Image_GeneratorV2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Image_Generator.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/LEChaney/ML-RayCast-Experiments/blob/master/Image_GeneratorV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jw1arH2vxVXK",
        "colab_type": "code",
        "outputId": "0d215b62-df5d-43b6-aa20-6833fe5e781d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "!git clone https://github.com/LEChaney/ML-RayCast-Experiments\n",
        "%cd ML-RayCast-Experiments\n",
        "%pwd"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ML-RayCast-Experiments'...\n",
            "remote: Enumerating objects: 59, done.\u001b[K\n",
            "remote: Counting objects:   1% (1/59)\u001b[K\rremote: Counting objects:   3% (2/59)\u001b[K\rremote: Counting objects:   5% (3/59)\u001b[K\rremote: Counting objects:   6% (4/59)\u001b[K\rremote: Counting objects:   8% (5/59)\u001b[K\rremote: Counting objects:  10% (6/59)\u001b[K\rremote: Counting objects:  11% (7/59)\u001b[K\rremote: Counting objects:  13% (8/59)\u001b[K\rremote: Counting objects:  15% (9/59)\u001b[K\rremote: Counting objects:  16% (10/59)\u001b[K\rremote: Counting objects:  18% (11/59)\u001b[K\rremote: Counting objects:  20% (12/59)\u001b[K\rremote: Counting objects:  22% (13/59)\u001b[K\rremote: Counting objects:  23% (14/59)\u001b[K\rremote: Counting objects:  25% (15/59)\u001b[K\rremote: Counting objects:  27% (16/59)\u001b[K\rremote: Counting objects:  28% (17/59)\u001b[K\rremote: Counting objects:  30% (18/59)\u001b[K\rremote: Counting objects:  32% (19/59)\u001b[K\rremote: Counting objects:  33% (20/59)\u001b[K\rremote: Counting objects:  35% (21/59)\u001b[K\rremote: Counting objects:  37% (22/59)\u001b[K\rremote: Counting objects:  38% (23/59)\u001b[K\rremote: Counting objects:  40% (24/59)\u001b[K\rremote: Counting objects:  42% (25/59)\u001b[K\rremote: Counting objects:  44% (26/59)\u001b[K\rremote: Counting objects:  45% (27/59)\u001b[K\rremote: Counting objects:  47% (28/59)\u001b[K\rremote: Counting objects:  49% (29/59)\u001b[K\rremote: Counting objects:  50% (30/59)\u001b[K\rremote: Counting objects:  52% (31/59)\u001b[K\rremote: Counting objects:  54% (32/59)\u001b[K\rremote: Counting objects:  55% (33/59)\u001b[K\rremote: Counting objects:  57% (34/59)\u001b[K\rremote: Counting objects:  59% (35/59)\u001b[K\rremote: Counting objects:  61% (36/59)\u001b[K\rremote: Counting objects:  62% (37/59)\u001b[K\rremote: Counting objects:  64% (38/59)\u001b[K\rremote: Counting objects:  66% (39/59)\u001b[K\rremote: Counting objects:  67% (40/59)\u001b[K\rremote: Counting objects:  69% (41/59)\u001b[K\rremote: Counting objects:  71% (42/59)\u001b[K\rremote: Counting objects:  72% (43/59)\u001b[K\rremote: Counting objects:  74% (44/59)\u001b[K\rremote: Counting objects:  76% (45/59)\u001b[K\rremote: Counting objects:  77% (46/59)\u001b[K\rremote: Counting objects:  79% (47/59)\u001b[K\rremote: Counting objects:  81% (48/59)\u001b[K\rremote: Counting objects:  83% (49/59)\u001b[K\rremote: Counting objects:  84% (50/59)\u001b[K\rremote: Counting objects:  86% (51/59)\u001b[K\rremote: Counting objects:  88% (52/59)\u001b[K\rremote: Counting objects:  89% (53/59)\u001b[K\rremote: Counting objects:  91% (54/59)\u001b[K\rremote: Counting objects:  93% (55/59)\u001b[K\rremote: Counting objects:  94% (56/59)\u001b[K\rremote: Counting objects:  96% (57/59)\u001b[K\rremote: Counting objects:  98% (58/59)\u001b[K\rremote: Counting objects: 100% (59/59)\u001b[K\rremote: Counting objects: 100% (59/59), done.\u001b[K\n",
            "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
            "remote: Total 265 (delta 23), reused 2 (delta 0), pack-reused 206\u001b[K\n",
            "Receiving objects: 100% (265/265), 44.83 MiB | 26.86 MiB/s, done.\n",
            "Resolving deltas: 100% (131/131), done.\n",
            "/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/ML-RayCast-Experiments\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/ML-RayCast-Experiments'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18ct-FwsPHaw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import pygame\n",
        "os.environ['SDL_VIDEODRIVER']='dummy'\n",
        "file_directory = os.getcwd()\n",
        "os.chdir(\"/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master\")\n",
        "print(os.getcwd())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ6tnEnBwSdM",
        "colab_type": "code",
        "outputId": "eb4ba311-49a9-4636-f015-25abb4b5e6f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "'''import numpy as np\n",
        "import sys\n",
        "import random\n",
        "import pygame\n",
        "import flappy_bird_utils\n",
        "import pygame.surfarray as surfarray\n",
