def ENVI_raster_binary_from_2d_array(envidata, file_out, post, image_array):
'''
Converts a numpy array back to an ENVI binary - requires geotransform and projection
information as imported using ENVI_raster_binary_to_2d_array() or load_ENVI(). If
resampling has taken place between the initial ENVI load (for which getransform and
projection info is specific) then the new posting size must also be passed in to
enable rescaling of pixels accordingly
'''
util.check_output_dir(file_out)
original_geotransform, inDs = envidata
rows, cols = image_array.shape
bands = 1
# Creates a new raster data source
outDs = driver.Create(file_out, cols, rows, bands, gdal.GDT_Float32)
# Write metadata
originX = original_geotransform[0]
originY = original_geotransform[3]
outDs.SetGeoTransform([originX, post, 0.0, originY, 0.0, -post])
outDs.SetProjection(inDs.GetProjection())
#Write raster datasets
outBand = outDs.GetRasterBand(1)
outBand.WriteArray(image_array)
new_geotransform = outDs.GetGeoTransform()
new_projection = outDs.GetProjection()
print("Output binary saved: %s" % file_out)
return new_geotransform, new_projection, file_out
def ENVI_raster_binary_from_2d_array(envidata, file_out, post, image_array):
'''
Converts a numpy array back to an ENVI binary - requires geotransform and projection
information as imported using ENVI_raster_binary_to_2d_array() or load_ENVI(). If
resampling has taken place between the initial ENVI load (for which getransform and
projection info is specific) then the new posting size must also be passed in to
enable rescaling of pixels accordingly
'''
util.check_output_dir(file_out)
original_geotransform, inDs = envidata
rows, cols = image_array.shape
bands = 1
# Creates a new raster data source
outDs = driver.Create(file_out, cols, rows, bands, gdal.GDT_Float32)
# Write metadata
originX = original_geotransform[0]
originY = original_geotransform[3]
outDs.SetGeoTransform([originX, post, 0.0, originY, 0.0, -post])
outDs.SetProjection(inDs.GetProjection())
#Write raster datasets
outBand = outDs.GetRasterBand(1)
outBand.WriteArray(image_array)
new_geotransform = outDs.GetGeoTransform()
new_projection = outDs.GetProjection()
print("Output binary saved: %s" %file_out)
return new_geotransform,new_projection,file_out
def write_stats(filename, spacings, orientations, wavelength, snrs, wave_type, noise_type, noise_scale):
util.check_output_dir(filename)
if os.path.exists(filename):
f = open(filename, 'a')
else:
f = open(filename, 'w')
#f.write("Run, RMS spacing approx vs obs, spacing mean, RMS orientations, SnR mean\n")
f.write("wave_type, noise_type, noise_scale, wavelength, RMS spacing approx. vs obs, spacing mean, RMS orientations, SnR mean\n")
f.write("%s, %s, %s, %s, %f, %f, %f, %f\n" %(wave_type, noise_type, noise_scale, wavelength, rms(spacings - wavelength), spacings.mean(), rms(orientations), snrs.mean() ))
f.close()
print("Stats file: %s" %(filename))
def write_stats(filename, data, date, kernel_size, stepsize):
util.check_output_dir(filename)
if os.path.exists(filename):
f = open(filename, 'a')
else:
f = open(filename, 'w')
f.write("Run, min, max, mean, median, std.dev., UQ, LQ, IQR\n")
f.write("%s_win_%s_step_%s, %f, %f, %f, %f, %f, %f, %f, %f\n" %
(date, kernel_size, stepsize, np.amin(data), np.amax(data),
np.mean(data), np.median(data), np.std(data),
np.percentile(data, 75), np.percentile(data, 25),
(np.percentile(data, 75) - np.percentile(data, 25))))
f.close()
def plot_image(img, title, filename, envidata, post, remove_empty_cols=0):
'''
Plots a given image as a png and also converts the input to a binary using
raster_functions.ENVI_raster_binary_from_2d_array() - this requires geotransform
info to be available (see raster_functions.load_envi()). ENVI output is hardwired.
'''
util.check_output_dir(filename)
#print("Inside plot image...")
