offset) + "\n")
          f.write("file type = ENVI Standard" + "\n")
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          f.write("data type = " + str(self.get_envi_type(self.data_type)) + "\n")
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          f.write("interleave = " + self.interleave + "\n")
          f.write("sensor type = " + self.sensor_type + "\n")
          f.write("byte order = " + str(self.byte_order) + "\n")
          f.write("x start = " + str(self.x_start) + "\n")
          f.write("y start = " + str(self.y_start) + "\n")
          f.write("wavelength units = " + self.wavelength_units + "\n")
          f.write("z plot titles = {" + self.z_plot_titles + "}" + "\n")
          
          # save the wavelength values
          if self.wavelength != []:
              if len(self.wavelength) == self.bands:
                  f.write("wavelength = {")
                  f.write(",".join(map(str, self.wavelength)))
                  f.write("}\n")
              else:
                  raise Exception("ENVI HEADER ERROR: Number of wavelengths does not match number of bands")
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          f.close()
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      #sets the properties of the header to match those of the input array
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      def setprops(self, A, interleave="BSQ", wavelength=[]):
          # determine the data type automatically
          self.type = A.dtype
          
          # determine the ordering based on the specified interleave
          if interleave == "BSQ":
              self.samples = A.shape[2]
              self.lines = A.shape[1]
              self.bands = A.shape[0]
          elif interleave == "BIP":
              self.samples = A.shape[1]
              self.lines = A.shape[2]
              self.bands = A.shape[0]
          elif interleave == "BIL":
              self.samples = A.shape[0]
              self.lines = A.shape[2]
              self.bands = A.shape[1]
          else:
              raise Exception("invalid interleave format (requires 'BSQ', 'BIP', or 'BIL') - interleave is set to {}".interleave)
              
          # if wavelength units are given, make sure that they match the number of bands
          if wavelength != []:
              if len(wavelength) != self.bands:
                  raise Exception("invalid number of wavelengths specified")
              else:
                  self.wavelength = wavelength
                  
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  class envi:
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      def __init__(self, filename, headername = "", mask = []):
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          self.open(filename, headername)
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          if mask == []:
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              self.mask = numpy.ones((self.header.lines, self.header.samples), dtype=numpy.bool)
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          elif type(mask) == numpy.ndarray:
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              self.mask = mask
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          else:
              print("ERROR: unrecognized mask format - expecting a boolean array")
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          self.idx = 0                                                               #initialize the batch IDX to 0 for batch reading
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      def open(self, filename, headername = ""):
          if headername == "":
              headername = filename + ".hdr"
              
          if not os.path.isfile(filename):
              print("ERROR: " + filename + " not found")
              return
          if not os.path.isfile(headername):
              print("ERROR: " + headername + " not found")
              return
          
          #open the file
          self.header = envi_header(headername)
          self.file = open(filename, "rb")
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      # load the entire ENVI file into memory and return it as an array
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      def loadall(self):
          X = self.header.samples
          Y = self.header.lines
          B = self.header.bands
          
          #load the data
          D = numpy.fromfile(self.file, dtype=self.header.data_type)
          
          if self.header.interleave == "bsq":
              return numpy.reshape(D, (B, Y, X))
              #return numpy.swapaxes(D, 0, 2)
          elif self.header.interleave == "bip":
              D = numpy.reshape(D, (Y, X, B))
              return numpy.rollaxis(D, 2)
          elif self.header.interleave == "bil":
              D = numpy.reshape(D, (Y, B, X))
              return numpy.rollaxis(D, 1)
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      #save an updated version of the file (all header information is assumed to be the same)
      def saveall(self, D, filename):
          
          new_header = self.header
          new_header.interleave = "bsq"
          new_header.save(filename + ".hdr")
          D.tofile(filename)
          
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      #loads all of the pixels where mask != 0 and returns them as a matrix
      def loadmask(self, mask):
          X = self.header.samples
          Y = self.header.lines
          B = self.header.bands
          
          P = numpy.count_nonzero(mask)           #count the number of zeros in the mask file
          M = numpy.zeros((B, P), dtype=self.header.data_type)
          type_bytes = numpy.dtype(self.header.data_type).itemsize
          
