def createBufferGeoPandas(inGDF,
bufferDistanceMeters=5,
bufferRoundness=1,
projectToUTM=True):
'''Create a buffer around the lines of the geojson'''
# inGDF = gpd.read_file(geoJsonFileName)
if len(inGDF) == 0:
return []
# Transform gdf Roadlines into UTM so that Buffer makes sense
if projectToUTM:
tmpGDF = osmnx_funcs.project_gdf(inGDF)
else:
tmpGDF = inGDF
gdf_utm_buffer = tmpGDF
# perform Buffer to produce polygons from Line Segments
gdf_utm_buffer['geometry'] = tmpGDF.buffer(bufferDistanceMeters,
bufferRoundness)
gdf_utm_dissolve = gdf_utm_buffer.dissolve(by='class')
gdf_utm_dissolve.crs = gdf_utm_buffer.crs
if projectToUTM:
gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
else:
gdf_buffer = gdf_utm_dissolve
return gdf_buffer
def create_speed_gdf_v0(image_path,
geojson_path,
mask_path_out_gray,
bin_conversion_func,
mask_burn_val_key='burnValue',
buffer_distance_meters=2,
buffer_cap_style=1,
dissolve_by='speed_m/s',
bin_conversion_key='speed_mph',
verbose=False):
'''
Create buffer around geojson for speeds, use bin_conversion_func to
assign values to the mask
'''
# get gdf_buffer
try:
inGDF = gpd.read_file(geojson_path)
except:
print("Empty mask for path:", geojson_path)
# create emty mask
h, w = cv2.imread(image_path, 0).shape[:2]
mask_gray = np.zeros((h, w)).astype(np.uint8)
skimage.io.imsave(mask_path_out_gray, mask_gray)
# cv2.imwrite(mask_path_out, mask_gray)
return []
# project
projGDF = osmnx_funcs.project_gdf(inGDF)
if verbose:
print("inGDF.columns:", inGDF.columns)
gdf_utm_buffer = projGDF.copy()
# perform Buffer to produce polygons from Line Segments
gdf_utm_buffer['geometry'] = gdf_utm_buffer.buffer(buffer_distance_meters,
buffer_cap_style)
gdf_utm_dissolve = gdf_utm_buffer.dissolve(by=dissolve_by)
gdf_utm_dissolve.crs = gdf_utm_buffer.crs
gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
if verbose:
print("gdf_buffer['geometry'].values[0]:",
gdf_buffer['geometry'].values[0])
# set burn values
speed_arr = gdf_buffer[bin_conversion_key].values
burnVals = [bin_conversion_func(s) for s in speed_arr]
gdf_buffer[mask_burn_val_key] = burnVals
# create mask
gdf_to_array(gdf_buffer,
image_path,
mask_path_out_gray,
mask_burn_val_key=mask_burn_val_key,
verbose=verbose)
return gdf_buffer
def create_speed_gdf(image_path, geojson_path, mask_path_out_gray,
bin_conversion_func, mask_burn_val_key='burnValue',
buffer_distance_meters=2, buffer_roundness=1,
dissolve_by='inferred_speed_mps', # 'speed_m/s',
bin_conversion_key='speed_mph',
# crs={'init': 'epsg:4326'},
zero_frac_thresh=0.05,
verbose=False):
'''Create buffer around geojson for speeds, use bin_conversion_func to
assign values to the mask'''
# get gdf_buffer
try:
inGDF = gpd.read_file(geojson_path)
except:
print("Can't read geojson:", geojson_path)
# create emty mask
h, w = skimage.io.imread(image_path).shape[:2]
#h, w = cv2.imread(image_path, 0).shape[:2]
mask_gray = np.zeros((h, w)).astype(np.uint8)
skimage.io.imsave(mask_path_out_gray, mask_gray)
# cv2.imwrite(mask_path_out, mask_gray)
return []
if len(inGDF) == 0:
print("Empty mask for path:", geojson_path)
# create emty mask
h, w = skimage.io.imread(image_path).shape[:2]
# h, w = cv2.imread(image_path, 0).shape[:2]
mask_gray = np.zeros((h, w)).astype(np.uint8)
skimage.io.imsave(mask_path_out_gray, mask_gray)
# cv2.imwrite(mask_path_out, mask_gray)
return []
# project
projGDF = osmnx_funcs.project_gdf(inGDF) #, to_crs=crs)
if verbose:
print("inGDF.columns:", inGDF.columns)
gdf_utm_buffer = projGDF.copy()
