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ImageUsing.ipynb
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224
ImageUsing.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50cc3f9e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"from tqdm import tqdm\n",
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"\n",
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"# Directory to Pictures\n",
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"DATA_DIR = \"C:/Users/muhle/Desktop/Code/DataSets/PetAI\"\n",
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"# Kategorien die zu erkennen sind\n",
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"CATEGORIES = [\"DOG\", \"CAT\"]\n",
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"\n",
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"# Die Ordner mit den Kategorien im Namen Suchen, anschließend alle Bilder in Schwarz-Weiß umformen \n",
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"for category in CATEGORIES:\n",
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" path = os.path.join(DATA_DIR, category)\n",
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" for img in os.listdir(path):\n",
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" img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
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" # Anzeigen eines Bildes mit Matplotlib\n",
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" plt.imshow(img_array, cmap=\"gray\")\n",
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" plt.show()\n",
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" break\n",
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" break"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7847ac85",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Größe des Bildes definieren\n",
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"IMG_SIZE = 50\n",
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"# Größe des Bild Array's ändern\n",
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"new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
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"# Neues verpixeltes Bild anzeigen\n",
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"plt.imshow(new_array, cmap = 'gray')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "709419fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Liste für Trainingsdaten definieren\n",
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"training_data = []\n",
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"\n",
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"def create_training_data():\n",
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" for category in CATEGORIES: # do dogs and cats\n",
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"\n",
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" path = os.path.join(DATA_DIR,category) # Pfad zu Hunde und Katzen erstellen\n",
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" class_num = CATEGORIES.index(category) # Klassifikation für Hund und Katze bekommen, Hund=0; Katze=1;\n",
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"\n",
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" for img in tqdm(os.listdir(path)): # Bilder von Katzen und Hunden wiederholen\n",
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" try:\n",
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" img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # Bild zu einen Array\n",
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" new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # Größe zu einer normalen Datengröße machen (50,50)\n",
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" training_data.append([new_array, class_num]) # Hinzufügen zu unserer Liste trainings_data\n",
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" except Exception as e: # Wenn ein Fehler auftritt soll es einfach übersrpingen und weitermachen\n",
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" pass\n",
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"create_training_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "41c1245c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Länge der training_data\n",
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"print(len(training_data))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cdfe3e93",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"# Random Werte aus training_data nehmen\n",
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"random.shuffle(training_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f1e9c8e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Array ausgeben\n",
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"for sample in training_data[:10]:\n",
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" print(sample[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5eaae186",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Input (X) und Output (y) definieren\n",
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"X = []\n",
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"y = []\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "510602f8",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"for features, label in training_data: \n",
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" X.append(features)\n",
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" y.append(label)\n",
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" \n",
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"# X in einen Numpy Array umwandeln und ihn eine neue Größe geben, 1 Farmkanal (Grau)\n",
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"X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b59339a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"# X und y in Pickle dateien schreiben\n",
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"pickle_out = open(\"X.pickle\", \"wb\")\n",
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"pickle.dump(X, pickle_out)\n",
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"pickle_out.close()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1b487159",
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"metadata": {},
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"outputs": [],
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"source": [
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"pickle_out = open(\"y.pickle\", \"wb\")\n",
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"pickle.dump(y, pickle_out)\n",
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"pickle_out.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6aabb96e",
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"metadata": {},
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"outputs": [],
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"source": [
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"pickle_out = open(\"X.pickle\", \"wb\")\n",
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"pickle.dump(X, pickle_out)\n",
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"pickle_out.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0f0e4ca0",
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"metadata": {},
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"outputs": [],
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"source": [
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"X[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7532702",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ff3800d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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