def show_in_8bits(fnames, fig=None, axes=None, ncols=3, figsize=(20,20), verbose=False):
"""
show raster image in 8 bits. It silently performs 16 to 8bit conversion
if the input is 16 bits by calling `convert_to_8bits` in apls_tools.py
"""
from math import ceil
nrows = max(1, int(ceil(len(fnames)/ncols)))
if fig is None or axes is None:
fig,axes = plt.subplots(nrows,ncols,figsize=figsize)
axes = axes.flatten()
for i,fname in enumerate(fnames):
if not isinstance(fname, str):
fname = str(fname)
img = cv2.imread(fname,-1)
if img.dtype == 'uint16':
if verbose:
print("{} is 16bits. We'll convert it to 8bits".format(fname))
apls_tools.convert_to_8Bit(fname, './temp.tif')
img = cv2.imread('./temp.tif',-1)
assert(img.dtype != 'uint16')
axes[i].imshow(img)
axes[i].set_title("_".join(fname.split('_')[-2:]))
# Delete any empty axes
for j in range(len(fnames),len(axes)):
fig.delaxes(axes[j])
return fig,axes[:len(fnames)]
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--buffer_meters',
default=2,
type=float,
help='Buffer distance (meters) around graph')
parser.add_argument('--burnValue',
default=150,
type=int,
help='Value of road pixels (for plotting)')
parser.add_argument(
'--test_data_loc',
default='AOI_2_Vegas_Train',
type=str,
help='Folder within sample_data directory of test data')
args = parser.parse_args()
# set paths
path_apls = os.path.dirname(path_apls_src)
path_data = os.path.join(path_apls, 'sample_data/' + args.test_data_loc)
path_outputs = os.path.join(path_apls,
'example_output_ims/' + args.test_data_loc)
path_images_raw = os.path.join(path_data, 'RGB-PanSharpen')
path_images_8bit = os.path.join(path_data, 'RGB-PanSharpen_8bit')
path_labels = os.path.join(path_data, 'truthGeoJson/spacenetroads')
# output directories
path_masks = os.path.join(path_outputs,
'masks_' + str(args.buffer_meters) + 'm')
path_masks_plot = os.path.join(
path_outputs, 'masks_' + str(args.buffer_meters) + 'm_plots')
# create directories
for d in [path_outputs, path_images_8bit, path_masks, path_masks_plot]:
if not os.path.exists(d):
os.mkdir(d)
# iterate through images, convert to 8-bit, and create masks
im_files = os.listdir(path_images_raw)
for im_file in im_files:
if not im_file.endswith('.tif'):
continue
name_root = im_file.split('_')[-1].split('.')[0]
# create 8-bit image
im_file_raw = os.path.join(path_images_raw, im_file)
im_file_out = os.path.join(path_images_8bit, im_file)
# convert to 8bit
apls_tools.convert_to_8Bit(im_file_raw,
im_file_out,
outputPixType='Byte',
outputFormat='GTiff',
rescale_type='rescale',
percentiles=[2, 98])
# determine output files
label_file = os.path.join(path_labels, 'spacenetroads_AOI_2_Vegas_' \
+ name_root + '.truthGeoJson')
label_file_tot = os.path.join(path_labels, label_file)
output_raster = os.path.join(path_masks, 'mask_' + name_root + '.png')
plot_file = os.path.join(path_masks_plot, 'mask_' + name_root + '.png')
print("\nname_root:", name_root)
print(" output_raster:", output_raster)
print(" output_plot_file:", plot_file)
# create masks
mask, gdf_buffer = apls_tools.get_road_buffer(
label_file_tot,
im_file_out,
output_raster,
buffer_meters=args.buffer_meters,
burnValue=args.burnValue,
bufferRoundness=6,
plot_file=plot_file,
figsize=(6, 6), #(13,4),
fontsize=8,
dpi=200,
show_plot=False,
verbose=False)
return
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--buffer_meters', default=2, type=float,
