Removed all the conflicting plates

.ipynb_checkpoints/ML_Task_1-checkpoint.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "_Mo2vxkP-0JK",
+    "outputId": "85d4c99f-8b12-41bb-faf7-6a0a3f1fb237"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'google'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-4007aab83f86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import google\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google'"
+     ]
+    }
+   ],
+   "source": [
+    "# import google\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 207
+    },
+    "colab_type": "code",
+    "id": "nmBbzycb_7O-",
+    "outputId": "7aef17ea-cf7f-46ea-9af8-0c0efddb24fb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X.shape is (12288, 307)\n",
+      "Proceed 0 of 307\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, cv2, itertools \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "PLATE_NO_DIR = './plate_number/'\n",
+    "NEG_IMG_DIR = './negative_images/'\n",
+    "\n",
+    "ROWS = 64\n",
+    "COLS = 64\n",
+    "CHANNELS = 3\n",
+    "\n",
+    "plate_no_images = [PLATE_NO_DIR + i for i in os.listdir(PLATE_NO_DIR)]\n",
+    "neg_img = [NEG_IMG_DIR + i for i in os.listdir(NEG_IMG_DIR)]\n",
+    "\n",
+    "\n",
+    "def read_image(file_path):\n",
+    "  img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n",
+    "  return cv2.resize(img, (ROWS, COLS), interpolation = cv2.INTER_CUBIC)\n",
+    "\n",
+    "def prep_data(images):\n",
+    "  m = len(images)\n",
+    "  n_x = ROWS*COLS*CHANNELS\n",
+    "  \n",
+    "  X = np.ndarray((n_x,m), dtype = np.uint8)\n",
+    "  y = np.zeros((1,m))\n",
+    "  print(\"X.shape is {}\".format(X.shape))\n",
+    "  \n",
+    "  for i,image_file in enumerate(images):\n",
+    "    image = read_image(image_file)\n",
+    "    X[:,i] = np.squeeze(image.reshape((n_x,1)))\n",
+    "    if '-' in image_file.lower() :\n",
+    "      y[0,i] = 1\n",
+    "    elif 'image' in image_file.lower() :\n",
+    "      y[0,i] = 0\n",
+    "    else : # for test data\n",
+    "      y[0,i] = image_file.split('/')[-1].split('.')[0]\n",
+    "      \n",
+    "    if i%5000 == 0 :\n",
+    "      print(\"Proceed {} of {}\".format(i, m))\n",
+    "    \n",
+    "  return X,y\n",
+    "\n",
+    "\n",
+    "X_img, y_img = prep_data(plate_no_images + neg_img)\n",
+    "\n",
+    "classes = {0: 'Normal Image',\n",
+    "           1: 'License Plate'}\n",
+    "\n",
+    "def show_images(X, y, idx) :\n",
+    "  image = X[idx]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize=(4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"This is a {}\".format(classes[y[idx,0]]))\n",
+    "  plt.show()\n",
+    "  \n",
+    "show_images(X_img.T, y_img.T, 0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "cfT5L_mMAOSm",
+    "outputId": "82fde8e9-a30a-4992-9cd2-4659fb236b61"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "\n",
+    "clf = LogisticRegressionCV()\n",
+    "\n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T.ravel()\n",
+    "\n",
+    "clf.fit(X_img_lr,y_img_lr)\n",
+    "\n",
+    "print(\"Model accuracy: {:.2f}%\".format(clf.score(X_img_lr, y_img_lr)*100))\n",
+    "\n",
+    "def show_image_prediction(X, idx, model) :\n",
+    "  image = X[idx].reshape(1,-1)\n",
+    "  image_class = classes[model.predict(image).item()]\n",
+    "  image = image.reshape((ROWS, COLS, CHANNELS))\n",
+    "  plt.figure(figsize = (4,2))\n",
+    "  plt.imshow(image)\n",
+    "  plt.title(\"Test {} : I think this is a {}\".format(idx, image_class))\n",
+    "  plt.show()\n",
+    "  \n",
+    "X_img_lr, y_img_lr = X_img.T, y_img.T\n",
+    "\n",
+    "for i in np.random.randint(0, len(X_img_lr), 10) :\n",
+    "  show_image_prediction(X_img_lr, i, clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "ML_Task_1.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}