mirror of
https://github.com/VitaAetaerna/PetDetectionPy.git
synced 2024-09-19 19:00:56 +02:00
72 lines
1.9 KiB
Plaintext
72 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bc93610d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importieren der Pakekte\n",
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"import cv2\n",
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"import tensorflow as tf\n",
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"\n",
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"# Kategorien defienieren. In diesem Falle nur 2 da wir mit einem Binary_Crossentropy arbeiten (Nur 2 Klassifizierungen)\n",
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"category = ['Dog', 'Cat'] \n",
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"\n",
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"# Dateipfad und funktion definieren\n",
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"def prepare(filepath):\n",
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" \n",
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" # Größe des Bildes bestimmen (Desto kleiner desto weniger Lernmaterial) \n",
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" IMG_SIZE = 50\n",
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" \n",
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" # Bild in einen Array machen und in Schwarz-Weiß umformen\n",
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" img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
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" \n",
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" # Bild größe anpassen\n",
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" new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
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" \n",
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" # Bild als Schwarz-Weiß und mit neuer Größe zurückgeben\n",
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" return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
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"\n",
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"#Model das wir bereits erstellt haben, hier reinladen und verwenden (WICHTIG: Name MUSS übereinstimmen)\n",
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"model = tf.keras.models.load_model(\"64x3-CNN.model\")\n",
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"\n",
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"\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": "2f07fa08",
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = model.predict([prepare('dog.jpg')])\n",
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"print(prediction)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (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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