help='Buffer distance (meters) around graph')
parser.add_argument('--burnValue', default=150, type=int,
help='Value of road pixels (for plotting)')
parser.add_argument('--filepath', type=str,
default='/home/ananya/Documents/data/spacenet/SpaceNet_Buildings_Competition_Round2_Sample/AOI_3_Paris_Roads_Train/')
parser.add_argument('--imgSizePix', type=int, default=416)
args = parser.parse_args()
# set paths
path_apls_src = os.path.dirname(os.path.realpath(__file__))
path_apls = os.path.dirname(path_apls_src)
path_data = args.filepath
path_outputs = os.path.join(args.filepath, 'annotations')
path_images_raw = os.path.join(path_data, 'RGB-PanSharpen')
path_images_8bit = os.path.join(path_data, 'RGB-PanSharpen_clip_8bit')
path_labels = os.path.join(path_data, 'geojson/spacenetroads')
# output directories
path_masks = os.path.join(
path_outputs, 'clip_masks_' + str(args.buffer_meters) + 'm')
path_masks_plot = os.path.join(
path_outputs, 'clip_masks_' + str(args.buffer_meters) + 'm_plots')
# create directories
for d in [path_outputs, path_images_8bit, path_masks, path_masks_plot]:
if not os.path.exists(d):
os.mkdir(d)
# iterate through images, chip, convert to 8-bit, and create masks
im_files = os.listdir(path_images_raw)
for im_file in im_files:
if not im_file.endswith('.tif'):
continue
# chip images
name_root = im_file.split('_')[-1].split('.')[0]
label_file = os.path.join(path_labels, 'spacenetroads_AOI_3_Paris_'
+ name_root + '.geojson')
chipSummaryList = processRasterChip(os.path.join(path_images_raw,im_file), path_images_raw,
label_file, os.path.dirname(label_file),
outputDirectory=path_outputs,
imagePixSize=args.imgSizePix, clipOverlap=0.0, randomClip=False,
minpartialPerc=0.0,
outputPrefix='')
for chipSummary in chipSummaryList:
# create 8-bit image
# im_file_raw = os.path.join(path_images_raw, im_file)
# im_file_out = os.path.join(path_images_8bit, im_file)
# annotationName = os.path.basename(chipSummary['rasterSource'])
# annotationName = os.path.join(path_outputs, annotationName)
im_file_raw = chipSummary['rasterSource']
im_file_out = os.path.join(
path_images_8bit, chipSummary['chipName'])
# convert to 8bit
apls_tools.convert_to_8Bit(im_file_raw, im_file_out,
outputPixType='Byte',
outputFormat='GTiff',
rescale_type='rescale',
percentiles=[2, 98])
# determine output files
# label_file = os.path.join(path_labels, 'spacenetroads_AOI_3_Paris_'
# + name_root + '.geojson')
# label_file_tot = os.path.join(path_labels, label_file)
label_file_tot = chipSummary['geoVectorName']
output_raster = os.path.join(
path_masks, 'mask_' + name_root + '.tif')
plot_file = os.path.join(
path_masks_plot, 'mask_' + name_root + '.png')
print("\nname_root:", name_root)
print(" output_raster:", output_raster)
print(" output_plot_file:", plot_file)
# create masks
mask, gdf_buffer = apls_tools.get_road_buffer(label_file_tot, im_file_out,
output_raster,
buffer_meters=args.buffer_meters,
burnValue=args.burnValue,
bufferRoundness=6,
plot_file=plot_file,
# (13,4),
figsize=(6, 6),
fontsize=8,
dpi=200, show_plot=False,
verbose=False)
return
def create_masks(path_data,
buffer_meters=2,
n_bands=3,
burnValue=150,
make_plots=True,
overwrite_ims=False,
output_df_file='',
header=[
'name', 'im_file', 'im_vis_file', 'mask_file',
'mask_vis_file'
]):
'''
Create masks from files in path_data.
Write 8bit images and masks to file.
