From 68180f1a57331380a0551253b17e2b8ff04a271a Mon Sep 17 00:00:00 2001
From: Alexandre Strube <a.strube@fz-juelich.de>
Date: Thu, 9 Sep 2021 15:03:29 +0200
Subject: [PATCH] First commit
---
logo.jpg | Bin 0 -> 22126 bytes
notebook-brian.ipynb | 847 +++++++++++++++++++++++++
notebook-christian.ipynb | 1274 ++++++++++++++++++++++++++++++++++++++
notebook-tabea.ipynb | 387 ++++++++++++
notebook.ipynb | 772 +++++++++++++++++++++++
submission.csv | 1047 +++++++++++++++++++++++++++++++
submission_valid.csv | 1047 +++++++++++++++++++++++++++++++
7 files changed, 5374 insertions(+)
create mode 100644 logo.jpg
create mode 100644 notebook-brian.ipynb
create mode 100644 notebook-christian.ipynb
create mode 100644 notebook-tabea.ipynb
create mode 100644 notebook.ipynb
create mode 100644 submission.csv
create mode 100644 submission_valid.csv
diff --git a/logo.jpg b/logo.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6654d4d742bcb65cb17e2c668f612db910f7c73a
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diff --git a/notebook-brian.ipynb b/notebook-brian.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The Simulated Root System Challenge"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Root systems are important actors in the overall plant development, growth and ultimetally, productivity. In the framework of agricultural research, improved root systems can help to acquire more soil resources, insure a better plant stability of store more carbon in the deep soil layers. However, due to their underground nature, roots are challenging to measure. \n",
+ " \n",
+ "For analysing root images classical measurements are the total root length (the summed length of all the individual roots) or the total number of roots. However, as root systems can quickly become very complex, root image analysis algorithms are prone to errors (see Lobet et al. 2017). For plant seedlings, we can assume that existing tools will be reliable, but as soon as the plants are several weeks old, the same tools will fail in their evaluation due to increasing root overlaps and crossing in the images."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This challenge focuses on the analysis of root systems, assuming the segmentation already given. Therefore we deal with simulated data, i.e., artificial black and white images, having the advantage of being (i) easy to generate, both the image and the groundtruth and (ii) to be close enough to real images such as the algorithms developed for simulated data might be transferred without too much trouble. The focus of this challenge is to extract the biologically relevant features from these images: (1) the total number of roots and (2) the total length of all the roots. Again, both are challenging to extract due to occlusions and overlap of roots within the images. As a general rule, for complex root systems, both are often underestimated by root image analysis software tools. \n",
+ "\n",
+ "We provide a library of 10.000 simulated plant root systems. For each root system in the simulated dataset, we have the whole structure stored in a data file (Root System Markup Language, RSML, Lobet et al 2015), a 2D black and white images (jpg, grayscale, 300 DPI, size between 1500 x 4700 px and 110 x 2100 px) of the root system, and the groundtruth data (e.g. total length, number of root, etc.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The challenge that we will offer to the machine learning community will be to extract : \n",
+ " \n",
+ "- the total root length\n",
+ "- the total number of roots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploratory Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from IPython.display import Image\n",
+ "df = pd.read_csv('train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(\"images/dicot-sim-145-2-25.rsml.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>6258.159151</td>\n",
+ " <td>387.820929</td>\n",
+ " <td>212.600726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>8740.875921</td>\n",
+ " <td>588.740714</td>\n",
+ " <td>82.306584</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>78.713036</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>49.953335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>931.466970</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2631.605200</td>\n",
+ " <td>133.000000</td>\n",
+ " <td>199.982670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>7974.766350</td>\n",
+ " <td>442.000000</td>\n",
+ " <td>259.926670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>73971.210000</td>\n",
+ " <td>4448.000000</td>\n",
+ " <td>504.952030</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " tot_root_length n_laterals depth\n",
+ "count 8371.000000 8371.000000 8371.000000\n",
+ "mean 6258.159151 387.820929 212.600726\n",
+ "std 8740.875921 588.740714 82.306584\n",
+ "min 78.713036 0.000000 49.953335\n",
+ "25% 931.466970 45.000000 149.944670\n",
+ "50% 2631.605200 133.000000 199.982670\n",
+ "75% 7974.766350 442.000000 259.926670\n",
+ "max 73971.210000 4448.000000 504.952030"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/p/software/juwelsbooster/stages/2020/software/Jupyter/2020.2.6-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+ " import pandas.util.testing as tm\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x146db6ed0820>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 540x540 with 12 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "sns.pairplot(df.sample(1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given an image, the goal is to predict the three columns: `tot_root_length`, `n_laterals`, and `depth`.\n",
+ "Let's first submit a dummy model that predicts the mean of each column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.dummy import DummyRegressor\n",
+ "import numpy as np\n",
+ "df = pd.read_csv(\"train.csv\")\n",
+ "reg = DummyRegressor(strategy='mean')\n",
+ "cols = ['tot_root_length', 'n_laterals', 'depth']\n",
+ "X = np.zeros(len(df))\n",
+ "y = df[cols]\n",
+ "reg.fit(X, y)\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X = np.zeros(len(df_valid))\n",
+ "df_valid.loc[:, cols] = reg.predict(X)\n",
+ "df_valid.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the following, we will develop a simple baseline solution based on classical image processing techniques. We will use HOG (Histogram of Oriented Gradients) as features, they can detect lines with different orientations. https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_hog.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x146da7cd6be0>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.io import imread \n",
+ "from skimage.morphology import thin\n",
+ "import matplotlib.pyplot as plt\n",
+ "image = imread(\"images/dicot-sim-145-2-25.rsml.jpg\")\n",
+ "image = 255 - image\n",
+ "image = image / 255\n",
+ "image = (image>.5).astype(float)\n",
+ "image = thin(image, max_iter=2)\n",
+ "image = image.astype(float)\n",
+ "#image = resize(image, (256,128))\n",
+ "#image = (image>127).astype('float')\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(image, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(244000,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x146da7c3be80>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.feature import hog\n",
+ "out, viz = hog(image, visualize=True, pixels_per_cell=(30, 30), cells_per_block=(10, 10), orientations=20)\n",
+ "print(out.shape)\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(viz, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from skimage.transform import resize\n",
+ "import numpy as np\n",
+ "def features(image):\n",
+ " image = 255 - image\n",
+ " image = image / 255\n",
+ " image = resize(image, (800,400))\n",
+ " image = (image>.5).astype(float)\n",
+ " image = thin(image, max_iter=2)\n",
+ " image = image.astype(float)\n",
+ " return hog(image, pixels_per_cell=(40, 40), cells_per_block=(10, 10), orientations=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(22000,)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "image = imread(\"images/dicot-sim-1-1-25.rsml.jpg\")\n",
+ "features(image).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dataset(df):\n",
+ " from tqdm import tqdm\n",
+ " X = []\n",
+ " Y = []\n",
+ " for _, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
+ " X.append(features(imread(f\"images/{row.image}\")))\n",
+ " Y.append([row.tot_root_length, row.n_laterals, row.depth])\n",
+ " X = np.array(X)\n",
+ " Y = np.array(Y)\n",
+ " return X, Y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 1000/1000 [01:57<00:00, 8.54it/s]\n",
+ "100%|██████████| 1046/1046 [01:59<00:00, 8.78it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 2min 57s, sys: 4.04 s, total: 3min 1s\n",
+ "Wall time: 3min 56s\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_train = pd.read_csv('train.csv')\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X_train, Y_train = get_dataset(df_train.sample(1000))\n",
+ "X_valid, _ = get_dataset(df_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((1000, 22000), (1000, 3), (1046, 22000))"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train.shape, Y_train.shape, X_valid.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.55627699, 0.51608773, 0.57481516, 0.52764447, 0.5245963 ])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "model = RandomForestRegressor(n_estimators=50, n_jobs=-1)\n",
+ "cross_val_score(model, X_train, Y_train, scoring='r2', )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestRegressor(n_estimators=50, n_jobs=-1)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(X_train, Y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Ypred = model.predict(X_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"submission_valid.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[:, [\"tot_root_length\", \"n_laterals\", \"depth\"]] = Ypred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import Model\n",
+ "from tensorflow.keras.layers import (\n",
+ " Input,\n",
+ " Conv2D,\n",
+ " Dense,\n",
+ " Flatten,\n",
+ " add,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_cnn():\n",
+ " input_0 = Input(shape=(None,None,1))\n",
+ " conv1 = Conv2D(8,3,padding='same',activation='relu')\n",
+ " conv2 = Conv2D(16,3,padding='same',activation='relu')\n",
+ " conv3 = Conv2D(32,3,padding='same',activation='relu')\n",
+ " dense = Dense(32,activation='relu')\n",
+ " out = Dense(4,activation='linear')\n",
+ " x = input_0\n",
+ " x = add([conv1(x),x])\n",
+ " x = add([conv2(x),x])\n",
+ " x = add([conv3(x),x])\n",
+ " x = dense(Flatten()(x))\n",
+ " x = out(x)\n",
+ " Model(inputs=input_0, outputs=x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "Operands could not be broadcast together with shapes (None, None, 16) (None, None, 8)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-32-917730d5b171>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbuild_cnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-31-14f1f3eff445>\u001b[0m in \u001b[0;36mbuild_cnn\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/layers/merge.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(inputs, **kwargs)\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 770\u001b[0m \"\"\"\n\u001b[0;32m--> 771\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mAdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 923\u001b[0m \u001b[0;31m# >> model = tf.keras.Model(inputs, outputs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 925\u001b[0;31m return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0m\u001b[1;32m 926\u001b[0m input_list)\n\u001b[1;32m 927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;31m# Build layer if applicable (if the `build` method has been\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;31m# overridden).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1099\u001b[0m \u001b[0mcast_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2641\u001b[0m \u001b[0;31m# operations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_init_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2643\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint:disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2644\u001b[0m \u001b[0;31m# We must set also ensure that the layer is marked as built, and the build\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2645\u001b[0m \u001b[0;31m# shape is stored since user defined build functions may not be calling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(instance, input_shape)\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minput_shape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0minput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_shapes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_tuples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m \u001b[0moutput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 324\u001b[0m \u001b[0;31m# Return shapes from `fn` as TensorShapes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_shape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/layers/merge.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0moutput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_elemwise_op_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;31m# If the inputs have different ranks, we have to reshape them\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;31m# to make them broadcastable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/p/software/juwelsbooster/stages/2020/software/TensorFlow/2.3.1-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/tensorflow/python/keras/layers/merge.py\u001b[0m in \u001b[0;36m_compute_elemwise_op_output_shape\u001b[0;34m(self, shape1, shape2)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;34m'Operands could not be broadcast '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m 'together with shapes ' + str(shape1) + ' ' + str(shape2))\n",
