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# BirdIdentifier # BirdIdentifier 🐦
## :memo: Todo ## Bonjour et bienvenue sur le dépôt du projet BirdIdentifier ! 👋
* Comment representer les images ? Traitement d'image ? Enlever le bg, identifier le oiseau etc...
*******
Sommaire
1. [Accessibilité](#acces)
2. [Présentation du projet](#presentation)
3. [Notre jeu de données](#dataset)
4. [Description des librairies](#libraries)
5. [Auteurs](#auteurs)
*******
<div id='acces'/>
## Accessibilité ↗️
> **Warning**: Le déploiement n'a pas encore été fait.
*******
<div id='dataset'/>
## Notre jeu de données 📁
Notre jeu de données contient plus de **80000 images** de plus de **500 espèces d'oiseaux** différentes ! Parmi ces images, nous avons des images **d'entraînement**, de **test** et **d'imagination**. Il s'agit d'un jeu de données de très haute qualité, où chaque image ne contient qu'**un seul oiseau** occupant généralement **au moins 50 % des pixels** de l'image. En conséquence, même un modèle modérément complexe atteindra des précisions d'entraînement et de test dans la plage des **90 %**.
Toutes les images sont au **format jpg** et ont une **taille de 224 x 224 x 3 pixels en couleur**. Le jeu de données inclut également un fichier **birds.csv**. Ce fichier CSV contient **5 colonnes** :
- la colonne **filepaths** contient le chemin relatif du fichier image,
- la colonne **labels** contient le nom de la classe d'espèce d'oiseau associée au fichier image,
- la colonne **scientific label** contient le nom scientifique latin de l'image,
- la colonne **data set** indique dans quel ensemble de données (entraînement, test ou validation) se trouve le chemin du fichier,
- la colonne **class_id** contient la valeur d'index de classe associée à la classe du fichier image.
*******
<div id='libraries'/>
## Librairies utilisées 📚
### **cv2**
> OpenCV (Open Source Computer Vision) est une bibliothèque open-source spécialisée dans le traitement d'images et la vision par ordinateur. En Python, la version la plus couramment utilisée de cette bibliothèque est appelée **cv2**.
***Fonctionnalités*** :
*Traitement d'Images* : OpenCV offre un ensemble complet de fonctionnalités pour lire, écrire, manipuler et traiter des images.
*Détection d'Objets* : La bibliothèque propose des outils puissants pour la détection d'objets, y compris la reconnaissance faciale, la détection de contours et la correspondance des formes.
*Transformation et Filtrage* : OpenCV permet la transformation d'images, la convolution, le filtrage et d'autres opérations permettant de modifier l'apparence des images.
*Vision par Ordinateur* : Idéale pour le développement de projets de vision par ordinateur, OpenCV fournit des algorithmes pour le suivi d'objets, la stéréovision, la calibration de caméra, etc.
[Documentation Officielle](https://opencv.org/)
### **os.path**
> Le module **os.path** fait partie du module **os** en Python et offre des fonctionnalités spécifiques pour la manipulation des chemins de fichiers et des noms de fichiers.
***Fonctionnalités*** :
*Manipulation de Chemins* : Le module os.path fournit des méthodes pour manipuler des chemins de fichiers de manière portable entre les systèmes d'exploitation, en prenant en compte les différences dans les séparateurs de répertoire (/ ou \) et les conventions spécifiques à chaque plateforme.
*Validation de Chemins* : Vous pouvez utiliser les fonctions du module pour vérifier l'existence de fichiers ou de répertoires, tester si un chemin est absolu ou relatif, et obtenir des informations sur les fichiers comme la taille ou la date de modification.
*Construction de Chemins* : Facilite la création de chemins de fichiers en combinant des répertoires et des noms de fichiers de manière sûre et portable.
[Documentation Officielle](https://docs.python.org/3/library/os.path.html)
### **matplotlib.pyplot**
> **matplotlib.pyplot** est un module de la bibliothèque Matplotlib, largement utilisée pour la création de graphiques et de visualisations en Python. Ce module spécifique fournit une interface similaire à celle de MATLAB, facilitant la création de graphiques de manière interactive.
***Fonctionnalités*** :
*Création de Graphiques* : Matplotlib permet de créer une variété de graphiques, y compris des tracés de lignes, des histogrammes, des diagrammes à barres, des diagrammes en boîte, etc.
*Personnalisation* : Vous pouvez personnaliser chaque aspect du graphique, y compris les étiquettes d'axe, les titres, les couleurs, les styles de ligne, et plus encore.
*Visualisation en Temps Réel* : Idéal pour l'exploration de données, le module pyplot facilite la création de graphiques interactifs pour visualiser des données en temps réel.
