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WIP on SelfDualAttributeProfiles

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This commit is contained in:
Florent Guiotte 2018-04-18 23:50:56 +02:00
parent 02657bea40
commit 8f38866761
7 changed files with 295 additions and 11 deletions
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47
Notebooks/Node Streaming Benchmark.ipynb
View File

@ -18,6 +18,42 @@
"from ld2dap.core import Filter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layers_files = [\n",
" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif',\n",
" '../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif',\n",
" '../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif',\n",
" '../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
" '../Data/phase1_rasters/Intensity_C2/UH17_GI2F051_TR.tif',\n",
" '../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l = ld2dap.LoadTIFF(layers_files[5])\n",
"t = ld2dap.Treshold(1e4)\n",
"a = ld2dap.AttributeProfiles(area = [1e3, 1e6])\n",
"f = ld2dap.Differential()\n",
"d = ld2dap.ShowFig('all', fname='../Res/aps.png', pad_inches=0, symb=True, vmax=255)\n",
"\n",
"t.input = l\n",
"a.input = t\n",
"f.input = a\n",
"d.input = f\n",
"d.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -28,16 +64,15 @@
"trsh = ld2dap.Treshold(1e4)\n",
"aps = ld2dap.AttributeProfiles(area=[1e4,1e5], moi=[.1,.5,.9])\n",
"diff = ld2dap.Differential()\n",
"\n",
"disp = ld2dap.ShowFig('all', True)\n",
"out = ld2dap.RawOutput()\n",
"out.input = aps\n",
"out2 = ld2dap.RawOutput()\n",
"\n",
"out2.input = diff\n",
"disp.input = diff\n",
"out.input = aps\n",
"#diff.input = aps\n",
"\n",
"diff.input = aps\n",
"#out2.input = diff\n",
"disp.input = aps\n",
"\n",
"aps.input = trsh\n",
"\n",
@ -52,7 +87,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(out.metadata[1])"
"'\\rho'.format()"
]
},
{

125
Notebooks/SDAPs.ipynb Normal file
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@ -0,0 +1,125 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"triskele_path = Path('../triskele/python/')\n",
"sys.path.append(str(triskele_path.resolve()))\n",
"import triskele\n",
"\n",
"def DFC_filter(raster):\n",
" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
"\n",
" \n",
"def show(im, im_size=1, save=None):\n",
" plt.figure(figsize=(16*im_size,3*im_size))\n",
" plt.imshow(im)\n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()\n",
"\n",
"def mshow(Xs, titles=None, im_size=1, save=None):\n",
" s = len(Xs)\n",
"\n",
" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
"\n",
" for i in range(s):\n",
" plt.subplot(s,1,i+1)\n",
" plt.imshow(Xs[i])\n",
" \n",
" if titles is not None:\n",
" plt.title(titles[i])\n",
" \n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layers_files = [\n",
" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layers = list()\n",
"\n",
"for file in layers_files:\n",
" print('Loading {}'.format(file))\n",
" layer = triskele.read(file)\n",
" DFC_filter(layer)\n",
" layers.append(layer)\n",
"\n",
"layers_stack = np.stack(layers, axis=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = triskele.Triskele(layers_stack, verbose=False)\n",
"\n",
"attributes = t.filter(tree='min-tree',\n",
" area=[1e3,1e5], \n",
" standard_deviation=[.3,.9],\n",
" moment_of_inertia=[.1,.4]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"attributes.shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

4
ld2dap/Differential.py
View File

@ -22,7 +22,7 @@ class Differential(Filter):
offset = 0
for stack in metadata:
print('Differential: {}'.format(stack))
raster_list.append(data[:,:,stack.begin+1:stack.end] -
raster_list.append(data[:,:,stack.begin+1:stack.end] -
data[:,:,stack.begin:stack.end-1])
size_new = stack.end - stack.begin - 1
@ -47,4 +47,4 @@ class Differential(Filter):
data = np.dstack(raster_list)
return data, metadata_new
return data, metadata_new