#print(img.shape)
if remove_empty_cols == 1:
img = util.trim_constant_rows_cols(img)
#fig, ax = plt.subplots()
#cax = ax.imshow(img, cmap=cm.coolwarm, interpolation='none')
#ax.set_xticklabels(" ")
#ax.set_yticklabels(" ")
#ax.set_title(title)
#cbar=fig.colorbar(cax)
plt.imshow(img, cmap=cm.coolwarm)
plt.title(title)
plt.colorbar()
plt.savefig(filename + '.png', dpi=300, transparent=True)
plt.clf()
## put into new function.... <<<<<<<<<<<<<<<<<<<<<<<<<<<
if envidata != False:
print("Geospatial data provided :)")
raster_functions.ENVI_raster_binary_from_2d_array(envidata, filename + '.bin', post, img)
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< PROBLEM WITH THIS CATCH STILL - ARRAYS DEVELOPING ZERO AXIS....
else:
print("No geospatial data provided - saving as txt file.")
ofile="%s.png" %filename
save_array_as_image(img, ofile)
def find_spacings(image_array, date, kernel_size, stepsize, envidata, post, output_dir, spectrum_n=1.0, interact=False):
'''
Main workhorse of the whole FT program - for a provided input image a moving window - of a prior specified
size - is iterated across an FT array of the original image. This returns new arrays representing maximum
peak spacing, orientation and related signal-to-noise ratio (standard deviation of a given point over the
mean). These arrays are then plotted and converted to binary files with headers matching that of the
original input file - the pixels are rescaled to account for the step frequency of the moving window.
Requires access to:
find_spacing_at_point()
more_fft_functions.border_indent()
plots.plot_image()
util.trim_constant_rows_cols
'''
startTime = time.time()
nywind,nxwind = image_array.shape
opath = output_dir + '/%s_win_%s_step_%s' % (date, kernel_size, stepsize)
###########################
## SET MOVING WINDOW UP ##
###########################
if kernel_size % 2 == 0 or kernel_size < 3:
error_msg = "kernel is even - it needs to be odd and at least of a value of 3"
sys.exit(error_msg)
space = np.zeros((nywind // stepsize, nxwind // stepsize))
orientation = np.zeros((nywind // stepsize, nxwind // stepsize))
SnR_imgout = np.zeros((nywind // stepsize, nxwind // stepsize))
orig = np.zeros((nywind // stepsize, nxwind // stepsize))
window_border_indent, window_border_indent_end_X, window_border_indent_end_Y = more_fft_functions.border_indent(kernel_size, nxwind, nywind)
time_stamp = time.strftime("%H.%M.%S")
file_name = "%s/FT_POST_log_%s_%s.txt" %(opath, date, time_stamp)
util.check_output_dir(file_name)
with open( file_name, 'w') as f:
f.write("Post: %f\n" %post)
f.write("Image dimensions (x,y): %f, %f\n" %(nywind,nxwind))
f.write("Noise value: %f\n" %(spectrum_n))
f.write("Kernel size: %f\n" %(kernel_size))
f.write("Stepsize: %f\n" %(stepsize))
f.write("%s, %s, %s\n" %("Feature orientation", "distance (frq)", "distance (m)"))
for out_i, ii in enumerate(range(window_border_indent, window_border_indent_end_Y, stepsize)):
for out_j, jj in enumerate(range(window_border_indent, window_border_indent_end_X, stepsize)):
angle, dist, snr = find_spacing_at_point(image_array, ii, jj, kernel_size, spectrum_n, show_fft=0)
space[out_i, out_j] = dist * post
orientation[out_i, out_j] = angle
SnR_imgout[out_i, out_j] = snr
orig[out_i, out_j] = image_array[ii, jj]
print("PRINTING IMAGES....")