+ "\u001b[0;31mValueError\u001b[0m: Operands could not be broadcast together with shapes (None, None, 16) (None, None, 8)"
+ ]
+ }
+ ],
+ "source": [
+ "build_cnn()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hhh_env",
+ "language": "python",
+ "name": "hhh_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook-christian.ipynb b/notebook-christian.ipynb
new file mode 100644
index 0000000..3cd475e
--- /dev/null
+++ b/notebook-christian.ipynb
@@ -0,0 +1,1274 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The Simulated Root System Challenge"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Root systems are important actors in the overall plant development, growth and ultimetally, productivity. In the framework of agricultural research, improved root systems can help to acquire more soil resources, insure a better plant stability of store more carbon in the deep soil layers. However, due to their underground nature, roots are challenging to measure. \n",
+ " \n",
+ "For analysing root images classical measurements are the total root length (the summed length of all the individual roots) or the total number of roots. However, as root systems can quickly become very complex, root image analysis algorithms are prone to errors (see Lobet et al. 2017). For plant seedlings, we can assume that existing tools will be reliable, but as soon as the plants are several weeks old, the same tools will fail in their evaluation due to increasing root overlaps and crossing in the images."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This challenge focuses on the analysis of root systems, assuming the segmentation already given. Therefore we deal with simulated data, i.e., artificial black and white images, having the advantage of being (i) easy to generate, both the image and the groundtruth and (ii) to be close enough to real images such as the algorithms developed for simulated data might be transferred without too much trouble. The focus of this challenge is to extract the biologically relevant features from these images: (1) the total number of roots and (2) the total length of all the roots. Again, both are challenging to extract due to occlusions and overlap of roots within the images. As a general rule, for complex root systems, both are often underestimated by root image analysis software tools. \n",
+ "\n",
+ "We provide a library of 10.000 simulated plant root systems. For each root system in the simulated dataset, we have the whole structure stored in a data file (Root System Markup Language, RSML, Lobet et al 2015), a 2D black and white images (jpg, grayscale, 300 DPI, size between 1500 x 4700 px and 110 x 2100 px) of the root system, and the groundtruth data (e.g. total length, number of root, etc.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The challenge that we will offer to the machine learning community will be to extract : \n",
+ " \n",
+ "- the total root length\n",
+ "- the total number of roots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploratory Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from IPython.display import Image\n",
+ "df = pd.read_csv('train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
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+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>195</th>\n",
+ " <td>dicot-sim-455-10-25.rsml.jpg</td>\n",
+ " <td>110.000010</td>\n",
+ " <td>0</td>\n",
+ " <td>104.902000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>379</th>\n",
+ " <td>dicot-sim-340-5-25.rsml.jpg</td>\n",
+ " <td>109.999080</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>391</th>\n",
+ " <td>dicot-sim-195-2-25.rsml.jpg</td>\n",
+ " <td>104.979600</td>\n",
+ " <td>0</td>\n",
+ " <td>99.906670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>555</th>\n",
+ " <td>dicot-sim-372-4-25.rsml.jpg</td>\n",
+ " <td>159.994400</td>\n",
+ " <td>0</td>\n",
+ " <td>154.940000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>635</th>\n",
+ " <td>dicot-sim-405-1-25.rsml.jpg</td>\n",
+ " <td>114.998540</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>701</th>\n",
+ " <td>monocot-sim-186-9-25.rsml.jpg</td>\n",
+ " <td>189.911880</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>844</th>\n",
+ " <td>dicot-sim-142-2-25.rsml.jpg</td>\n",
+ " <td>139.999080</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1041</th>\n",
+ " <td>dicot-sim-415-1-25.rsml.jpg</td>\n",
+ " <td>109.999275</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1115</th>\n",
+ " <td>monocot-sim-186-6-25.rsml.jpg</td>\n",
+ " <td>188.549000</td>\n",
+ " <td>0</td>\n",
+ " <td>109.897340</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1484</th>\n",
+ " <td>dicot-sim-142-5-25.rsml.jpg</td>\n",
+ " <td>139.999790</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1603</th>\n",
+ " <td>dicot-sim-311-8-25.rsml.jpg</td>\n",
+ " <td>94.947830</td>\n",
+ " <td>0</td>\n",
+ " <td>89.916000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1763</th>\n",
+ " <td>monocot-sim-455-5-25.rsml.jpg</td>\n",
+ " <td>246.723630</td>\n",
+ " <td>0</td>\n",
+ " <td>129.963330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1774</th>\n",
+ " <td>dicot-sim-390-4-25.rsml.jpg</td>\n",
+ " <td>154.997040</td>\n",
+ " <td>0</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1872</th>\n",
+ " <td>monocot-sim-90-9-25.rsml.jpg</td>\n",
+ " <td>109.997820</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1918</th>\n",
+ " <td>dicot-sim-311-5-25.rsml.jpg</td>\n",
+ " <td>94.934975</td>\n",
+ " <td>0</td>\n",
+ " <td>89.916000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1935</th>\n",
+ " <td>monocot-sim-239-6-25.rsml.jpg</td>\n",
+ " <td>586.982540</td>\n",
+ " <td>0</td>\n",
+ " <td>99.737335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1942</th>\n",
+ " <td>dicot-sim-142-10-25.rsml.jpg</td>\n",
+ " <td>139.991930</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2030</th>\n",
+ " <td>dicot-sim-390-6-25.rsml.jpg</td>\n",
+ " <td>154.998670</td>\n",
+ " <td>0</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2248</th>\n",
+ " <td>dicot-sim-311-2-25.rsml.jpg</td>\n",
+ " <td>94.986200</td>\n",
+ " <td>0</td>\n",
+ " <td>89.916000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2404</th>\n",
+ " <td>dicot-sim-195-8-25.rsml.jpg</td>\n",
+ " <td>104.999794</td>\n",
+ " <td>0</td>\n",
+ " <td>99.906670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2637</th>\n",
+ " <td>monocot-sim-75-7-25.rsml.jpg</td>\n",
+ " <td>756.273400</td>\n",
+ " <td>0</td>\n",
+ " <td>84.158670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2673</th>\n",
+ " <td>dicot-sim-195-7-25.rsml.jpg</td>\n",
+ " <td>104.995060</td>\n",
+ " <td>0</td>\n",
+ " <td>99.991330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2711</th>\n",
+ " <td>dicot-sim-40-8-25.rsml.jpg</td>\n",
+ " <td>78.713036</td>\n",
+ " <td>0</td>\n",
+ " <td>73.660000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2733</th>\n",
+ " <td>dicot-sim-415-10-25.rsml.jpg</td>\n",
+ " <td>109.996340</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3065</th>\n",
+ " <td>dicot-sim-405-8-25.rsml.jpg</td>\n",
+ " <td>114.996060</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3282</th>\n",
+ " <td>dicot-sim-405-6-25.rsml.jpg</td>\n",
+ " <td>114.996930</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3421</th>\n",
+ " <td>dicot-sim-372-7-25.rsml.jpg</td>\n",
+ " <td>159.996900</td>\n",
+ " <td>0</td>\n",
+ " <td>154.940000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3806</th>\n",
+ " <td>dicot-sim-340-4-25.rsml.jpg</td>\n",
+ " <td>109.998320</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4224</th>\n",
+ " <td>dicot-sim-500-4-25.rsml.jpg</td>\n",
+ " <td>104.780380</td>\n",
+ " <td>0</td>\n",
+ " <td>99.737335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4473</th>\n",
+ " <td>monocot-sim-239-1-25.rsml.jpg</td>\n",
+ " <td>587.290040</td>\n",
+ " <td>0</td>\n",
+ " <td>99.737335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4636</th>\n",
+ " <td>dicot-sim-122-5-25.rsml.jpg</td>\n",
+ " <td>124.998560</td>\n",
+ " <td>0</td>\n",
+ " <td>119.888000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4725</th>\n",
+ " <td>monocot-sim-186-1-25.rsml.jpg</td>\n",
+ " <td>186.151460</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4877</th>\n",
+ " <td>dicot-sim-195-1-25.rsml.jpg</td>\n",
+ " <td>104.986590</td>\n",
+ " <td>0</td>\n",
+ " <td>99.906670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5376</th>\n",
+ " <td>dicot-sim-340-7-25.rsml.jpg</td>\n",
+ " <td>109.998085</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5378</th>\n",
+ " <td>dicot-sim-390-9-25.rsml.jpg</td>\n",
+ " <td>154.999860</td>\n",
+ " <td>0</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5718</th>\n",
+ " <td>dicot-sim-340-1-25.rsml.jpg</td>\n",
+ " <td>109.999855</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6146</th>\n",
+ " <td>monocot-sim-186-2-25.rsml.jpg</td>\n",
+ " <td>189.541020</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6376</th>\n",
+ " <td>dicot-sim-142-9-25.rsml.jpg</td>\n",
+ " <td>139.981500</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6415</th>\n",
+ " <td>dicot-sim-142-8-25.rsml.jpg</td>\n",
+ " <td>139.991260</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6512</th>\n",
+ " <td>dicot-sim-142-1-25.rsml.jpg</td>\n",
+ " <td>139.995160</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6580</th>\n",
+ " <td>dicot-sim-340-9-25.rsml.jpg</td>\n",
+ " <td>109.998290</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6709</th>\n",
+ " <td>dicot-sim-311-10-25.rsml.jpg</td>\n",
+ " <td>94.986290</td>\n",
+ " <td>0</td>\n",
+ " <td>89.916000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6896</th>\n",
+ " <td>dicot-sim-142-7-25.rsml.jpg</td>\n",
+ " <td>139.993240</td>\n",
+ " <td>0</td>\n",
+ " <td>134.958660</td>\n",
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+ " <tr>\n",
+ " <th>6947</th>\n",
+ " <td>dicot-sim-340-3-25.rsml.jpg</td>\n",
+ " <td>110.000000</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7114</th>\n",
+ " <td>dicot-sim-395-9-25.rsml.jpg</td>\n",
+ " <td>154.999880</td>\n",
+ " <td>0</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7205</th>\n",
+ " <td>dicot-sim-415-9-25.rsml.jpg</td>\n",
+ " <td>109.998924</td>\n",
+ " <td>0</td>\n",
+ " <td>104.986670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7268</th>\n",
+ " <td>dicot-sim-179-8-25.rsml.jpg</td>\n",
+ " <td>104.883060</td>\n",
+ " <td>0</td>\n",
+ " <td>99.822010</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7298</th>\n",
+ " <td>dicot-sim-405-10-25.rsml.jpg</td>\n",
+ " <td>114.997430</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7388</th>\n",
+ " <td>dicot-sim-222-7-25.rsml.jpg</td>\n",
+ " <td>94.998350</td>\n",
+ " <td>0</td>\n",
+ " <td>89.916000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7471</th>\n",
+ " <td>monocot-sim-455-3-25.rsml.jpg</td>\n",
+ " <td>251.026290</td>\n",
+ " <td>0</td>\n",
+ " <td>129.963330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7617</th>\n",
+ " <td>monocot-sim-455-10-25.rsml.jpg</td>\n",
+ " <td>257.927200</td>\n",
+ " <td>0</td>\n",
+ " <td>129.963330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8110</th>\n",
+ " <td>dicot-sim-500-5-25.rsml.jpg</td>\n",
+ " <td>104.765090</td>\n",
+ " <td>0</td>\n",
+ " <td>99.737335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8294</th>\n",
+ " <td>dicot-sim-195-5-25.rsml.jpg</td>\n",
+ " <td>104.995674</td>\n",
+ " <td>0</td>\n",
+ " <td>99.906670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8342</th>\n",
+ " <td>dicot-sim-405-2-25.rsml.jpg</td>\n",
+ " <td>114.997570</td>\n",
+ " <td>0</td>\n",
+ " <td>109.982000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "195 dicot-sim-455-10-25.rsml.jpg 110.000010 0 104.902000\n",
+ "379 dicot-sim-340-5-25.rsml.jpg 109.999080 0 104.986670\n",
+ "391 dicot-sim-195-2-25.rsml.jpg 104.979600 0 99.906670\n",
+ "555 dicot-sim-372-4-25.rsml.jpg 159.994400 0 154.940000\n",
+ "635 dicot-sim-405-1-25.rsml.jpg 114.998540 0 109.982000\n",
+ "701 monocot-sim-186-9-25.rsml.jpg 189.911880 0 109.982000\n",
+ "844 dicot-sim-142-2-25.rsml.jpg 139.999080 0 134.958660\n",
+ "1041 dicot-sim-415-1-25.rsml.jpg 109.999275 0 104.986670\n",
+ "1115 monocot-sim-186-6-25.rsml.jpg 188.549000 0 109.897340\n",
+ "1484 dicot-sim-142-5-25.rsml.jpg 139.999790 0 134.958660\n",
+ "1603 dicot-sim-311-8-25.rsml.jpg 94.947830 0 89.916000\n",
+ "1763 monocot-sim-455-5-25.rsml.jpg 246.723630 0 129.963330\n",
+ "1774 dicot-sim-390-4-25.rsml.jpg 154.997040 0 149.944670\n",
+ "1872 monocot-sim-90-9-25.rsml.jpg 109.997820 0 104.986670\n",
+ "1918 dicot-sim-311-5-25.rsml.jpg 94.934975 0 89.916000\n",
+ "1935 monocot-sim-239-6-25.rsml.jpg 586.982540 0 99.737335\n",
+ "1942 dicot-sim-142-10-25.rsml.jpg 139.991930 0 134.958660\n",
