envi.py
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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
import progressbar
import sys
class envi_header:
def __init__(self, filename = ""):
if filename != "":
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"
self.sensor_type = "Unknown"
self.byte_order = int(0)
self.x_start = int(0)
self.y_start = int(0)
self.z_plot_titles = "Unknown, Unknown"
self.pixel_size = [float(0), float(0)]
self.pixel_size_units = "Meters"
self.wavelength_units = "Wavenumber"
self.description = "no description"
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
#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"):
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]]
'''
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
li += 1
desc.append(l[li])
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()
#save an ENVI header
def save(self, fname):
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")
f.write("data type = " + str(self.get_envi_type(self.type)) + "\n")
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")
f.close()
#sets the properties of the header to match those of the input array
def set(self, A):
self.type = A.dtype
self.samples = A.shape[2]
self.lines = A.shape[1]
self.bands = A.shape[0]
class envi:
def __init__(self, filename, headername = "", mask = []):
self.open(filename, headername)
if mask == []:
self.mask = numpy.ones((self.header.lines, self.header.samples), dtype=numpy.bool)
elif type(mask) == numpy.ndarray:
self.mask = mask
else:
print("ERROR: unrecognized mask format - expecting a boolean array")
self.idx = 0 #initialize the batch IDX to 0 for batch reading
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")
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)
#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
prev_pos = self.file.tell()
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)
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)
elif self.header.interleave == "bsq":
band = numpy.zeros(mask.shape, dtype=self.header.data_type)
i = numpy.nonzero(mask)
bar = progressbar.ProgressBar(max_value=B)
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)
elif self.header.interleave == "bil":
plane = numpy.zeros((B, X), dtype=self.header.data_type)
p = 0
bar = progressbar.ProgressBar(max_value=Y)
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)
self.file.seek(prev_pos)
return M
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
prev_pos = self.file.tell()
if self.header.interleave == "bsq":
self.file.seek(n * X * Y * type_bytes)
self.file.readinto(band)
self.file.seek(prev_pos)
return band
#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))
# verify verify that there are no NaN or Inf values
def loadtrain(self, classimages, verify=True):
# get number of classes
C = classimages.shape[0]
F = []
T = []
for c in range(0, C):
print("\nLoading class " + str(c+1) + "...")
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)
return numpy.nan_to_num(numpy.concatenate(F, 1).transpose()), numpy.concatenate(T)
#read a batch of data based on the mask
def loadbatch(self, npixels):
i = numpy.flatnonzero(self.mask) #get the indices of valid pixels
if len(i) == self.idx: #if all of the pixels have been read, return an empyt array
return []
npixels = min(npixels, len(i) - self.idx) #if there aren't enough pixels, change the batch size
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
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
#returns an image of the pixels that have been read using batch loading
def batchmask(self):
#allocate a new mask
outmask = numpy.zeros(self.mask.shape, dtype=numpy.bool)
#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
def close(self):
self.file.close()
def __del__(self):
self.file.close()
#saves an array as an ENVI file
def save_envi(A, fname):
#create and save a header file
header = envi_header();
header.set(A)
header.save(fname + ".hdr")
#save the raw data
file = open(fname, "wb")
file.write(bytearray(A))
file.close()