534 lines
12 KiB
Plaintext
534 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Histogram APs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"triskele_path = Path('../triskele/python/')\n",
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"sys.path.append(str(triskele_path.resolve()))\n",
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"import triskele"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Specific Utils\n",
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"\n",
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"def DFC_filter(raster):\n",
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" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
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"\n",
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"def show(im, im_size=1, save=None):\n",
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" plt.figure(figsize=(16*im_size,3*im_size))\n",
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" plt.imshow(im)\n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()\n",
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"\n",
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"def mshow(Xs, titles=None, im_size=1, save=None):\n",
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" s = len(Xs)\n",
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"\n",
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" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
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"\n",
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" for i in range(s):\n",
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" plt.subplot(s,1,i+1)\n",
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" plt.imshow(Xs[i])\n",
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" \n",
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" if titles is not None:\n",
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" plt.title(titles[i])\n",
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" \n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load a single raster"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"raster_p = Path('../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif')\n",
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"raster = triskele.read(raster_p)\n",
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"DFC_filter(raster)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"show(raster)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute attributes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#area = np.array([25, 100, 500, 1e3, 5e3, 10e3, 50e3, 100e3, 150e3])\n",
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"area = [1e3, 1e6]\n",
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"moi = [.5, .7, .9]\n",
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"sd = [.1, .5, .9]\n",
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"moi = None\n",
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"sd = None\n",
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"\n",
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"t = triskele.Triskele(raster, verbose=False)\n",
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"attributes = t.filter(tree='tos-tree',\n",
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" area=area,\n",
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" moment_of_inertia=moi,\n",
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" standard_deviation=sd\n",
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" )\n",
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"attributes.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"show(attributes[:,:,-1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = list()\n",
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"for i in range(attributes.shape[2]):\n",
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" figs.append(attributes[:,:,i])\n",
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" \n",
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"mshow(figs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = attributes\n",
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"i = 1\n",
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"show(a[:,:,i].astype(np.float) - a[:,:,i+1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute patches"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"patch_size = 5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"\n",
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"offset_stack = None\n",
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"def create_patches(array, patch_size=3):\n",
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" amp = int((patch_size - 1 ) / 2)\n",
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"\n",
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" stack = list()\n",
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" for i in range(-amp, amp+1):\n",
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" ai = i if i > 0 else None\n",
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" bi = i if i < 0 else None\n",
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" ci = -bi if bi is not None else None\n",
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" di = -ai if ai is not None else None\n",
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"\n",
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" for j in range(-amp, amp+1):\n",
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" offset = np.zeros_like(array)\n",
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" #offset = np.empty(array.shape)\n",
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" aj = j if j > 0 else None\n",
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" bj = j if j < 0 else None\n",
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" cj = -bj if bj is not None else None\n",
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" dj = -aj if aj is not None else None\n",
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" print('{}:{} {}:{} - {}:{} {}:{}'.format(ai, bi, ci, di, aj, bj, cj, dj))\n",
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" offset[ai:bi, aj:bj] = array[ci:di, cj:dj]\n",
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" stack.append(offset)\n",
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" return np.stack(stack, axis=-1)\n",
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"\n",
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"offset_stack = create_patches(attributes, patch_size)\n",
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"offset_stack.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" init | user | sys | total\n",
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" --- | ---: | ---: | ---:\n",
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" empty | 27.9 s | 15.2 s | 43.1 s\n",
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" zeros | 27.2 s | 14.1 s | 41.3 s\n",
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" zeros_like | 6.47 s | 4.01 s | 10.5 s"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%whos"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"offset_stack[-2,-3,:].reshape(-1,1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stack_std = np.std(offset_stack, axis=-1)\n",
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"stack_std.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = list()\n",
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"ttls = list()\n",
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"for d in range(stack_std.shape[-1]):\n",
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" if d == 0:\n",
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" ttls.append('Origin')\n",