+ "2030 dicot-sim-390-6-25.rsml.jpg 154.998670 0 149.944670\n",
+ "2248 dicot-sim-311-2-25.rsml.jpg 94.986200 0 89.916000\n",
+ "2404 dicot-sim-195-8-25.rsml.jpg 104.999794 0 99.906670\n",
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+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[df.n_laterals == df.n_laterals.min()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(\"images/dicot-sim-405-2-25.rsml.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>6258.159151</td>\n",
+ " <td>387.820929</td>\n",
+ " <td>212.600726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>8740.875921</td>\n",
+ " <td>588.740714</td>\n",
+ " <td>82.306584</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>78.713036</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>49.953335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>931.466970</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2631.605200</td>\n",
+ " <td>133.000000</td>\n",
+ " <td>199.982670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>7974.766350</td>\n",
+ " <td>442.000000</td>\n",
+ " <td>259.926670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>73971.210000</td>\n",
+ " <td>4448.000000</td>\n",
+ " <td>504.952030</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " tot_root_length n_laterals depth\n",
+ "count 8371.000000 8371.000000 8371.000000\n",
+ "mean 6258.159151 387.820929 212.600726\n",
+ "std 8740.875921 588.740714 82.306584\n",
+ "min 78.713036 0.000000 49.953335\n",
+ "25% 931.466970 45.000000 149.944670\n",
+ "50% 2631.605200 133.000000 199.982670\n",
+ "75% 7974.766350 442.000000 259.926670\n",
+ "max 73971.210000 4448.000000 504.952030"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/p/software/juwelsbooster/stages/2020/software/Jupyter/2020.2.6-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+ " import pandas.util.testing as tm\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x14cac66e56d0>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 540x540 with 12 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "sns.pairplot(df.sample(1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given an image, the goal is to predict the three columns: `tot_root_length`, `n_laterals`, and `depth`.\n",
+ "Let's first submit a dummy model that predicts the mean of each column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.dummy import DummyRegressor\n",
+ "import numpy as np\n",
+ "df = pd.read_csv(\"train.csv\")\n",
+ "reg = DummyRegressor(strategy='mean')\n",
+ "cols = ['tot_root_length', 'n_laterals', 'depth']\n",
+ "X = np.zeros(len(df))\n",
+ "y = df[cols]\n",
+ "reg.fit(X, y)\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X = np.zeros(len(df_valid))\n",
+ "df_valid.loc[:, cols] = reg.predict(X)\n",
+ "df_valid.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the following, we will develop a simple baseline solution based on classical image processing techniques. We will use HOG (Histogram of Oriented Gradients) as features, they can detect lines with different orientations. https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_hog.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x14cabce01520>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.io import imread \n",
+ "from skimage.morphology import thin\n",
+ "import matplotlib.pyplot as plt\n",
+ "image = imread(\"images/dicot-sim-145-2-25.rsml.jpg\")\n",
+ "image = 255 - image\n",
+ "image = image / 255\n",
+ "image = (image>.5).astype(float)\n",
+ "image = thin(image, max_iter=2)\n",
+ "image = image.astype(float)\n",
+ "#image = resize(image, (256,128))\n",
+ "#image = (image>127).astype('float')\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(image, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(244000,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x14cabcddfc10>"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.feature import hog\n",
+ "out, viz = hog(image, visualize=True, pixels_per_cell=(30, 30), cells_per_block=(10, 10), orientations=20)\n",
+ "print(out.shape)\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(viz, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from skimage.transform import resize\n",
+ "import numpy as np\n",
+ "def features(image):\n",
+ " image = 255 - image\n",
+ " image = image / 255\n",
+ " image = resize(image, (800,400))\n",
+ " image = (image>.5).astype(float)\n",
+ " image = thin(image, max_iter=2)\n",
+ " image = image.astype(float)\n",
+ " return hog(image, pixels_per_cell=(40, 40), cells_per_block=(10, 10), orientations=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0., 0., 0., ..., 0., 0., 0.])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "image = imread(\"images/dicot-sim-1-1-25.rsml.jpg\")\n",
+ "features(image)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dataset(df):\n",
+ " from tqdm import tqdm\n",
+ " X = []\n",
+ " Y = []\n",
+ " for _, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
+ " X.append(features(imread(f\"images/{row.image}\")))\n",
+ " Y.append([row.tot_root_length, row.n_laterals, row.depth])\n",
+ " X = np.array(X)\n",
+ " Y = np.array(Y)\n",
+ " return X, Y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 1000/1000 [01:49<00:00, 9.10it/s]\n",
+ "100%|██████████| 1046/1046 [01:36<00:00, 10.82it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 2min 54s, sys: 3.26 s, total: 2min 57s\n",
+ "Wall time: 3min 26s\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_train = pd.read_csv('train.csv')\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X_train, Y_train = get_dataset(df_train.sample(1000))\n",
+ "X_valid, _ = get_dataset(df_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((1000, 22000), (1000, 3), (1046, 22000))"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train.shape, Y_train.shape, X_valid.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0., 0., 0., ..., 0., 0., 0.])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.51420135, 0.56140878, 0.50610067, 0.48608223, 0.45654623])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "model = RandomForestRegressor(n_estimators=50, n_jobs=-1)\n",
+ "cross_val_score(model, X_train, Y_train, scoring='r2', )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestRegressor(n_estimators=50, n_jobs=-1)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(X_train, Y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Ypred = model.predict(X_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"submission_valid.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[:, [\"tot_root_length\", \"n_laterals\", \"depth\"]] = Ypred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hhh_env",
+ "language": "python",
+ "name": "hhh_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook-tabea.ipynb b/notebook-tabea.ipynb
new file mode 100644
index 0000000..7c57f92
--- /dev/null
+++ b/notebook-tabea.ipynb
@@ -0,0 +1,387 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The Simulated Root System Challenge"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Root systems are important actors in the overall plant development, growth and ultimetally, productivity. In the framework of agricultural research, improved root systems can help to acquire more soil resources, insure a better plant stability of store more carbon in the deep soil layers. However, due to their underground nature, roots are challenging to measure. \n",
+ " \n",
+ "For analysing root images classical measurements are the total root length (the summed length of all the individual roots) or the total number of roots. However, as root systems can quickly become very complex, root image analysis algorithms are prone to errors (see Lobet et al. 2017). For plant seedlings, we can assume that existing tools will be reliable, but as soon as the plants are several weeks old, the same tools will fail in their evaluation due to increasing root overlaps and crossing in the images."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This challenge focuses on the analysis of root systems, assuming the segmentation already given. Therefore we deal with simulated data, i.e., artificial black and white images, having the advantage of being (i) easy to generate, both the image and the groundtruth and (ii) to be close enough to real images such as the algorithms developed for simulated data might be transferred without too much trouble. The focus of this challenge is to extract the biologically relevant features from these images: (1) the total number of roots and (2) the total length of all the roots. Again, both are challenging to extract due to occlusions and overlap of roots within the images. As a general rule, for complex root systems, both are often underestimated by root image analysis software tools. \n",
+ "\n",
+ "We provide a library of 10.000 simulated plant root systems. For each root system in the simulated dataset, we have the whole structure stored in a data file (Root System Markup Language, RSML, Lobet et al 2015), a 2D black and white images (jpg, grayscale, 300 DPI, size between 1500 x 4700 px and 110 x 2100 px) of the root system, and the groundtruth data (e.g. total length, number of root, etc.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The challenge that we will offer to the machine learning community will be to extract : \n",
+ " \n",
+ "- the total root length\n",
+ "- the total number of roots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploratory Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import sknw\n",
+ "import networkx as nx\n",
+ "from skimage.morphology import skeletonize\n",
+ "import numpy as np\n",
+ "from IPython.display import Image\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Image(\"images/dicot-sim-145-2-25.rsml.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image = plt.imread(\"images/dicot-sim-145-2-25.rsml.jpg\")\n",
+ "image = 255 - image\n",
+ "image = image / 255\n",
+ "image = (image>.5).astype(float)\n",
+ "skel = skeletonize(image)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x153a93bd1a30>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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P8e6773o9DseOHSMyMpKuXbt6PUqTKMpWsGLFimY7JZ5rdu3aRXZ2ttdjABdfLqxcuTLs/64VZSuoqakhJyeH7t27ez1KswkEAvTu3ZvevXuTmJjo9Th1Dh06RCAQICMjw+tRGk0HY24lvXv3ZsKECTz//PNej9IoqampREVFMXXqVDp37kxNTQ0FBQXAxbNKZ2dns3z5co+nvKhPnz6MGzeOF154wetRrkkHY/bYoUOH+OIXv0ggEHD+/BoxMTEkJCRgjOG+++4jMTGRc+fOUVNTw6pVq8jNzaW2trZub5m0tDQeeughZ6LMzs5mypQpZGRkhOVBnxVlK7HWsnXrVkaMGMGmTZu8HqeOz+ere/o5bNgwBg8eXHeWaGtt3Yl6SktLw+Z8KdZa1q1bx/jx43njjTfC7ozXivIGjR8/nu3bt1NaWnrDP7ty5UqefPJJtmzZ4tkveExMDH6/n44dO3LfffdhjKn7BP6OHTt4/vnnqaioaNTjc8n+/fvp3bs3o0eP5oMPPvB6nBuiKG/QuXPn+Ju/+RuOHDnCkiVLKC8vb/DPVlVVsWvXLkaNGtUqa8uIiAiioqLo3r07kydPBiAYDBIIBCgsLOS1116jurqaM2fOtPgsXli1ahXf/e532bRpk6dHErhRivIG7dq1i3379jFw4EC+8Y1v8Prrr3PkyJEG/3x2djbDhg1r9ij9fj8REREEAgFmzZpFZGRk3WvD48eP88477wBQUFAQlp+caIzKykq2b9/ObbfdxoYNG7wep8EUZSNUVFSwfft29uzZwwMPPADQ4DD37t3LlClT6Nq1a5NOC3fpc4xjxowhMzOTpKQkEhISqKysZNGiRZSXl1NSUtIqe9r06dOHgwcPtvj9NMa6dev4zne+wwcffBA2R4FQlE1QUVHB7t27GTVq1A2tLffs2cPAgQMbHOWlT/d37tyZ8ePHEwwG6w5OvHHjRrZs2UJ+fj5FRUU3+Aiax7hx43j66ac9ue/rKS8vZ9u2bYwbN46srCyvx2kQRdlExcXFxMXF3dD5Kbdt28bXv/51VqxYcdXr9OnTh4EDB2KM4aabbiIYDJKYmMi8efMoKytz/j04l2RlZfHNb35TUbYXR44coUePHvh8vgZvUb1w4QI7d+5k/PjxZGVlERMTw4QJE4iKimLYsGFERESQnZ3Nnj17sNaydOlSRo8eTWRkpGcHqgpnpaWlbN26lUmTJrFu3Tqvx7kuRdkM8vPzSUtLa/DT0cTERKKiohg7dizTpk3jwoULZGVlcebMGd59993PxW2M4dZbb+Wpp55qifHbhU2bNvHAAw+wd+9e8vPzvR7nmhRlM1izZg1Tpkzh5Zdf/tz3jDEMGzaMYDDI2LFjSUhI4OzZs+zcuZPf/va3DXo7YtKkSezYsYOSkpKWGL9dKCkp4f3332f69Om88MILTm/0UZTNoLy8nMjIyLo34gcPHkxkZCR33HEHkZGR7Nixg7KyMp5++ukbPhiwz+dj5MiRPPPMMy00ffuRnZ3NjBkzSElJ4dSpU16Pc1WKsol69OhBQkICAwYM4Ec/+hGVlZXs3r2byspKfvnLX97QzgVXMnHiRHbs2BEWR/YOB2+//TYzZ87k+eefd3ZtqShvUGZmJlFRUdx+++2kpqZy7NgxSkpKeOqppzh27BjW2mb7n+33+xk2bBjPPvtss9yeXNwwFx8fT3Jyct2nXFyjKK8jISGB1NRU7rnnHuLj4zlx4gRVVVW89dZbFBQUUFNT0yL/4hpjmDRpEtu3b9daspldOo/oc8895+TaUlFeR0ZGBqNHj+all17i/PnzVFZWtsr/yKSkJAYNGsRvfvObFr+vpiotLaV79+507tyZ1atXO7+f6bFjx4iJiSEpKcnzYwtd0aWnW65eANveLsYYO3fuXNutWzfPZ2nIpV+/fvbTTz+1f//3f2+DwaDn8zTkkpiYaL/+9a/b6Ohoz2a42u+8DgfioIkTJ3Lo0KGw+YDugw8+yPz583nxxRfD5vR/RUVF7N27lzFjxng9yucoSscEg0GGDh3Kxo0bnXy9cyVVVVU8//zzzJkzh9jYWK/HabCNGzcyZMgQgsGg16NcRlE6ZsSIEXz66adcuHDB61FuSFFREcYYpw6idT0XLlzg008/ZdSoUV6PchlF6ZC4uDiGDx8eNjtOA8TGxlJZWUllZSULFy5k9uzZXo90Q7Kyshg+fLhTZ0dTlA75i7/4C15//fWwOhRHx44dKSsro7y8nNLS0rB6+goXtxzv3buX4cOHez1KHUXpiK5du2KtdX5n6WupqqqiqqrKqbVOQ6xdu5ZRo0Y58w+KonRAbGws06ZNY+HChc6/x3ctZWVllJaWkpSU5PUoN6S8vJxXX32Vxx9/3OtRAEXphGHDhpGbmxvWa8lLcnNzw/JcHnl5eVRXV9OtWzevR1GUXouJieG2225j1apVXo/SLFauXMntt9/u9Rg3zFrrzGkLtZudx0aOHMlHH30UNgcMvnTqu2HDhtG1a1cSEhI4cOBA3fcrKiooLCxs8oHBvJCbm0t5eTmZmZk3dMyl5qYoPdSrVy969eoVFsfb6d+/Pz169GDo0KF8/PHHvPXWW+Tl5ZGRkcHMmTPrrnfpdAauvSHfENZali9fzr333uvpgcAUpYfuuusu3n77bWf33ElMTGTMmDHcdttt7N+/n6NHj/Kzn/3suj+3fv16Jk6c6Mwp8m5ETk4O5eXl9OrVi0OHDnkyg6L0SHp6OgUFBeTk5Hg9ymWMMfTu3ZvZs2dTU1PD5s2b+eEPf3hDW4UPHz4clmvKS1atWsX06dOZN2+eJ/evKD2Sm5vLggULvB6jTseOHZk4cSKDBg3i2LFj/P73v6ekpKRR5zypra0N66PuHT9+nKKiIgYNGuTJ41CUHnHhKWvHjh1JTU1l1qxZlJWVsXHjRpYuXdpuTmtwLW+88Qbf+9732L9/P5WVla1634qynQkEAiQmJtYdzqSoqIinnnqK8vJy58+b2Zpqa2vZtGkT48aNY82aNa1634qynYiPjyc9PZ27774bay2rVq1iwYIFCvEa3n//ff7+7/+e999/v1XfslKUbZjf7ycYDHLPPffQuXNnTp48yR//+EeKi4u9Hi0sVFdXs3HjRiZPnszKlStb7X4VZRvk9/sZNGgQEyZMIBgMsnjxYg4ePNgi/9r7/f6wOcNzY2zYsIEnn3ySNWvWtNrjNC5scLgWY4