        "from pygame.locals import *\n",
        "from itertools import cycle'''\n",
        "\n",
        "SCREENWIDTH  = 288\n",
        "SCREENHEIGHT = 512\n",
        "def Image_Generator(filepath):\n",
        "\n",
        "    file = open(filepath,\"r\")\n",
        "    running = True\n",
        "    while running:\n",
        "        current = file.readline()\n",
        "        if file.readline() == \"\":\n",
        "            break;\n",
        "        data_list = current.split(\" \")\n",
        "        data_list.pop()\n",
        "        \n",
        "        if (len(data_list) < 16):\n",
        "            loop_times = 16 - len(data_list)\n",
        "            counter = 0\n",
        "            while counter < loop_times:\n",
        "                data_list.append(None)\n",
        "                counter += 1\n",
        "        #print(data_list)\n",
        "        #data_list = list(map(int, data_list))\n",
        "        bird_index = int(data_list[3])\n",
        "        upper_left_x = int(int(data_list[4]) + SCREENWIDTH /2)\n",
        "        upper_left_y = int(int(data_list[5]) + SCREENHEIGHT /2)\n",
        "        '''CaseNo = data_list[0]\n",
        "        bird_x = data_list[1] + SCREENWIDTH * 2\n",
        "        bird_y = data_list[2] + SCREENHEIGHT * 2\n",
        "        \n",
        "\n",
        "        down_left_x = data_list[6] + SCREENWIDTH * 2\n",
        "        down_left_y = data_list[7] + SCREENHEIGHT * 2\n",
        "        upper_middle_x = data_list[8] + SCREENWIDTH * 2\n",
        "        upper_middle_y = data_list[9] + SCREENHEIGHT * 2\n",
        "        down_middle_x = data_list[10] + SCREENWIDTH * 2\n",
        "        down_middle_y = data_list[11] + SCREENHEIGHT * 2\n",
        "        upper_right_x = data_list[12] + SCREENWIDTH * 2\n",
        "        upper_right_y = data_list[13] + SCREENHEIGHT * 2\n",
        "        down_right_x = data_list[14] + SCREENWIDTH * 2\n",
        "        down_right_y = data_list[15] + SCREENHEIGHT * 2'''\n",
        "        pipe_green = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/pipe-green.png\") #numpy array\n",
        "        background = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/background-black.png\")\n",
        "        bird_down = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/redbird-downflap.png\")\n",
        "        bird_mid = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/redbird-midflap.png\")\n",
        "        bird_up = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/redbird-upflap.png\")\n",
        "        #bird_download = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/redbird-downflap.png\")\n",
        "        \n",
        "        print(\"pipe_green.shape: \",pipe_green.shape)\n",
        "        print()\n",
        "        print(\"upper_left_x: \", upper_left_x)\n",
        "        print()\n",
        "        print(\"bird_down.shape[0]: \",bird_down.shape[0])\n",
        "        print()\n",
        "        print(\"upper_left_y: \", upper_left_y)\n",
        "        print()\n",
        "        print(\"bird_down.shape[1]: \", bird_down.shape[1])\n",
        "        print(\"background.shape: \", background.shape)\n",
        "        \n",
        "        print(\"bird_down: \", bird_down)\n",
        "        #background[x:pip.shape[1]e_green[0]]\n",
        "        reverse_pipe = rotate(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/pipe-green.png\")\n",
        "        #plt.imshow(pipe_green)\n",
        "        #plt.show()\n",
        "        running = False\n",
        "        if bird_index == 0:\n",
        "          background[upper_left_x:bird_down.shape[0], upper_left_y:bird_down.shape[1], :] = pipe_green\n",
        "          plt.imshow(background)\n",
        "          plt.show()\n",
        "        #print(type(pipe_green))\n",
        "        elif bird_index == 1:\n",
        "          background[upper_left_x:bird_mid.shape[0], upper_left_y:bird_mid.shape[1], :] = pipe_green\n",
        "          plt.imshow(background)\n",
        "          plt.show()\n",
        "        else:\n",
        "          background[upper_left_x:bird_up.shape[0], upper_left_x:bird_up.shape[1], :] = pipe_green\n",
        "          plt.imshow(background)\n",
        "          plt.show()  \n",
        "\n",
        "          \n",
        "    #print(\"bird_down: \", bird_down)      \n",
        "    file.close()\n",
        "      \n",
        "\n",
        "    \n",
        "\n",
        "'''def csv_image_generator(inputPath, bs, lb, mode=\"train\", aug=None):\n",
        "\t# open the CSV file for reading\n",
        "\tf = open(inputPath, \"r\")\n",
        "\n",
        "\t# loop indefinitely\n",
        "\twhile True:\n",