# perform Buffer to produce polygons from Line Segments
gdf_utm_buffer['geometry'] = gdf_utm_buffer.buffer(buffer_distance_meters,
buffer_roundness)
gdf_utm_dissolve = gdf_utm_buffer.dissolve(by=dissolve_by)
gdf_utm_dissolve.crs = gdf_utm_buffer.crs
gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
if verbose:
print("gdf_buffer['geometry'].values[0]:",
gdf_buffer['geometry'].values[0])
# set burn values
speed_arr = gdf_buffer[bin_conversion_key].values
burnVals = [bin_conversion_func(s) for s in speed_arr]
gdf_buffer[mask_burn_val_key] = burnVals
# create mask
gdf_to_array(gdf_buffer, image_path, mask_path_out_gray,
mask_burn_val_key=mask_burn_val_key,
verbose=verbose)
# check to ensure no mask outside the image pixels (some images are
# largely black)
im_bgr = skimage.io.imread(image_path)
# im_bgr = cv2.imread(image_path, 1)
try:
im_gray = np.sum(im_bgr, axis=2)
# check if im_gray is more than X percent black
zero_frac = 1. - float(np.count_nonzero(im_gray)) / im_gray.size
if zero_frac >= zero_frac_thresh:
print("zero_frac:", zero_frac)
print("create_speed_gdf(): checking to ensure masks are null where "
"image is null")
# ensure the label doesn't extend beyond the image
mask_gray = cv2.imread(mask_path_out_gray, 0)
zero_locs = np.where(im_gray == 0)
# set mask_gray to zero at location of zero_locs
mask_gray[zero_locs] = 0
# overwrite
cv2.imwrite(mask_path_out_gray, mask_gray)
except:
# something is wrong with the image...
pass
# h0, w0 = 100, 100
# mask_gray = np.zeros((h0, w0)).astype(np.uint8)
# skimage.io.imsave(mask_path_out_gray, mask_gray)
return gdf_buffer
def create_buffer_geopandas(inGDF,
buffer_distance_meters=2,
buffer_cap_style=1,
dissolve_by='class',
projectToUTM=True,
verbose=False):
"""
Create a buffer around the lines of the geojson
Arguments
---------
inGDF : geodataframe
Geodataframe from a SpaceNet geojson.
buffer_distance_meters : float
Width of buffer around geojson lines. Formally, this is the distance
to each geometric object. Optional. Defaults to ``2``.
buffer_cap_style : int
Cap_style of buffer, see: (https://shapely.readthedocs.io/en/stable/manual.html#constructive-methods)
Defaults to ``1`` (round).
dissolve_by : str
Method for differentiating rows in geodataframe, and creating unique
mask values. Defaults to ``'class'``.
projectToUTM : bool
Switch to project gdf to UTM coordinates. Defaults to ``True``.
verbose : bool
Switch to print relevant values. Defaults to ``False``.
Returns
-------
gdf_buffer : geopandas dataframe
Dataframe created from geojson
"""
# inGDF = gpd.read_file(geoJsonFileName)
if len(inGDF) == 0:
return []
# if we want a geojson instead of gdf for input
# try:
# inGDF = gpd.read_file(geoJsonFileName)
# except:
# return []
# Transform gdf Roadlines into UTM so that Buffer makes sense
if projectToUTM:
tmpGDF = osmnx_funcs.project_gdf(inGDF, inGDF.crs)
else:
tmpGDF = inGDF
if verbose:
print("inGDF.columns:", tmpGDF.columns)
gdf_utm_buffer = tmpGDF.copy()
# perform Buffer to produce polygons from Line Segments
gdf_utm_buffer['geometry'] = tmpGDF.buffer(buffer_distance_meters,
cap_style=buffer_cap_style)
gdf_utm_dissolve = gdf_utm_buffer.dissolve(by=dissolve_by)
gdf_utm_dissolve.crs = gdf_utm_buffer.crs
if projectToUTM:
gdf_buffer = gdf_utm_dissolve.to_crs(inGDF.crs)
else:
gdf_buffer = gdf_utm_dissolve
if verbose:
print("gdf_buffer['geometry'].values[0]:",
gdf_buffer['geometry'].values[0])
# add the dissolve_by column back into final gdf, since it's now the index
gdf_buffer[dissolve_by] = gdf_buffer.index.values
return gdf_buffer