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          prev_pos = self.file.tell()
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          self.file.seek(0)
          if self.header.interleave == "bip":
              spectrum = numpy.zeros(B, dtype=self.header.data_type)
              flatmask = numpy.reshape(mask, (X * Y))
              i = numpy.flatnonzero(flatmask)
              bar = progressbar.ProgressBar(max_value = P)
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              #bar = pyprind.ProgBar(P)
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              for p in range(0, P):
                  self.file.seek(i[p] * B * type_bytes)
                  self.file.readinto(spectrum)
                  M[:, p] = spectrum
                  bar.update(p+1)
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                  #bar.update()
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          elif self.header.interleave == "bsq":
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              band = numpy.zeros(mask.shape, dtype=self.header.data_type)
              i = numpy.nonzero(mask)
              bar = progressbar.ProgressBar(max_value=B)
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              #bar = pyprind.ProgBar(P)
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              for b in range(0, B):
                  self.file.seek(b * X * Y * type_bytes)
                  self.file.readinto(band)
                  M[b, :] = band[i]
                  bar.update(b+1)
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                  #bar.update()
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          elif self.header.interleave == "bil":
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              plane = numpy.zeros((B, X), dtype=self.header.data_type)
              p = 0
              bar = progressbar.ProgressBar(max_value=Y)
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              #bar = pyprind.ProgBar(P)
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              for l in range(0, Y):
                  i = numpy.flatnonzero(mask[l, :])
                  self.file.readinto(plane)
                  M[:, p:p+i.shape[0]] = plane[:, i]
                  p = p + i.shape[0]
                  bar.update(l+1)
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                  #bar.update()
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          self.file.seek(prev_pos)
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          return M
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      def loadband(self, n):
          X = self.header.samples
          Y = self.header.lines
          B = self.header.bands
  
          band = numpy.zeros((Y, X), dtype=self.header.data_type)
          type_bytes = numpy.dtype(self.header.data_type).itemsize
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          prev_pos = self.file.tell()
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          if self.header.interleave == "bsq":
              self.file.seek(n * X * Y * type_bytes)
              self.file.readinto(band)
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          self.file.seek(prev_pos)
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          return band
  
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      #create a set of feature/target pairs for classification
      #input: envi file object, stack of class masks C x Y x X
      #output: feature matrix (features x pixels), target matrix (1 x pixels)
      #example: generate_training(("class_coll.bmp", "class_epith.bmp"), (1, 2))
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      #   verify      verify that there are no NaN or Inf values
      def loadtrain(self, classimages, verify=True):
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          # get number of classes
          C = classimages.shape[0]
  
          F = []
          T = []
          for c in range(0, C):
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              print("\nLoading class " + str(c+1) + "...")
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              f = self.loadmask(classimages[c, :, :])            #load the feature matrix for class c
              t = numpy.ones((f.shape[1])) * (c+1)         #generate a target array                 
              F.append(f)
              T.append(t)
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          return numpy.nan_to_num(numpy.concatenate(F, 1).transpose()), numpy.concatenate(T)
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      #create a set of feature/target pairs for classification with balanced data
      #input: envi file object, stack of class masks C x Y x X, number of samples per class
      #output: feature matrix (features x pixels), target matrix (1 x pixels)
      #example: generate_training(("class_coll.bmp", "class_epith.bmp"), (1, 2))
      #   verify      verify that there are no NaN or Inf values
      def loadtrain_balance(self, classimages, num_samples=None):
  
          # get number of classes
          C = classimages.shape[0]
  
          F = []
          T = []
  
          # get number of samples per class
          samples_per_class = numpy.zeros(C, dtype=numpy.int32)
          for c in range(0, C):
              if num_samples is None:
                  samples_per_class[c] = numpy.count_nonzero(classimages[c, :, :])
              else:
                  # if user has specified a max number of samples per class
                  if num_samples > numpy.count_nonzero(classimages[c, :, :]):
                      samples_per_class[c] = numpy.count_nonzero(classimages[c, :, :])
                  else:
                      samples_per_class[c] = num_samples
  
          for c in range(0, C):
              print("\nLoading class " + str(c+1) + "...")
              # row, col index of valid pixels
              temp = classimages[c,:]
              flat_temp = numpy.reshape(temp, temp.shape[0]*temp.shape[1])
  
              idx = numpy.flatnonzero(temp)  # indices of nonzero values
              if num_samples:
                  # use specific number of samples for training
                  numpy.random.shuffle(idx)
                  idx = idx[0:samples_per_class[c]]
  