        "\t\t# initialize our batches of images and labels\n",
        "\t\timages = []\n",
        "\t\tlabels = []\n",
        "\n",
        "\t\t# keep looping until we reach our batch size\n",
        "\t\twhile len(images) < bs:\n",
        "\t\t\t# attempt to read the next line of the CSV file\n",
        "\t\t\tline = f.readline()\n",
        "\n",
        "\t\t\t# check to see if the line is empty, indicating we have\n",
        "\t\t\t# reached the end of the file\n",
        "\t\t\tif line == \"\":\n",
        "\t\t\t\t# reset the file pointer to the beginning of the file\n",
        "\t\t\t\t# and re-read the line\n",
        "\t\t\t\tf.seek(0)\n",
        "\t\t\t\tline = f.readline()\n",
        "\n",
        "\t\t\t\t# if we are evaluating we should now break from our\n",
        "\t\t\t\t# loop to ensure we don't continue to fill up the\n",
        "\t\t\t\t# batch from samples at the beginning of the file\n",
        "\t\t\t\tif mode == \"eval\":\n",
        "\t\t\t\t\tbreak\n",
        "\n",
        "\t\t\t# extract the label and construct the image\n",
        "\t\t\tline = line.strip().split(\",\")\n",
        "\t\t\tlabel = line[0]\n",
        "\t\t\timage = np.array([int(x) for x in line[1:]], dtype=\"uint8\")\n",
        "\t\t\timage = image.reshape((64, 64, 3))\n",
        "\n",
        "\t\t\t# update our corresponding batches lists\n",
        "\t\t\timages.append(image)\n",
        "\t\t\tlabels.append(label)\n",
        "\n",
        "\t\t# one-hot encode the labels\n",
        "\t\tlabels = lb.transform(np.array(labels))\n",
        "\n",
        "\t\t# if the data augmentation object is not None, apply it\n",
        "\t\tif aug is not None:\n",
        "\t\t\t(images, labels) = next(aug.flow(np.array(images),\n",
        "\t\t\t\tlabels, batch_size=bs))\n",
        "\n",
        "\t\t# yield the batch to the calling function\n",
        "\t\tyield (np.array(images), labels)'''\n",
        "\n",
        "\n",
        "'''1. the number of case\n",
        "2. the x of bird\n",
        "3. the y of bird\n",
        "4. the index of bird\n",
        "5. the x of pipe1(upper left)\n",
        "6. the y of pipe1(upper left)\n",
        "7. the x of pipe2(down left)\n",
        "8. the y of pipe2(down left)\n",
        "9. the x of pipe3(upper middle)\n",
        "10. the y of pipe3(upper middle)\n",
        "11. the x of pipe4(down middle)\n",
        "12. the y of pipe4(down middle)\n",
        "13. the x of pipe5(upper right)\n",
        "14. the y of pipe5(upper right)\n",
        "15. the x of pipe6(down right)\n",
        "16. the y of pipe6(down right)'''\n",
        "\n",
        "file= \"data.txt\" #file path or a file in the current folder\n",
        "Image_Generator(file)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pipe_green.shape:  (320, 52, 3)\n",
            "\n",
            "upper_left_x:  428\n",
            "\n",
            "bird_down.shape[0]:  24\n",
            "\n",
            "upper_left_y:  76\n",
            "\n",
            "bird_down.shape[1]:  34\n",
            "background.shape:  (512, 288, 3)\n",
            "bird_down:  [[[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]\n",
            "\n",
            " [[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]\n",
            "\n",
            " [[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [70 56 83]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]\n",
            "\n",
            " [[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]\n",
            "\n",
            " [[ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  ...\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]\n",
            "  [ 0  0  0]]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-860673a3a622>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m\"data.txt\"\u001b[0m \u001b[0;31m#file path or a file in the current folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mImage_Generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-83-860673a3a622>\u001b[0m in \u001b[0;36mImage_Generator\u001b[0;34m(filepath)\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0mrunning\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbird_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m           \u001b[0mbackground\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mupper_left_x\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbird_down\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupper_left_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbird_down\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe_green\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m           \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackground\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m           \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (320,52,3) into shape (0,0,3)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLprYgbcNKfz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3bwG0JXNLKo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mf04d7Nywqo6",