[Documentation Officielle](https://matplotlib.org/3.5.3/api/_as_gen/matplotlib.pyplot.html)
### **sklearn**
> **scikit-learn**, également connu sous le nom de **sklearn**, est une bibliothèque open-source en Python dédiée à l'apprentissage automatique (machine learning). Elle offre des outils simples et efficaces pour la classification, la régression, le clustering, la réduction de dimensionnalité, et bien plus encore.
***Fonctionnalités*** :
*Large Gamme d'Algorithmes* : scikit-learn propose une variété d'algorithmes d'apprentissage automatique, allant des méthodes classiques aux techniques avancées, couvrant la plupart des besoins en modélisation.
*Facilité d'Utilisation* : La bibliothèque est conçue pour être conviviale, avec une API cohérente et une documentation détaillée, facilitant la prise en main même pour les débutants.
*Traitement de Données* : sklearn fournit des outils pour la préparation et la transformation des données, y compris la normalisation, la standardisation, le traitement des valeurs manquantes, et plus encore.
*Évaluation des Modèles* : Des fonctions d'évaluation de modèles telles que la validation croisée, les courbes ROC, et les métriques de performance facilitent l'évaluation des performances des modèles.
[Documentation Officielle](https://scikit-learn.org/stable/)
*******
<div id='presentation'/>
## **Présentation** 🎉
BirdIdentifier : Votre identificateur d'oiseaux à partir d'une photo !
*******
<div id='auteurs'/>
## **Auteurs** 👥
Étudiant 3ème Annnée - BUT Informatique - IUT Clermont Auvergne - 2023-2024
`BRODA Lou` - `FRANCO Nicolas`

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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "ValueError", "name": "stdout",
"evalue": "Found array with dim 4. DecisionTreeClassifier expected <= 2.", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "[[[[0.16862745 0.18823529 0.19215686]\n",
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", " [0.16862745 0.18823529 0.19215686]\n",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", " [0.17254902 0.19215686 0.20392157]\n",
"Cell \u001b[1;32mIn[27], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m clf \u001b[38;5;241m=\u001b[39m tree\u001b[38;5;241m.\u001b[39mDecisionTreeClassifier()\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m clf \u001b[38;5;241m=\u001b[39m \u001b[43mclf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Predict on the test set\u001b[39;00m\n\u001b[0;32m 13\u001b[0m predictions \u001b[38;5;241m=\u001b[39m clf\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", " ...\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:1351\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1344\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1346\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1347\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1348\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1349\u001b[0m )\n\u001b[0;32m 1350\u001b[0m ):\n\u001b[1;32m-> 1351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", " [0.49411765 0.60392157 0.67058824]\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\tree\\_classes.py:1009\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m 978\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 979\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m 980\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[0;32m 981\u001b[0m \n\u001b[0;32m 982\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1006\u001b[0m \u001b[38;5;124;03m Fitted estimator.