346 lines
8.0 KiB
Plaintext
346 lines
8.0 KiB
Plaintext
{
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"cells": [
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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 numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"\n",
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"# Triskele\n",
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"import sys\n",
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"from pathlib import Path\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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"fs = np.array([16,3]) * 2 # Figure Size"
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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 ground truth"
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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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"gt = triskele.read('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.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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"plt.figure(figsize=fs)\n",
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"plt.imshow(gt, cmap=plt.get_cmap('GnBu'))\n",
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"plt.colorbar()\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": "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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"count = np.unique(gt, return_counts=True)\n",
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"lbl = pd.read_csv('../labels.csv', index_col=0)\n",
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"lbl['Count'] = count[1]\n",
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"lbl"
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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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"plt.bar(*count, log=True)\n",
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"plt.xticks(lbl.index.values,lbl['label'], rotation=90)\n",
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"plt.show()\n",
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"\n",
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"plt.bar(count[0][1:], count[1][1:])\n",
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"plt.xticks(lbl.index.values[1:], lbl['label'][1:], rotation=90)\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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"# Split data\n",
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"\n",
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"Create a filter raster `split` with values:\n",
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"\n",
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"- `0` No Data\n",
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"- `1` Train\n",
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"- `2` Test"
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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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"train_part = .9\n",
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"\n",
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"split = np.zeros_like(gt)\n",
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"\n",
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"for lbli, lblc in zip(count[0][1:], count[1][1:]):\n",
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" treshold = int(lblc * train_part)\n",
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" #print('lbli:{}, count:{}, train:{}'.format(lbli, lblc, treshold))\n",
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" f = np.nonzero(gt == lbli)\n",
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" split[f[0][:treshold], f[1][:treshold]] = 1\n",
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" split[f[0][treshold:], f[1][treshold:]] = 2\n",
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" \n",
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"plt.figure(figsize=fs)\n",
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"plt.imshow(split)\n",
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"#plt.imshow(split * (gt==1))\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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"## First Semantic Cross Validation Generator Prototype\n",
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"\n",
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"**WORK IN PROGRESS** you 'll have fun"
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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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"nb_split = 5\n",
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"\n",
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"def semantic_cvg(gt, nb_split, step=0):\n",
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" test_part = 1 / nb_split\n",
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"\n",
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" split = np.zeros_like(gt)\n",
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"\n",
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" for lbli, lblc in zip(count[0][1:], count[1][1:]):\n",
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" treshold = int(lblc * test_part)\n",
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" #print('lbli:{}, count:{}, train:{}'.format(lbli, lblc, treshold))\n",
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" f = np.nonzero(gt == lbli)\n",
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" t_int, t_ext = treshold * step, treshold * (step + 1)\n",
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" split[f[0], f[1]] = 1\n",
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" split[f[0][t_int:t_ext], f[1][t_int:t_ext]] = 2\n",
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"\n",
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" return split\n",
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"\n",
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"fig, axs = plt.subplots(nb_split, figsize=fs * nb_split)\n",
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"\n",
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"for i in range(nb_split):\n",
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" split = semantic_cvg(gt, nb_split, i)\n",
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"\n",
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" axs[i].imshow(split)\n",
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" axs[i].set_title(i)\n",
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"#plt.imshow(split * (gt==1))\n",
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"plt.savefig('../Res/cross_valid_split_{}.png'.format(nb_split))\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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"# Metaclasses "
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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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"metaclasses_A = {'Grass': [1, 2, 3],\n",
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" 'Trees': [4, 5],\n",
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" 'Hard Ground': [6, 17],\n",
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" 'Water': [7],\n",
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" 'Buildings': [8, 9],\n",
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" 'Roads': [10, 11, 12, 13, 14, 16],\n",
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" 'Railways': [15],\n",
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" 'Cars': [18],\n",
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" 'Trains': [19],\n",
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" 'Stadium': [20]}"
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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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"plt.figure(figsize=fs)\n",
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"plt.imshow(np.isin(gt, metaclasses_A['Roads']))\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": "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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"plt.figure(figsize=fs)\n",
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"plt.imshow(gt * np.isin(gt, metaclasses_A['Roads']))\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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"## Database-style"
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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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"dfc_lbl = pd.read_csv('../labels.csv')\n",
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"meta_idx = pd.read_csv('../metaclass_indexes.csv')\n",
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"meta_lbl = pd.read_csv('../metaclass_labels.csv')"
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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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"dfc_meta_view = pd.merge(dfc_lbl, meta_idx)\n",
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"all_meta_view = pd.merge(dfc_meta_view, meta_lbl)\n",
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"all_meta_view"
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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.array(all_meta_view[all_meta_view['metaclass_label'] == 'Roads']['index'])"
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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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"plt.figure(figsize=fs)\n",
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"plt.imshow(gt * np.isin(gt, np.array(all_meta_view[all_meta_view['metaclass_label'] == 'Roads']['index'])))\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": "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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"count = np.unique(gt, return_counts=True)\n",
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"df_count = pd.DataFrame(np.array(count).T, columns=['index', 'count'])\n",
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"df_count.head()"
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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 seaborn as sns\n",
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"\n",
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"df_tmp = meta_idx.merge(df_count).merge(meta_lbl).merge(dfc_lbl).drop(index=0)\n",
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"display(df_tmp.head())\n",
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"\n",
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"g = sns.barplot(data=df_tmp, x='label', y='count')\n",
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"plt.xticks(rotation=80)\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": "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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"meta_count_view = pd.merge(meta_idx, df_count)\n",
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"display(meta_count_view.head())\n",
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"meta_count_view.drop('index', 1, inplace=True)\n",
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"meta_count_view = meta_count_view.groupby('metaclass_index', as_index=False).sum()\n",
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"display(meta_count_view.head())\n",
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"\n",
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"pd.merge(meta_lbl, meta_count_view)"
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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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"meta_index = np.array(meta_idx['metaclass_index'])"
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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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"plt.figure(figsize=fs)\n",
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"plt.imshow(meta_index[gt])\n",
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"plt.colorbar()\n",
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"plt.show()"
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]
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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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