        "colab_type": "code",
        "outputId": "126bed66-7c4b-4b99-f621-6d4bdeb3e773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "im = cv2.imread(\"Keras-FlappyBird-master/Keras-FlappyBird-master/assets/sprites/pipe-green.png\")\n",
        "#print (im.shape[0])\n",
        "#print (im.shape[1])\n",
        "print(im.row)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "320\n",
            "52\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-6b5cdd27a42a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'row'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaXKLzgLx_8D",
        "colab_type": "code",
        "outputId": "1ee0f17e-64bf-4dd0-a4df-c7eab509dd05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "#from PIL import Image\n",
        "from matplotlib import pyplot as plt\n",
        "#image = Image.fromarray(im,'RGB')\n",
        "#image.save(\"000.png\")\n",
        "#pyt.show()\n",
        "plt.imshow(im)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFEAAAD8CAYAAAAPDUgGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR2rN0ieyYQ5",
        "colab_type": "code",
        "outputId": "e5445d4d-bf0a-4a95-c078-63d5e5df7583",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "source": [
        "from PIL import Image\n",
        "def rotate(image_path):\n",
        "    \"\"\"\n",
        "    Rotate the given photo the amount of given degreesk, show it and save it\n",
        "    @param image_path: The path to the image to edit\n",
        "    @param degrees_to_rotate: The number of degrees to rotate the image\n",
        "    @param saved_location: Path to save the cropped image\n",
        "    \"\"\"\n",
        "    degrees_to_rotate = 180\n",
        "    image_obj = Image.open(image_path)\n",
        "    rotated_image = image_obj.rotate(degrees_to_rotate)\n",
        "    rotated_image.save(\"000.png\")\n",
        "    im = cv2.imread(\"000.png\")\n",
        "    #plt.imshow(im)\n",
        "    #plt.show()\n",
        "    return im\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[255, 255, 255],\n",
              "        [255, 255, 255],\n",
              "        [ 71,  56,  84],\n",
              "        ...,\n",
              "        [ 71,  56,  84],\n",
              "        [255, 255, 255],\n",
              "        [255, 255, 255]],\n",
              "\n",
              "       [[255, 255, 255],\n",
              "        [255, 255, 255],\n",
              "        [ 71,  56,  84],\n",
              "        ...,\n",
              "        [ 71,  56,  84],\n",
              "        [255, 255, 255],\n",
              "        [255, 255, 255]],\n",
              "\n",
              "       [[255, 255, 255],\n",
              "        [255, 255, 255],\n",
              "        [ 71,  56,  84],\n",
              "        ...,\n",
              "        [ 71,  56,  84],\n",
              "        [255, 255, 255],\n",
              "        [255, 255, 255]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        [ 38, 132,  90],\n",
              "        ...,\n",
              "        [113, 221, 192],\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84]],\n",
              "\n",
              "       [[ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        ...,\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84]],\n",
              "\n",
              "       [[ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        ...,\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84],\n",
              "        [ 71,  56,  84]]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqsexcF4qXfE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "74231e29-9343-498d-eae8-f634434b5c63"
      },
      "source": [
        "import os\n",
        "os.environ['SDL_VIDEODRIVER']='dummy'\n",
        "file_directory = os.getcwd()\n",
        "os.chdir(\"/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master\")\n",
        "print(os.getcwd())"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iY7nyqTkNMef",
        "colab_type": "code",
        "outputId": "18dc250f-7c03-4ba2-b872-7ca143acfc97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "import sys\n",
        "\n",
        "#sys.path.pop(1)\n",
