johanvandegriff
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BoggleCV
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Using OpenCV and Tensorflow to identify a Boggle board in an image and decode what letters are on the board.
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BoggleCV/tensorflow-boggle.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#https://www.tensorflow.org/tutorials/keras/classification\n",
"\n",
"from __future__ import absolute_import, division, print_function, unicode_literals\n",
"\n",
"# TensorFlow and tf.keras\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import datasets, layers, models\n",
"\n",
"# Helper libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#DATA_FILE=\"/home/johanv/downloads/labelled.json\"\n",
"DATA_FILE=\"/home/johanv/nextcloud/projects/boggle2.0/labelled.json\"\n",
"#DATA_FILE=\"/home/johanv/nextcloud/projects/boggle2.0/labelled-20200124_155523.mp4.json\"\n",
"\n",
"#import os\n",
"#DATA_FILE = \"/labelled.json\"\n",
"#DATA_URL = \"https://drive.confuzer.cloud/index.php/s/2fQ5KkGi3Bi2SLq/download\"\n",
"#os.system(\"curl \" + DATA_URL + \" > \" + DATA_FILE)\n",
"\n",
"MODEL_SAVE_FILE=\"/home/johanv/nextcloud/projects/boggle2.0/model.h5\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(tf.__version__)\n",
"\n",
"#fashion_mnist = keras.datasets.fashion_mnist\n",
"#(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
"#class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
"# 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
"\n",
"with open(DATA_FILE, 'r') as f:\n",
" data = json.load(f)\n",
"\n",
"IMG_DIM = 30\n",
"\n",
"images_in = data[\"imgs\"]\n",
"labels_in = data[\"labels\"]\n",
"\n",
"images = []\n",
"labels = []\n",
"for rot in range(4):\n",
" for i in range(len(images_in)):\n",
" #https://artemrudenko.wordpress.com/2014/08/28/python-rotate-2d-arraymatrix-90-degrees-one-liner/\n",
" images_in[i] = list(zip(*images_in[i][::-1])) #rotate 90 degrees (still the same letter!)\n",
" images.extend(images_in)\n",
" labels.extend(labels_in)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"images = np.array(images, dtype=np.uint8).reshape((-1, IMG_DIM, IMG_DIM, 1))\n",
"\n",
"split = int(0.15 * len(images))\n",
"\n",
"train_images = np.array(images[split:],dtype=np.uint8)\n",
"train_labels = np.array(labels[split:],dtype=np.uint8)\n",
"\n",
"test_images = np.array(images[:split],dtype=np.uint8)\n",
"test_labels = np.array(labels[:split],dtype=np.uint8)\n",
"\n",
"class_names = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
"\n",
"L = len(class_names)\n",
"\n",
"print(train_images.shape)\n",
"print(train_labels.shape)\n",
"print(test_images.shape)\n",
"print(test_labels.shape)\n",
"\n",
"# plt.figure()\n",
"# plt.imshow(train_images[1])\n",
"# plt.colorbar()\n",
"# plt.grid(False)\n",
"# plt.show()\n",
"\n",
"train_images = train_images / 255.0\n",
"test_images = test_images / 255.0\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(10,10))\n",
"for i in range(25):\n",
" print(\"{}/25\".format(i))\n",
" plt.subplot(5,5,i+1)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.grid(False)\n",
" plt.imshow(train_images[i].reshape((IMG_DIM, IMG_DIM)), cmap=plt.cm.binary)\n",
" plt.xlabel(class_names[train_labels[i]])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#model = keras.Sequential([\n",
"# keras.layers.Flatten(input_shape=(IMG_DIM,IMG_DIM)),\n",
"# keras.layers.Dense(128, activation=\"sigmoid\"),\n",
"# keras.layers.Dense(64, activation=\"sigmoid\"),\n",
"# keras.layers.Dense(L, activation=\"softmax\")\n",
"#])\n",
"\n",
"model = models.Sequential()\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(IMG_DIM, IMG_DIM, 1)))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(32, (3, 3), activation='relu'))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(16, (3, 3), activation='relu'))\n",
"\n",
"model.add(layers.Flatten())\n",
"model.add(layers.Dense(100, activation='relu'))\n",
"# model.add(layers.Dense(52, activation='relu'))\n",
"model.add(layers.Dense(L, activation=\"softmax\"))\n",
"\n",
"print(model.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#model.compile(\n",
"# loss='sparse_categorical_crossentropy',\n",
"# optimizer='adam',\n",
"# metrics=['accuracy']\n",
"#)\n",
"#\n",
"#model.fit(train_images, train_labels, epochs=7)\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['accuracy'])\n",
"\n",
"history = model.fit(train_images, train_labels, epochs=8,\n",