zbAzokPj6ee+65h6SkJPLy8lizZg1nz55t0fv81re+xcsvv+zmAaiaycSJE4mOjmb58uXNervWWnOl5VpThjmfz0dsbCz33HMPGRkZvPvuu+Tn57fa2ZljYmLC7igJN+rDDz9k6tSprXZ/ijJMXTrpz5gxY4iMjGTx4sW8+uqrnnz069L5M9uqiooK3n333Va7Pz19DTNJSUnceeed3HzzzXz44Yd88MEHFBYWejbPT3/6U2pra9m3bx8vv/yytubeAD19DXNjxoyhR48edO7cmRUrVvDKK694PRJw8YBZv/71r+nSpQtPPvkkb7/9dljvzeMCRekwYwyjR49m8uTJ7Nq1iz179vDqq696PdYVHTp0iJ/97GfMmTOHzp07k5WV1eSTG7VXbfvFQBgLBAJ8+9vfJiUlhf/6r/9iyZIlzq+BampqWLBgAZWVlTz00EP06dPH65HCktaUjqqqquJXv/qV12PcsNraWtavX092dja33347GRkZbN68OayO0Oc1rSmlReTk5PDSSy8RERHBgw8+SGZmptcjhQ2tKaXFXNrHtmvXrkybNo0DBw6wY8eONv++ZlNpTSktLjc3l+eff57Y2FgeeuihsDzaXWvSmlJahbWWZcuWkZaWxpw5c/j444/ZsWOHXmtegdaU0qry8/OZN28e8fHxPPbYY6SkpGDMFd9Db7e0ppRWV1tby9KlS0lOTuaRRx5h06ZNbN261euxnKEoxTOnT5/mf/7nf4iMjPR6FKfo6at4qra2Vnv+fEaTozTGHDHGfGyM2WmM2Rpa1skYs8IYcyD0346h5cYY81tjzEFjzC5jjDvnHxNxRHOtKadYa4daa0eG/vwPwCprbV9gVejPANOBvqHLXOCpZrp/kTajpZ6+zgIuHYv/BWB2veUv2os2AYnGmC4tNINIWGqOKC2w3BizzRgzN7QszVqbF/r6JJAW+jodOF7vZ0+ElolISHNsfR1vrc0xxqQCK4wxe+t/01prb/SDyqG45173iiJtUJPXlNbanNB/TwELgdFA/qWnpaH/ngpdPQeof1bOjNCyz97mPGvtyHqvUUXajSZFaYyJNcbEX/oauAvYDSwGnghd7QlgUejrxcDjoa2wY4Diek9zRYSmP31NAxaGdpPyA3+21i41xmwBXjPGfAU4CjwUuv57wAzgIFAKfLmJ9y/S5jQpSmvtIWDIFZafAe64wnILfK0p9ynS1mmPHhHHKEoRxyhKEccoShHHKEoRxyhKabQOHTpQWlraqidUbQ8UpTRaUlIS586d0/lDmpmiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjE7wIzckEAjQqVMnAFJSUjyepm1SlHJVkZGRxMXFATBjxoy6CC99KiQ5OZnjx49f9eelcRSlABAVFUVUVBR+v58vfOELBINBfD4fPt/FVzjvvfceBQUFVFdXc+7cOQB69erFpEmTvBy7TVKU7ZDP5yMqKooOHTpw3333YYwhGAwSGRlJVVUVCxcupKysjPLycoqLi70et91RlO1AIBDAGEPnzp254447iIyMpFOnTpw/f57FixdTW1tLUVERJSUlXo8qKMo2KSIigoiICGbMmEFsbCwZGRlERkaSn5/PypUrqaioICfnc2eLEEcoyjAXOjo9wWCQadOmERkZSd++fTHG8N5773HhwgXeeOMNKioqPJ5UGkpRhqkxY8aQmppKz5496dSpE+Xl5SxbtoyKigreeOMNampqvB5RGklROs4Yw7hx4+remrjllluIj4/nww8/JDc3l6ysLM6ePevxlNKcFKVj/H4/Y8aMISoqqu6/GzZs4Pz58wA888wzdW9JSNukKD3Wv39/kpKSGDhwIN27d6eqqopNmzZRUVHBL37xC70WbIcUZSsbMGAAsbGxjB07lpSUFPbv38+ZM2dYunSp9o4RQFG2KGMM/fv3JzIykjvvvJOEhAT27t1LWVkZr776KqdOnbr+jUi7oyhbyPjx45kyZQqHDx+msrKS5557jnPnzlFbW+v1aOI4RdlCPvjgAzZt2qRD+ssNU5QtpLa2VmtFaRR9yFnEMYpSGi0yMpLKykqvx2hzFKU02n333cfixYu9HqPNUZTSaFFRUdq5oQUoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKaTFTpkyhT58+Xo8RdhSltJikpKS6k91KwylKaTFZWVmMGzfO6zHCjqKUFpOfn09aWprXY4QdRSniGEUpLcZay6FDh+jVq5fXo4QVRSkt6syZMyQnJ3s9RlhRlCKOUZTSKCkpKZw9e5aampprXu/TTz9lwIABrTRV26AopVGSkpIoKiq6bpQHDx7UDgQ3SFGKOEZRSovLy8ujS5cuXo8RNhSltLjs7Gx69+7t9RhhQ1FKi7PW4vPpV62h9DclLW7jxo2MGTPG6zHCht/rASR8feELX6B///4AHD9+nBMnTnD48GH27t172fVKSkqIj4/3YsSwpCil0RYsWMDLL78MQPfu3cnIyGDYsGE8+uijbN68mdLSUrZs2UJJSQlnz56lY8eOFBYWejy1+4y11usZrskY4/aA7dSAAQMYMWJEXZT1GWMYPXo0MTExjBo1ivj4eCorK8nNzeW5557zYFo3WWvNlZYrSmmUa0V5JaNHj6Z79+4MHjyY8+fPs27dOg4fPkxBQUELT+ouRSnN6kajrC8+Pp5JkybRs2dPkpOT2bNnD/v27aOgoIDc3NwWmNZNV4tSryml1Z0/f5533nkHYy7+Tg4cOJCbbrqJ2267jZSUFJYsWUJJSQmHDx+moqLC42lbn6IUz1x6lrZ79252796NMYZAIMD06dOJi4tj1qxZnDlzhtWrV1NRUUFOTo7HE7cORSnOsNZSWVnJokWLAAgEAqSnpzNlyhQiIyPp1KkT69at49ixYxQWFnLhwgWPJ24ZilKcVVVVxZEjR/jjH/+Iz+cjKiqKyZMnM3PmTILBIJGRkWzbto2PP/6YgoICXN8+0lCKUsJCbW0tZWVlLFmyBIDo6GgiIyMZMWIEDz/8MH6/n9raWhYsWEBOTg61tbUeT9x4ilLCUnl5OeXl5axZs4a1a9fSoUMHAoEA9913HwsWLAjrnRQUpYQ9ay1FRUUAbWLnBO2QLuIYrSml0caOHYsxhvLyct555x2qqqque3gQuT5FKY12+PBhtmzZQjAY5Lvf/S4+n4+DBw9y4MABtm/fjrW2zWwRbU2KUhotLy+Pffv2AbBz5058Ph8DBgygX79+3HvvvRQWFnLo0CFOnTrFpk2bPJ42fGjfV2mUhuz72rFjR3r37k1KSgq33XYbe/bsobi4mB07dpCfn9+K07pJ+75KqyssLGTr1q0ALFmyhIEDB5KYmMgDDzxAQkICmzdvpry8nE2bNum1aD2KUlrNnj17ANiwYQPBYJBbb72V+Ph4vv/973PkyBEOHDjAyZMnOXTokMeTektPX6VRmvLRrSvp3bs3/fr1Iy0tjUAgwI4dOwD45JNPKC0tbZb7cI2evorTsrOzyc7OJhAI8K1vfYv09HQA7rjjDi5cuMDatWspLy/n4MGDHk/a8hSlOGX8+PFs27aNNWvWALB48WJSU1OZNGkS0dHRPPbYY2zcuJGjR4+Sn59ftydPW6IoxRmRkZEMHTqUZ599tm6ZtZb8/Hxee+01APx+P2PGjGHUqFEkJSURGRnJ4sWLqaio4Pjx421ig5GilEaJiYlp9td6CQkJWGs5f/78Va9TXV1NVlYWWVlZ+P1+YmJimDVrFlFRUXTo0IFnn32W4uLiZp2r1V3a68LVC2B1ce/y/e9/30ZFRTXb7Rlj7Ny5c223bt0afRvBYNCGNgyGxeVqv/NaU0qj+P1+qqurm+32kpKSiI6O5sSJE42+jbKysmabx0v6lIg44f7772fhwoXaVxa9ppQGioqKonPnzkybNg2gWc+iFR0dTXR0dFh/MLk5KUq5Kp/Ph9/vZ/r06fTr14/CwsK6w3E059mZhw8fzv79+ykpKWm22wxnilIuc+kwjzNmzCAtLY3U1FSWLFnC0qVLLzsGa3NteQ0Gg4wePZp58+Y1y+21BYpSMMYQHR3NXXfdRdeuXUlOTua9995j8+bNLX7E8sTERKqqqtrsrnSNoSjbsbi4OCZNmkRmZiYJCQmsWLGCzZs3k5eX12oz3HPPPbz99tutdn/hQFG2Q8OHD2fChAlER0ezdu1atmzZwqlTp1p9jrS0NPx+f5PeBmmLFGU7MWLECLp3787w4cPZtm0bL730EmfOnPF0pm7duoX9MVpbgqJso4wxDBs2jJ49ezJkyBC2b9/O4cOHWbhwodejARffBpkyZQq/+c1vvB7FOYqyDfH7/QwcOJABAwZw00038dFHH5Gdnc2bb77p9Wifk5GRQUFBAZWVlV6P4hxF2Qakp6fTpUsX7rzzTg4cOMCePXt47bXXnN47ZsKECaxfv97rMZykKMNUVFQUt9xyCzNmzCA/P5+TJ0/yy1/+MizWPOnp6QQCAQ4fPuz1KE5SlGEkGAySlpbG1KlT6dSpE/v27eMnP/kJVVVVTq8VP2vq1KmsXr1aG3iuQlGGgdTUVHr27Mn48eMpLi5m5cqV5ObmhsVa8bMCgQDdunVr9wfHuhZF6ahgMEhCQgKzZ8/G7/dz9OhR/vu//5vy8nKvR2uSIUOGsGvXLq0lr0FROsQYQ2pqKl/4wheIjIykpqaGt956i9zc3DbzSzx9+nR++tOfej2G0xSlI+Li4vjSl76E3+9nwYIFFBcXt7nTh3ft2pXTp0+H5dPu1qQoHVFaWsqCBQva1GnCP2vixIl6G6QBFKUjamtrPdn/tDWVlZXVHSVdrk6HA5FWs2jRIq9HCAuKUsQxjY7SGNPfGLOz3uWcMebbxpgfGGNy6i2fUe9n/tEYc9AYs88YM615HoJI29Lo15TW2n3AUABjTASQAywEvgz8ylr7i/rXN8bcDDwCDAS6AiuNMf2steF/SGuRZtRcT1/vALKttUevcZ1ZwCvW2gpr7WHgIDC6me5fpM1origfAebX+/PXjTG7jDF/MMZ0DC1LB47Xu86J0DIRqafJURpjIoH7gNdDi54CenPxqW0e8MtG3OZcY8xWY8zWps4n7oiJiSEhIcHrMZzXHGvK6cB2a20+gLU231pbY62tBX7P/z5FzQG61fu5jNCyz7HWzrPWjrTWjmyG+cQRt9xyCxMmTPB6DOc1R5SPUu+pqzGmS73vzQF2h75eDDxijIkyxvQE+gKbm+H+JUy0xJm62qImRWmMiQXuBBbUW/wzY8zHxphdwBTgOwDW2j3Aa8AnwFLga9ryGp4yMjIatZP85MmTWbduXQtN1XY0aTc7a+0FIOkzy/7iGtf/N+DfmnKf4r2kpCQKCwtveB9dn8/XZj7t0pK0R4+IYxSltIoePXpw9Oi13saWSxSltIrMzEyOHDni9RhhQVGKOEZRSqswxrTZD283N0UprWLMmDFs3LjR6zHCgqKUVhEbG6sdBxpIUYo4RlFKi+vUqZPnp90LJ4pSWtzIkSPZulUf+GkoRSniGEUpN6RXr17079/f6zHaNEUpDTZy5EgmTpxI9+7db+jnBgwYwN69e1toqrZHUcp1xcTE8NWvfpWuXbvywgsvsGLFihv6+eTkZE6fPt1C07U9OkK6XFPnzp2ZPn06ixYtIi8vT3vltAJFKVc1evRoRowYwcKFCzl58qTX47QbilI+Jzo6mqFDh5KUlMQzzzzTpA8mDxgwgH379jXjdG2fopTLjB07lrFjx5KUlMQ//dM/NflIAX379uXAgQPNNF37oA09Alw87fm4ceNISEjgueee4+TJkzocpEcUpRAfH8/cuXOJj49n+fLlFBYWUlRURHJystejtUt6+trOdejQgQceeID58+dz9uzZuuXZ2dn07t2bgwcPNun2jTE6WNYN0pqynTLGMGnSJH71q1+xePHiy4IE+PDDD7n11lubfB8DBw5k9+7d17+y1NGash1KTEzky1/+MgkJCbz66qst+sZ+MBikvLy8xW6/LVKU7czo0aMZOHAgBw8epKKiguXLl1/xetXV1Rw7doxevXpx6NChVp6yfVOU7cgdd9xBTEwMp0+fvmaQANZaKioqCAaDrTihgF5TtgudOnVi1qxZVFdXs3btWgYMGHDNIC85ffr0VbfAPvTQQ/z4xz/mkUceYeTIkURERHzuOklJSfpwcyNoTdnGJSUlcd9997Fr1y7279/PAw88wB//+McG/ezq1av5l3/5lyue/+O1117j9ddfZ+DAgfTu3ZuZM2eSnZ3N3r17KSgo4OjRozp3SCMpyjbKGMO0adPo378/f/zjH7HW8sADD/D22283eMOOtZaysjKio6OvurFmz5497Nmzh8WLF9OvXz9uuukmhgwZQrduF896eP78+WZ7TO2FcX2vf2OM2wM6yBjDXXfdxfnz59m4cSPWWr75zW/y5z//+Ya3tM6ZM4fDhw+zc+fOumXjx48nLi6OpUuXXvXnIiIiuOuuu+jatSsZGRmsWrWK48eP69QF9VhrzZWWa03ZxqSmpvL444/zwQcf8MEHHwAXj7l64MCBRr31UVpaSlxc3GXLpk6dyo9//ONr/lxNTQ1LlizBGENERAS33347U6dOpWvXrrz//vtkZ2dz/PjxG56nXbDWOn0BrC7Xv/h8PturVy/75S9/2SYkJNQtHzNmjJ0+fboNPeO44UtERIT9wQ9+cNmyH/7wh426PWOMjYqKslOnTrVz58613/zmN21GRkajZ4uPj2/0z7pwudrvvNaUbYDP52P69Omkpqby2muvceHCBeDie5LJycm8++67jf5wcm1tLTU1NQQCAaqqqpo056W3WVauXIkxhuTkZO6//37S0tIoLi5m4cKFnD17tkGvQzt27MjDDz/Ms88+S3V1dZPmco2iDHOxsbFMnjyZ/Px83n333brlMTExDBs2jNdff71JRwuw1vLRRx8xZMiQZj1MpLWWM2fOcOTIEfbt28fHH3/MnDlziImJwRjDW2+9xenTpykpKfncz/p8PsaNG8fWrVvbXJCgKMNaly5deOihh9i4ceNlwcTExPDwww/z1ltvfW6f1saoqKggOjq6ybfzWXfddRfnzp2re+37+9//nqioKOLj45k1axbx8fFUV1ezZMkSjhw5Qk1NDXDxM5qJiYm88847zT6TE7x+zajXlDd+8fl8dubMmfbLX/6yDQaDl30vKirKzp0716alpTXb/UVFRdnvf//7dX9u7GvK+pekpCT79a9/3QYCgWveb+fOne2Xv/xl+7Wvfc0+8MADNj093X71q1+1sbGxnv9/aOpFrynbCGMM9957L/n5+axevZqysrLLvj9kyBDy8vLIz89vtvusqKggEAg02+nsjDH85V/+Jc8999w1X6dWVFRw8uRJ/vjHP5KSkkJ6ejpPPfUUO3fu5KGHHqKsrIzFixdTXV1NTU1N3Zo03CnKMJKens706dPZvn0727dv/9z3R44cSWpq6mWvLZvLli1bGDVqFJs3b27ybd1xxx1s2bKFoqKiBv9MQUEBaWlpvPTSSyxcuJBu3boRExPDt771LW655RY+/vhjTpw4wYEDB9iyZUtYB6oow0R6ejp33HEHy5cv59ixY5/7/ujRo0lNTeW9995rkQ8Vl5WVNcvO6QMGDCAlJYX58+ff0M8lJSUxatQo3njjDaqqquo+ubJ79266dOnC//k//4du3boxZcoUHnroIXbs2EF5eTlr1qzhzJkzYXVoTO3REwYuBfn6669/7ukqXPzM4t/+7d/y1FNPtdhnF+Pi4vi7v/s7fvazn/HrX/+aNWvWUFtbS3l5OatXr27Qmqljx47Mnj2bhQsX3tBaEmD27NkcP36cbdu2XfU6ycnJZGRkcOeddxIMBsnPzyc6OprIyEj27t3Lli1bnDpUpvboCUPGGO6//36SkpJ48cUXrxhkTEwMDz30EK+88kqLfpi4pKSE+Ph4AKqqqjh58iQ1NTXExMTw/e9/H2st27Zto6KigqysrCvOMm7cOD755JMbDnLEiBEA1wwSLn6q5fTp0+zcuZNBgwaRmZnJ6NGjOXr0KD179iQjI4MuXbpw4MABjh07RlZWlpNPc7WmdJTP52PWrFkcOnSIjz766IrXiY2N5cEHH2TZsmXk5eW1+EwTJ07kzjvvpH///jz88MOXPSX0+/2MHDmSqKgoxo8fT1RUFBs3bqS0tJTt27czcuRIANasWXND93npY2cLFiyguLi4UXMPGjSIHj16cOutt7Jnzx4iIyPJz89n7NixHD16lMOHD3PixIkmH4/oRl1tTakoHZSRkcH48eP55JNP2LVr11WvN2nSJCIjI2/43B5N9cMf/pAf/OAH132dNmbMGGJjY7n99tsZPHgw8+bN4+jRo9d8TJ/14IMPsm/fvhv6mWsZOHBg3Rp079695OTkEAgEyMjIoFevXrz//vscO3aM/fv3N8v9XYuiDBOZmZlMmTKFjRs3XvNMVSNHjqRr164sXry4Fae7qKFRXvKd73yH559/nr59+9K9e3cGDx7MhQsXWLVqFceOHePUqVNX/LkxY8aQlJTUIluT4eJGp4kTJ9K5c2dWrVpFTk4OPXr0oFu3bvTr14+CggKWL1/eYkd4V5Rhok+fPhw9evSa79+NGDGC9PR0T4KEG4ty/PjxBIPBy9bmxhhiY2O544476NatGykpKXzyySd8/PHH7N+/n+rqahITE5kzZw5vvfUWhYWFLflwCAQCTJ06lfT0dDIyMlixYgU5OTmUlpbSr18/srKyWuR+taEnTFzvdU0wGOTOO+/kJz/5SStN1HgJCQncdttt/PznP79subWWkpISFi1ahDEGYww333wzQ4YMYdasWfj9fqy1bNy4scWDhIsbri59zMzn83HXXXcxYMCAus+BxsTEUFpa2uJzXKI1ZRgJBoM89NBDrFy5kpycHM/maMia0ufz8bWvfY358+ff0Oc4/X4/Y8eOZeDAgVhrSU5OZvny5Zw6dYr8/PwrboFuCZc+B3rHHXdwyy23UFZWxrvvvnvVneQbQ2vKMBcdHe1EkA01evRoDh48eMMfrA4Gg/Tv35/58+dz7tw5AoEA06ZNY/DgwXTo0IELFy7w9ttvU11d3ay7En6WtZbq6mqWLVvG6tWrSUpKYubMmSQkJFBTU1O3s39zBVqfogwT06ZNY/fu3WERZHx8POPHj+eXv/zlDf/s/fffz+rVq+vey6yoqKh77RwVFUVycjL33nsvERERxMTEsHXrVnbv3k1paWnd50ib26X3ZZ977jmioqJISEhgzpw5BINB9u7dy7Zt2zh79myz7UmlKMPAkCFD8Pl8lx0nx1V+v5/HHnuMP/3pTze8a9uYMWPIz8/n8OHDV/x+RUUFOTk5PP300xhjSEhIYPjw4fz2t79l165dlJSUsHDhQkpKSjh37lyL7G5YUVFBQUEB8+bNIzo6mpEjR/LYY49hjCE7O5v333+f8+fPN2mnBEXpuMGDB9OrVy8WL17s5N4nnzV8+HBycnJu+KllbGwsw4YN4+WXX25QTNZaiouLWbNmDYMHD2bhwoWUl5czZ84c4uLiqKys5Pjx46xcuZKampoWeS1aXl5OVlYWWVlZdOzYkX79+vGVr3yFyspKTpw4wdKlSxt1v4rSYcFgkOnTp/OLX/wiLIIcPXo0KSkpjXpf8Ytf/CLvvvsu586da9R9l5WVUVBQwDPPPANc3A82MzOTr33ta1RXV3P+/HnWrl3L8ePHqaysbPa/z8LCQj788EM+/PBDkpOT6dGjB3/3d3/H+fPnWb16NcePH6eioqJBt6UoHeX3+3nwwQd55ZVXwiLIYDDIrbfeygsvvHDDTxtvvfVWTpw4wYkTJ274fi8dEeGza6RL+8Fu3bqVqKgo0tLSmDx5Mg8++CBnz57lzJkzdRuMmnrsoc+6dN+7d+8mLS2NKVOm0LlzZ/bv38+iRYuu+/ejKB1VU1PD22+/3Srv0zWHxx57jMWLF9/wmi4mJoYxY8bw+9//vlGvAS8d/vJaW0ErKio4duwYL774InDxMJydO3fm29/+NhERERw5coS9e/eyY8eOZv0H8NL9vvDCC0RGRtK5c+cGPUZF6ShrbdgEOXz4cE6dOtWoNd1f/MVf8Oabb7bqm/OnTp3i1KlT7Nq1C5/PR9++fbnpppuYOXMm+/fvp7y8nLVr11JUVNRsn8OsrKy84udgr0RRSpNER0czadIknn766Rtey4wcOZLc3Fxyc3NbaLrrq62tZd++fezbt49FixbRv39/oqOj+cu//Et8Ph/79u2jqKioxXa1uxJFKU1yaU13o1sZg8EgkydP5re//a0zp1+31tZ9CGDnzp0kJCRw8803k5iYyI9+9CM+/fRTCgoK2Lt3b6OeFTSUopRG69ixIwUFBY36BX3iiSd45ZVXqKysbIHJmse5c+fYtGkTAEuXLmXAgAGkpqYyc+ZMUlJS2LBhAxUVFWzZsqVZNxYpSmm0wsJC3nrrrRv+ueHDh3Py5Mlm2TspOTn5qh/9am579+5l7969rF+/nsjISMaNG0dCQgLf/e53eeqppxr9IezP8fq4rjrua/hdmnLc12AwaL/3ve9Zn8/XLLM8+eSTNjEx0fO/k8ZcrvY7rzM5yw3p06cPhw4davRWyccff7zBe+20V4pSbkinTp0afSqEwYMHc+bMmRbdSNIWKEppFVFRUcycOZMFCxZ4PYrzFKW0ii996Uv8+c9/1tPWBlCU0uJuvvlmSktLm/3U6vHx8VRVVbXa0Qhai6KUFhUIBJg1axavvfZas992fHw8lZWVDf70RbjQ+5Ryw2bMmEFGRkbdwa/Kysqu+ub5F7/4RV577bWw+KSLKxSl3LAPP/yQ5cuXEx8fz9/+7d9SVlZGcXExb7311mWfEunXrx+1tbVkZ2d7OG34UZRyw86cOVO3E/nPfvYzOnbsSEpKCnPnzuXMmTPs27eP7du3c//99zfqOD0N1adPnzYZvF5TSpMVFhayf/9+/vM//5N169bRoUMHvve971FeXs6YMWPw+Vrm12zs2LF1p2ZvS7SmlGZTW1vLoUOHOHToEEuWLKFPnz706tWLf/3Xf+XAgQMcP36c9evXh9W5Ir2gKKXFHDx4kIMHD7J8+XL69etH9+7d+eEPf1gX6Lp16xToFShKaRX79+9n//79rFy5kn79+tGtWzd+8IMfcOjQIQ4fPswHH3xAdXV1g28vOjqaqqqqG/qZcKEopdVdCnTVqlX06dOHnj178n//7/8lJyeHtWvXNmjjTVpaGsXFxS16olyvKErx1KWnuCtWrKBHjx5MnjyZxx9/vO5wkK19IlcXKEpxxtGjR3nhhRcwxjB58mRuu+02Hn/8cdatW8fRo0fbTaCKUpxjra07DfulQMeOHcuXvvQlNmzYwKFDh4iLi2u+T/o7RlGK0+oHGhERwbhx4xg/fjxf+tKX+N3vfkdmZibHjh1rU58+UZQSNmpqali/fj3r168nMzOTc+fOcccdd5Camsonn3zCtm3byMvLC/v9bBWlhKWamho2btzImjVriI6OZtCgQXXnjzxy5Ajvv/8+p0+fDsu3TBSlhB2fz0dNTU3djgfl5eVs3bqVrVu3EhsbS+/evXnggQcIBAKcPHmS5cuXc+7cuWY/Z0hLUZQSdnr27MnJkyev+DnKCxcusGvXLnbt2kV8fDzdunXj8ccfp7a2lrNnz7J48WJKS0udDlRRStgxxjRow8758+f55JNP+OSTT0hMTCQtLY25c+dSXl5e91Gza30W1CuKUtqFoqIiioqK+PnPf05iYiLJycn8zd/8DRUVFRQVFbF48WJKSkqceA2qKCXsRERENOktkPqBXvos6F/91V9RXV3Nli1b2LBhQzNOe+MUpYSdGTNm1J1rsqkKCwspLCzkF7/4BR06dCAmJqZZbrcpFKWEnYSEhEafhv1aiouLndhLSEcekLCzf//+NrUHz2dpTSlhZ/78+V6P0KK0phRxjKKUG5KYmEhhYaHXY7RpilIaLDY2lmHDhpGVleX1KG2aopQGGzZsGDt37vR6jDZPG3rkquLi4khNTaVr165MmTKFHj16tMg5QeRyilLqJCQkkJCQwE033cTIkSMpLy+nqKiI3NxcfvrTnzJu3DgCgYDXY7Z5irIdS0hIIDo6mjFjxjBgwAAqKiqoqKjg008/5Te/+Q3V1dVUVlbWXb+qqkpRtgJF2U4EAgHi4+NJTU1l+vTpwMUjmtfW1rJx40bWrl1LeXn5ZRGKNxRlGxUIBAgGg/Ts2ZOJEydijCEiIoJTp07xwgsvAFBSUqIIHaQo2wBjDNHR0URHRzN79mwiIyOJiooiOjqaw4cP89prr9V9REncpyjDkDGGQCBAYmIiM2fOxO/3k5ycTHl5OYsWLaKyspKSkhJFGKYUZRiIiIgAoEuXLkyaNIno6Gi6du1KcXEx7777LlVVVRw/flwny2kjFKWDjDH4/X6mTZtGMBikT58+REVFkZeXx7p16ygrK+Po0aNejyktRFE6IhgMcvvttxMZGcktt9wCwLJly7hw4QKLFy++4kGipG1SlB4ZOXIkSUlJDBgwgKSkJMrKylizZg3l5eUsXrw47A8oLI2nKFvJ6NGjiYmJYdSoUSQkJLB161ZOnz7NSy+9xJkzZ7weTxyiKFuA3+9n+PDhREZGMmHCBILBIJs3b+bChQs888wzLXIoC2k7FGUzyMzMJCkpiWAwyB133IG1lh07dlBZWcmvfvWrsD+x6YABA4iNjWXAgAEcO3bM63HaPEXZCL179yYYDDJhwgQ6d+7MkSNHKCwspKysjH/7t39z4tihjeXz+ejXrx9RUVHcddddxMXFsXfvXsrKykhJSVGUrUBRXocxhh49ehAVFcX06dPp0KEDR48epaysjEWLFnHy5MmwP4hTamoqnTt3rtsR4dChQ1RWVvLMM89QUlJS9/hOnz7txCEY2zpF+RkRERFkZGQQFRXFrFmziIyMJDc3l6qqKv70pz9RXFx82cllwlFaWhpdunRh5syZREZGUlpayqFDh/j1r39NVVVVWK/p24J2H2UgECA1NZX4+Hhmz56NtZYzZ85QUVHBM888U/dxpnAXFRVFcnIyKSkp3HbbbZw8eZJf/vKX9OjRg/Hjx/P66697PaKEtLsoo6Ki6NChA126dOGuu+6itraW0tJSioqK+N3vfkdNTQ1lZWVej9lkwWCQ+Pj4uk+JVFdXU15ezsmTJ5k3b17d+6Bt4R+ctqbNRxkVFUVMTAx9+/Zl3LhxWGupra0lLy+PefPmUV1dzYULF7wes8mCwSDR0dEMHTqUoUOH1j3Ow4cPM2/ePKqqqigtLfV6TGmANhelMYbY2Fiio6P5whe+QDAYxO/3c+DAAV588UUqKys5f/6812M22