        "print(sys.path)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['', '/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/game', '/env/python', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynload', '/usr/local/lib/python3.6/dist-packages', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.6/dist-packages/IPython/extensions', '/root/.ipython']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGMYTHT4OLy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import wrapped_flappy_bird as game\n",
        "import numpy as np\n",
        "import flappy_bird_utils\n",
        "import cv2\n",
        "import sys\n",
        "import random\n",
        "import pygame\n",
        "import pygame.surfarray as surfarray\n",
        "from pygame.locals import *\n",
        "from itertools import cycle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Es1C7Y7Iqonc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5dc7a2d7-bb30-4788-bc1e-54cae996b1d5"
      },
      "source": [
        "os.chdir(\"/content/ML-RayCast-Experiments\")\n",
        "print(os.getcwd())"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/ML-RayCast-Experiments\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epVQmcWzpGRU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "88bc532c-fb5f-4d76-8e01-b5b17321b775"
      },
      "source": [
        "GAME = 'bird' # the name of the game being played for log files\n",
        "CONFIG = 'nothreshold'\n",
        "ACTIONS = 2 # number of valid actions\n",
        "GAMMA = 0.99 # decay rate of past observations\n",
        "OBSERVATION = 3200. # timesteps to observe before training\n",
        "EXPLORE = 3000000. # frames over which to anneal epsilon\n",
        "FINAL_EPSILON = 0.0001 # final value of epsilon\n",
        "INITIAL_EPSILON = 0.1 # starting value of epsilon\n",
        "REPLAY_MEMORY = 50000 # number of previous transitions to remember\n",
        "BATCH = 32 # size of minibatch\n",
        "FRAME_PER_ACTION = 1\n",
        "LEARNING_RATE = 1e-4\n",
        "\n",
        "FPS = 30\n",
        "SCREENWIDTH  = 288\n",
        "SCREENHEIGHT = 512\n",
        "\n",
        "pygame.init()\n",
        "FPSCLOCK = pygame.time.Clock()\n",
        "SCREEN = pygame.display.set_mode((SCREENWIDTH, SCREENHEIGHT))\n",
        "pygame.display.set_caption('Flappy Bird')\n",
        "\n",
        "IMAGES, SOUNDS, HITMASKS = flappy_bird_utils.load()\n",
        "PIPEGAPSIZE = 100 # gap between upper and lower part of pipe\n",
        "BASEY = SCREENHEIGHT * 0.79\n",
        "\n",
        "PLAYER_WIDTH = IMAGES['player'][0].get_width()\n",
        "PLAYER_HEIGHT = IMAGES['player'][0].get_height()\n",
        "PIPE_WIDTH = IMAGES['pipe'][0].get_width()\n",
        "PIPE_HEIGHT = IMAGES['pipe'][0].get_height()\n",
        "BACKGROUND_WIDTH = IMAGES['background'].get_width()\n",
        "\n",
        "PLAYER_INDEX_GEN = cycle([0, 1, 2, 1])\n",
        "\n",
        "img_rows , img_cols = 80, 80\n",
        "#Convert image into Black and white\n",
        "img_channels = 4 #We stack 4 frames\n",
        "\n",
        "game_state = game.GameState()\n",
        "do_nothing = np.zeros(ACTIONS)\n",
        "do_nothing[0] = 1\n",
        "game_state.frame_step(do_nothing)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-8dea7bfe1f55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_caption\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Flappy Bird'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mIMAGES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOUNDS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHITMASKS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflappy_bird_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0mPIPEGAPSIZE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m \u001b[0;31m# gap between upper and lower part of pipe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mBASEY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSCREENHEIGHT\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.79\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/game/flappy_bird_utils.py\u001b[0m in \u001b[0;36mload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m# numbers sprites for score display\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     IMAGES['numbers'] = (\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'assets/sprites/0.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'assets/sprites/1.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mpygame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'assets/sprites/2.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31merror\u001b[0m: Couldn't open assets/sprites/0.png"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFd4h0qxcNXw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "'''import numpy as np\n",
        "import sys\n",
        "import random\n",
        "import pygame\n",
        "import flappy_bird_utils\n",
        "import pygame.surfarray as surfarray\n",