              # increase number of samples by copying them over multiple times
              max_samples = numpy.amax(samples_per_class)
              # num of times to copy for even division
              copy_times = int(floor(max_samples / samples_per_class[c]))
              rem = max_samples % samples_per_class[c]  # remaining samples
  
              for i in range(0, copy_times):
                  numpy.random.shuffle(idx)
                  shuffle_temp = numpy.zeros(flat_temp.shape, dtype=bool)
                  shuffle_temp[idx] = flat_temp[idx]
                  f = self.loadmask(numpy.reshape(shuffle_temp, (temp.shape[0], temp.shape[1])))  # load the feature matrix for class c
                  t = numpy.ones((f.shape[1])) * (c+1)  # generate a target array
                  F.append(f)
                  T.append(t)
  
              # copy the remaning samples so the total matches the max number of samples chosen by user
              if rem > 0:
                  numpy.random.shuffle(idx)
                  idx = idx[0:rem]
                  shuffle_temp = numpy.zeros(flat_temp.shape, dtype=bool)
                  shuffle_temp[idx] = flat_temp[idx]
                  f = self.loadmask(numpy.reshape(shuffle_temp, (temp.shape[0], temp.shape[1])))  # load the feature matrix for class c
                  t = numpy.ones((f.shape[1])) * (c+1)  # generate a target array
                  F.append(f)
                  T.append(t)
  
          return numpy.nan_to_num(numpy.concatenate(F, 1).transpose()), numpy.concatenate(T)
  
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      #read a batch of data based on the mask
      def loadbatch(self, npixels):
          i = numpy.flatnonzero(self.mask)                                      #get the indices of valid pixels
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          if len(i) == self.idx:                                                    #if all of the pixels have been read, return an empyt array
              return []
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          npixels = min(npixels, len(i) - self.idx)                        #if there aren't enough pixels, change the batch size
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          B = self.header.bands
          
          batch = numpy.zeros((B, npixels), dtype=self.header.data_type)          #allocate space for the batch
          pixel = numpy.zeros((B), dtype=self.header.data_type)                   #allocate space for a single pixel
          type_bytes = numpy.dtype(self.header.data_type).itemsize                #calculate the size of a single value
          if self.header.interleave == "bip":
              for n in range(0, npixels):                                          #for each pixel in the batch
                  self.file.seek(i[self.idx] * B * type_bytes)                 #seek to the current pixel in the file
                  self.file.readinto(pixel)                                       #read a single pixel
                  batch[:, n] = pixel                                             #save the pixel into the batch matrix
                  self.idx = self.idx + 1
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              return batch
          elif self.header.interleave == "bsq":
              print("ERROR: BSQ batch loading isn't implemented yet!")
          elif self.header.interleave == "bil":
              print("ERROR: BIL batch loading isn't implemented yet!")        
         
      #returns the current batch index         
      def getidx(self):
          return self.idx
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      #returns an image of the pixels that have been read using batch loading
      def batchmask(self):
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          #allocate a new mask
          outmask = numpy.zeros(self.mask.shape, dtype=numpy.bool)
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          #zero out any unclassified pixels 
          idx = self.getidx()
          i = numpy.nonzero(self.mask)
          outmask[i[0][0:idx], i[1][0:idx]] = self.mask[i[0][0:idx], i[1][0:idx]]
          return outmask
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      def close(self):
          self.file.close()
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      def __del__(self):
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          self.file.close()
  
  #saves an array as an ENVI file
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  def save_envi(A, fname, interleave="BSQ", wavelength=[]):
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      #create and save a header file
      header = envi_header();
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      header.setprops(A, interleave, wavelength)
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      header.save(fname + ".hdr")
  
      #save the raw data
      file = open(fname, "wb")
      file.write(bytearray(A))
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      file.close()