finger_lang.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "finger_lang.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ar3ee3ar/finger-lang/blob/main/finger_lang.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PR0rAq2ussBj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7c7e08a-59b1-45cd-ccd6-db6e142596bb"
      },
      "source": [
        "!git clone https://github.com/ardamavi/Sign-Language-Digits-Dataset.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'Sign-Language-Digits-Dataset' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGGM9WqX43a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2d91340b-ad27-4c1f-dfd1-85e7b865fe25"
      },
      "source": [
        "!pip install opencv-contrib-python\n",
        "import cv2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.6/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from opencv-contrib-python) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQsSJ-vBvNNc"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "folders =['/content/Sign-Language-Digits-Dataset/Dataset/0',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/1',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/2',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/3',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/4',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/5',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/6',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/7',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/8',\n",
        "          '/content/Sign-Language-Digits-Dataset/Dataset/9']\n",
        "images=[]\n",
        "labels=[]\n",
        "def load_images_from_folder(folder):\n",
        "  for filename in os.listdir(folder):\n",
        "      img = cv2.imread(os.path.join(folder,filename))\n",
        "      img = cv2.resize(img,(32,32))\n",
        "      #img=image.img_to_array(img)\n",
        "      images.append(img)\n",
        "      label = folder.split(os.path.sep)[-1]\n",
        "      labels.append(label)\n",
        "def convert_images(images):\n",
        "  X=np.array(images)\n",
        "  #X=X.reshape(X.shape[0],100*100*3)\n",
        "  return X\n",
        "def convert_labels(labels):\n",
        "  enc = LabelEncoder()\n",
        "  P=enc.fit_transform(labels)\n",
        "  Y=to_categorical(P)\n",
        "  return Y\n",
        "\n",
        "for folder in folders:\n",
        "  #print('Readiing from path: '+folder)\n",
        "  load_images_from_folder(folder)\n",
        "X=convert_images(images)\n",
        "Y = convert_labels(labels)\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3)\n",
        "\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /=255\n",
        "X_test /=255\n",
        "\n",
        "#print(X_train.shape)\n",
        "#print(X_test.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgSSKNgC1uIK"
      },
      "source": [
        "from keras.layers import Dense, Flatten,Conv2D,MaxPooling2D,Activation\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import SGD,Adam\n",
        "cnn = Sequential()\n",
        "##\n",
        "cnn.add(Conv2D(6,(5,5),activation='relu',input_shape=(32,32,3)))\n",
        "cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
        "cnn.add(Conv2D(10,(5,5),activation='relu'))\n",
        "cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
        "## loop for depend on your need\n",
        "cnn.add(Flatten())\n",
        "cnn.add(Dense(60,activation='relu'))\n",
        "cnn.add(Dense(240,activation='relu'))\n",
        "cnn.add(Dense(60,activation='relu'))\n",
        "cnn.add(Dense(10,activation='softmax'))\n",
        "\n",
        "adm = Adam(lr=0.002)\n",
        "cnn.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer=adm)\n",
        "#print(cnn.summary())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwnlKFKN3vwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4b85c3ef-fd93-4aa2-abdd-8863ace3a52c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history = cnn.fit(X_train,Y_train,epochs=60,verbose=1)\n",
        "plt.plot(history.history['loss'])\n",
        "##plt.title('Loss value')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.2105 - accuracy: 0.2065\n",
            "Epoch 2/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.2476 - accuracy: 0.5502\n",
            "Epoch 3/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.8515 - accuracy: 0.7117\n",
            "Epoch 4/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.5381 - accuracy: 0.8205\n",
            "Epoch 5/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.4055 - accuracy: 0.8656\n",
            "Epoch 6/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.3193 - accuracy: 0.8960\n",
            "Epoch 7/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.2234 - accuracy: 0.9342\n",
            "Epoch 8/50\n",
            "46/46 [==============================] - 1s 28ms/step - loss: 0.1882 - accuracy: 0.9480\n",
            "Epoch 9/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.1618 - accuracy: 0.9432\n",
            "Epoch 10/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.1580 - accuracy: 0.9446\n",
            "Epoch 11/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.1159 - accuracy: 0.9667\n",
            "Epoch 12/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0830 - accuracy: 0.9723\n",
            "Epoch 13/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0442 - accuracy: 0.9882\n",
            "Epoch 14/50\n",
            "46/46 [==============================] - 1s 26ms/step - loss: 0.0259 - accuracy: 0.9965\n",
            "Epoch 15/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0157 - accuracy: 0.9972\n",
            "Epoch 16/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0210 - accuracy: 0.9931\n",
            "Epoch 17/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0266 - accuracy: 0.9924\n",
            "Epoch 18/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0373 - accuracy: 0.9903\n",
            "Epoch 19/50\n",
            "46/46 [==============================] - 1s 28ms/step - loss: 0.0416 - accuracy: 0.9875\n",
            "Epoch 20/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0493 - accuracy: 0.9834\n",
            "Epoch 21/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0882 - accuracy: 0.9681\n",
            "Epoch 22/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0770 - accuracy: 0.9785\n",
            "Epoch 23/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0204 - accuracy: 0.9931\n",
            "Epoch 24/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0097 - accuracy: 0.9986\n",
            "Epoch 25/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 0.0018 - accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 8.1238e-04 - accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 5.8523e-04 - accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 4.8042e-04 - accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 4.1671e-04 - accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 3.6863e-04 - accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 3.2935e-04 - accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.9888e-04 - accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.6688e-04 - accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.3874e-04 - accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.1907e-04 - accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 2.0353e-04 - accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.8611e-04 - accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.6959e-04 - accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.5872e-04 - accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.4670e-04 - accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.3731e-04 - accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.3741e-04 - accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.2212e-04 - accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.1169e-04 - accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.0457e-04 - accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 1.0032e-04 - accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 9.3124e-05 - accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 8.9768e-05 - accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 8.2834e-05 - accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "46/46 [==============================] - 1s 27ms/step - loss: 7.8344e-05 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xn_cwW628hV5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3377c374-c248-4285-9eb0-a779bfb3e4b9"
      },
      "source": [
        "img_path = '/content/9 HAND5c753a6285524.png'\n",
        "img = cv2.imread(img_path)\n",
        "img = cv2.resize(img,(32,32))\n",
        "lala = [0,1,2,3,4,5,6,7,8,9]\n",
        "lala = convert_labels(lala)\n",
        "x=convert_images(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "preds = cnn.predict(x)\n",
        "preds = preds.reshape(10,)\n",
        "imgplot = plt.subplots(1, 1)\n",
        "for i in range(10):\n",
        "  comparison = preds == lala[i]\n",
        "  if (comparison.all()):\n",
        "    print(i)\n",
        "imgplot = plt.imshow(img)\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGORbFvzJ4q9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c6b99716-8b35-49d1-de23-0c18082a5abc"
      },
      "source": [
        "score = cnn.evaluate(X_test, Y_test, verbose=0)\n",
        " #y= cnn.predict(X_test)\n",
        "print(cnn.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'accuracy']\n",
            "[0.4747413992881775, 0.9386106729507446]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}