        "from pygame.locals import *\n",
        "from itertools import cycle'''\n",
        "\n",
        "SCREENWIDTH  = 288\n",
        "SCREENHEIGHT = 512\n",
        "\n",
        "def Image_Generator(filepath):\n",
        "\n",
        "    file = open(filepath,\"r\")\n",
        "    running = True\n",
        "    while running:\n",
        "        current = file.readline()\n",
        "        if file.readline() == \"\":\n",
        "            break;\n",
        "        data_list = current.split(\" \")\n",
        "        data_list.pop()\n",
        "        \n",
        "        if (len(data_list) < 16):\n",
        "            loop_times = 16 - len(data_list)\n",
        "            counter = 0\n",
        "            while counter < loop_times:\n",
        "                data_list.append(None)\n",
        "                counter += 1\n",
        "        #print(data_list)\n",
        "        #data_list = list(map(int, data_list))\n",
        "\n",
        "        CaseNo = int(data_list[0])\n",
        "        game.playerx = int(int(data_list[1]) + SCREENWIDTH /2)\n",
        "        game.playery = int(int(data_list[2]) + SCREENWIDTH /2)\n",
        "        game.playerIndex = int(data_list[3])        \n",
        "        \n",
        "        game.upperPipes = []\n",
        "        game.lowerPipes = []\n",
        "        \n",
        "        for i in range(4,len(data_list),4):\n",
        "          \n",
        "            newPipe = getRandomPipe()\n",
        "            game.upperPipes.append(newPipe1[0])\n",
        "            game.upperPipes[int((i-4)/4)]['x'] = int(int(data_list[i]) + SCREENWIDTH /2)\n",
        "            game.upperPipes[int((i-4)/4)]['y'] = int(int(data_list[i+1]) + SCREENWIDTH /2)\n",
        "\n",
        "            newPipe = getRandomPipe()     \n",
        "            game.lowerPipes.append(newPipe2[0])\n",
        "            game.lowerPipes[int((i-4)/4)]['x'] = int(int(data_list[i+2]) + SCREENWIDTH /2)\n",
        "            game.lowerPipes[int((i-4)/4)]['y'] = int(int(data_list[i+3]) + SCREENWIDTH /2)\n",
        "        \n",
        "        # draw sprites\n",
        "        SCREEN.blit(IMAGES['background'], (0,0))\n",
        "\n",
        "        for uPipe, lPipe in zip(self.upperPipes, self.lowerPipes):\n",
        "            SCREEN.blit(IMAGES['pipe'][0], (uPipe['x'], uPipe['y']))\n",
        "            SCREEN.blit(IMAGES['pipe'][1], (lPipe['x'], lPipe['y']))\n",
        "\n",
        "        SCREEN.blit(IMAGES['base'], (self.basex, BASEY))\n",
        "        # print score so player overlaps the score\n",
        "        # showScore(self.score)\n",
        "        SCREEN.blit(IMAGES['player'][self.playerIndex],\n",
        "                    (self.playerx, self.playery))\n",
        "\n",
        "        image_data = pygame.surfarray.array3d(pygame.display.get_surface())\n",
        " \n",
        "    plt.show(image_data)\n",
        "    plt.show()      \n",
        "                       \n",
        "    #print(\"bird_down: \", bird_down)      \n",
        "    file.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoY9XL6ERYaG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "be9fbf8d-52d2-4f7c-ab93-070201e685f1"
      },
      "source": [
        "file= \"data.txt\" #file path or a file in the current folder\n",
        "Image_Generator(file)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-77b220083953>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m\"data.txt\"\u001b[0m \u001b[0;31m#file path or a file in the current folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mImage_Generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-40-609dc58bcdf4>\u001b[0m in \u001b[0;36mImage_Generator\u001b[0;34m(filepath)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mImage_Generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mrunning\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mrunning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data.txt'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaPBa5MHO8Yx",
        "colab_type": "code",
        "outputId": "796dde5c-e9c3-499d-b604-a8eeba1e2092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "%pip install pygame"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pygame\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/24/ede6428359f913ed9cd1643dd5533aefeb5a2699cc95bea089de50ead586/pygame-1.9.6-cp36-cp36m-manylinux1_x86_64.whl (11.4MB)\n",
            "\u001b[K     |████████████████████████████████| 11.4MB 2.7MB/s \n",
            "\u001b[?25hInstalling collected packages: pygame\n",
            "Successfully installed pygame-1.9.6\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}