def grid_difference_filter_based(fn_ref, res_ref, data_mms, nonvalue,
raster_size, threshold_height_diff):
d_mms = rasterize(data_mms, res_ref, dim=2)
# read reference
data_ref, d_ref = read_ascii_xyz(fn_ref,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=res_ref,
dim=2)
# calculate the difference
difference = nonvalue * np.ones((raster_size, raster_size))
reduce_list = []
for key, value in d_mms.iteritems():
m, n = np.int0(key.split('+'))
z_mms = data_mms[value, 2]
mean_mms = np.mean(z_mms)
mean_ref = data_ref[d_ref[key], 2]
delta = mean_mms - mean_ref
if delta > threshold_height_diff:
reduce_list.extend(value)
else:
difference[m, n] = mean_mms - mean_ref
return difference, reduce_list
def curbdetection(fn):
t = time.time()
[fx, fy] = fn[:-4].split('_')
x0, y0 = tool.coord(fx, fy, r, x_offset, y_offset)
X0 = [x0, y0, 0]
data, d = tool.read_ascii_xyz(inputdir + fn,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=eps,
dim=3)
## data,d = tool.iter_loadraster(inputdir+'\\'+fn, delimiter=' ', skiprows=9, dtype=np.float, args = args)
# data,d = tool.read_bin_xyz(inputdir+'\\'+fn, delimiter=' ', skiprows=7, dtype=np.float, args = args)
if len(d) > 0:
print 'Number of rasters: ', len(d), ',Loading time: ', time.time() - t
# Neighbourhood mask
m = tool.gen_mat(*c)
result = Curb.curb(d, data, m, threshold, 6)
# result = Curb.curb_single_core(d,data,m,threshold)
result = np.array(result)
list_points = list(flatten(result[:, 0]))
feature = np.array(list(flatten(result[:, 1]))).reshape(-1, 3)
intensity = np.array(list(flatten(result[:, 2])))
featurePoint = np.hstack((feature, intensity[:, None]))
featurePoint = tool.unique(featurePoint)
## tool.write_xyzi('test.ply', featurePoint[:,0:3] + X0, featurePoint[:,3])
filtered = filterNeigh(outputdir + 'curb_' + fn, featurePoint,
intensity, X0)
print time.time() - t
return filtered
else:
print 'No proper data'
return 0
def grid_difference(fn_ref, res_ref, data_mms, nonvalue, raster_size):
d_mms = rasterize(data_mms, res_ref, dim=2)
# read reference
data_ref, d_ref = read_ascii_xyz(fn_ref,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=res_ref,
dim=2)
# calculate the difference
difference = nonvalue * np.ones((raster_size, raster_size))
for key, value in d_mms.iteritems():
m, n = np.int0(key.split('+'))
z_mms = data_mms[value, 2]
mean_mms = np.mean(z_mms)
mean_ref = data_ref[d_ref[key], 2]
difference[m, n] = mean_mms - mean_ref
return difference
# kn = "00000000_ffffff40"
# inputdir = "..\\pydtm\\testData\\" + kn
outputdir = "..\\pydtm\\testData\\Curb\\"
# refdir = "..\\pydtm\\testData\\REF_"+kn+".ply"
# gfdir = "..\\pydtm\\testData\\GF_"+kn+".ply"
# directory = [ x for x in os.listdir(inputdir) if x[-4:] == ".ply" ]
# gf, _ = tool.read_bin_xyz_return_list(gfdir, delimiter=' ', skiprows=7, dtype=np.float, args = [0,200,0.1])
fn = "0000003e_fffffee1.ply"
inputdir = "..\\pydtm\\"
gfdir = "..\\pydtm\\"
gf, _ = tool.read_ascii_xyz(gfdir + fn,
delimiter=' ',
skiprows=7,
dtype=np.float,
e=eps,
dim=3)
height_min = min(np.array(gf)[:, 2]) + global_offset - height_range
height_max = max(np.array(gf)[:, 2]) + global_offset + height_range
args = [height_min, height_max, eps]
#ref,_ = tool.read_ascii_xyz(refdir, delimiter=' ', skiprows=7, dtype=np.float, args = args)
t = time.time()
[fx, fy] = fn[:-4].split('_')
x0, y0 = tool.coord(fx, fy, r, x_offset, y_offset)
X0 = [x0, y0, 0]