aXHNX78eHr37o3f7yciIoKdO3fy4osvUl5e3ib+sWmPwj5Kn89HZGQkHTt25J577iEiIoIOHTpQUVHBggULKCwsbBMRRkVF4fP5mDZtGqmpqcTGxhIZGcn777/PG2+8wblz57QmbCPCLkqfz4ff76dLly5MmTKFyMhIUlJSKCoq4p133qGqqorc3Fyvx2yyQCBAREQE99xzT91R5QKBAEuXLmX79u2cOnUq7HdKkCsLiyiNMfTr14/hw4cTHx9P586dycvLq9uB+8SJE16P2GTGGGJiYpgxYwaRkZFkZmYC8O6771JSUsKxY8da5NAdFy5cIDo6moiICO0E74iwiPK2226jV69ebN++neLiYnJycrweqcmMMQDceuutpKenk56eTkxMDO+99x4VFRW88sorrRJJQUEBCQkJREVF6emvI8Iiyg8++IAPPvjA6zGaxfjx4+nYsSP9+vWjQ4cOfPjhh2RnZ7N27Vp9WkSAMIkyXPl8PsaPH08wGGT48OEEg0GysrLIz89n3bp1+rSIXJGibEZRUVGMGjWK6Ohoxo4di8/nY8OGDVy4cIFf//rXYblTQnJyMgMHDgSgR48efPLJJx5P1PY1KEpjzB+Ae4BT1tpBoWWdgFeBTOAI8JC1ttBcfLH0G2AGUAr8pbV2e+hnngC+H7rZ/2etfaH5Hkrri42NZdCgQcTFxTFx4kSqqqrYsmUL5eXl/Pu//3vY7UMaERHB0KFDiYqKYtKkScTExFBQUFAX4qhRo3jxxRc9nrLtMw3ZsdoYMxEoAV6sF+XPgLPW2p8YY/4B6Git/Z4xZgbwDS5GeSvwG2vtraGItwIjAQtsA0ZYa695skNjjDN7ficmJpKZmUlaWhrjxo2jpKSEPXv2UFJSwvr168NqJ/Xo6GgGDBgAwNe//nUKCgooKyvjo48+oqKiou4AXfX9+Mc/5p//+Z+9GLdNstaaKy1v0JrSWrveGJP5mcWzgMmhr18A1gLfCy1/0V78Dd1kjEk0xnQJXXeFtfYsgDFmBXA34Oy5sn0+H3379qVfv36MHDmSoqIijhw5QkFBAf/6r/8aNhHGx8fTvXt3ACZPnkxqaiplZWXs27ev7jr/7//9P0pLS8PmMbVlTXlNmWatzQt9fRJIC32dDhyvd70ToWVXW+4Mv99PZmYm0dHR3HvvvURERHDkyBH279/Pj370o7r9SV3m8/nIzMzE5/PRu3dvbr31Vs6fP8/JkycBePPNNykoKLjssQwYMABrrYJ0RLNs6LHW2uZ8mmmMmQvMba7bu5bU1FSCwSDjx4+nW7du5OXlUV5ezm9+8xuqqqqoqqpqjTEaJRAI0LVrV+Di670BAwZQU1NT98Hr7Oxs/uM//oPa2lrtGBBGmhJlvjGmi7U2L/T09FRoeQ7Qrd71MkLLcvjfp7uXlq+90g1ba+cB86D5X1NGR0fTqVMnpk+fTkpKCsXFxVRVVZGVlcUbb7zh9EeZkpOT63YrnD59OjU1NRQVFWGtZcuWLbzzzjtYa51+DHJ9TYlyMfAE8JPQfxfVW/51Y8wrXNzQUxwKdxnw78aYjqHr3QX8YxPuv8E6depEv379GD9+PDU1NZSXl7NkyRJOnz5NWVmZc2sRYwwdO3bE5/PRp08fxo8fD0B5eTk1NTWcPn26TX32Uy7X0LdE5nNxLZdsjDkB/CsXY3zNGPMV4CjwUOjq73Fxy+tBLr4l8mUAa+1ZY8yPgS2h6/3o0kaf5mSMqTsZ6he+8AUiIyMB2L9/P88++yyVlZXO7U4WExNDZGQkwWCQL3zhC/j9//u/5eDBgzz77LMAnD9/vkX+Adm5cyeDBw9m06ZNzX7bcuMauvX10at8644rXNcCX7vK7fwB+EODp2ugSztUT5gwoe5Ic5c+ulVRUcHZs2ed2UATERFBdHR03VNQ+N/5y8rKWLhwYd3MrbXhZdeuXdx3332K0hFhu0dPZGRk3evCDh06EAgEeP/993n99dc5derU9W+glURERBAIBOjRowcTJkwgEAjQoUMHTp8+zaJFF5/xXzo8pQiEcZSPPfYYH3/8MTt37uTUqVPO/FL7fD4iIiIYOHAgQ4YMISYmhqSkJI4cOcKKFSsoKytz6h8NcU/YRvnHP/7R6xGA/z135KhRo+jZsycpKSkkJiayZ88eNmzYwLlz5xSh3JCwjdIrxhhSUlIYN25c3QG5jDFs3ryZXbt2cfLkSX0ES5pEUTZA9+7dueWWW+jUqRN9+/bl1KlTZGVlUVZWxssvv+z1eNLGKMor6NOnD5mZmXTr1o1evXpx7NgxPv74Yw4cOMCf/vQnr8eTNq7dRxkXF8fQoUMBGDRoEOnp6ezfv59jx46xZs0aZ167SvvR7qLs2LEjffv2JSkpidtuu42SkhJ27twJwOLFi9vEkfBuVGVlZd1WY9f2bmqP2nSUERER3HTTTURGRnLnnXcSGxtLYWEhBw8epKCggH/5l3/xekQnFBQUEBMTQ1xcHMXFxV6P0+61qSgjIyPp1asXsbGxTJ8+HWMM+/fvp7Kykt/97nd1u9fpI0risrCOMj09nWAwyOTJk+natStVVVUcOXKE8vJyfv7zn1NdXa2nYxJ2wibKuLg4OnXqBMDMmTPp1KkTBQUFVFZWsnLlSnJzc7HWOv35R5GGcD7KhIQEvvrVr1JRUVF3uIpFixZRWFhIRUWFMzuaizQX56Osra3lf/7nf6isrNSHd6VdcD7KkpISr0cQaVU+rwcQkcspSgEuvkzw+fTr4AL9XxAAFi5cyOzZs70eQ1CUElJSUkJ8fLzXYwiKUsQ5ilLEMYpSxDGKUoCLR9RbsGDBVb/foUMHioqKWm+gdkxRCgBVVVWcOHHiqt8fNWoUW7dubcWJ2i9FKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGOfPuiXuePTRRxk9ejSFhYW8/fbbFBcXU15e7vVYbY6x1no9wzUZY9wesJ2YOnUqgUCADz74gI4dO3LvvfcSERFBaWkpb775JmfOnPF6xLBjrTVXWq4opUGmTp1KVVUV69atq1t26ZT3s2fPpra2ll27drF9+3YuXLiA679XLlCU0iRXivISn89HcnIyQ4cOZejQoZw7d46DBw+yYcMGysvLFehVKEppkmtFWZ8xhi5dutC3b1/Gjh3L6dOneeeddygoKKC6urqVpg0PV4tSG3qkWVlryc3NJTc3l3Xr1tGtWzdmz55NXFwcJ06cICsri5ycHGpra70e1VlaU0qDNHRNeTUxMTF0796diRMn0qlTJw4ePMj27ds5dOhQM08aPvT0VZqkqVHWFx0dTf/+/Rk+fDgREREcP36c1atXU1VV1QyThg9FKU3SnFHW1717d7p3786UKVM4deoU2dnZbNiwgbKysma9HxcpSmmSJ598kt///vcUFxe32H2kp6fTu3dvxo0bx/nz51m7di27d+9usfvzmjb0SJN06NChRYMEyMnJIScnh/Xr15OWlsbkyZN5+OGH+eCDDzh+/HibDrQ+7fsqTsrPz+fVV1/ln//5nyksLGTgwIF85zvfYfjw4XTq1Mnr8VqU1pTivE2bNrFp0yY6duzIpEmTmDhxItnZ2ezfv58DBw60ubdXFKWEjcLCQt566y2MMYwaNYpRo0Yxa9Ys3n33XfLz8zl9+rTXIzYLRSlhx1rL5s2b2bx5M3Fxcdx7770kJSURHx/PokWL2L9/f1jvPaQo5Zqio6Pp1KkTgUDA61GuqKSkhPnz5xMREUF0dDT33XcfM2fO5MSJEyxbtoyzZ896PeINU5RyVdHR0Tz66KN1YbqspqaGCxcuMH/+fILBID169OCxxx4jKiqKpUuXsm/fvrDZOUFRyhVdCnLZsmXk5uby4x//2OuRGqysrIy9e/dy8OBBYmNjufvuu5k6dSqFhYUsXLiQc+fOeT3iNSlK+ZyEhATuv/9+li9fTm5urtfjNFp1dTXFxcW8+uqrJCQkkJqayle+8hVOnz7Nn//8Z2pqarwe8YoUpVxm5MiRTJo0ifnz54d1kJ917tw5zp07x29/+1s6derkbJCgKKWekSNH0rlzZ3796187/UvbFDU1NRQUFHg9xjUpSgH+N8j33nuvzb0ZH24UZTsXGxvLjBkzKCsrU5COUJTtWGxsLA899BDbtm1j9+7dCtIRirIdmzhxIu+++y6nTp3yehSpR1G2Y0uWLPF6BLkCfXRLxDFaU8oVGWPo378/0dHRwMUDX0nrUJQCQFpaGklJSfTv358hQ4ZgrWX//v1UVlYC1MUpLU9RtkNdu3YlJiaGhIQEZsyYgc/no6CggKKiIj799FMWL14McNkOBEOHDvVo2vZHUbYDXbp0IRAIcPfdd5OSksLp06cpLy/n3Llz/PznP6e2tpaamhq9JeIIRdnGxMXFERcXR0RERN2Ryc+dO0dNTQ1Lly6loKCAiooKBegwRRnm4uLiiIqKYvTo0dx8881UV1dTXV1NTU0Nb775JiUlJZSWlirCMKIow0xsbCx+v5+pU6eSkZGBMRcPHbpp0yaee+45ysvLdSLXMKcoHeb3+4mKiiIzM5OJEycCF7eC+v1+VqxYwfr16zl37hwVFRUeTyrNSVE6xO/3ExERwS233MLQoUOJjo4mPj6ew4cPs2jRIgBOnz5d9zaFtE2K0gHx8fHcd999JCcnEx8fz8cff8zKlSs5f/68TlveDilKB5SXl/Phhx9y+vRpioqKvB5HPKYoHVBVVcXBgwe9HkMcoR3S5bpSU1P18a5WpCjlukaNGsWWLVu8HqPdUJQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijtHnKaVBJk6cSFpaGgAffvghJ0+e9HiitktRSoMUFBSQk5MDwJw5c+jSpQsHDx6koKCATz/9lCNHjng7YBtirLVez3BNxhi3B2wHZs6cyZkzZ9i0adNly/v06UNaWhoDBgwgMzOTI0eOcOjQIXJzc9m3b59H04YPa6250nJFKdd1tSg/q0ePHvTq1Yv09HT69+9Pbm4uu3btorCwkE8++aSVpg0fV4tST1+l2Rw9epSjR4/W/blz584MGzaMbt268cgjj1BYWMj7779PWVkZe/bs8XBStylKaTEnT5687GzRnTp1YsKECURHR/Pggw9SVlbGsmXLqKysZN++fZed5as9U5TSas6ePVt3UGmfz0dMTAx33nkn0dHR3H///VhreeeddygrK+Pw4cPt9qDTilI8UVtbS0lJCQsXLgQuHh3e7/czc+ZMYmJimDNnDn6/n6VLl3L69GlOnjzZbs6RoijFCZfOFvbmm28CEAgE8Pl83H333dxyyy106tSJyMhI1q1bx7FjxygsLOTChQseT90yFKU4qaqqCqDu6W5UVBQRERFMmjSJe+65h+joaCIjI9m2bRsff/wxpaWllJSUeDlys1GUEhYunVns0oajS1GOGDGCRx55BGMMPp+PAwcOsGHDhroT5YYjvU8p19XQ9ym9FB0dTUxMDP369eO2227jT3/6E6dPn/Z6rGvS+5TSpl06We6mTZuc/sejIfQpEbkmn8+H369/u1uTopTPMcYQCAS47777+Ku/+iseeOABr0dqV/RPoNQxxtC/f38mTZpEWloaS5cuZdu2bXWfDpHWoSiFQYMG0bdvX2655Rays7N5++23yc3Nrfv+lClTdP7MVqQo26m4uDjGjh1Lv379yM/P58CBAyxevPhzbyMYY+jXrx8vvfSSR5O2P4qynRkyZAhTpkwhMjKSDz74gNdff538/Hyvx5J6FGU70KlTJ8aPH8+oUaPYuXMnL774ImfPnvV6LLkKRdlGpaSk0KNHD26//XYqKyvJysrin//5n70eSxpAUbYhERER9O3bl4EDB9KlSxeOHz/OU089xfnz570eTW7AdaM0xvwBuAc4Za0dFFr2c+BeoBLIBr5srS0yxmQCnwKXDtCyyVr7t6GfGQE8DwSB94BvWdf38QsTaWlp3H777fTu3Zvs7Gw+/fRTFixYgP56w5S19poXYCIwHNhdb9ldgD/09U+Bn4a+zqx/vc/czmZgDGCAJcD069136OesLle/zJ492371q1+1N998s/X7/c1++8YY+6Mf/cjzx9kWL1f7nb/umtJauz60Bqy/bHm9P24CrrnLhzGmC5Bgrd0U+vOLwGwuxilN8O6771JbWxu2n4iQz2uO3ez+Py6Pq6cxZocxZp0xZkJoWTpwot51ToSWSRNVVVUpyDamSRt6jDH/BFQDL4cW5QHdrbVnQq8h3zLGDGzE7c4F5jZlNpFw1eg1pTHmL7m4AeixSxtsrLUV1tozoa+3cXEjUD8gB8io9+MZoWVXZK2dZ60daa0d2dj5pPlkZmbyta99jQceeICIiAivx2nzGhWlMeZu4P8H3GetLa23PMUYExH6uhfQFzhkrc0DzhljxhhjDPA4sKjJ00uryMvLY8GCBRw8eJD777+fqKgor0dq064bpTFmPrAR6G+MOWGM+QrwOyAeWGGM2WmMeTp09YnALmPMTuAN4G+ttZd2Hfk74FngIBfXoNrIEyYqKirIy8tj586d7N69m29961v06dMHn0+f/GsRDXlbwssLDmy6bs+XK70lEhkZaZ944gn72GOP2UAg4PmM4Xq52u+8/qmTG1ZZWckLL7zAli1bePLJJ7n33nsJBAJej9VmKEpptP379/OLX/yC/Px8nnzySQYOvOEN7XIF2vdVmqSqqorNmzezbds2HnzwQcaPH8+6devYu3ev16OFLUUpzaKmpoZXXnmF9PR0JkyYQI8ePXj//fcpLS29/g/LZfT0VZpVTk4Or776KhcuXOCb3/wmd911F7GxsV6PFVYUpTQ7ay1ZWVn85Cc/obS0lAcffJA+ffp4PVbYUJTSorKysliyZAljx47lm9/8JvHx8V6P5Dy9ppQWl5+fz4svvkhKSgrx8fH60PV1KEppNQUFBV6PEBb09FXEMYpSrqtnz55897vfpV+/fl6P0i4oSrmuEydO8NxzzzF8+HAeffRR4uLivB6pTVOUcl1VVVUUFRXx5ptvsmPHDv76r/+am2++2eux2ixFKQ1WVVXF3r17+a//+i8GDhzII488oh3RW4CilBtWXV3NW2+9xe7du3n00UeJjo72eqQ2RVHKNRljrnj82KqqKnbv3s369ev59re/zaBBgzyYrm0yrh+w1xjj9oBt3B133EF1dTXr1q276nX8fj/33nsvBw4cYPfu3a04XXiz1porLdeaUq4pISGB4uLia16nurqaxYsXc9NNNzF37lw9nW0iRSnNoqamhtdff5333nuPRx55RFtnm0BRSrM6ceIEy5Yt45ZbbmHEiBFejxOWFKU0u7y8PF599VWKioq8HiUsKUppMdnZ2V6PEJYUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGN0gh+5TEpKCl26dKFnz54MHz6cAQMG8B//8R9ej9WuKMp2LhAI1AXYv39/zpw5Q35+PocPH+btt99mzpw5Xo/Y7ijKdiY6OpquXbuSmJjIjBkzqKmp4cSJE2zfvp033niD2tpaamtr665f/2tpHYqyHUhLSyMQCHD33XeTmJjImTNnKCoq4uc//znWWiorK70eUepRlG2MMYbk5GT8fj+zZ8+uO3OytZYlS5Zw8uRJKioqvB5TrkFRhjmfz0eHDh2IjY1lzpw5+P1+qqurqa6uZtGiRZw/f54LFy7oaWgYUZRhKCYmhtTUVO69916MMURERFBWVsarr75ad9o6109HIVenKMNAdHQ0Pp+PGTNmkJSURExMDOfOneO1116jpqaG06dPez2iNCNF6aDIyEj8fj/33XcfwWCQlJQUIiIiWLJkCZs2baKgoECvC9swRemIhIQEZsyYQSAQoHv37tTW1vL2229TWlrK8ePHqaqqavWZjDF06tSJ7du3t/p9t2eK0gGdOnXiiSeeYPny5VRUVPDyyy87sWHG5/PRvXt3jh496vUo7YqidMDZs2f51a9+5fUY4gjtkC7iGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4pjrRmmM+YMx5pQxZne9ZT8wxuQYY3aGLjPqfe8fjTEHjTH7jDHT6i2/O7TsoDHmH5r/oYi0DQ1ZUz4P3H2F5b+y1g4NXd4DMMbcDDwCDAz9zP8YYyKMMRHAfwPTgZuBR0PXFZHP8F/vCtba9caYzAbe3izgFWttBXDYGHMQGB363kFr7SEAY8wroet+cuMji7RtTXlN+XVjzK7Q09uOoWXpwPF61zkRWna15SLyGY2N8imgNzAUyAN+2VwDARhj5hpjthpjtjbn7YqEg+s+fb0Sa23+pa+NMb8H3gn9MQfoVu+qGaFlXGP5lW5/HjAvdPu2MTOKhKtGrSmNMV3q/XEOcGnL7GLgEWNMlDGmJ9AX2AxsAfoaY3oaYyK5uDFocePHFmm7rrumNMbMByYDycaYE8C/ApONMUMBCxwB/gbAWrvHGPMaFzfgVANfs9bWhG7n68AyIAL4g7V2T3M/GJG2oCFbXx+9wuLnrnH9fwP+7QrL3wPeu6HpRNoh7dEj4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSlXNXDgQPbs0Y5XrU1RylUpSm8oShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRxyhKEccoShHHKEq5qoiICKqrq70eo91RlHJFgUCAzMxMDhw44PUo7Y6ilKvy+XzU1tZ6PUa7oyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyjliqy1FBUVeT1Gu2SstV7PcE3GGLcHbMMiIiKoqanxeow2y1prrrRca0q5KgXpDUUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4pjrRmmM+YMx5pQxZne9Za8aY3aGLkeMMTtDyzONMWX1vvd0vZ8ZYYz52Bhz0BjzW2OMaZFHJBLurLXXvAATgeHA7qt8/5fAv4S+zrzG9TYDYwADLAGmX+++Qz9nddGlLV6u9jt/3TWltXY9cPZK3wut7R4C5l/rNowxXYAEa+0me7G0F4HZ17tvkfaoqa8pJwD51toD9Zb1NMbsMMasM8ZMCC1LB07Uu86J0DIR+Qx/E3/+US5fS+YB3a21Z4wxI4C3jDEDb/RGjTFzgblNnE0kLDU6SmOMH/gCMOLSMmttBVAR+nqbMSYb6AfkABn1fjwjtOyKrLXzgHmh+7GNnVEkHDXl6etUYK+1tu5pqTEmxRgTEfq6F9AXOGStzQPOGWPGhF6HPg4sasJ9i7RZDXlLZD6wEehvjDlhjPlK6FuP8PkNPBOBXaG3SN4A/tZae2kj0d8BzwIHgWwuboEVkc8wobcdnKWnr9JWWWuv+F699ugRcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxjKIUcYyiFHGMohRxzHWjNMZ0M8asMcZ8YozZY4z5Vmh5J2PMCmPMgdB/O4aWG2PMb40xB40xu4wxw+vd1hOh6x8wxjzRcg9LJIxZa695AboAw0NfxwP7gZuBnwH/EFr+D8BPQ1/PAJYABhgDfBha3gk4FPpvx9DXHRtw/1YXXdri5Wq/89ddU1pr86y120Nfnwc+BdKBWcALoau9AMwOfT0LeNFetAlINMZ0AaYBK6y1Z621hcAK4O7r3b9Ie3NDrymNMZnAMOBDIM1amxf61kkgLfR1OnC83o+dCC272nIRqcff0CsaY+KAN4FvW2vPGWPqvmettcYY21xDGWPmAnOb6/ZEwkmD1pTGmAAXg3zZWrsgtDg/9LSU0H9PhZbnAN3q/XhGaNnVln+OtXaetXaktXZkQx+ISFvRkK2vBngO+NRa+5/1vrUYeCL09RPAonrLHw9thR0DFIee5i4D7jLGdAxtqb0rtExE6mvA1s/xXNxatAvYGbrMAJKAVcABYCXQKXR9A/w3kA18DIysd1v/H3AwdPny9e5bW191acuXq/3Om9AvvrOa87WqiEusteZKy7VHj4hjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIY/xeD9AAJcA+r4doJsnAaa+HaEZ6PI3X42rfCIco91lrR3o9RHMwxmxtK48F9Hhaip6+ijhGUYo4JhyinOf1AM2oLT0W0ONpEcZa6/UMIlJPOKwpRdoVZ6M0xtxtjNlnjDlojPkHr+dpCGPMH4wxp4wxu+st62SMWWGMORD6b8fQcmOM+W3o8e0yxgz3bvLPM8Z0M8asMcZ8YozZY4z5Vmh5uD6eaGPMZmPMR6HH88PQ8p7GmA9Dc79qjIkMLY8K/flg6PuZrTastda5CxABZAO9gEjgI+Bmr+dqwNwTgeHA7nrLfgb8Q+jrfwB+Gvp6BrAEMMAY4EOv5//MY+kCDA99HQ/sB24O48djgLjQ1wHgw9CcrwGPhJY/DXw19PXfAU+Hvn4EeLXVZvX6L+sqf4G3Acvq/fkfgX/0eq4Gzp75mSj3AV1CX3fh4vuuAM8Aj17pei5egEXAnW3h8QAxwHbgVi7uLOAPLa/7vQOWAbeFvvaHrmdaYz5Xn76mA8fr/flEaFk4SrPW5oW+Pgmkhb4Om8cYeuo2jItrl7B9PMaYCGPMTuAUsIKLz8aKrLXVoavUn7nu8YS+XwwktcacrkbZJtmL/+yG1eZuY0wc8CbwbWvtufrfC7fHY62tsdYOBTKA0cAAbye6MlejzAG61ftzRmhZOMo3xnQBCP33VGi584/RGBPgYpAvW2sXhBaH7eO5xFpbBKzh4tPVRGPMpd1N689c93hC3+8AnGmN+VyNcgvQN7RlLJKLL7QXezxTYy0Gngh9/QQXX5tdWv54aKvlGKC43tNCzxljDPAc8Km19j/rfStcH0+KMSYx9HWQi6+PP+VinA+ErvbZx3PpcT4ArA49M2h5Xr/ovsaL8Rlc3OKXDfyT1/M0cOb5QB5QxcXXJ1/h4uuQVcABYCXQKXRdA/x36PF9DIz0ev7PPJbxXHxqugvYGbrMCOPHMxjYEXo8u4F/CS3vBWwGDgKvA1Gh5dGhPx8Mfb9Xa82qPXpEHOPq01eRdktRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijjm/w9Vco/X+izDGgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(skel, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build graph from skeletonized image\n",
+ "G = sknw.build_sknw(skel, multi=False)\n",
+ "\n",
+ "# need to avoid np.arrays - so we convert it to a list\n",
+ "for (s, e) in G.edges():\n",
+ " G[s][e]['pts'] = G[s][e]['pts'].tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Name: \n",
+ "Type: Graph\n",
+ "Number of nodes: 96\n",
+ "Number of edges: 102\n",
+ "Average degree: 2.1250\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(nx.info(G))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4824\n"
+ ]
+ }
+ ],
+ "source": [
+ "# calculate total root length based on number of pixels\n",
+ "tot_root_len = 0\n",
+ "for (s, e) in G.edges():\n",
+ " tot_root_len += len(G[s][e]['pts'])\n",
+ "print(tot_root_len)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[(0, 1), (1, 3), (2, 1), (3, 3), (4, 1), (5, 3), (6, 1), (7, 3), (8, 1), (9, 3), (10, 1), (11, 1), (12, 3), (13, 1), (14, 3), (15, 1), (16, 3), (17, 3), (18, 1), (19, 3), (20, 3), (21, 3), (22, 1), (23, 5), (24, 3), (25, 1), (26, 1), (27, 1), (28, 3), (29, 1), (30, 1), (31, 3), (32, 1), (33, 1), (34, 3), (35, 3), (36, 1), (37, 3), (38, 3), (39, 1), (40, 3), (41, 1), (42, 1), (43, 1), (44, 3), (45, 3), (46, 3), (47, 3), (48, 3), (49, 1), (50, 3), (51, 4), (52, 2), (53, 2), (54, 3), (55, 4), (56, 4), (57, 1), (58, 1), (59, 4), (60, 3), (61, 1), (62, 1), (63, 1), (64, 1), (65, 3), (66, 3), (67, 4), (68, 1), (69, 1), (70, 3), (71, 3), (72, 3), (73, 1), (74, 3), (75, 4), (76, 1), (77, 1), (78, 1), (79, 3), (80, 3), (81, 1), (82, 1), (83, 3), (84, 1), (85, 3), (86, 1), (87, 3), (88, 1), (89, 3), (90, 1), (91, 3), (92, 1), (93, 3), (94, 1), (95, 1)]\n",
+ "number of roots: 45\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get number of root (via number of nodes with 1 edge only)\n",
+ "num_roots = 1\n",
+ "degree = G.degree()\n",
+ "print(degree)\n",
+ "# degree_list = []\n",
+ "for (n, d) in degree:\n",
+ "# degree_list.append(d)\n",
+ " if G.degree(n) == 1:\n",
+ " num_roots += 1\n",
+ "# print(degree_list)\n",
+ "# print(len(degree_list)\n",
