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diff --git a/wildfireDemo/utilitization.ipynb b/wildfireDemo/utilitization.ipynb
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+++ b/wildfireDemo/utilitization.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import os\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "import cv2\n",
+ "import re"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tf.test.is_built_with_cuda()\n",
+ "tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.25, 0.5 , 0.75, 1. ])"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test = np.array([1,2,3,4])\n",
+ "test/4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pathToModel = \"/userdata/kerasData/firstSplitmodel.h5\"\n",
+ "pathToImageDir = \"/userdata/kerasData/images/setForLk\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loadData(datasetPath):\n",
+ " tempF = []\n",
+ " fireLabel = []\n",
+ " for element in os.listdir(datasetPath):\n",
+ " if re.search('\\+', element):\n",
+ " fireLabel.append(1)\n",
+ " else:\n",
+ " fireLabel.append(0)\n",
+ " \n",
+ " image = cv2.imread(datasetPath + \"/\"+ element)\n",
+ " image = cv2.resize(image, (128,128))\n",
+ " tempF.append(image)\n",
+ " labels = np.array(fireLabel)\n",
+ " labels = tf.keras.utils.to_categorical(labels, num_classes=2)\n",
+ " data = np.array(tempF)\n",
+ " data = np.true_divide(data, 255)\n",
+ " return data, labels\n",
+ "\n",
+ "data, Label = loadData(pathToImageDir)\n",
+ "# cv2.imread(\"/userdata/kerasData/images/setForLk/1512676384_+02400.jpg\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = tf.keras.models.load_model(pathToImageDirectory)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.5107457 , 0.4892543 ],\n",
+ " [0.41123033, 0.5887697 ],\n",
+ " [0.43183708, 0.56816286],\n",
+ " [0.44897774, 0.55102223],\n",
+ " [0.47739947, 0.52260053],\n",
+ " [0.3578842 , 0.6421158 ],\n",
+ " [0.4941746 , 0.5058254 ],\n",
+ " [0.34056282, 0.6594372 ],\n",
+ " [0.35624158, 0.64375836],\n",
+ " [0.47470272, 0.5252973 ],\n",
+ " [0.4808885 , 0.5191116 ],\n",
+ " [0.431317 , 0.568683 ],\n",
+ " [0.5134212 , 0.4865788 ],\n",
+ " [0.40128127, 0.59871876],\n",
+ " [0.4639625 , 0.53603756],\n",
+ " [0.35375822, 0.64624184],\n",
+ " [0.36264202, 0.63735795],\n",
+ " [0.45890424, 0.5410958 ],\n",
+ " [0.33185333, 0.66814667],\n",
+ " [0.3523977 , 0.6476023 ],\n",
+ " [0.34115818, 0.6588418 ],\n",
+ " [0.48866385, 0.5113361 ],\n",
+ " [0.455409 , 0.544591 ],\n",
+ " [0.46895435, 0.5310457 ],\n",
+ " [0.38263088, 0.6173691 ],\n",
+ " [0.5233783 , 0.47662166],\n",
+ " [0.44299665, 0.5570033 ],\n",
+ " [0.47639135, 0.5236086 ],\n",
+ " [0.3761411 , 0.6238589 ],\n",
+ " [0.47419727, 0.52580273],\n",
+ " [0.40086877, 0.5991312 ],\n",
+ " [0.48351654, 0.5164834 ],\n",
+ " [0.43688735, 0.5631126 ],\n",
+ " [0.4520515 , 0.5479485 ],\n",
+ " [0.3890876 , 0.6109124 ],\n",
+ " [0.4929448 , 0.50705516],\n",
+ " [0.35062018, 0.64937985],\n",
+ " [0.46544933, 0.53455067],\n",
+ " [0.4987746 , 0.5012254 ],\n",
+ " [0.4678061 , 0.53219396],\n",
+ " [0.468355 , 0.53164506],\n",
+ " [0.37291136, 0.62708867],\n",
+ " [0.5106242 , 0.4893759 ],\n",
+ " [0.4318202 , 0.5681798 ],\n",
+ " [0.39998996, 0.60001004],\n",
+ " [0.40117145, 0.59882855],\n",
+ " [0.45087582, 0.5491242 ],\n",
+ " [0.3823646 , 0.6176354 ],\n",
+ " [0.3389846 , 0.6610154 ],\n",
+ " [0.36759573, 0.63240427],\n",
+ " [0.4042852 , 0.5957148 ],\n",
+ " [0.39802316, 0.6019768 ],\n",
+ " [0.34497583, 0.6550242 ],\n",
+ " [0.4933747 , 0.5066253 ],\n",
+ " [0.4834825 , 0.51651746],\n",
+ " [0.43642092, 0.5635791 ],\n",
+ " [0.47396407, 0.52603596],\n",
+ " [0.4887493 , 0.5112507 ],\n",
+ " [0.47656032, 0.52343965],\n",
+ " [0.46176285, 0.53823715],\n",
+ " [0.37929055, 0.6207095 ],\n",
+ " [0.49937135, 0.50062865],\n",
+ " [0.48522255, 0.5147774 ],\n",
+ " [0.45080933, 0.5491907 ],\n",
+ " [0.46133736, 0.5386627 ],\n",
+ " [0.39081547, 0.60918456],\n",
+ " [0.41144982, 0.58855015],\n",
+ " [0.52494085, 0.47505915],\n",
+ " [0.38156646, 0.61843354],\n",
+ " [0.36862728, 0.6313727 ],\n",
+ " [0.4544768 , 0.5455232 ],\n",
+ " [0.34168276, 0.6583172 ],\n",
+ " [0.37399516, 0.6260049 ],\n",
+ " [0.47957534, 0.52042466],\n",
+ " [0.50483143, 0.49516863],\n",
+ " [0.4877852 , 0.5122148 ],\n",
+ " [0.42307466, 0.57692534],\n",
+ " [0.51999307, 0.4800069 ],\n",
+ " [0.39840838, 0.60159165],\n",
+ " [0.4879313 , 0.5120687 ],\n",
+ " [0.4254754 , 0.57452464]], dtype=float32)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.predict(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "81/1 [==============================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 588us/sample - loss: 0.5996 - accuracy: 0.5926\n",
+ "[0.6112031642301583, 0.5925926]\n",
+ "['loss', 'accuracy']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(model.evaluate(x=data, y=Label))\n",
+ "print(model.metrics_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}