Return a dataframe of file locations with the following columns:
['name', 'im_file', 'im_vis_file', 'mask_file', 'mask_vis_file']
We record locations of im_vis_file and mask_vis_file in case im_file
or mask_file is not 8-bit or has n_channels != [1,3]
if using 8band data, the RGB-PanSharpen_8bit should already exist, so
3band should be run prior to 8band
'''
t0 = time.time()
# set paths
#path_apls = os.path.dirname(path_apls_src)
#path_data = os.path.join(path_apls, args.test_data_loc)
path_labels = os.path.join(path_data, 'geojson/spacenetroads')
# output directories
path_masks = os.path.join(path_data, 'masks_' + str(buffer_meters) + 'm')
path_masks_plot = os.path.join(path_data, 'masks_' \
+ str(buffer_meters) + 'm_plots')
# image directories
path_images_vis = os.path.join(path_data, 'RGB-PanSharpen_8bit')
if n_bands == 3:
path_images_raw = os.path.join(path_data, 'RGB-PanSharpen')
path_images_8bit = os.path.join(path_data, 'RGB-PanSharpen_8bit')
else:
path_images_raw = os.path.join(path_data, 'MUL-PanSharpen')
path_images_8bit = os.path.join(path_data, 'MUL-PanSharpen_8bit')
if not os.path.exists(path_images_vis):
print("Need to run 3band prior to 8band!")
return
# create directories
for d in [path_images_8bit, path_masks, path_masks_plot]:
if not os.path.exists(d):
os.mkdir(d)
# iterate through images, convert to 8-bit, and create masks
outfile_list = []
im_files = os.listdir(path_images_raw)
nfiles = len(im_files)
for i, im_name in enumerate(im_files):
if not im_name.endswith('.tif'):
continue
# define files
name_root = 'AOI' + im_name.split('AOI')[1].split('.')[0]
im_file_raw = os.path.join(path_images_raw, im_name)
im_file_out = os.path.join(path_images_8bit, im_name)
im_file_out_vis = im_file_out.replace('MUL', 'RGB')
## get visible file (if using 8band imagery we want the 3band file
## for plotting purposes)
#if n_bands == 3:
# im_file_out_vis = im_file_out
#else:
# name_vis = im_name.replace('MUL', 'RGB')
# im_file_out_vis = os.path.join(path_images_vis, name_vis)
# convert to 8bit, if desired
if not os.path.exists(im_file_out) or overwrite_ims:
apls_tools.convert_to_8Bit(im_file_raw,
im_file_out,
outputPixType='Byte',
outputFormat='GTiff',
rescale_type='rescale',
percentiles=[2, 98])
# determine output files
#label_file = os.path.join(path_labels, 'spacenetroads_AOI_2_Vegas_' \
# + name_root + '.geojson')
label_file = os.path.join(path_labels, 'spacenetroads_' + name_root \
+ '.geojson')
label_file_tot = os.path.join(path_labels, label_file)
mask_file = os.path.join(path_masks, name_root + '.png')
if make_plots:
plot_file = os.path.join(path_masks_plot, name_root + '.png')
else:
plot_file = ''
print("\n", i + 1, "/", nfiles)
print(" im_name:", im_name)
print(" name_root:", name_root)
print(" im_file_out:", im_file_out)
print(" mask_file:", mask_file)
print(" output_plot_file:", plot_file)
# create masks
if not os.path.exists(mask_file) or overwrite_ims:
mask, gdf_buffer = apls_tools.get_road_buffer(
label_file_tot,
im_file_out_vis,
mask_file,
buffer_meters=buffer_meters,
burnValue=burnValue,
bufferRoundness=6,
plot_file=plot_file,
figsize=(6, 6), #(13,4),
fontsize=8,
dpi=500,
show_plot=False,
verbose=False)
# resize in ingest so we don't have to save the very large arrays
outfile_list.append(
[im_name, im_file_out, im_file_out_vis, mask_file, mask_file])
# make dataframe and save
df = pd.DataFrame(outfile_list, columns=header)
if len(output_df_file) > 0:
df.to_csv(output_df_file, index=False)
print("\ndf.ix[0]:", df.ix[0])
print("\nTotal data length:", len(df))
t4 = time.time()
print("Time to run create_masks():", t4 - t0, "seconds")
return df