+ "print(\"number of roots: \", num_roots-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2592</th>\n",
+ " <td>dicot-sim-145-2-25.rsml.jpg</td>\n",
+ " <td>444.57877</td>\n",
+ " <td>47</td>\n",
+ " <td>129.96333</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "2592 dicot-sim-145-2-25.rsml.jpg 444.57877 47 129.96333"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[df.image==\"dicot-sim-145-2-25.rsml.jpg\"]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hhh_env",
+ "language": "python",
+ "name": "hhh_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook.ipynb b/notebook.ipynb
new file mode 100644
index 0000000..b27bf5e
--- /dev/null
+++ b/notebook.ipynb
@@ -0,0 +1,772 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The Simulated Root System Challenge"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Root systems are important actors in the overall plant development, growth and ultimetally, productivity. In the framework of agricultural research, improved root systems can help to acquire more soil resources, insure a better plant stability of store more carbon in the deep soil layers. However, due to their underground nature, roots are challenging to measure. \n",
+ " \n",
+ "For analysing root images classical measurements are the total root length (the summed length of all the individual roots) or the total number of roots. However, as root systems can quickly become very complex, root image analysis algorithms are prone to errors (see Lobet et al. 2017). For plant seedlings, we can assume that existing tools will be reliable, but as soon as the plants are several weeks old, the same tools will fail in their evaluation due to increasing root overlaps and crossing in the images."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This challenge focuses on the analysis of root systems, assuming the segmentation already given. Therefore we deal with simulated data, i.e., artificial black and white images, having the advantage of being (i) easy to generate, both the image and the groundtruth and (ii) to be close enough to real images such as the algorithms developed for simulated data might be transferred without too much trouble. The focus of this challenge is to extract the biologically relevant features from these images: (1) the total number of roots and (2) the total length of all the roots. Again, both are challenging to extract due to occlusions and overlap of roots within the images. As a general rule, for complex root systems, both are often underestimated by root image analysis software tools. \n",
+ "\n",
+ "We provide a library of 10.000 simulated plant root systems. For each root system in the simulated dataset, we have the whole structure stored in a data file (Root System Markup Language, RSML, Lobet et al 2015), a 2D black and white images (jpg, grayscale, 300 DPI, size between 1500 x 4700 px and 110 x 2100 px) of the root system, and the groundtruth data (e.g. total length, number of root, etc.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The challenge that we will offer to the machine learning community will be to extract : \n",
+ " \n",
+ "- the total root length\n",
+ "- the total number of roots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploratory Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from IPython.display import Image\n",
+ "df = pd.read_csv('train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(\"images/dicot-sim-145-2-25.rsml.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " <td>8371.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>6258.159151</td>\n",
+ " <td>387.820929</td>\n",
+ " <td>212.600726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>8740.875921</td>\n",
+ " <td>588.740714</td>\n",
+ " <td>82.306584</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>78.713036</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>49.953335</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>931.466970</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>149.944670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2631.605200</td>\n",
+ " <td>133.000000</td>\n",
+ " <td>199.982670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>7974.766350</td>\n",
+ " <td>442.000000</td>\n",
+ " <td>259.926670</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>73971.210000</td>\n",
+ " <td>4448.000000</td>\n",
+ " <td>504.952030</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " tot_root_length n_laterals depth\n",
+ "count 8371.000000 8371.000000 8371.000000\n",
+ "mean 6258.159151 387.820929 212.600726\n",
+ "std 8740.875921 588.740714 82.306584\n",
+ "min 78.713036 0.000000 49.953335\n",
+ "25% 931.466970 45.000000 149.944670\n",
+ "50% 2631.605200 133.000000 199.982670\n",
+ "75% 7974.766350 442.000000 259.926670\n",
+ "max 73971.210000 4448.000000 504.952030"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/p/software/juwelsbooster/stages/2020/software/Jupyter/2020.2.6-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+ " import pandas.util.testing as tm\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.PairGrid at 0x7fe6310b6880>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 540x540 with 12 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "sns.pairplot(df.sample(1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given an image, the goal is to predict the three columns: `tot_root_length`, `n_laterals`, and `depth`.\n",
+ "Let's first submit a dummy model that predicts the mean of each column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.dummy import DummyRegressor\n",
+ "import numpy as np\n",
+ "df = pd.read_csv(\"train.csv\")\n",
+ "reg = DummyRegressor(strategy='mean')\n",
+ "cols = ['tot_root_length', 'n_laterals', 'depth']\n",
+ "X = np.zeros(len(df))\n",
+ "y = df[cols]\n",
+ "reg.fit(X, y)\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X = np.zeros(len(df_valid))\n",
+ "df_valid.loc[:, cols] = reg.predict(X)\n",
+ "df_valid.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the following, we will develop a simple baseline solution based on classical image processing techniques. We will use HOG (Histogram of Oriented Gradients) as features, they can detect lines with different orientations. https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_hog.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7fe621d8cb50>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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o0KFN7h9YUVFBbGwsI0aM8Pz4t+Xl5bz99tt84Qtf8HSO5qAoW1hVVRW7du1i+PDhXo/S7LKzs1m9ejVf+9rXiImJ8Xocjh07Rnh4OF26dPF6lCZRlK1g+fLlzXZKPNfs3LmTQ4cOeT0GcOHtwooVK0L+71pRtoKamhqys7Pp1q2b16M0m0AgQO/evenduzcdO3b0epw6hw4dIhAIkJGR4fUojaZtX1uBtZaVK1dyyy238NJLL3k9TqOkpqYSERHBrbfeSqdOnaipqSE/Px+AXbt2ebq5XX3WWj788EOmTZvGyy+/7PU4jaIoW8mhQ4f4whe+QCAQcP78GtHR0cTHx2OM4Z577qFjx44UFRVRU1PDBx98QE5ODrW1tXVby6SlpfHQQw95PPX/ysrKYurUqWRkZHDixAmvx7luirKVWGvZsmULo0aNYsOGDV6PU8fn89W9/BwxYgRDhw6tO0u0tbbuRD2lpaUhc74Uay2rV69m4sSJvP322yF3xmtFeZ0mTpzItm3bKC0tve6fXbFiBT/4wQ/YvHmzZ7/g0dHR+P1+EhISuOeeezDG1H2csX37dl566SUqKioa9fhcsn//fnr37s3YsWP56KOPvB7nuijK61RUVMQ//uM/cuTIERYvXkx5eXmDf7aqqoqdO3cyZsyYVnm2DAsLIyIigm7dujFlyhQAoqKiCAQCFBQUMGfOHKqrqzlz5kyLz+KFDz74gO9///ts2LDB0yMJXC9FeZ127tzJvn37GDRoEP/0T//EW2+9xZEjRxr881lZWYwYMaLZo/T7/YSFhREIBJg1axbh4eF17w2PHz/Oe++9B0B+fn5I7jnRGJWVlWzbto2bbrqJdevWeT1OgynKRqioqGDbtm3s2bOHBx54AKDBYe7du5epU6fSpUuXJp0W7uJ+jOPGjaNHjx4kJSURHx9PZWUlCxYsoLy8nJKSEk6fPt3o+2ioIUOGOHsukNWrV/O9732Pjz76KGSOAuHm32SIqKioYPfu3YwZM+a6ni337NnDoEGDGhzlxY8bpk2bRkJCAlFRUXUHJ16/fj2bN28mLy+PwsLC63wEzeOWW25h+/btntz3tZSXl7N161YmTJhAZmam1+M0iKJsonPnzhEbG4sxpsH/Em/dupVvfetbLF++/IrX6dOnD4MGDcIYww033EBkZCS5ublkZmZSVlbm1GdwhYWFTp9iPjMzk29/+9uKsr04cuQI3bt3x+fzNXiN6vnz59mxYwcTJ04kMzOT6Ohobr75ZiIiIuo27s7KymLPnj1Ya1myZAljx44lPDzcswNVhbLS0lK2bNnC5MmTWb16tdfjXJOibAZ5eXmkpaU1+OVox44diYiIYPz48dxxxx2cP3+ezMxMzpw5w6JFiz4XtzGGG2+8kaeffrolxm8XNmzYwAMPPMDevXvJy8vzepyrUpTNYOXKlUydOpXXXnvtc98zxjBixAiioqIYP3488fHxnD17lh07dvDnP/+5QR9HTJ48me3bt1NSUtIS47cLJSUlrF27lunTp/Pyyy87vdJHUTaD8vJywsPD6z6IHzp0KOHh4UybNo3w8HC2b99OWVkZzzzzzHUfDNjn8zF69GieffbZFpq+/cjKymLGjBmkpKRw6tQpr8e5IkXZRN27dyc+Pp4BAwbwi1/8gsrKSnbv3k1lZSW///3vr2vjgsuZNGkS27dvd/rI3oFAgMTExBY74U5zevfdd5k5cyYvvfSSs8+WivI69ejRg4iICG655RZSU1M5duwYJSUlPP300xw7dgxrbbP9z/b7/YwYMYLnn3++WW6vpSQlJfHoo4/y7//+716Pck1HjhwhLi6O5OTkur1cXKMoryE+Pp7U1FTuuusu4uLiOHHiBFVVVcyfP5/8/Hxqampa5F9cYwyTJ09m27ZtTj9LXuTKrlsNcfE8oi+88IKTz5aK8hoyMjIYO3Ysr776KsXFxVRWVrbK/8ikpCQGDx7Mn/70pxa/r+Zw1113sXPnThYtWuT8dqbHjh0jOjqapKSkVtni6XqFzj9vHvnkk0946aWXOH36NBUVFa0SpDGG++67j7lz57b4fTWXYcOGMWjQoJA5mtwrr7zCI4884uQBzRSlgyZNmsShQ4dCagfdoqIiXn/99ZA5/V9hYSF79+5l3LhxXo/yOYrSMVFRUQwfPpz169c7+X7ncs6fP893vvMdpk+f7sRR7Rpq/fr1DBs2jKioKK9HuYSidMyoUaP49NNPOX/+vNejNFhxcTGLFy8mEAgQGxvr9TgNdv78eT799FPGjBnj9SiXUJQOiY2NZeTIkSGz4TRATEwMAwcOBGDfvn0hd/q/zMxMRo4c6dTZ0RSlQ774xS/y1ltvhdShOBISEkhMTKSyspINGzY4+R7takpLS9m7dy8jR470epQ6itIRXbp0wVrr/MbSl7No0SLKy8upqqqiqqrKqWedhli1ahVjxoxx5v2wonRATEwMd9xxB/PmzXP+M76rKSsro7S0lKSkJK9HuS7l5eW8+eabPPHEE16PAihKJ4wYMYKcnJyQfJb8rJycnJA8l0dubi7V1dV07drV61EUpdeio6O56aab+OCDD7wepVmsWLGCW265xesxrpu11pnTFmozO4+NHj2ajz/+OGQOGBwbG8vgwYMZMWIEXbp0IT4+ngMHDtR9v6KigoKCgiYfGMwLOTk5lJeX06NHj+s65lJzU5Qe6tWrF7169XLqeDtX0r9/f7p3787w4cPZtWsX8+fPJzc3l4yMDGbOnFl3vYunM3DtA/mGsNaybNky7r77bp555hnP5lCUHrr99tt59913nd1yp2PHjowbN46bbrqJ/fv3c/ToUX7zm99c8+fWrFnDpEmTyMrKaoUpm1d2djbl5eX06tXLs1P8KUqPpKenk5+fT3Z2ttejXMIYQ+/evbn33nupqalh06ZN/PznP7+utcKHDx8OyWfKiz744AOmT5/Oc88958n9K0qP5OTkOLUXSEJCApMmTWLw4MEcO3aM//7v/6akpKRR5zypra0N6aPuHT9+nMLCQgYPHuzJ41CUHnHhJWtCQgKpqanMmjWLsrIy1q9fz5IlS9rNaQ2u5u233+aHP/wh+/fvp7KyslXvW1G2M4FAgI4dO9YdzqSwsJCnn366boscuaC2tpYNGzYwYcIEVq5c2ar3rSjbibi4ONLT07nzzjux1vLBBx8wd+5chXgVa9eu5f/8n//D2rVrW/UjK0XZhvn9fqKiorjrrrvo1KkTJ0+e5MUXX+TcuXNejxYSqqurWb9+PVOmTGHFihWtdr+Ksg3y+/0MHjyYm2++maioKBYuXMjBgwdb5F97v9+PMabZb9cV69at4wc/+AErV65stRP9Kso2JC4ujrvuuoukpCRyc3N57bXXOHv2bIve59e+9jXnPtZpTlVVVaxbt45p06axbNmyVrlPRRnifD4fMTEx3HXXXWRkZLBo0SLy8vJa7ezMpaWlvPXWW61yX17ZuHEjt956a6vdn6IMURdP+jNu3DjCw8NZuHAhb775Zqvv+lVVVRUSx6VtioqKChYtWtRq92dc+Lzsaowxbg/YypKSkrjtttsYOHAgGzdu5KOPPqKgoMCzeX79619TW1vLvn37eO2117Q29zpYay/7ZlzPlCFi3LhxdO/enU6dOrF8+XLeeOMNr0cCLhyq8Y9//COdO3fmBz/4Ae+++25Ib83jAkXpMGMMY8eOZcqUKezcuZM9e/bw5ptvej3WZR06dIjf/OY3zJ49m06dOpGZmdnkkxu1V9rJ2VGBQIDvfve7pKSk8Je//IXFixc7/wxUU1PD3Llzqays5KGHHqJPnz5ejxSS9EzpqKqqKv7whz94PcZ1q62tZc2aNWRlZXHLLbeQkZHBpk2bQuoIfV7TM6W0iOzsbF599VXCwsJ48MEH6dGjh9cjhQw9U0qLubiNbZcuXbjjjjs4cOAA27dvD6mjv3tBz5TS4nJycnjppZeIiYnhoYceCsmj3bUmPVNKq7DWsnTpUtLS0pg9eza7du1i+/bteq95GXqmlFaVl5fHc889R1xcHI899hgpKSlteoP2xtAzpbS62tpalixZQnJyMo888ggbNmxgy5YtXo/lDEUpnjl9+jT/9V//RXh4uNejOEUvX8VTtbW12vLnM5ocpTHmiDFmlzFmhzFmS3BZojFmuTHmQPC/CcHlxhjzZ2PMQWPMTmOMO+cfE3FEcz1TTrXWDrfWjg7++Z+BD6y1fYEPgn8GmA70DV6eAp5upvsXaTNa6uXrLODisfhfBu6tt/wVe8EGoKMxpnMLzSASkpojSgssM8ZsNcY8FVyWZq3NDX59EkgLfp0OHK/3syeCy0QkqDnWvk601mYbY1KB5caYvfW/aa2117ujcjDup655RZE2qMnPlNba7OB/TwHzgLFA3sWXpcH/ngpePRuof1bOjOCyz97mc9ba0fXeo4q0G02K0hgTY4yJu/g1cDuwG1gIPBm82pPAguDXC4EngmthxwHn6r3MFRGa/vI1DZgX3EzKD/yPtXaJMWYzMMcY8xXgKPBQ8PrvAzOAg0Ap8OUm3r9Im9OkKK21h4Bhl1l+Bph2meUW+GZT7lOkrdMWPSKOUZQijlGUIo5RlCKOUZQijlGU0mgdOnRgyJAhXo/R5ihKabSkpCTCwsJa9SzH7YGilCZZsmSJTurTzBSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGN0gh+5LoFAgMTERABSUlI8nqZtUpRyReHh4cTGxgIwY8aMuggvboCenJzM8ePHr/jz0jiKUgCIiIggIiICv9/PfffdR1RUFD6fD5/vwjuc999/n/z8fKqrqykqKgKgV69eTJ482cux2yRF2Q75fD4iIiLo0KED99xzD8YYoqKiCA8Pp6qqinnz5lFWVkZ5eTnnzp3zetx2R1G2A4FAAGMMnTp1Ytq0aYSHh5OYmEhxcTELFy6ktraWwsJCSkpKvB5VUJRtUlhYGGFhYcyYMYOYmBgyMjIIDw8nLy+PFStWUFFRQXb2584WIY5QlCEueHR6oqKiuOOOOwgPD6dv374YY3j//fc5f/48b7/9NhUVFR5PKg2lKEPUuHHjSE1NpWfPniQmJlJeXs7SpUupqKjg7bffpqamxusRpZEUpeOMMUyYMKHuo4khQ4YQFxfHxo0bycnJITMzk7Nnz3o8pTQnRekYv9/PuHHjiIiIqPvvunXrKC4uBuDZZ5+t+0hC2iZF6bH+/fuTlJTEoEGD6NatG1VVVWzYsIGKigp+97vf6b1gO6QoW9mAAQOIiYlh/PjxpKSksH//fs6cOcOSJUu0dYwAirJFGWPo378/4eHh3HbbbcTHx7N3717Kysp48803OXXq1LVvRNodRdlCJk6cyNSpUzl8+DCVlZW88MILFBUVUVtb6/Vo4jhF2UI++ugjNmzYoKOHy3VTlC2ktrZWz4rSKNrJWcQxilIaLSoqyusR2iRFKY32jW98o25/S2k++huVRjt9+jTvvPOO12O0OYpSGq26uprS0lKvx2hzFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSliGMUpYhjFKWIYxSltJipU6fSp08fr8cIOYpSWkxSUlLdyW6l4RSltJjMzEwmTJjg9RghR1FKi8nLyyMtLc3rMUKOohRxjKKUFmOt5dChQ/Tq1cvrUUKKopQWdebMGZKTk70eI6QoShHHKEpplJSUFIYPH37N63366acMGDCg5QdqQxSlNEpSUhJnz56lpqbmqtc7ePCgNiC4TopSGm3VqlXXjFKun6KUFpebm0vnzp29HiNkKEppcVlZWfTu3dvrMUKGopQWZ63Vadivg/6mpMWtX7+ecePGeT1GyPB7PYCErvvuu4/+/fsDcPz4cU6cOMHhw4fZu3fvJdcrKSkhLi7OixFDkqKURps7dy6vvfYaAN26dSMjI4MRI0bw6KOPsmnTJkpLS9m8eTMlJSWcPXuWhIQECgoKPJ7afcZa6/UMV2WMcXvAdmrAgAGMGjWqLsr6jDGMHTuW6OhoxowZQ1xcHJWVleTk5PDCCy94MK2brLXmcssVpTTK1aK8nLFjx9KtWzeGDh1KcXExq1ev5vDhw+Tn57fwpO5SlNKsrjfK+uLi4pg8eTI9e/YkOTmZPXv2sG/fPvLz88nJyWmBad10pSj1nlJaXXFxMe+99x7GXPidHDRoEDfccAM33XQTKSkpLF68mJKSEg4fPkxFRYXH07Y+RSmeufgqbffu3ezevRtjDIFAgOnTpxMbG8usWbM4c+YMH374IRUVFWRnZ3s8cetQlOIMay2VlZUsWLAAgEAgQHp6OlOnTiU8PJzExERWr17NsWPHKCgo4Pz58x5P3DIUpTirqqqKI0eO8OKLL+Lz+YiIiGDKlCnMnDmTqKgowsPD2bp1K7t27SI/Px/X1480lKKUkFBbW0tZWRmLFy8GIDIykvDwcEaNGsXDDz+M3++ntraWuXPnkp2dTW1trccTN56ilJBUXl5OeXk5K1euZNWqVXTo0IFAIMA999zD3LlzQ3ojBUUpIc9aS2FhIUCb2DhBG6SLOEbPlNJo48ePxxhDeXk57733HlVVVToSQTNQlNJohw8fZvPmzURFRfH9738fn8/HwYMHOXDgANu2bcNa22bWiLYmRSmNlpuby759+wDYsWMHPp+PAQMG0K9fP+6++24KCgo4dOgQp06dYsOGDR5PGzq07as0SkO2fU1ISKB3796kpKRw0003sWfPHs6dO8f27dvJy8trxWndpG1fpdUVFBSwZcsWABYvXsygQYPo2LEjDzzwAPHx8WzatIny8nI2bNig96L1KEppNXv27AFg3bp1REVFceONNxIXF8ePf/xjjhw5woEDBzh58iSHDh3yeFJv6eWrNEpTdt26nN69e9OvXz/S0tIIBAJs374dgE8++YTS0tJmuQ/X6OWrOC0rK4usrCwCgQDf+c53SE9PB2DatGmcP3+eVatWUV5ezsGDBz2etOUpSnHKxIkT2bp1KytXrgRg4cKFpKamMnnyZCIjI3nsscdYv349R48eJS8vr25LnrZEUYozwsPDGT58OM8//3zdMmsteXl5zJkzBwC/38+4ceMYM2YMSUlJhIeHs3DhQioqKjh+/HibWGGkKKVR4uPjW+Q2rbUUFxdf8TrV1dVkZmaSmZmJ3+8nOjqaWbNmERERQYcOHXj++ec5d+5cs8/Wqi5udeHqBbC6uHd58cUX7cMPP9xst2eMsU899ZTt2rVro28jKirKBlcMhsTlSr/zeqaURjl+/DgLFy5stttLSkoiMjKSEydONPo2ysrKmm0eL2kvEWmU2tpaKisrm+327r//fubNm6dtZdF7SmmgiIgIOnXqxB133AHQrGfRioyMJDIyMqR3TG5OilKuyOfz4ff7mT59Ov369aOgoKDucBzNeXbmkSNHsn//fkpKSprtNkOZopRLXDzM44wZM0hLSyM1NZXFixezZMmSS47B2lxb2URFRTF27Fiee+65Zrm9tkBRCsYYIiMjuf322+nSpQvJycm8//77bNq0qcWPWN6xY0eqqqra7KZ0jaEo27HY2FgmT55Mjx49iI+PZ/ny5WzatInc3NxWm+Guu+7i3XffbbX7CwWKsh0aOXIkN998M5GRkaxatYrNmzdz6tSpVp8jLS0Nv9/fpI9B2iJF2U6MGjWKbt26MXLkSLZu3cqrr77KmTNnPJ2pa9euIX+M1pagKNsoYwwjRoygZ8+eDBs2jG3btnH48GHmzZvn9WjAhY9Bpk6dyp/+9CevR3GOomxD/H4/gwYNYsCAAdxwww18/PHHZGVl8c4773g92udkZGSQn5/frBsgtBWKsg1IT0+nc+fO3HbbbRw4cIA9e/YwZ84cp7eOufnmm1mzZo3XYzhJUYaoiIgIhgwZwowZM8jLy+PkyZP8/ve/D4lnnvT0dAKBAIcPH/Z6FCcpyhASFRVFWloat956K4mJiezbt49f/epXVFVVOf2s+Fm33norH374oVbwXIGiDAGpqan07NmTiRMncu7cOVasWEFOTk5IPCt+ViAQoGvXru3+4FhXoygdFRUVRXx8PPfeey9+v5+jR4/yn//5n5SXl3s9WpMMGzaMnTt36lnyKhSlQ4wxpKamct999xEeHk5NTQ3z588nJyenzfwST58+nV//+tdej+E0RemI2NhYHn/8cfx+P3PnzuXcuXNt7vThXbp04fTp0yH5srs1KUpHlJaWMnfu3DZ1mvDPmjRpkj4GaQBF6Yja2lpPtj9tTWVlZXVHSZcr0+FApNUsWLDA6xFCgqIUcUyjozTG9DfG7Kh3KTLGfNcY8zNjTHa95TPq/cyPjDEHjTH7jDF3NM9DEGlbGv2e0lq7DxgOYIwJA7KBecCXgT9Ya39X//rGmIHAI8AgoAuwwhjTz1ob+oe0FmlGzfXydRqQZa09epXrzALesNZWWGsPAweBsc10/yJtRnNF+Qjwer0/f8sYs9MY8zdjTEJwWTpwvN51TgSXiUg9TY7SGBMO3AO8FVz0NNCbCy9tc4HfN+I2nzLGbDHGbGnqfOKO6OjoFjkHSVvTHM+U04Ft1to8AGttnrW2xlpbC/w3//sSNRvoWu/nMoLLPsda+5y1drS1dnQzzCeOGDJkCDfffLPXYzivOaJ8lHovXY0xnet9bzawO/j1QuARY0yEMaYn0BfY1Az3LyEiOjpah5JsgCZFaYyJAW4D5tZb/BtjzC5jzE5gKvA9AGvtHmAO8AmwBPim1ryGpoyMDEaNGnXdPzdlyhRWr17dAhO1LU3azM5aex5I+syyL17l+v8K/GtT7lO8l5SUxL59+657zxWfz9dm9nZpSdqiRxpl48aNbXbDea8pSmkV3bt35+jRq32MLRcpSmkVPXr04MiRI16PERIUpYhjFKW0CmOM3oM2kKKUVjFu3DjWr1/v9RghQVFKq4iJidGGAw2kKEUcoyilxSUmJnp+2r1QoiilxY0ePZotW7TDT0MpShHHKEq5Lr169aJ///5ej9GmKUppsNGjRzNp0iS6det2XT83YMAA9u7d20JTtT2KUq4pOjqar3/963Tp0oWXX36Z5cuXX9fPJycnc/r06Raaru3REdLlqjp16sT06dNZsGABubm52iqnFShKuaKxY8cyatQo5s2bx8mTJ70ep91QlPI5MTExPP7445w4cYJnn322STsmDxgwgH379jXjdG2fopRLjB8/nrFjx5KXl8fixYubfKSAvn37cuDAgWaarn3Qih4BLpz2fMKECcTHx/Pyyy+Tmpqqw0F6RFEKcXFxPPXUU8TFxbFs2TIKCgooLCwkOTnZ69HaJb18becmTJjAwIEDef311zl79mzd8qysLHr37s3BgwebdPvGGB0s6zrpmbKdMsYwefJkxo0bx9y5cy8JEi4cGOvGG29s8n0MGjSI3bt3X/vKUkfPlO1Qx44d+fKXv0x8fDwbNmz4XJDNKSoqivLy8ha7/bZIUbYzY8eOZdCgQRw8eJCKigqWLVt22etVV1dz7NgxevXqxaFDh1p5yvZNUbYj06ZNIzo6mtOnT181SABrLRUVFURFRbXihAJ6T9kuJCYmMmvWLKqrq1m1ahUDBgy4apAXnT59+oprYB966CF++ctf8sgjjzB69GjCwsI+d52kpCTt3NwIeqZs45KSkrjnnnvYuXMn+/fv54EHHuDFF19s0M9++OGH/Mu//Mtlz/8xZ84c3nrrLQYNGkTv3r2ZOXMmWVlZ7N27l/z8fI4ePapzhzSSomyjjDHccccd9O/fnxdffBFrLQ888ADvvvtug/fYsNZSVlZGZGTkFVfW7Nmzhz179rBw4UL69evHDTfcwLBhw+ja9cJZD4uLi5vtMbUXxvWt/o0xbg/oIGMMt99+O8XFxaxfvx5rLd/+9rf5n//5n+vehWr27NkcPnyYHTt21C2bNm0aiYmJvPXWW1f8ubCwMG6//Xa6dOlCRkYGH3zwAcePH9epC+qx1prLLdczZRuTmprKE088wUcffcRHH30EXDjm6oEDBxq1T2NpaSmxsbGXLHvqqad45ZVXrvpzNTU1LF68GGMMYWFh3HLLLdx666106dKFtWvXkpWVxfHjx697nnbBWuv0BbC6XPvi8/lsr1697Je//GUbHx9ft3zcuHF2+vTpNviK47ovYWFh9mc/+9kly375y19av99/3bdljLERERH21ltvtU899ZT99re/bTMyMho9W1xcXKN/1oXLlX7n9UzZBvh8PqZPn05qaipz5szh/PnzwIXPJJOTk1m0aFGjd06ura2lpqaGQCBAVVXVJcuu18WPWVasWIExhuTkZO6//37S0tI4d+4c8+bN4+zZsw16H5qQkMDDDz/M888/T3V19XXP4jJFGeJiYmKYMmUKeXl5LFq0qG55dHQ0I0aM4K233mrS0QKstXz88ccMGzasWQ8Taa3lzJkzHDlyhH379rFr1y5mz55NdHQ0xhjmz5/P6dOnKSkp+dzP+nw+JkyYwJYtW9pckKAoQ1rnzp156KGHWL9+/SXBREdH8/DDDzN//vxm2YSuoqKCyMjIJt/OZ91+++0UFRXVvff97//+byIiIoiLi2PWrFnExcVRXV3N4sWLOXLkSN2zc9++fenYsSPvvfdes8/kBK/fM+o95fVffD6fnTlzpv3yl79so6KiLvleRESEfeqpp2xaWlqz3V9ERIT98Y9/XPfnn//8501+L5eUlGS/9a1v2UAgcNX77dSpk/3yl79sv/nNb9oHHnjApqen23/4h3+wMTExnv9/aOrlSr/z2qInxBhjuPvuuzlz5gxvvPEGZWVll3x/2LBh5ObmkpeX12z3WVFRQSAQwJjLrsG/bsYYvvSlL/Hqq6/WvU+90v2ePHmSF198kTlz5nDw4EGefvppOnbsyMyZM4mKisLnu/ArfPG/bYFevoaQ9PR0pk+fzrZt29i2bdvnvj969GhSU1MveW/ZXDZv3syYMWPYtGlTk29r2rRpbN68mcLCwgb/TH5+Pmlpabz66qusWrWKTp068d3vfpeioiKKiopISEigoKCAAwcOsHnz5katiHKFogwR6enpTJs2jWXLlnHs2LHPfX/s2LGkpqby/vvvt8hOxWVlZc2ycfqAAQNISUnh9ddfv66fS0pKYsyYMbz99tsUFxdz6tQpdu7cSUpKCp07d+aLX/wiAwYMYOrUqTz00ENs376d8vJyVq5cyZkzZ0Lq0JjaoicEXAzyrbfe+tzLVbiwz+LXvvY1nn766RbbdzE2NpZvfOMb/OY3v+GPf/wjK1eupLa2lvLycj788MMGPTMlJCRw7733Mm/evOt6lgS49957OX78OFu3br3idZKTk8nIyOC2224jKiqKvLw8IiMjCQ8PZ+/evWzevNmpQ2Vqi54QZIzh/vvvJykpiVdeeeWyQUZHR/PQQw/xxhtvtOjOxCUlJcTFxQFQVVXFyZMnqampITo6mh//+MdYa9m6dSsVFRVkZmZedpYJEybwySefXHeQo0aNArhqkHBhr5bTp0+zY8cOBg8eTI8ePRg7dixHjx6lZ8+eZGRk0LlzZw4cOMCxY8fIzMx08mWunikd5fP5mDVrFocOHeLjjz++7HViYmJ48MEHWbp0Kbm5uS0+06RJk7jvvvsYMmQIt9566yUvCf1+P6NHjyYiIoKJEycSERHB+vXrKS0tZdu2bYwePRqAlStXXtd9XtztbO7cuZw7d65Rcw8ePJju3btz4403smfPHsLDw8nLy2P8+PEcPXqUw4cPc+LEiSYfj+h6XemZUlE6KCMjg4kTJ/LJJ5+wc+fOK15v8uTJhIeHX/e5PZpi4MCB/PWvf2XatGnXfJ82btw4YmJiuOWWWxg6dCjPPfccR48evepj+qwHH3yQffv2XdfPXM2gQYPqnkH37t1LdnY2gUCAjIwMevXqxdq1azl27Bj79+9vlvu7GkUZInr06MHUqVNZv379Vc9UNXr0aLp06cLChQtbcboLfvGLX/DTn/60wStPvve97/HSSy/Rt29funXrxtChQzl//jwffPABx44d49SpU5f9uXHjxpGUlNQia5PhwkqnSZMm0alTJz744AOys7Pp3r07Xbt2pV+/fuTn57Ns2bIWO8K7ogwRffr04ejRo1f9/G7UqFGkp6d7EiTAz3/+c372s581KMqJEycSFRV1ybO5MYaYmBimTZtG165dSUlJ4ZNPPmHXrl3s37+f6upqOnbsyOzZs5k/fz4FBQUt+XAIBALceuutpKenk5GRwfLly8nOzqa0tJR+/fqRmZnZIverFT0h4lrva6Kiorjtttv41a9+1UoTNV58fDw33XQTv/3tby9Zbq2lpKSEBQsWYIzBGMPAgQMZNmwYs2bNwu/3Y61l/fr1LR4kXFhxdXE3M5/Px+23386AAQPq9gONjo6mtLS0xee4SM+UISQqKoqHHnqIFStWkJ2d7dkcDXmm9Pl8fPOb3+T111+/rv04/X4/48ePZ9CgQVhrSU5OZtmyZZw6dYq8vLzLroFuCRf3A502bRpDhgyhrKyMRYsWXXEj+cbQM2WIi4yMdCLIhho7diwHDx687h2ro6Ki6N+/P6+//jpFRUUEAgHuuOMOhg4dSocOHTh//jzvvvsu1dXVzbop4WdZa6murmbp0qV8+OGHJCUlMXPmTOLj46mpqanb2L+5Aq1PUYaIO+64g927d4dEkHFxcUycOJHf//731/2z999/Px9++GHdZ5kVFRV1750jIiJITk7m7rvvJiwsjOjoaLZs2cLu3bspLS2t24+0uV38XPaFF14gIiKC+Ph4Zs+eTVRUFHv37mXr1q2cPXu22bakUpQhYNiwYfh8vkuOk+Mqv9/PY489xt///vfr3rRt3Lhx5OXlcfjw4ct+v6KiguzsbJ555hmMMcTHxzNy5Ej+/Oc/s3PnTkpKSpg3bx4lJSUUFRW1yOaGFRUV5Ofn89xzzxEZGcno0aN57LHHMMaQlZXF2rVrKS4ubtJGCYrScUOHDqVXr14sXLjQya1PPmvkyJFkZ2df90vLmJgYRowYwWuvvdagmKy1nDt3jpUrVzJ06FDmzZtHeXk5s2fPJjY2lsrKSo4fP86KFSuoqalpkfei5eXlZGZmkpmZSUJCAv369eMrX/kKlZWVnDhxgiVLljTqfhWlw6Kiopg+fTq/+93vQiLIsWPHkpKS0qjPFb/whS+waNEiioqKGnXfZWVl5Ofn8+yzzwIXtoPt0aMH3/zmN6murqa4uJhVq1Zx/PhxKisrm/3vs6CggI0bN7Jx40aSk5Pp3r073/jGNyguLubDDz/k+PHjVFRUNOi2FKWj/H4/Dz74IG+88YZzQfp8Pnw+3yVzRUVFceONN/Lyyy9f98vGG2+8kRMnTnDixInrnuXiERE++4x0cTvYLVu2EBERQVpaGlOmTOHBBx/k7NmznDlzpm6F0dU+E26Mi/e9e/du0tLSmDp1Kp06dWL//v0sWLDg2n8/Xh9ZQEceuPzFGGMTEhI8n+Nylzlz5tjp06dfsuyrX/2q7d69+3XfVnR0tP3Od75jo6OjGzVLcnKy/c53vnNdP5OammqHDh1qf/SjH9kf//jH9vHHH7ejR4+2YWFhLfZ3Fh4ebrt163bJMh3NLsRYa1vlg/PGOHz4MOvXr6/788iRIzl16lSjnum++MUv8s4777Tqh/OnTp2q2x/T5/PRt29fbrjhBmbOnMn+/fspLy9n1apVFBYWNtt+mJWVlZfdD/ZyFKVct/Ly8ro9NiIjI5k8eTLPPPPMdb/MHj16NDk5OeTk5LTEmA1SW1vLvn372LdvHwsWLKB///5ERkbypS99CZ/Px759+ygsLGyxTe0uR1FKk1x8prvetYxRUVFMmTKFP//5z86cft1aW7cTwI4dO4iPj2fgwIF07NiRX/ziF3z66afk5+ezd+/eRr0qaChFKY2WkJBAfn5+o35Bn3zySd544w0qKytbYLLmUVRUxIYNGwBYsmQJAwYMIDU1lZkzZ5KSksK6deuoqKhg8+bNzbqySFFKoxUUFDB//vzr/rmRI0dy8uTJZtk6KTk5+Yq7fjW3vXv3snfvXtasWUN4eDgTJkwgPj6e73//+zz99NON3gn7c7xeu6q1r6F3acpxX6OiouwPf/hD6/P5mmWWH/zgB7Zjx46e/5005qLjvkqz6NOnD2PGjGn0zz/xxBMN3mqnvVKUcl0SExP56KOPGvVRwdChQzlz5kyLriRpCxSlXLcrHcjraiIiIpg5cyZz585tgYnaFkUpreLxxx/nf/7nf/SytQEUpbS4gQMHUlpa2uynVo+Li6OqqqrVjkbQWhSltKhAIMCsWbOYM2dOs992XFwclZWVDd77IlToc0q5bjNmzCAjI6Pu4FdlZWVX/PD8C1/4AnPmzHFuTxeXKUq5bhs3bmTZsmXExcXxta99jbKyMs6dO8f8+fMv2R+yX79+1NbWkpWV5eG0oUdRynU7c+ZM3Ubkv/nNb0hISCAlJYWnnnqKM2fOsG/fPrZt28b999/fqOP0NFSfPn3aZPB6TylNVlBQwP79+/mP//gPVq9eTYcOHfjhD39IeXk548aNa7ETuo4fP77u1OxtiZ4ppdnU1tZy6NAhDh06xOLFi+nTpw+9evXipz/9KQcOHOD48eOsWbMmpM4V6QVFKS3m4MGDHDx4kGXLltGvXz+6devGz3/+87pAV69erUAvQ1FKq9i/fz/79+9nxYoV9OvXj65du/Kzn/2MQ4cOcfjwYT766COqq6sbfHuRkZFUVVVd18+ECkUpre5ioB988AF9+vShZ8+e/N//+3/Jzs5m1apVDVp5k5aWxrlz51r0RLleUZTiqYsvcZcvX0737t2ZMmUKTzzxRN3hIFv7RK4uUJTijKNHj/Lyyy9jjGHKlCncdNNNPPHEE6xevZqjR4+2m0AVpTjHWlt3GvaLgY4fP57HH3+cdevWcejQIWJjY5tvT3/HKEpxWv1Aw8LCmDBhAhMnTuTxxx/nr3/9Kz169ODYsWNtau8TRSkho6amhjVr1rBmzRp69OhBUVER06ZNIzU1lU8++YStW7eSm5sb8tvZKkoJSTU1Naxfv56VK1cSGRnJ4MGD684feeTIEdauXcvp06dD8iMTRSkh5+J5TC5ueFBeXs6WLVvYsmULMTEx9O7dmwceeIBAIMDJkydZtmwZRUVFzX7OkJaiKCXk9OzZk5MnT152P8rz58+zc+dOdu7cSVxcHF27duWJJ56gtraWs2fPsnDhQkpLS50OVFFKyDHGNGjFTnFxMZ988gmffPIJHTt2JC0tjaeeeqrutAvz58+/6r6gXlGU0i4UFhZSWFjIb3/7Wzp27EhycjL/+I//SEVFBYWFhSxcuJCSkhIn3oMqSgk5YWFhTfoIpH6gF/cF/epXv0p1dTWbN29m3bp1zTjt9VOUEnJmzJjBK6+80iy3VVBQQEFBAb/73e/o0KED0dHRzXK7TaEoJeTEx8c3+jTsV3Pu3DknthLSkQck5Ozfv79NbcHzWXqmlJDz+uuvez1Ci9IzpYhjFKVcl/T0dIwxXo/RpilKuS5PPPFEi6xkkf9lXD9wUfDkpOKAAQMGMGrUKF577TWvR2kTrLWXfcmhFT1yRbGxsaSmptKlSxemTp1K9+7dW+ScIHIpRSl14uPjiY+P54YbbmD06NGUl5dTWFhITk4Ov/71r5kwYQKBQMDrMds8RdmOxcfHExkZybhx4xgwYAAVFRVUVFTw6aef8qc//Ynq6moqKyvrrl9VVaUoW4GibCcCgQBxcXGkpqYyffp04MIRzWtra1m/fj2rVq2ivLz8kgjFG4qyjQoEAkRFRdGzZ08mTZqEMYawsDBOnTrFyy+/DEBJSYkidJCibAOMMURGRhIZGcm9995LeHg4ERERREZGcvjwYebMmVO3i5K4T1GGIGMMgUCAjh07MnPmTPx+P8nJyZSXl7NgwQIqKyspKSlRhCFKUYaAsLAwADp37szkyZOJjIykS5cunDt3jkWLFlFVVcXx48d1spw2QlE6yBiD3+/njjvuICoqij59+hAREUFubi6rV6+mrKyMo0ePej2mtBBF6YioqChuueUWwsPDGTJkCABLly7l/PnzLFy48LIHiZK2SVF6ZPTo0SQlJTFgwACSkpIoKytj5cqVlJeXs3DhwpA/oLA0nqJsJWPHjiU6OpoxY8YQHx/Pli1bOH36NK+++ipnzpzxejxxiKJsAX6/n5EjRxIeHs7NN99MVFQUmzZt4vz58zz77LPay0KuSlE2gx49epCUlERUVBTTpk3DWsv27duprKzkD3/4Q5s4sWlcXBwDBgzg2LFjXo/S5inKRujduzdRUVHcfPPNdOrUiSNHjlBQUEBZWRn/+q//6sSxQ5sqISGB9PR0MjIyuOmmmyguLiYQCCjKVqD9Ka99/3Tv3p2IiAimT59Ohw4dOHr0KGVlZaxdu5aTJ0+2iYM4paSkkJCQwMCBAxk2bBiFhYWcPHmSEydOsH79eqy1TJgwgejoaJYtW+b1uG2C9qdsoLCwMDIyMoiIiGDWrFmEh4eTk5NDVVUVf//73zl37twlJ5cJVampqURHRxMbG8vdd99NUVERRUVFfPLJJ7z33nt1G6tL62v3UQYCAVJTU4mLi+Pee+/FWsuZM2eoqKjg2WefrdudKdRFRESQnJxMSkoK06dPp7CwkMrKSsLDw9m8eTMrV67UxzCOaHdRRkRE0KFDBzp37sztt99ObW0tpaWlFBYW8te//pWamhrKysq8HrPJoqKiiIuLq9tLpLq6mvLyck6ePMmf/vQnKioqqKmpoUePHkybNk1BOqTNRxkREUF0dDR9+/ZlwoQJWGupra0lNzeX5557jurqas6fP+/1mE0WFRVFZGQkw4cPZ/jw4XWP8/Dhwzz33HNUVVVRWlrq9ZjSAG0uSmMMMTExREZGct999xEVFYXf7+fAgQO88sorVFZWUlxc7PWYTXbxcU2cOJHevXvj9/sJCwtjx44dvPLKK5SXl7eJf2zao5CP0ufzER4eTkJCAnfddRdhYWF06NCBiooK5s6dS0FBQZuIMCIiAp/Pxx133EFqaioxMTGEh4ezdu1a3n77bYqKivRM2EaEXJQ+nw+/30/nzp2ZOnUq4eHhpKSkUFhYyHvvvUdVVRU5OTlej9lkgUCAsLAw7rrrrrqjygUCAZYsWcK2bds4depUm9goQT4vJKI0xtCvXz9GjhxJXFwcnTp1Ijc3t24D7hMnTng9YpMZY4iOjmbGjBmEh4fTo0cPABYtWkRJSQnHjh1rkUN3nD9/nsjISMLCwrSyxxEhEeVNN91Er1692LZtG+fOnSM7O9vrkZrs4qH/b7zxRtLT00lPTyc6Opr333+fiooK3njjjVaJJD8/n/j4eCIiIvTy1xEhEeVHH33ERx995PUYzWLixIkkJCTQr18/OnTowMaNG8nKymLVqlXaW0SAEIkyVPl8PiZOnEhUVBQjR44kKiqKzMxM8vLyWL16tfYWkctSlM0oIiKCMWPGEBkZyfjx4/H5fKxbt47z58/zxz/+MSQ3SkhOTmbQoEEADB06lH379nk8UdvXoCiNMX8D7gJOWWsHB5clAm8CPYAjwEPW2gJz4c3Sn4AZQCnwJWvttuDPPAn8OHiz/39r7cvN91BaX0xMDIMHDyY2NpZJkyZRVVXF5s2bKS8v59/+7d9Cbm+RsLAwhg8fTkREBJMnTyY6Opr8/Hw++eQT4EKgmzZt8njKtq9Be4kYYyYBJcAr9aL8DXDWWvsrY8w/AwnW2h8aY2YA/8SFKG8E/mStvTEY8RZgNGCBrcAoa23BNe7bmS2/O3bsSI8ePUhLS2PChAmUlJSwZ88eSkpKWLNmTUhtpB4ZGcmAAQMAuP/++/H5fFRUVPDxxx9TUVFRd4Cu+n75y1/yk5/8xItx26Qm7SVirV1jjOnxmcWzgCnBr18GVgE/DC5/xV74Dd1gjOlojOkcvO5ya+1ZAGPMcuBOwNlzZft8Pvr27Uu/fv0YPXo0hYWFHDlyhPz8fH7605+GTIRxcXF069YNgClTppCamkpZWVndS9GioiL+8pe/UFFRETKPqS1rynvKNGttbvDrk0Ba8Ot04Hi9650ILrvScmf4/X569OhBZGQkd999N2FhYRw5coT9+/fzi1/8om57Upf5fD569OiBz+ejd+/e3HjjjRQXF3Py5EkA3nnnHfLz8y95LD/60Y/w+XwK0hHNsqLHWmub82WmMeYp4Knmur2rSU1NJSoqiokTJ9K1a1dyc3MpLy/nT3/6E1VVVVRVVbXGGI0SCATo0qULAGPGjGHAgAHU1NTU7XidlZXFv//7v1NbW6sNA0JIU6LMM8Z0ttbmBl+engouzwa61rteRnBZNv/7cvfi8lWXu2Fr7XPAc9D87ykjIyNJTExk+vTppKSkcO7cOaqqqsjMzOTtt992et/J5OTkus0Kp0+fTk1NDYWFhVhr2bx5M++99x7WWqcfg1xbU6JcCDwJ/Cr43wX1ln/LGPMGF1b0nAuGuxT4N2NMQvB6twM/asL9N1hiYiL9+vVj4sSJ1NTUUF5ezuLFizl9+jRlZWXOPYsYY0hISMDn89GnTx8mTpwIQHl5OTU1NZw+fbpN7fspl2roRyKvc+FZLtkYcwL4KRdinGOM+QpwFHgoePX3ubDm9SAXPhL5MoC19qwx5pfA5uD1fnFxpU9zMsbUnQz1vvvuIzw8HID9+/fz/PPPU1lZ6dzmZNHR0YSHhxMVFcV9992H3/+//1sOHjzI888/D0BxcXGL/AOyY8cOhg4dyoYNG5r9tuX6NXTt66NX+Na0y1zXAt+8wu38Dfhbg6droIsbVN988811R5q7uOtWRUUFZ8+edWYFTVhYGJGRkXUvQeF/5y8rK2PevHl1M7fWipedO3dyzz33KEpHhOwWPeHh4XXvCzt06EAgEGDt2rW89dZbnDp16to30ErCwsIIBAJ0796dm2++mUAgQIcOHTh9+jQLFlx4xX/x8JQiEMJRPvbYY+zatYsdO3Zw6tQpZ36pfT4fYWFhDBo0iGHDhhEdHU1SUhJHjhxh+fLllJWVOfWPhrgnZKN88cUXvR4B+N9zR44ZM4aePXuSkpJCx44d2bNnD+vWraOoqEgRynUJ2Si9YowhJSWl7sDEffv2xRjDpk2b2LlzJydPntQuWNIkirIBunXrxpAhQ0hMTKRv376cOnWKzMxMysrKeO2117weT9oYRXkZffr0oUePHnTt2pVevXpx7Ngxdu3axYEDB/j73//u9XjSxrX7KGNjYxk+fDgAgwcPJj09nf3793Ps2DFWrlzpzHtXaT/aXZQJCQn07duXpKQkbrrpJkpKStixYwcACxcubBNHwrtelZWVdWuNXdu6qT1q01GGhYVxww03EB4ezm233UZMTAwFBQUcPHiQ/Px8/uVf/sXrEZ2Qn59fd7Kfc+fOeT1Ou9emogwPD6dXr17ExMQwffp0jDHs37+fyspK/vrXv9ZtXqddlMRlIR1leno6UVFRTJkyhS5dulBVVcWRI0coLy/nt7/9LdXV1Xo5JiEnZKKMjY0lMTERgJkzZ5KYmEh+fj6VlZWsWLGCnJwcrLVO7/8o0hDORxkfH8/Xv/51KioqKC0txVrLggULKCgooKKiwpkNzUWai/NR1tbW8l//9V9UVlZq511pF5yPsqSkxOsRRFqVz+sBRORSilKAC28TfD79OrhA/xcEgHnz5nHvvfd6PYagKCWopKSEuLg4r8cQFKWIcxSliGMUpYhjFKUAF46oN3fu3Ct+f+TIkWzdurUVJ2q/FKUAUFVVxYkTJ674/cTERAoKrnrWQmkmilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDHG9VONG2PcHrCd6NOnDw8++CDV1dUUFBTw7rvvcu7cOcrLy70eLWRZa83llitKaZApU6YQHh7Oxo0bSUhI4O677yYsLIzS0lLeeecdzpw54/WIIUdRSpPceuutVFVVsXr16rplF095f++991JbW8vOnTvZtm0b58+fx/XfKxcoSmmSy0V5kc/nIzk5meHDhzN8+HCKioo4ePAg69ato7y8XIFegaKUJrlalPUZY+jcuTN9+/Zl/PjxnD59mvfee4/8/Hyqq6tbadrQcKUonT+9uoQWay05OTnk5OSwevVqunbtyr333ktsbCwnTpwgMzOT7OxsamtrvR7VWXqmlAZp6DPllURHR9OtWzcmTZpEYmIiBw8eZNu2bRw6dKiZJw0devkqTdLUKOuLjIykf//+jBw5krCwMI4fP86HH35IVVVVM0waOhSlNMmDDz7Ip59+yu7du5v1drt160a3bt2YOnUqp06dIisri3Xr1lFWVtas9+MiRSlN8stf/pKf/OQnLXof6enp9O7dmwkTJlBcXMyqVaua/R8Bl2hFjzgvOzub7Oxs1qxZQ1paGlOmTOHhhx/mo48+4vjx42060Pq07as4KS8vjzfffJOf/OQnFBQUMGjQIL73ve8xcuRIEhMTvR6vRemZUpy3YcMGNmzYQEJCApMnT2bSpElkZWWxf/9+Dhw40OY+XlGUEjIKCgqYP38+xhjGjBnDmDFjmDVrFosWLSIvL4/Tp097PWKzUJQScqy1bNq0iU2bNhEbG8vdd99NUlIScXFxLFiwgP3794f01kOKUq4qMjKSxMREAoGA16NcVklJCa+//jphYWFERkZyzz33MHPmTE6cOMHSpUs5e/as1yNeN0UpVxQZGcmjjz5aF6bLampqOH/+PK+//jpRUVF0796dxx57jIiICJYsWcK+fftCZuMERSmXdTHIpUuXkpOTwy9/+UuvR2qwsrIy9u7dy8GDB4mJieHOO+/k1ltvpaCggHnz5lFUVOT1iFelKOVz4uPjuf/++1m2bBk5OTlej9No1dXVnDt3jjfffJP4+HhSU1P5yle+wunTp/mf//kfampqvB7xshSlXGL06NFMnjyZ119/PaSD/KyioiKKior485//TGJiorNBgqKUekaPHk2nTp344x//6PQvbVPU1NSQn5/v9RhXpSgF+N8g33///Tb3YXyoUZTtXExMDDNmzKCsrExBOkJRtmMxMTE89NBDbN26ld27dytIRyjKdmzSpEksWrSIU6dOeT2K1KMo27HFixd7PYJchnbdEnGMninlsowx9O/fn8jISODCga+kdShKASAtLY2kpCT69+/PsGHDsNayf/9+KisrAerilJanKNuhLl26EB0dTXx8PDNmzMDn85Gfn09hYSGffvopCxcuBLhkA4Lhw4d7NG37oyjbgc6dOxMIBLjzzjtJSUnh9OnTlJeXU1RUxG9/+1tqa2upqanRRyKOUJRtTGxsLLGxsYSFhdUdmbyoqIiamhqWLFlCfn4+FRUVCtBhijLExcbGEhERwdixYxk4cCDV1dVUV1dTU1PDO++8Q0lJCaWlpYowhCjKEBMTE4Pf7+fWW28lIyMDYy4cOnTDhg288MILlJeX60SuIU5ROszv9xMREUGPHj2YNGkScGEtqN/vZ/ny5axZs4aioiIqKio8nlSak6J0iN/vJywsjCFDhjB8+HAiIyOJi4vj8OHDLFiwAIDTp0/XfUwhbZOidEBcXBz33HMPycnJxMXFsWvXLlasWEFxcbFOW94OKUoHlJeXs3HjRk6fPk1hYaHX44jHFKUDqqqqOHjwoNdjiCO0QbpcU2pqqnbvakWKUq5pzJgxbN682esx2g1FKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIY7U8p15SXl8eIESNIS0sDYOPGjZw8edLjqdouRSnXFBkZSWFhIdnZ2QDMnj2bzp07c/DgQfLz8/n00085cuSIt0O2IcZa6/UMV2WMcXvAdmDmzJmcOXOGDRs2XLK8T58+pKWlMWDAAHr06MGRI0c4dOgQOTk57Nu3z6NpQ4e11lxuuaKUa7pSlJ/VvXt3evXqRXp6Ov379ycnJ4edO3dSUFDAJ5980krTho4rRamXr9Jsjh49ytGjR+v+3KlTJ0aMGEHXrl155JFHKCgoYO3atZSVlbFnzx4PJ3WbopQWc/LkyUvOFp2YmMjNN99MZGQkDz74IGVlZSxdupTKykr27dt3yVm+2jNFKa3m7NmzdQeV9vl8REdHc9tttxEZGcn999+PtZb33nuPsrIyDh8+3G4POq0oxRO1tbWUlJQwb9484MLR4f1+PzNnziQ6OprZs2fj9/tZsmQJp0+f5uTJk+3mHCmKUpxw8Wxh77zzDgCBQACfz8edd97JkCFDSExMJDw8nNWrV3Ps2DEKCgo4f/68x1O3DEUpTqqqqgKoe7kbERFBWFgYkydP5q677iIyMpLw8HC2bt3Krl27KC0tpaSkxMuRm42ilJBw8cxiF1ccXYxy1KhRPPLIIxhj8Pl8HDhwgHXr1tWdKDcU6XNKuaaGfk7ppcjISKKjo+nXrx833XQTf//73zl9+rTXY12VPqeUNu3iyXI3bNjg9D8eDaG9ROSqfD4ffr/+7W5NilI+xxhDIBDgnnvu4atf/SoPPPCA1yO1K/onUOoYY+jfvz+TJ08mLS2NJUuWsG3btrq9Q6R1KEph8ODB9O3blyFDhpCVlcW7775LTk4OcCHUxx9/nD//+c8eT9l+KMp2KjY2lvHjx9OvXz/y8vI4cOAACxcuvOzHCNHR0ZSWlnowZfukKNuZYcOGMXXqVMLDw/noo4946623yMvL83osqUdRtgOJiYlMnDiRMWPGsGPHDl555RXOnj3r9VhyBYqyjUpJSaF79+7ccsstVFZWkpmZyU9+8hOvx5IGUJRtSFhYGH379mXQoEF07tyZ48eP8/TTT1NcXOz1aHIdrhmlMeZvwF3AKWvt4OCy3wJ3A5VAFvBla22hMaYH8Clw8QAtG6y1Xwv+zCjgJSAKeB/4jnV9G78QkZaWxi233ELv3r3Jysri008/Ze7cueivN0RZa696ASYBI4Hd9ZbdDviDX/8a+HXw6x71r/eZ29kEjAMMsBiYfq37Dv6c1eXKl3vvvdd+/etftwMHDrR+v7/Zb98YY3/xi194/jjb4uVKv/PXfKa01q4JPgPWX7as3h83AFfd5MMY0xmIt9ZuCP75FeBeLsQpTbBo0SJqa2tDdo8I+bzm2Mzu/+HSuHoaY7YbY1YbY24OLksHTtS7zongMmmiqqoqBdnGNGlFjzHm/wWqgdeCi3KBbtbaM8H3kPONMYMacbtPAU81ZTaRUNXoZ0pjzJe4sALosYsrbKy1FdbaM8Gvt3JhJVA/IBvIqPfjGcFll2Wtfc5aO9paO7qx80nzsNaSl5fHN7/5TR544AHCwsK8HqnNa1SUxpg7gf8fcI+1trTe8hRjTFjw615AX+CQtTYXKDLGjDPGGOAJYEGTp5cWZ4whISGBuXPncvDgQe6//34iIiK8HqtNu2aUxpjXgfVAf2PMCWPMV4C/AnHAcmPMDmPMM8GrTwJ2GmN2AG8DX7PWXtx05BvA88BBLjyDaiVPiAgPDyc3N5cdO3awe/duvvOd79CnTx98Pu351yIa8rGElxccWHXdni+X+0gkPDzcPvnkk/axxx6zgUDA8xlD9XKl33n9UyfXrbKykpdffpnNmzfzgx/8gLvvvptAIOD1WG2GopRG279/P7/73e/Iy8vjBz/4AYMGXfeKdrkMbfsqTVJVVcWmTZvYunUrDz74IBMnTmT16tXs3bvX69FClqKUZlFTU8Mbb7xBeno6N998M927d2ft2rXaOboR9PJVmlV2djZvvvkm58+f59vf/ja33347MTExXo8VUhSlNDtrLZmZmfzqV7+itLSUBx98kD59+ng9VshQlNKiMjMzWbx4MePHj+fb3/42cXFxXo/kPL2nlBaXl5fHK6+8QkpKCnFxcdrp+hoUpbSa/Px8r0cICXr5KuIYRSlXZa0lNzeX73//+/Tr18/rcdoFRSlXZYwhJSWFF154gZEjR/Loo48SGxvr9VhtmqKUa/L7/RQWFvLOO++wfft2/uEf/oGBAwd6PVabpSilwaqqqti7dy9/+ctfGDRoEI888og2RG8BilKuW3V1NfPnz2f37t08+uijREZGej1Sm6Io5aoyMjI4fvz455ZXVVWxe/du1qxZw3e/+10GDx7swXRtk3H9gL3GGLcHbONmz57N4cOH2bFjxxWv4/f7ufvuuzlw4AC7d+9uveFCnLXWXG65nimlyaqrq1m4cCE33HADTz31lF7ONpGilGZRU1PDW2+9xfvvv88jjzyitbNNoCilWZ04cYKlS5cyZMgQRo0a5fU4IUlRSrPLzc3lzTffpLCw0OtRQpKilBaTlZXl9QghSVGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4Rif4kUukpKTQuXNnevbsyciRIxkwYAD//u//7vVY7YqibOcCgUBdgP379+fMmTPk5eVx+PBh3n33XWbPnu31iO2OomxnIiMj6dKlCx07dmTGjBnU1NRw4sQJtm3bxttvv01tbS21tbV116//tbQORdkOpKWlEQgEuPPOO+nYsSNnzpyhsLCQ3/72t1hrqays9HpEqUdRtjHGGJKTk/H7/dx77711Z0621rJ48WJOnjxJRUWF12PKVSjKEOfz+ejQoQMxMTHMnj0bv99PdXU11dXVLFiwgOLiYs6fP6+XoSFEUYag6OhoUlNTufvuuzHGEBYWRllZGW+++SZVVVUUFhbi+uko5MoUZQiIjIzE5/MxY8YMkpKSiI6OpqioiDlz5lBTU8Pp06e9HlGakaJ0UHh4OH6/n3vuuYeoqChSUlIICwtj8eLFbNiwgfz8/FZ7X+jzafuS1qYoHREfH8+MGTMIBAJ069aN2tpa3n33XUpLSzl+/DhVVVWtPpPP56Nfv368++67rX7f7ZmidEBiYiJPPvkky5Yto6Kigtdee82JFTPGGCIjI/WRSStTlA44e/Ysf/jDH7weQxyhNwwijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo5RlCKOUZQijlGUIo65ZpTGmL8ZY04ZY3bXW/YzY0y2MWZH8DKj3vd+ZIw5aIzZZ4y5o97yO4PLDhpj/rn5H4pI29CQZ8qXgDsvs/wP1trhwcv7AMaYgcAjwKDgz/yXMSbMGBMG/CcwHRgIPBq8roh8hv9aV7DWrjHG9Gjg7c0C3rDWVgCHjTEHgbHB7x201h4CMMa8EbzuJ9c/skjb1pT3lN8yxuwMvrxNCC5LB47Xu86J4LIrLReRz2hslE8DvYHhQC7w++YaCMAY85QxZosxZktz3q5IKLjmy9fLsdbmXfzaGPPfwHvBP2YDXetdNSO4jKssv9ztPwc8F7x925gZRUJVo54pjTGd6/1xNnBxzexC4BFjTIQxpifQF9gEbAb6GmN6GmPCubAyaGHjxxZpu675TGmMeR2YAiQbY04APwWmGGOGAxY4AvwjgLV2jzFmDhdW4FQD37TW1gRv51vAUiAM+Ju1dk9zPxiRtqAha18fvcziF65y/X8F/vUyy98H3r+u6UTaIW3RI+IYRSniGEUp4hhFKeIYRSniGEUp4hhFKeIYRSlXFBERQXl5uddjtDuKUq5o+vTpLF682Osx2h1FKVcUHh5OVVWV12O0O4pSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUsQxilLEMYpSxDGKUi4rLCyMlJQUTp486fUo7Y6ilMvy+Xx07NiRM2fOeD1Ku6MoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyjlsmpqanj//fe9HqNdMtZar2e4KmOM2wOKNJK11lxuuZ4pRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRxzzSiNMX8zxpwyxuyut+xNY8yO4OWIMWZHcHkPY0xZve89U+9nRhljdhljDhpj/myMMS3yiERCnbX2qhdgEjAS2H2F7/8e+Jfg1z2ucr1NwDjAAIuB6de67+DPWV10aYuXK/3OX/OZ0lq7Bjh7ue8Fn+0eAl6/2m0YYzoD8dbaDfZCaa8A917rvkXao6a+p7wZyLPWHqi3rKcxZrsxZrUx5ubgsnTgRL3rnAguE5HP8Dfx5x/l0mfJXKCbtfaMMWYUMN8YM+h6b9QY8xTwVBNnEwlJjY7SGOMH7gNGXVxmra0AKoJfbzXGZAH9gGwgo96PZwSXXZa19jngueD92MbOKBKKmvLy9VZgr7W27mWpMSbFGBMW/LoX0Bc4ZK3NBYqMMeOC70OfABY04b5F2qyGfCTyOrAe6G+MOWGM+UrwW4/w+RU8k4CdwY9I3ga+Zq29uJLoG8DzwEEgiwtrYEXkM0zwYwdn6eWrtFXW2st+Vq8tekQcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRyjKEUcoyhFHKMoRRxzzSiNMV2NMSuNMZ8YY/YYY74TXJ5ojFlujDkQ/G9CcLkxxvzZGHPQGLPTGDOy3m09Gbz+AWPMky33sERCmLX2qhegMzAy+HUcsB8YCPwG+Ofg8n8Gfh38egawGDDAOGBjcHkicCj434Tg1wkNuH+riy5t8XKl3/lrPlNaa3OttduCXxcDnwLpwCzg5eDVXgbuDX49C3jFXrAB6GiM6QzcASy31p611hYAy4E7r3X/Iu3Ndb2nNMb0AEYAG4E0a21u8FsngbTg1+nA8Xo/diK47ErLRaQef0OvaIyJBd4BvmutLTLG1H3PWmuNMba5hjLGPAU81Vy3JxJKGvRMaYwJcCHI16y1c4OL84IvSwn+91RweTbQtd6PZwSXXWn551hrn7PWjrbWjm7oAxFpKxqy9tUALwCfWmv/o963FgJPBr9+ElhQb/kTwbWw44BzwZe5S4HbjTEJwTW1tweXiUh9DVj7OZELa4t2AjuClxlAEvABcABYASQGr2+A/wSygF3A6Hq39f8AB4OXL1/rvrX2VZe2fLnS77wJ/uI7qznfq4q4xFprLrdcW/SIOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijhGUYo4RlGKOEZRijjG7/UADVAC7PN6iGaSDJz2eohmpMfTeN2v9I1QiHKftXa010M0B2PMlrbyWECPp6Xo5auIYxSliGNCIcrnvB6gGbWlxwJ6PC3CWGu9nkFE6gmFZ0qRdsXZKI0xdxpj9hljDhpj/tnreRrCGPM3Y8wpY8zuessSjTHLjTEHgv9NCC43xpg/Bx/fTmPMSO8m/zxjTFdjzEpjzCfGmD3GmO8El4fq44k0xmwyxnwcfDw/Dy7vaYzZGJz7TWNMeHB5RPDPB4Pf79Fqw1prnbsAYUAW0AsIBz4GBno9VwPmngSMBHbXW/Yb4J+DX/8z8Ovg1zOAxYABxgEbvZ7/M4+lMzAy+HUcsB8YGMKPxwCxwa8DwMbgnHOAR4LLnwG+Hvz6G8Azwa8fAd5stVm9/su6wl/gTcDSen/+EfAjr+dq4Ow9PhPlPqBz8OvOXPjcFeBZ4NHLXc/FC7AAuK0tPB4gGtgG3MiFjQX8weV1v3fAUuCm4Nf+4PVMa8zn6svXdOB4vT+fCC4LRWnW2tzg1yeBtODXIfMYgy/dRnDh2SVkH48xJswYswM4BSznwquxQmttdfAq9WeuezzB758DklpjTlejbJPshX92Q2p1tzEmFngH+K61tqj+90Lt8Vhra6y1w4EMYCwwwNuJLs/VKLOBrvX+nBFcForyjDGdAYL/PRVc7vxjNMYEuBDka9baucHFIft4LrLWFgIrufBytaMx5uLmpvVnrns8we93AM60xnyuRrkZ6BtcMxbOhTfaCz2eqbEWAk8Gv36SC+/NLi5/IrjWchxwrt7LQs8ZYwzwAvCptfY/6n0rVB9PijGmY/DrKC68P/6UC3E+ELzaZx/Pxcf5APBh8JVBy/P6TfdV3ozP4MIavyzg//V6ngbO/DqQC1Rx4f3JV7jwPuQD4ACwAkgMXtcA/xl8fLuA0V7P/5nHMpELL013AjuClxkh/HiGAtuDj2c38C/B5b2ATcBB4C0gIrg8Mvjng8Hv92qtWbVFj4hjXH35KtJuKUoRxyhKEccoShHHKEoRxyhKEccoShHHKEoRx/x/B4vlvBvZDs0AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.io import imread \n",
+ "from skimage.morphology import thin\n",
+ "import matplotlib.pyplot as plt\n",
+ "image = imread(\"images/dicot-sim-145-2-25.rsml.jpg\")\n",
+ "image = 255 - image\n",
+ "image = image / 255\n",
+ "image = (image>.5).astype(float)\n",
+ "image = thin(image, max_iter=2)\n",
+ "image = image.astype(float)\n",
+ "#image = resize(image, (256,128))\n",
+ "#image = (image>127).astype('float')\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(image, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(244000,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7fe621c3e430>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from skimage.feature import hog\n",
+ "out, viz = hog(image, visualize=True, pixels_per_cell=(30, 30), cells_per_block=(10, 10), orientations=20)\n",
+ "print(out.shape)\n",
+ "fig = plt.figure(figsize=(20,20))\n",
+ "plt.imshow(viz, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from skimage.transform import resize\n",
+ "import numpy as np\n",
+ "def features(image):\n",
+ " image = 255 - image\n",
+ " image = image / 255\n",
+ " image = resize(image, (800,400))\n",
+ " image = (image>.5).astype(float)\n",
+ " image = thin(image, max_iter=2)\n",
+ " image = image.astype(float)\n",
+ " return hog(image, pixels_per_cell=(40, 40), cells_per_block=(10, 10), orientations=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(22000,)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "image = imread(\"images/dicot-sim-1-1-25.rsml.jpg\")\n",
+ "features(image).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>image</th>\n",
+ " <th>tot_root_length</th>\n",
+ " <th>n_laterals</th>\n",
+ " <th>depth</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>monocot-sim-30-10-25.rsml.jpg</td>\n",
+ " <td>2375.78270</td>\n",
+ " <td>124</td>\n",
+ " <td>164.93068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>monocot-sim-348-4-18.rsml.jpg</td>\n",
+ " <td>10114.11100</td>\n",
+ " <td>496</td>\n",
+ " <td>189.99200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>dicot-sim-407-1-21.rsml.jpg</td>\n",
+ " <td>8269.03400</td>\n",
+ " <td>475</td>\n",
+ " <td>189.90733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>monocot-sim-427-10-15.rsml.jpg</td>\n",
+ " <td>9242.80500</td>\n",
+ " <td>516</td>\n",
+ " <td>204.97801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>dicot-sim-6-10-25.rsml.jpg</td>\n",
+ " <td>403.78925</td>\n",
+ " <td>25</td>\n",
+ " <td>104.98667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " image tot_root_length n_laterals depth\n",
+ "0 monocot-sim-30-10-25.rsml.jpg 2375.78270 124 164.93068\n",
+ "1 monocot-sim-348-4-18.rsml.jpg 10114.11100 496 189.99200\n",
+ "2 dicot-sim-407-1-21.rsml.jpg 8269.03400 475 189.90733\n",
+ "3 monocot-sim-427-10-15.rsml.jpg 9242.80500 516 204.97801\n",
+ "4 dicot-sim-6-10-25.rsml.jpg 403.78925 25 104.98667"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dataset(df):\n",
+ " from tqdm import tqdm\n",
+ " X = []\n",
+ " Y = []\n",
+ " for _, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
+ " X.append(features(imread(f\"images/{row.image}\")))\n",
+ " Y.append([row.tot_root_length, row.n_laterals, row.depth])\n",
+ " X = np.array(X)\n",
+ " Y = np.array(Y)\n",
+ " return X, Y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 1000/1000 [01:52<00:00, 8.89it/s]\n",
+ "100%|██████████| 1046/1046 [01:52<00:00, 9.28it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 3min 1s, sys: 4.45 s, total: 3min 6s\n",
+ "Wall time: 3min 45s\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_train = pd.read_csv('train.csv')\n",
+ "df_valid = pd.read_csv('submission_valid.csv')\n",
+ "X_train, Y_train = get_dataset(df_train.sample(1000))\n",
+ "X_valid, _ = get_dataset(df_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((1000, 22000), (1000, 3), (1046, 22000))"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train.shape, Y_train.shape, X_valid.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.51420135, 0.56140878, 0.50610067, 0.48608223, 0.45654623])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "model = RandomForestRegressor(n_estimators=50, n_jobs=-1)\n",
+ "cross_val_score(model, X_train, Y_train, scoring='r2', )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestRegressor(n_estimators=50, n_jobs=-1)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(X_train, Y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Ypred = model.predict(X_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"submission_valid.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[:, [\"tot_root_length\", \"n_laterals\", \"depth\"]] = Ypred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(\"submission.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can open the submision.csv file (File -> Open) file and download it!\n",
+ "\n",
+ "After you download it, you can upload it to the challenge frontend."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hhh_env",
+ "language": "python",
+ "name": "hhh_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
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diff --git a/submission.csv b/submission.csv
new file mode 100644
index 0000000..5986b70
--- /dev/null
+++ b/submission.csv
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diff --git a/submission_valid.csv b/submission_valid.csv
new file mode 100644
index 0000000..86cfa90
--- /dev/null
+++ b/submission_valid.csv
@@ -0,0 +1,1047 @@
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--
GitLab