\u001b[39;00m\n\u001b[0;32m 1007\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1009\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1010\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1011\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1012\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1013\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1014\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1015\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", " [0.49019608 0.6 0.66666667]\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\tree\\_classes.py:252\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[1;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m 248\u001b[0m check_X_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m 249\u001b[0m dtype\u001b[38;5;241m=\u001b[39mDTYPE, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 250\u001b[0m )\n\u001b[0;32m 251\u001b[0m check_y_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 252\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_separately\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_y_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 254\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 256\u001b[0m missing_values_in_feature_mask \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 257\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_missing_values_in_feature_mask(X)\n\u001b[0;32m 258\u001b[0m )\n\u001b[0;32m 259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n", " [0.49019608 0.6 0.66666667]]\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:645\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m 643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_X_params:\n\u001b[0;32m 644\u001b[0m check_X_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_X_params}\n\u001b[1;32m--> 645\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_y_params:\n\u001b[0;32m 647\u001b[0m check_y_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params}\n", "\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\utils\\validation.py:997\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 992\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 993\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 994\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 995\u001b[0m )\n\u001b[0;32m 996\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_nd \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m--> 997\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 998\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 999\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m 1000\u001b[0m )\n\u001b[0;32m 1002\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m 1003\u001b[0m _assert_all_finite(\n\u001b[0;32m 1004\u001b[0m array,\n\u001b[0;32m 1005\u001b[0m input_name\u001b[38;5;241m=\u001b[39minput_name,\n\u001b[0;32m 1006\u001b[0m estimator_name\u001b[38;5;241m=\u001b[39mestimator_name,\n\u001b[0;32m 1007\u001b[0m allow_nan\u001b[38;5;241m=\u001b[39mforce_all_finite \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 1008\u001b[0m )\n", " [[0.16862745 0.18823529 0.19215686]\n",
"\u001b[1;31mValueError\u001b[0m: Found array with dim 4. DecisionTreeClassifier expected <= 2." " [0.17254902 0.19215686 0.19607843]\n",
" [0.17647059 0.19607843 0.20784314]\n",
" ...\n",
" [0.49411765 0.60392157 0.67058824]\n",
" [0.49411765 0.60392157 0.67058824]\n",
" [0.49411765 0.60392157 0.67058824]]\n",
"\n",
" [[0.17254902 0.19215686 0.20392157]\n",
" [0.17254902 0.19215686 0.20392157]\n",
" [0.17647059 0.19607843 0.20784314]\n",
" ...\n",
" [0.50196078 0.60392157 0.67058824]\n",
" [0.49803922 0.6 0.66666667]\n",
" [0.49803922 0.6 0.66666667]]\n",
"\n",
" ...\n",
"\n",
" [[0.41568627 0.38823529 0.37647059]\n",
" [0.41568627 0.38823529 0.37647059]\n",
" [0.41568627 0.38823529 0.37647059]\n",
" ...\n",
" [0.58039216 0.63921569 0.65098039]\n",