" validation_data=(test_images, test_labels))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.save(MODEL_SAVE_FILE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = tf.keras.models.load_model(MODEL_SAVE_FILE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(history)\n",
"\n",
"plt.plot(history.history['accuracy'], label='accuracy')\n",
"plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.ylim([0.5, 1])\n",
"plt.legend(loc='lower right')\n",
"\n",
"test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"\n",
"print(\"\\n\\nacc: \", test_acc)\n",
"\n",
"predictions = model.predict(test_images)\n",
"print(\"predictions[0]: \", predictions[0])\n",
"print(\"np.argmax(predictions[0]): \", np.argmax(predictions[0]))\n",
"print(\"test_labels[0]: \", test_labels[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_label(predictions_array):\n",
" predicted_label = np.argmax(predictions_array)\n",
" if class_names[predicted_label] == 'E' and 100*np.max(predictions_array) < 99.4:\n",
" predicted_label = class_names.index('Q')\n",
" if class_names[predicted_label] == 'T' and 100*np.max(predictions_array) < 96:\n",
" predicted_label = class_names.index('L')\n",
" if class_names[predicted_label] == 'U' and 100*np.max(predictions_array) < 67:\n",
" predicted_label = class_names.index('L')\n",
" if class_names[predicted_label] == 'R' and 100*np.max(predictions_array) < 85:\n",
" predicted_label = class_names.index('U')\n",
" return predicted_label\n",
"\n",
"def plot_image(i, predictions_array, true_label, img):\n",
" predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
" plt.grid(False)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"\n",
" plt.imshow(img.reshape((IMG_DIM, IMG_DIM)), cmap=plt.cm.binary)\n",
"\n",
" predicted_label = get_label(predictions_array)\n",
" if predicted_label == true_label:\n",
" color = 'blue'\n",
" else:\n",
" color = 'red'\n",
"\n",
" plt.xlabel(\"{} {:2.2f}% ({})\".format(class_names[predicted_label],\n",
" 100*np.max(predictions_array),\n",
" class_names[true_label]),\n",
" color=color)\n",
"\n",
"def plot_value_array(i, predictions_array, true_label):\n",
" predictions_array, true_label = predictions_array, true_label[i]\n",
" plt.grid(False)\n",
" plt.xticks(range(L))\n",
" plt.yticks([])\n",
" thisplot = plt.bar(range(L), predictions_array, color=\"#777777\")\n",
" plt.ylim([0, 1])\n",
" predicted_label = get_label(predictions_array)\n",
"\n",
" thisplot[predicted_label].set_color('red')\n",
" thisplot[true_label].set_color('blue')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i = 0\n",
"plt.figure(figsize=(6,3))\n",
"plt.subplot(1,2,1)\n",
"plot_image(i, predictions[i], test_labels, test_images)\n",
"plt.subplot(1,2,2)\n",
"plot_value_array(i, predictions[i], test_labels)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i = 12\n",
"plt.figure(figsize=(6,3))\n",
"plt.subplot(1,2,1)\n",
"plot_image(i, predictions[i], test_labels, test_images)\n",
"plt.subplot(1,2,2)\n",
"plot_value_array(i, predictions[i], test_labels)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the first X test images, their predicted labels, and the true labels.\n",
"# Color correct predictions in blue and incorrect predictions in red.\n",
"for j in range(30):\n",
" num_rows = 5\n",
" num_cols = 3\n",
" num_images = num_rows*num_cols\n",
" plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
" for i in range(num_images):\n",
" plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
" plot_image(i+j*num_images, predictions[i+j*num_images], test_labels, test_images)\n",
" plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
" plot_value_array(i+j*num_images, predictions[i+j*num_images], test_labels)\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Grab an image from the test dataset.\n",
"img = test_images[1]\n",
"\n",
"print(img.shape)\n",
"\n",
"# Add the image to a batch where it's the only member.\n",
"img = (np.expand_dims(img,0))\n",
"\n",
"print(img.shape)\n",
"\n",
"predictions_single = model.predict(img)\n",
"\n",
"print(predictions_single)\n",
"\n",
"plot_value_array(1, predictions_single[0], test_labels)\n",
"_ = plt.xticks(range(L), class_names, rotation=45)\n",
"\n",
"print(\"np.argmax(predictions_single[0]): \", np.argmax(predictions_single[0]))"
]
},
{
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"execution_count": null,
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