img_prefix = opath + '/%s_winsize_%i_stepsize_%i_nvalue_%f_%s' % (date, kernel_size, stepsize, spectrum_n, time_stamp)
new_post = post * stepsize
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< PROBLEM inside plot function if envidata==False such as using sine_wave_TEST.py (see plots.py)
plots.plot_image(space, 'Spacing (m)', img_prefix + '_spacing_m', envidata, new_post, remove_empty_cols=1)
plots.plot_image(orientation, 'Orientation (degN.)', img_prefix + '_orientation', envidata, new_post, remove_empty_cols=1)
plots.plot_image(SnR_imgout, 'Signal-to-noise (%s)' % date, img_prefix + '_snr', envidata, new_post, remove_empty_cols=1)
plots.plot_image(orig, 'Original input image (%s)' % date, img_prefix + '_original', envidata, new_post, remove_empty_cols=1)
spacing_no_zeros = util.trim_constant_rows_cols(space)
orientation_no_zeros = util.trim_constant_rows_cols(orientation)
FT_spacing_orientation_stats.write_stats(output_dir + '/spacing_stats.txt', spacing_no_zeros, date, kernel_size, stepsize)
FT_spacing_orientation_stats.write_stats(output_dir + '/orientation_stats.txt', orientation_no_zeros, date, kernel_size, stepsize)
## CREATE QUIVER PLOT
plot_title = "%s_winsize_%i_stepsize_%i" %(date, kernel_size, stepsize)
quiver_plotter.quiver_plot(orientation, space, opath, plot_title, img_prefix + '_quiver', interact=interact)
endTime = time.time()
totalTime = endTime - startTime
print("Run took %f seconds to run" %(totalTime))
#print "==========================="
#print "Kernel: ", kernel
#print "Spacing (pixels): ", max_dist_px
#print "Spacing (m): ", max_dist_m
kernel_size_LIST.append(kernel)
feature_spacing_m_LIST.append(max_dist_m)
snr_LIST.append(snr)
## write values out to array and/or text file
kernel = 2 * int(kernel * 1.2 / 2) + 1 ## what does this bit do??
# Write file out
fout = '%s/kernel_estimation_out/kernel_spacing_%s_smooth_factor_%0.1f.csv' % (
opath, aoi, spectrum_n)
util.check_output_dir(fout)
f = open(fout, 'w')
f.write("Kernel size (m), Feature spacing (m), SnR\n")
for i in range(len(kernel_size_LIST)):
f.write("%f, %f, %f\n" %
(kernel_size_LIST[i], feature_spacing_m_LIST[i], snr_LIST[i]))
f.close()
## Plot data
ofigOut = '%s/kernel_estimation_out/kernel_size_to_spacing_%s_smooth_factor_%0.1f.png' % (
opath, aoi, spectrum_n)
util.check_output_dir(fout)
fig = plt.figure()
#print "==========================="
#print "Kernel: ", kernel
#print "Spacing (pixels): ", max_dist_px
#print "Spacing (m): ", max_dist_m
kernel_size_LIST.append(kernel)
feature_spacing_m_LIST.append(max_dist_m)
snr_LIST.append(snr)
## write values out to array and/or text file
kernel = 2 * int(kernel * 1.2 / 2) + 1 ## what does this bit do??
# Write file out
fout='%s/kernel_estimation_out/kernel_spacing_%s_smooth_factor_%0.1f.csv' %(opath, aoi, spectrum_n)
util.check_output_dir(fout)
f=open(fout, 'w')
f.write("Kernel size (m), Feature spacing (m), SnR\n")
for i in range(len(kernel_size_LIST)):
f.write("%f, %f, %f\n" %(kernel_size_LIST[i], feature_spacing_m_LIST[i], snr_LIST[i]))
f.close()
## Plot data
ofigOut='%s/kernel_estimation_out/kernel_size_to_spacing_%s_smooth_factor_%0.1f.png' %(opath, aoi, spectrum_n)
util.check_output_dir(fout)
fig=plt.figure()
ax1=fig.add_subplot(111)
def find_spacings(image_array,
date,
kernel_size,
stepsize,
envidata,
post,
output_dir,
spectrum_n=1.0,
interact=False):
'''
Main workhorse of the whole FT program - for a provided input image a moving window - of a prior specified
size - is iterated across an FT array of the original image. This returns new arrays representing maximum
peak spacing, orientation and related signal-to-noise ratio (standard deviation of a given point over the
mean). These arrays are then plotted and converted to binary files with headers matching that of the
original input file - the pixels are rescaled to account for the step frequency of the moving window.