" [0.61176471 0.6627451 0.69411765]\n",
" [0.6 0.6627451 0.69019608]]\n",
"\n",
" [[0.38431373 0.35686275 0.34509804]\n",
" [0.38039216 0.35294118 0.34117647]\n",
" [0.37647059 0.34901961 0.3372549 ]\n",
" ...\n",
" [0.58039216 0.63921569 0.65098039]\n",
" [0.60784314 0.65882353 0.69019608]\n",
" [0.6 0.65098039 0.68235294]]\n",
"\n",
" [[0.36078431 0.33333333 0.32156863]\n",
" [0.34117647 0.31372549 0.30196078]\n",
" [0.3254902 0.29803922 0.28627451]\n",
" ...\n",
" [0.59607843 0.64313725 0.65882353]\n",
" [0.58431373 0.63529412 0.66666667]\n",
" [0.58039216 0.63137255 0.6627451 ]]]\n",
"\n",
"\n",
" [[[0.85882353 0.7372549 0.60784314]\n",
" [0.8627451 0.74117647 0.61176471]\n",
" [0.8627451 0.74117647 0.61176471]\n",
" ...\n",
" [0.87058824 0.74509804 0.60784314]\n",
" [0.86666667 0.74117647 0.60392157]\n",
" [0.8627451 0.7372549 0.6 ]]\n",
"\n",
" [[0.85882353 0.7372549 0.60784314]\n",
" [0.86666667 0.74509804 0.61568627]\n",
" [0.86666667 0.74509804 0.61568627]\n",
" ...\n",
" [0.87058824 0.74509804 0.60784314]\n",
" [0.87058824 0.74509804 0.60784314]\n",
" [0.86666667 0.74117647 0.60392157]]\n",
"\n",
" [[0.85882353 0.7372549 0.60784314]\n",
" [0.86666667 0.74509804 0.61568627]\n",
" [0.86666667 0.74509804 0.61568627]\n",
" ...\n",
" [0.8627451 0.74117647 0.61176471]\n",
" [0.8627451 0.74117647 0.61176471]\n",
" [0.85882353 0.7372549 0.60784314]]\n",
"\n",
" ...\n",
"\n",
" [[0.8745098 0.74509804 0.61568627]\n",
" [0.87058824 0.74117647 0.61176471]\n",
" [0.88235294 0.74509804 0.61568627]\n",
" ...\n",
" [0.85882353 0.7372549 0.60784314]\n",
" [0.85882353 0.7372549 0.60784314]\n",
" [0.85882353 0.7372549 0.60784314]]\n",
"\n",
" [[0.88235294 0.74509804 0.61568627]\n",
" [0.87843137 0.74117647 0.61176471]\n",
" [0.89411765 0.74509804 0.61960784]\n",
" ...\n",
" [0.85490196 0.73333333 0.60392157]\n",
" [0.85490196 0.73333333 0.60392157]\n",
" [0.85490196 0.73333333 0.60392157]]\n",
"\n",
" [[0.8745098 0.7372549 0.60784314]\n",
" [0.8745098 0.7372549 0.60784314]\n",
" [0.89019608 0.74117647 0.61568627]\n",
" ...\n",
" [0.85490196 0.73333333 0.60392157]\n",
" [0.85490196 0.73333333 0.60392157]\n",
" [0.85490196 0.73333333 0.60392157]]]\n",
"\n",
"\n",
" [[[0.35686275 0.56862745 0.56862745]\n",
" [0.36078431 0.57254902 0.57254902]\n",
" [0.36862745 0.58039216 0.58039216]\n",
" ...\n",
" [0.11764706 0.33333333 0.2745098 ]\n",
" [0.11764706 0.32941176 0.27843137]\n",
" [0.12156863 0.33333333 0.28235294]]\n",
"\n",
" [[0.34509804 0.55686275 0.55686275]\n",
" [0.34901961 0.56078431 0.56078431]\n",
" [0.35686275 0.56862745 0.56862745]\n",
" ...\n",
" [0.11764706 0.33333333 0.2745098 ]\n",
" [0.12156863 0.33333333 0.28235294]\n",
" [0.12156863 0.33333333 0.28235294]]\n",
"\n",
" [[0.31764706 0.54117647 0.5372549 ]\n",
" [0.32156863 0.54509804 0.54117647]\n",
" [0.32941176 0.55294118 0.54901961]\n",
" ...\n",
" [0.11372549 0.3372549 0.27843137]\n",
" [0.11372549 0.33333333 0.28235294]\n",
" [0.11764706 0.3372549 0.28627451]]\n",
"\n",
" ...\n",
"\n",
" [[0.28627451 0.23921569 0.23921569]\n",
" [0.28627451 0.23921569 0.23921569]\n",
" [0.28627451 0.23921569 0.23921569]\n",
" ...\n",
" [0.31372549 0.39607843 0.52156863]\n",
" [0.31764706 0.4 0.5254902 ]\n",
" [0.31372549 0.39607843 0.52156863]]\n",
"\n",
" [[0.27843137 0.23921569 0.23921569]\n",
" [0.27843137 0.23921569 0.23921569]\n",
" [0.28235294 0.24313725 0.24313725]\n",
" ...\n",
" [0.22352941 0.30588235 0.43137255]\n",
" [0.23137255 0.31372549 0.43529412]\n",
" [0.23921569 0.32156863 0.44313725]]\n",
"\n",
" [[0.27843137 0.23921569 0.23921569]\n",
" [0.28235294 0.24313725 0.24313725]\n",
" [0.28627451 0.24705882 0.24705882]\n",
" ...\n",
" [0.23921569 0.32156863 0.44705882]\n",
" [0.2745098 0.35686275 0.47843137]\n",
