Merge branch 'master' of https://github.com/X-ray-Dawgz/XRayDawgz

README.md

@@ -1,5 +1,5 @@
 <p align="left">
-  <img src="https://github.com/X-ray-Dawgz/XRayDawgz/blob/master/banner.png" width="900">
+  <img src="https://github.com/X-ray-Dawgz/XRayDawgz/blob/master/icon/banner.png" width="900">
 </p>
 
 
@@ -75,7 +75,7 @@ In all cases, the users will inputting the XRD pattern that they obtained into o
 
 2. Use the following method to clone repo
 
-   - press the green botton <img src="https://github.com/X-ray-Dawgz/XRayDawgz/blob/master/icon.png" width="80"> at 
+   - press the green botton <img src="https://github.com/X-ray-Dawgz/XRayDawgz/blob/master/icon/icon.png" width="80"> at 
 the home page of our repo, and choose "Download Zip". 
 
    - unzip the file and you would get the file name "XRayDawgz" in your path.
@@ -84,9 +84,9 @@ the home page of our repo, and choose "Download Zip".
 
 
 ## How to use our software
-1.First, the user needs to prepare the XRD diagram pictures which they want to make the prediction. These pictures need to   
+1. First, the user needs to prepare the XRD diagram pictures which they want to make the prediction. These pictures need to   
 
-  match our standard to test. 
+   match our standard to test. 
 
   - The standard size is 372*288pxi
   - the range of the diffraction angle(2θ) needs to be 0-80 degree

Untitled.ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'load_weights' from 'keras.models' (/opt/anaconda3/lib/python3.7/site-packages/keras/models.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-bc89f4016bf8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'load_weights' from 'keras.models' (/opt/anaconda3/lib/python3.7/site-packages/keras/models.py)"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import load_weights\n",
+    "model.h5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CNN.ipynb\r\n",
+      "CNN_test_0312.ipynb\r\n",
+      "Image_Preprocess.ipynb\r\n",
+      "Image_Preprocess.py\r\n",
+      "\u001b[34mImages\u001b[m\u001b[m/\r\n",
+      "LICENSE\r\n",
+      "Most_Updated_CS_Classifier.ipynb\r\n",
+      "README.md\r\n",
+      "Untitled.ipynb\r\n",
+      "With_new_testset_Updated_CS_Classifier.ipynb\r\n",
+      "\u001b[34mXRayDawgz\u001b[m\u001b[m/\r\n",
+      "banner.png\r\n",
+      "best_model.h5\r\n",
+      "\u001b[34mcut_image\u001b[m\u001b[m/\r\n",
+      "\u001b[34mdocs\u001b[m\u001b[m/\r\n",
+      "environment.yml\r\n",
+      "package.json\r\n",
+      "requirements.txt\r\n",
+      "setup.py\r\n",
+      "\u001b[34mtest\u001b[m\u001b[m/\r\n",
+      "\u001b[34mtest 2\u001b[m\u001b[m/\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

prediction.ipynb

@@ -0,0 +1,65 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow import keras\n",
+    "from eras.preprocessing import image\n",
+    "import os\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_model = keras.models.load_model('Model_92_86.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_image(path_to_image):\n",
+    "    for file in os.listder(path_to_imege):\n",
+    "        predic_img = image.load_img(path_to_imege = file, \n",
+    "                                    target_size=(432,288))\n",
+    "        predic_img = image.img_to_array(predict_img) \n",
+    "        predic_img = np.expand_dims(predict_img,axis = 0)\n",
+    "        result = new_model.predict(predict_img)\n",
+    "        if np.round(result,0) == 0:\n",
+    "            print(str(file) + 'is BCC') \n",
+    "        else:\n",
+    "            print(str(file) + 'is FCC') "
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}