Requires access to:
find_spacing_at_point()
more_fft_functions.border_indent()
plots.plot_image()
util.trim_constant_rows_cols
'''
startTime = time.time()
nywind, nxwind = image_array.shape
opath = output_dir + '/%s_win_%s_step_%s' % (date, kernel_size, stepsize)
###########################
## SET MOVING WINDOW UP ##
###########################
if kernel_size % 2 == 0 or kernel_size < 3:
error_msg = "kernel is even - it needs to be odd and at least of a value of 3"
sys.exit(error_msg)
space = np.zeros((nywind // stepsize, nxwind // stepsize))
orientation = np.zeros((nywind // stepsize, nxwind // stepsize))
SnR_imgout = np.zeros((nywind // stepsize, nxwind // stepsize))
orig = np.zeros((nywind // stepsize, nxwind // stepsize))
window_border_indent, window_border_indent_end_X, window_border_indent_end_Y = more_fft_functions.border_indent(
kernel_size, nxwind, nywind)
time_stamp = time.strftime("%H.%M.%S")
file_name = "%s/FT_POST_log_%s_%s.txt" % (opath, date, time_stamp)
util.check_output_dir(file_name)
with open(file_name, 'w') as f:
f.write("Post: %f\n" % post)
f.write("Image dimensions (x,y): %f, %f\n" % (nywind, nxwind))
f.write("Noise value: %f\n" % (spectrum_n))
f.write("Kernel size: %f\n" % (kernel_size))
f.write("Stepsize: %f\n" % (stepsize))
f.write("%s, %s, %s\n" %
("Feature orientation", "distance (frq)", "distance (m)"))
for out_i, ii in enumerate(
range(window_border_indent, window_border_indent_end_Y, stepsize)):
for out_j, jj in enumerate(
range(window_border_indent, window_border_indent_end_X,
stepsize)):
angle, dist, snr = find_spacing_at_point(image_array,
ii,
jj,
kernel_size,
spectrum_n,
show_fft=0)
space[out_i, out_j] = dist * post
orientation[out_i, out_j] = angle
SnR_imgout[out_i, out_j] = snr
orig[out_i, out_j] = image_array[ii, jj]
print("PRINTING IMAGES....")
img_prefix = opath + '/%s_winsize_%i_stepsize_%i_nvalue_%f_%s' % (
date, kernel_size, stepsize, spectrum_n, time_stamp)
new_post = post * stepsize
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< PROBLEM inside plot function if envidata==False such as using sine_wave_TEST.py (see plots.py)
plots.plot_image(space,
'Spacing (m)',
img_prefix + '_spacing_m',
envidata,
new_post,
remove_empty_cols=1)
plots.plot_image(orientation,
'Orientation (degN.)',
img_prefix + '_orientation',
envidata,
new_post,
remove_empty_cols=1)
plots.plot_image(SnR_imgout,
'Signal-to-noise (%s)' % date,
img_prefix + '_snr',
envidata,
new_post,
remove_empty_cols=1)
plots.plot_image(orig,
'Original input image (%s)' % date,
img_prefix + '_original',
envidata,
new_post,
remove_empty_cols=1)
spacing_no_zeros = util.trim_constant_rows_cols(space)
orientation_no_zeros = util.trim_constant_rows_cols(orientation)
FT_spacing_orientation_stats.write_stats(output_dir + '/spacing_stats.txt',
spacing_no_zeros, date,
kernel_size, stepsize)
FT_spacing_orientation_stats.write_stats(
output_dir + '/orientation_stats.txt', orientation_no_zeros, date,
kernel_size, stepsize)
## CREATE QUIVER PLOT
plot_title = "%s_winsize_%i_stepsize_%i" % (date, kernel_size, stepsize)
quiver_plotter.quiver_plot(orientation,
space,
opath,
plot_title,
img_prefix + '_quiver',
interact=interact)
endTime = time.time()
totalTime = endTime - startTime
print("Run took %f seconds to run" % (totalTime))
def save_array_as_image(arr, ofile): "Saves a numpy array as an image file - type depends on extension set by user" util.check_output_dir(ofile) scipy.misc.imsave(ofile, arr)