" [0.31372549 0.39607843 0.51764706]]]\n",
"\n",
"\n",
" ...\n",
"\n",
"\n",
" [[[0.74509804 0.7372549 0.7372549 ]\n",
" [0.74901961 0.74117647 0.74117647]\n",
" [0.74509804 0.74509804 0.74509804]\n",
" ...\n",
" [0.69803922 0.69019608 0.69019608]\n",
" [0.69019608 0.68235294 0.68235294]\n",
" [0.68627451 0.67843137 0.67843137]]\n",
"\n",
" [[0.74901961 0.74117647 0.74117647]\n",
" [0.75294118 0.74509804 0.74509804]\n",
" [0.74901961 0.74901961 0.74901961]\n",
" ...\n",
" [0.69803922 0.69019608 0.69019608]\n",
" [0.69019608 0.68235294 0.68235294]\n",
" [0.68627451 0.67843137 0.67843137]]\n",
"\n",
" [[0.74901961 0.74117647 0.74117647]\n",
" [0.75294118 0.74509804 0.74509804]\n",
" [0.75686275 0.74901961 0.74901961]\n",
" ...\n",
" [0.69803922 0.69019608 0.69019608]\n",
" [0.69411765 0.68627451 0.68627451]\n",
" [0.69019608 0.68235294 0.68235294]]\n",
"\n",
" ...\n",
"\n",
" [[0.76078431 0.77254902 0.78823529]\n",
" [0.77647059 0.78823529 0.80392157]\n",
" [0.61568627 0.62745098 0.64313725]\n",
" ...\n",
" [0.70196078 0.68235294 0.67843137]\n",
" [0.69803922 0.67843137 0.6745098 ]\n",
" [0.69411765 0.6745098 0.67058824]]\n",
"\n",
" [[0.78039216 0.79607843 0.81568627]\n",
" [0.75294118 0.76862745 0.78823529]\n",
" [0.62352941 0.63921569 0.65882353]\n",
" ...\n",
" [0.70196078 0.68235294 0.67843137]\n",
" [0.69803922 0.67843137 0.6745098 ]\n",
" [0.69411765 0.6745098 0.67058824]]\n",
"\n",
" [[0.73333333 0.74901961 0.76862745]\n",
" [0.71764706 0.73333333 0.75294118]\n",
" [0.70980392 0.7254902 0.74509804]\n",
" ...\n",
" [0.70196078 0.68235294 0.67843137]\n",
" [0.69803922 0.67843137 0.6745098 ]\n",
" [0.69411765 0.6745098 0.67058824]]]\n",
"\n",
"\n",
" [[[0.00784314 0.2627451 0.2 ]\n",
" [0.00784314 0.2627451 0.2 ]\n",
" [0.00784314 0.2627451 0.20392157]\n",
" ...\n",
" [0.0745098 0.75294118 0.63529412]\n",
" [0.07843137 0.75686275 0.63921569]\n",
" [0.07843137 0.75686275 0.63921569]]\n",
"\n",
" [[0.00392157 0.25882353 0.19607843]\n",
" [0.00784314 0.2627451 0.2 ]\n",
" [0.01176471 0.26666667 0.20784314]\n",
" ...\n",
" [0.07843137 0.75686275 0.63921569]\n",
" [0.08235294 0.76078431 0.64313725]\n",
" [0.08627451 0.76470588 0.64705882]]\n",
"\n",
" [[0.00392157 0.25882353 0.19607843]\n",
" [0.00784314 0.2627451 0.2 ]\n",
" [0.01176471 0.26666667 0.20784314]\n",
" ...\n",
" [0.08235294 0.76470588 0.64705882]\n",
" [0.09019608 0.77254902 0.65490196]\n",
" [0.09411765 0.77647059 0.65882353]]\n",
"\n",
" ...\n",
"\n",
" [[0.1372549 0.30980392 0.19607843]\n",
" [0.1254902 0.29803922 0.18431373]\n",
" [0.11372549 0.29411765 0.18039216]\n",
" ...\n",
" [0.35686275 0.94509804 0.85098039]\n",
" [0.34509804 0.94509804 0.84705882]\n",
" [0.34509804 0.94509804 0.84705882]]\n",
"\n",
" [[0.15686275 0.31764706 0.21176471]\n",
" [0.1372549 0.29803922 0.19215686]\n",
" [0.1254902 0.29411765 0.18823529]\n",
" ...\n",
" [0.4 0.97254902 0.8745098 ]\n",
" [0.37254902 0.97254902 0.8745098 ]\n",
" [0.35294118 0.95686275 0.85882353]]\n",
"\n",
" [[0.16862745 0.32156863 0.21960784]\n",
" [0.14509804 0.30588235 0.2 ]\n",
" [0.1372549 0.29803922 0.19215686]\n",
" ...\n",
" [0.41960784 0.99215686 0.89411765]\n",
" [0.38431373 0.97254902 0.87843137]\n",
" [0.36078431 0.96470588 0.86666667]]]\n",
"\n",
"\n",
" [[[0.41176471 0.40784314 0.39215686]\n",
" [0.41176471 0.40784314 0.39215686]\n",
" [0.41960784 0.41568627 0.4 ]\n",
" ...\n",
" [0.5254902 0.52156863 0.50588235]\n",
" [0.52156863 0.51764706 0.50196078]\n",
" [0.51372549 0.50980392 0.49411765]]\n",
"\n",
" [[0.40784314 0.40392157 0.38823529]\n",
" [0.41176471 0.40784314 0.39215686]\n",
" [0.41960784 0.41568627 0.4 ]\n",
" ...\n",
" [0.52156863 0.51764706 0.50196078]\n",
" [0.51764706 0.51372549 0.49803922]\n",
" [0.50980392 0.50588235 0.49019608]]\n",