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" else:\n",
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" ttls.append('STD 5x5 from area {}'.format(area[d-1]))\n",
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" figs.append(stack_std[:,:,d])\n",
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" \n",
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"mshow(figs, ttls, 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stack_mean = np.mean(offset_stack, axis=-1)\n",
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"stack_avr = np.average(offset_stack, axis=-1)\n",
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"stack_var = np.var(offset_stack, axis=-1)\n",
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"stack_min = np.min(offset_stack, axis=-1)\n",
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"stack_max = np.max(offset_stack, axis=-1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.std.__name__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"create_patches.__name__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for func in (np.mean, np.var):\n",
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" tmp = func(offset_stack, axis=-1)\n",
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" show(tmp[:,:,0])\n",
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" print(tmp.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"size = '{}x{}'.format(patch_size, patch_size)\n",
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"figs = [raster,\n",
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" stack_avr,\n",
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" stack_mean,\n",
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" stack_min,\n",
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" stack_max,\n",
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" stack_var,\n",
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" stack_std\n",
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" ]\n",
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"ttls = ['Origin',\n",
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" 'Average ' + size,\n",
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" 'Mean ' + size,\n",
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" 'Minimum ' + size,\n",
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" 'Maximum ' + size,\n",
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" 'Variance ' + size,\n",
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" 'STD ' + size\n",
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" ]\n",
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"mshow(figs, ttls)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"size = '{}x{}'.format(patch_size, patch_size)\n",
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"figs = [raster,\n",
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" stack_avr[:,:,-1],\n",
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" stack_mean[:,:,-1],\n",
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" stack_min[:,:,-1],\n",
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" stack_max[:,:,-1],\n",
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" stack_var[:,:,-1],\n",
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" stack_std[:,:,-1]\n",
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" ]\n",
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"ttls = ['Origin',\n",
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" 'Average ' + size,\n",
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" 'Mean ' + size,\n",
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" 'Minimum ' + size,\n",
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" 'Maximum ' + size,\n",
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" 'Variance ' + size,\n",
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" 'STD ' + size\n",
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" ]\n",
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"mshow(figs, ttls, 2, save='mstack2.png')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### TMP TRISLEK DBG"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"normal = triskele.read('../triskele/build/out/test-default.tif')\n",
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"sd = triskele.read('../triskele/build/out/test-sd.tif')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"normal.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"areas = np.loadtxt('../triskele/data/areaThresholds.txt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = [normal[:,:,0]]\n",
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"ttls = ['Origin']\n",
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"\n",
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"for area, i in zip(areas, range(1,normal.shape[2])):\n",
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" figs.append(normal[:,:,i])\n",
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" ttls.append('SDAP area {}'.format(area))\n",
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"\n",
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"mshow(figs, ttls, 2, '../Res/trkldbg_triskele_ap.png')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = [sd[:,:,0]]\n",
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"ttls = ['Origin']\n",
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"\n",
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"for area, i in zip(areas, range(1,sd.shape[2])):\n",
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" figs.append(sd[:,:,i])\n",
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" ttls.append('Triskele LFSDAP (SD) area {}'.format(area))\n",
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"\n",
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"mshow(figs, ttls, 2, '../Res/trkldbg_triskele_sd.png')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"(normal == sd).all()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"offset_stack = create_patches(normal, 5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stack_std = np.std(offset_stack, axis=-1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = [stack_std[:,:,0]]\n",
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"ttls = ['Origin std 5x5']\n",
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"\n",
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"for area, i in zip(areas, range(1,sd.shape[2])):\n",
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" figs.append(stack_std[:,:,i])\n",
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" ttls.append('LD2DAPs LFSDAP std 5x5 area {}'.format(area))\n",
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"\n",
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"mshow(figs, ttls, 2, '../Res/trkldbg_ld2dap_sd.png')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figs = list()\n",
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"ttls = list()\n",
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"for d in range(stack_std.shape[-1]):\n",
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" if d == 0:\n",
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" ttls.append('Origin')\n",
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" else:\n",
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" ttls.append('STD 5x5 from area {}'.format(area[d-1]))\n",
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" figs.append(stack_std[:,:,d])\n",
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" \n",
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"mshow(figs, ttls, 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"assert True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"assert False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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