116
ld2dap/SelfDualAttributeProfiles.py Normal file
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@ -0,0 +1,116 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
# \file SelfDualAttributeProfiles.py
# \brief TODO
# \author Florent Guiotte <florent.guiotte@gmail.com>
# \version 0.1
# \date 17 avril 2018
#
# TODO details
from .core import Filter, Stack
## TODO: dep
import sys
import numpy as np
sys.path.append('../triskele/python')
import triskele
class AttributeProfiles(Filter):
def __init__(self, area=None, sd=None, moi=None):
super().__init__(self.__class__.__name__)
self.area = np.sort(area) if area is not None else None
self.sd = np.sort(sd) if sd is not None else None
self.moi = np.sort(moi) if moi is not None else None
def _process_desc(self):
att_desc = dict()
for att in ['area', 'sd', 'moi']:
att_desc[att] = list()
if self.__getattribute__(att) is not None:
att_desc[att].append(None)
att_desc[att].extend(
['Self-dual {} {}'.format(att, x) for x in self.__getattribute__(att)])
return att_desc
def _process_symb(self):
att_symb = dict()
for att in ['area', 'sd', 'moi']:
att_symb[att] = list()
if self.__getattribute__(att) is not None:
att_symb[att].append(None)
att_symb[att].extend(
['\rho^{{{}}}_{{{}}}'.format(att, x) for x in self.__getattribute__(att)])
return att_symb
def _process_len(self):
att_len = dict()
for att in ['area', 'sd', 'moi']:
values = self.__getattribute__(att)
att_len[att] = len(values) if values is not None else 0
return att_len
def _process(self, data, metadata):
t = triskele.Triskele(data, verbose=False)
att_min = t.filter(tree='tos-tree', area=self.area,
standard_deviation=self.sd,
moment_of_inertia=self.moi)
## Create new metadata
### Pre-process descriptions
att_desc = self._process_desc()
att_symb = self._process_symb()
### Compute stack offsets and att length
att_len = self._process_len()
raster_offset = sum(att_len.values())
### Merge old and new descriptions
metadata_new = list()
### Re-order to create original APs
raster_list = list()
for stack in metadata:
if stack.end - stack.begin > 1:
raise NotImplementedError('Nested filtering not implemented yet')
do = 0
sb = stack.begin * (raster_offset + 1)
for att in ['area', 'sd', 'moi']:
if att_offset[att] == 0:
continue
al = att_len[att]
raster_list.append(att_min[:,:,sb+do+al:sb+do:-1])
raster_list.append(att_min[:,:,sb])
print('DEBUG: copying layer {}'.format(sb))
raster_list.append(att_max[:,:,sb+do+1:sb+do+al+1])
do += al
stack_new = Stack(dso + stack_offset * stack.begin, att_offset[att],
stack.desc[0], stack.symb[0])
for old_desc, new_desc in zip(stack_new.desc, att_desc[att]):
print('DESCRIPTION: {} > {}'.format(old_desc, new_desc))
old_desc.append(new_desc)
for old_symb, new_symb in zip(stack_new.symb, att_symb[att]):
print('symbRIPTION: {} > {}'.format(old_symb, new_symb))
old_symb.append(new_symb)
metadata_new.append(stack_new)
data_new = np.dstack(raster_list)
return data_new, metadata_new
if __name__ == '__main__':
area = [10, 100, 1000]
sd = [.1, .9]
ap = AttributeProfiles(area, sd)
print(ap._process_desc())
print(ap._process_offset())

8
ld2dap/ShowFig.py
View File

@ -13,12 +13,14 @@ from ld2dap.core import Output
import matplotlib.pyplot as plt
class ShowFig(Output):
def __init__(self, stack_id=0, symb=False, bbox_inches='tight', pad_inches=1):
def __init__(self, stack_id=0, symb=False, fname=None, bbox_inches='tight', pad_inches=1, vmax=None):
super().__init__(self.__class__.__name__)
self.bbox_inches = bbox_inches
self.pad_inches = pad_inches
self.stack_id = stack_id
self.symb = symb
self.fname = fname
self.vmax = vmax
def _process(self, data, metadata):
if self.stack_id == 'all':
@ -36,7 +38,7 @@ class ShowFig(Output):
for i, di in enumerate(range(stack.begin, stack.end)):
f1 = fig.add_subplot(fig_count, 1, i + 1)
img = f1.imshow(data[:,:,di])
img = f1.imshow(data[:,:,di], vmax=self.vmax)
plt.colorbar(img)
if self.symb:
plt.rc('text', usetex=True)
@ -44,5 +46,7 @@ class ShowFig(Output):
else:
f1.set_title(' > '.join(filter(None, stack.desc[i])))
if self.fname is not None:
fig.savefig(self.fname, bbox_inches=self.bbox_inches, pad_inches=self.pad_inches)
plt.show()
plt.close(fig)

4
ld2dap/Treshold.py
View File

@ -17,6 +17,7 @@ class Treshold(Filter):
self.max_value = max_value #if max_value is not None else treshold
def _process(self, data, metadata):
# TODO: UPGRADE RASTER DEPENDANCE
if self.max_value is None:
self.max_value = data[data < self.treshold].max()
@ -25,4 +26,7 @@ class Treshold(Filter):
d.append('treshold {}'.format(self.treshold))
#s.append('T_{{{}}}'.format(self.treshold))
## TODO: TMP FIX
#data[:,:,stack.begin][data[:,:,stack.begin] > self.treshold] = data[:,:,stack.begin][data[:,:,stack.begin]].max()
return data * (data < self.treshold) + self.max_value * (data >= self.treshold), metadata

2
test.py
View File

@ -17,7 +17,7 @@ import numpy as np
def main():
i = LoadTIFF(['Data/test.tiff', 'Data/test2.tiff'])
t = Treshold(1e4)
ap = APs(np.array([100,1e3,1e4]))
ap = APs([100,1e3,1e4])
o = SaveFig('Res/test.png')
s = ShowFig(0, True)
d = Differential()