"\n",
" [[0.40784314 0.40784314 0.38431373]\n",
" [0.41176471 0.41176471 0.38823529]\n",
" [0.42352941 0.41960784 0.40392157]\n",
" ...\n",
" [0.50980392 0.50588235 0.49019608]\n",
" [0.50588235 0.50588235 0.48235294]\n",
" [0.50196078 0.50196078 0.47843137]]\n",
"\n",
" ...\n",
"\n",
" [[0.88235294 0.84313725 0.84313725]\n",
" [0.90588235 0.86666667 0.86666667]\n",
" [0.90980392 0.87058824 0.87058824]\n",
" ...\n",
" [0.39607843 0.38039216 0.37647059]\n",
" [0.39215686 0.37254902 0.36078431]\n",
" [0.38431373 0.36470588 0.35294118]]\n",
"\n",
" [[0.8745098 0.83137255 0.83921569]\n",
" [0.89803922 0.85490196 0.8627451 ]\n",
" [0.90196078 0.85882353 0.86666667]\n",
" ...\n",
" [0.40392157 0.38823529 0.38431373]\n",
" [0.39607843 0.37647059 0.36470588]\n",
" [0.38823529 0.36862745 0.35686275]]\n",
"\n",
" [[0.8745098 0.83137255 0.83921569]\n",
" [0.89803922 0.85490196 0.8627451 ]\n",
" [0.89411765 0.85098039 0.85882353]\n",
" ...\n",
" [0.41568627 0.4 0.39607843]\n",
" [0.40392157 0.38431373 0.37254902]\n",
" [0.39215686 0.37254902 0.36078431]]]]\n",
"[[[[0.36078431 0.27843137 0.27058824]\n",
" [0.36470588 0.28235294 0.2745098 ]\n",
" [0.36862745 0.29019608 0.27058824]\n",
" ...\n",
" [0.26666667 0.20784314 0.22352941]\n",
" [0.26666667 0.21176471 0.21960784]\n",
" [0.2627451 0.20784314 0.21568627]]\n",
"\n",
" [[0.35686275 0.2745098 0.26666667]\n",
" [0.36078431 0.27843137 0.27058824]\n",
" [0.36470588 0.28627451 0.26666667]\n",
" ...\n",
" [0.26666667 0.20784314 0.22352941]\n",
" [0.25882353 0.20392157 0.21176471]\n",
" [0.25098039 0.19607843 0.20392157]]\n",
"\n",
" [[0.35294118 0.27058824 0.2627451 ]\n",
" [0.36078431 0.27843137 0.27058824]\n",
" [0.36470588 0.28235294 0.2745098 ]\n",
" ...\n",
" [0.25882353 0.20392157 0.21176471]\n",
" [0.25098039 0.19607843 0.20392157]\n",
" [0.24705882 0.19215686 0.2 ]]\n",
"\n",
" ...\n",
"\n",
" [[0.98823529 0.99607843 0.99607843]\n",
" [0.98823529 0.99607843 0.99607843]\n",
" [0.99607843 0.99607843 0.99607843]\n",
" ...\n",
" [0.61176471 0.6627451 0.71764706]\n",
" [0.61960784 0.67058824 0.73333333]\n",
" [0.62745098 0.67843137 0.73333333]]\n",
"\n",
" [[0.99215686 0.99215686 0.99215686]\n",
" [0.99215686 0.99215686 0.99215686]\n",
" [0.99607843 0.99607843 0.99607843]\n",
" ...\n",
" [0.69411765 0.74117647 0.78823529]\n",
" [0.70196078 0.74509804 0.8 ]\n",
" [0.69411765 0.74117647 0.78823529]]\n",
"\n",
" [[1. 1. 1. ]\n",
" [1. 1. 1. ]\n",
" [1. 1. 1. ]\n",
" ...\n",
" [0.71764706 0.76470588 0.81176471]\n",
" [0.7254902 0.76078431 0.81176471]\n",
" [0.71764706 0.75294118 0.80392157]]]\n",
"\n",
"\n",
" [[[0.40392157 0.45882353 0.45490196]\n",
" [0.36862745 0.42352941 0.41960784]\n",
" [0.34901961 0.41176471 0.40784314]\n",
" ...\n",
" [0.40392157 0.38431373 0.38039216]\n",
" [0.39607843 0.37647059 0.37254902]\n",
" [0.39215686 0.37254902 0.36862745]]\n",
"\n",
" [[0.2627451 0.3254902 0.32156863]\n",
" [0.31372549 0.37647059 0.37254902]\n",
" [0.37254902 0.44313725 0.43921569]\n",
" ...\n",
" [0.31372549 0.29411765 0.29019608]\n",
" [0.32941176 0.30980392 0.30588235]\n",
" [0.34901961 0.32941176 0.3254902 ]]\n",
"\n",
" [[0.17254902 0.2627451 0.24705882]\n",
" [0.20392157 0.29411765 0.27843137]\n",
" [0.24705882 0.3372549 0.32156863]\n",
" ...\n",
" [0.2627451 0.23921569 0.24313725]\n",
" [0.25490196 0.23137255 0.23529412]\n",
" [0.25490196 0.23137255 0.23529412]]\n",
"\n",
" ...\n",
"\n",
" [[0.39215686 0.32941176 0.30196078]\n",
" [0.35686275 0.30588235 0.2745098 ]\n",
" [0.34901961 0.29803922 0.26666667]\n",
" ...\n",
" [0.16470588 0.27843137 0.26666667]\n",
" [0.17647059 0.29019608 0.27843137]\n",
" [0.16470588 0.28627451 0.2745098 ]]\n",
"\n",
" [[0.36078431 0.30588235 0.28235294]\n",
" [0.35294118 0.30588235 0.28235294]\n",
" [0.34117647 0.29411765 0.27058824]\n",
" ...\n",
" [0.21568627 0.34117647 0.32156863]\n",
" [0.23921569 0.36470588 0.34509804]\n",
" [0.23137255 0.36470588 0.34117647]]\n",
"\n",
" [[0.37647059 0.32941176 0.30588235]\n",
" [0.36470588 0.31764706 0.29411765]\n",
" [0.38823529 0.34117647 0.31764706]\n",
" ...\n",
" [0.30980392 0.43529412 0.41568627]\n",
" [0.37647059 0.50980392 0.48627451]\n",
" [0.38431373 0.51764706 0.49411765]]]\n",
"\n",
"\n",
" [[[0.25882353 0.25882353 0.25882353]\n",
" [0.25490196 0.25490196 0.25490196]\n",
" [0.25490196 0.25490196 0.25490196]\n",
" ...\n",
" [0.30588235 0.28235294 0.28627451]\n",
" [0.31764706 0.29411765 0.29803922]\n",
" [0.3254902 0.30196078 0.30588235]]\n",
"\n",
" [[0.25882353 0.25882353 0.25882353]\n",
" [0.25490196 0.25490196 0.25490196]\n",
" [0.25490196 0.25490196 0.25490196]\n",
" ...\n",
" [0.30196078 0.27843137 0.28235294]\n",
" [0.30588235 0.28235294 0.28627451]\n",
" [0.30980392 0.28627451 0.29019608]]\n",
"\n",
" [[0.25882353 0.25490196 0.2627451 ]\n",
" [0.25490196 0.25098039 0.25882353]\n",
" [0.25490196 0.25490196 0.25490196]\n",
" ...\n",
" [0.29803922 0.2745098 0.27843137]\n",
" [0.29803922 0.2745098 0.27843137]\n",
" [0.29803922 0.2745098 0.27843137]]\n",
"\n",
" ...\n",
"\n",
" [[0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" ...\n",
" [0.58431373 0.62745098 0.70588235]\n",
" [0.49411765 0.5372549 0.62352941]\n",
" [0.42352941 0.46666667 0.55294118]]\n",
"\n",
" [[0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" ...\n",
" [0.34117647 0.39607843 0.46666667]\n",
" [0.28235294 0.3372549 0.41176471]\n",
" [0.31764706 0.37254902 0.44705882]]\n",
"\n",
" [[0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" [0.25490196 0.25882353 0.2745098 ]\n",
" ...\n",
" [0.4745098 0.52941176 0.6 ]\n",
" [0.51372549 0.56862745 0.64313725]\n",
" [0.49019608 0.54509804 0.61960784]]]\n",
"\n",
"\n",
" ...\n",
"\n",
"\n",
" [[[0.70588235 0.52941176 0.39607843]\n",
" [0.70196078 0.5254902 0.39215686]\n",
" [0.69803922 0.52156863 0.38823529]\n",
" ...\n",
" [0.68235294 0.50588235 0.37254902]\n",
" [0.67843137 0.50196078 0.36862745]\n",
" [0.6745098 0.49803922 0.36470588]]\n",
"\n",
" [[0.70196078 0.5254902 0.39215686]\n",
" [0.70196078 0.5254902 0.39215686]\n",
" [0.69803922 0.52156863 0.38823529]\n",
" ...\n",
" [0.68235294 0.50588235 0.37254902]\n",
" [0.67843137 0.50196078 0.36862745]\n",
" [0.67843137 0.50196078 0.36862745]]\n",
"\n",
" [[0.70196078 0.5254902 0.39215686]\n",
" [0.70196078 0.5254902 0.39215686]\n",
" [0.70196078 0.5254902 0.39215686]\n",
" ...\n",
" [0.67843137 0.50196078 0.36862745]\n",
" [0.67843137 0.50196078 0.36862745]\n",
" [0.67843137 0.50196078 0.36862745]]\n",
"\n",
" ...\n",
"\n",
" [[0.68627451 0.5254902 0.39607843]\n",
" [0.68627451 0.52941176 0.39215686]\n",
" [0.69411765 0.52941176 0.38431373]\n",
" ...\n",
" [0.6745098 0.50588235 0.37647059]\n",
" [0.6745098 0.50588235 0.37647059]\n",
" [0.6745098 0.50588235 0.37647059]]\n",
"\n",
" [[0.6745098 0.5254902 0.39215686]\n",
" [0.68627451 0.52941176 0.39215686]\n",
" [0.69411765 0.52941176 0.38431373]\n",
" ...\n",
" [0.67843137 0.50980392 0.38039216]\n",
" [0.67843137 0.50980392 0.38039216]\n",
" [0.67843137 0.50980392 0.38039216]]\n",
"\n",
" [[0.6745098 0.5254902 0.39215686]\n",
" [0.68235294 0.5254902 0.38823529]\n",
" [0.69411765 0.52941176 0.38431373]\n",
" ...\n",
" [0.67843137 0.50980392 0.38039216]\n",
" [0.67843137 0.50980392 0.38039216]\n",
" [0.6745098 0.50588235 0.37647059]]]\n",
"\n",
"\n",
" [[[0.38431373 0.36862745 0.36470588]\n",
" [0.38431373 0.36862745 0.36470588]\n",
" [0.38823529 0.37254902 0.36862745]\n",
" ...\n",
" [0.35294118 0.34509804 0.34117647]\n",
" [0.34901961 0.34117647 0.3372549 ]\n",
" [0.35294118 0.34509804 0.34117647]]\n",
"\n",
" [[0.38039216 0.36470588 0.36078431]\n",
" [0.38431373 0.36862745 0.36470588]\n",
" [0.38431373 0.36862745 0.36470588]\n",
" ...\n",
" [0.35294118 0.34509804 0.34117647]\n",
" [0.35294118 0.34509804 0.34117647]\n",
" [0.35294118 0.34509804 0.34117647]]\n",
"\n",
" [[0.38039216 0.36470588 0.36078431]\n",
" [0.38039216 0.36470588 0.36078431]\n",
" [0.38431373 0.36862745 0.36470588]\n",
" ...\n",
" [0.36078431 0.34509804 0.34117647]\n",
" [0.35294118 0.34509804 0.34117647]\n",
" [0.35294118 0.34509804 0.34117647]]\n",
"\n",
" ...\n",
"\n",
" [[0.0627451 0.19607843 0.24705882]\n",
" [0.09411765 0.22745098 0.27843137]\n",
" [0.09803922 0.23137255 0.28235294]\n",
" ...\n",
" [0.29803922 0.28235294 0.27843137]\n",
" [0.30196078 0.28627451 0.28235294]\n",
" [0.30196078 0.28627451 0.28235294]]\n",
"\n",
" [[0.16862745 0.30588235 0.34509804]\n",
" [0.21176471 0.34901961 0.38823529]\n",
" [0.2 0.3372549 0.38823529]\n",
" ...\n",
" [0.29803922 0.28235294 0.27843137]\n",
" [0.30196078 0.28627451 0.28235294]\n",
" [0.30196078 0.28627451 0.28235294]]\n",
"\n",
" [[0.14117647 0.27843137 0.31764706]\n",
" [0.16470588 0.30196078 0.34117647]\n",
" [0.21568627 0.35294118 0.40392157]\n",
" ...\n",
" [0.29803922 0.28235294 0.27843137]\n",
" [0.30196078 0.28627451 0.28235294]\n",
" [0.29803922 0.28235294 0.27843137]]]\n",
"\n",
"\n",
" [[[0.29411765 0.30588235 0.32156863]\n",
" [0.30980392 0.32156863 0.3372549 ]\n",
" [0.32156863 0.34117647 0.35294118]\n",
" ...\n",
" [0.24705882 0.25882353 0.2745098 ]\n",
" [0.24313725 0.25490196 0.27058824]\n",
" [0.23921569 0.25098039 0.26666667]]\n",
"\n",
" [[0.32156863 0.33333333 0.34901961]\n",
" [0.33333333 0.34509804 0.36078431]\n",
" [0.34509804 0.36470588 0.37647059]\n",
" ...\n",
" [0.28627451 0.29803922 0.31372549]\n",
" [0.27843137 0.29019608 0.30588235]\n",
" [0.2745098 0.28627451 0.30196078]]\n",
"\n",
" [[0.33333333 0.34509804 0.36078431]\n",
" [0.34901961 0.36078431 0.37647059]\n",
" [0.36470588 0.37647059 0.39215686]\n",
" ...\n",
" [0.32941176 0.34509804 0.36470588]\n",
" [0.31764706 0.33333333 0.35294118]\n",
" [0.31372549 0.32941176 0.34901961]]\n",
"\n",
" ...\n",
"\n",
" [[0.25098039 0.25490196 0.27058824]\n",
" [0.25882353 0.27058824 0.28627451]\n",
" [0.27843137 0.29411765 0.29803922]\n",
" ...\n",
" [0.19215686 0.18039216 0.18823529]\n",
" [0.18823529 0.17647059 0.18431373]\n",
" [0.18039216 0.16862745 0.17647059]]\n",
"\n",
" [[0.23137255 0.22745098 0.24313725]\n",
" [0.23529412 0.23921569 0.25490196]\n",
" [0.24313725 0.24705882 0.2627451 ]\n",
" ...\n",
" [0.19215686 0.18039216 0.18823529]\n",
" [0.18823529 0.17647059 0.18431373]\n",
" [0.18039216 0.16862745 0.17647059]]\n",
"\n",
" [[0.22745098 0.22352941 0.23921569]\n",
" [0.22745098 0.22352941 0.23921569]\n",
" [0.21568627 0.21960784 0.23529412]\n",
" ...\n",
" [0.18823529 0.17647059 0.18431373]\n",
" [0.18039216 0.16862745 0.17647059]\n",
" [0.17254902 0.16078431 0.16862745]]]]\n",
"[2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1]\n",
"[1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2]\n"
] ]
} }
], ],
@ -265,6 +871,10 @@
"from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import train_test_split\n",
"\n", "\n",
"X_train, X_test, y_train, y_test = train_test_split(processed_images, labels, test_size=0.2, random_state=42)\n", "X_train, X_test, y_train, y_test = train_test_split(processed_images, labels, test_size=0.2, random_state=42)\n",
"print(X_train)\n",
"print(X_test)\n",
"print(y_train)\n",
"print(y_test)\n",
"\n", "\n",
"# Initialize the model\n", "# Initialize the model\n",
"#clf = tree.DecisionTreeClassifier()\n", "#clf = tree.DecisionTreeClassifier()\n",

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