Image_GeneratorV7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Image_Generator.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python [conda env:tensorflow113]",
      "language": "python",
      "name": "conda-env-tensorflow113-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.2"
    }
  },
  "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_GeneratorV7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jw1arH2vxVXK",
        "outputId": "c37400f2-fd95-44ef-9b2d-232d9c4983f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "!git clone https://github.com/LEChaney/ML-RayCast-Experiments\n",
        "%cd ML-RayCast-Experiments\n",
        "%pwd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ML-RayCast-Experiments'...\n",
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            "remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
            "remote: Total 279 (delta 29), reused 2 (delta 0), pack-reused 206\u001b[K\n",
            "Receiving objects: 100% (279/279), 44.85 MiB | 13.22 MiB/s, done.\n",
            "Resolving deltas: 100% (137/137), done.\n",
            "/content/ML-RayCast-Experiments\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/ML-RayCast-Experiments'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nR2rN0ieyYQ5",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BqsexcF4qXfE",
        "outputId": "c680cc02-6d56-45b6-a996-4c42d694a164",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[WinError 3] The system cannot find the path specified: '/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master'",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-60-96bcbabebfe6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SDL_VIDEODRIVER'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dummy'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfile_directory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: '/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iY7nyqTkNMef",
        "outputId": "0dd1b142-ad01-4e69-8de9-a02ef498781e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "import sys\n",
        "#sys.path.pop(1)\n",
        "sys.path.insert(1,\"/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/game\")\n",
        "print(sys.path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['C:\\\\Users\\\\Lance Chaney\\\\Desktop\\\\ml-experiments', '/content/ML-RayCast-Experiments/Keras-FlappyBird-master/Keras-FlappyBird-master/game', 'Keras-FlappyBird-master/Keras-FlappyBird-master/game', './game', './game', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\python37.zip', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\DLLs', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\lib', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113', '', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\lib\\\\site-packages', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\lib\\\\site-packages\\\\mask_rcnn-2.1-py3.7.egg', 'C:\\\\Anaconda3\\\\envs\\\\tensorflow113\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\Lance Chaney\\\\.ipython']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F-j-vysiwaMd",
        "outputId": "92e3abef-a8fa-4c77-9210-45e2a623f1f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "%pip install pygame"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pygame in c:\\anaconda3\\envs\\tensorflow113\\lib\\site-packages (1.9.6)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GGMYTHT4OLy4",
        "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\n",
        "import matplotlib.pyplot as plt\n",
        "import PIL.Image\n",
        "#from cStringIO import StringIO\n",
        "import IPython.display\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "epVQmcWzpGRU",
        "outputId": "5547abaa-ec02-48c4-c7d6-b4df14788b51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        }
      },
      "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",
        "pygame.display.init()\n",
        "pygame.display.set_mode((1, 1))\n",
        "FPSCLOCK = pygame.time.Clock()\n",
        "SCREEN = pygame.Surface((SCREENWIDTH, SCREENHEIGHT)).convert_alpha()\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": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[0, 0, 0]]], dtype=uint8), 0.1, False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oFd4h0qxcNXw",
        "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",
        "def Image_Generator(filepath):\n",
        "\n",
        "    file = open(filepath,\"r\")\n",
        "    running = True\n",
        "    count = 100\n",
        "    while running:\n",
        "        current = file.readline()\n",
        "        count -= 1\n",
        "        if current == \"\":\n",
        "            break;\n",
        "        if current[0] == \"-\":\n",
        "            continue\n",
        "        if count > 1:\n",
        "            continue\n",
        "        if count == 0:\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(float(data_list[0]))\n",
        "        game_state.playerx = int(float(data_list[1]))# + SCREENWIDTH /2)\n",
        "        game_state.playery = int(float(data_list[2]))# + SCREENWIDTH /2)\n",
        "        game_state.playerIndex = int(data_list[3])        \n",
        "        \n",
        "        game_state.upperPipes = []\n",
        "        game_state.lowerPipes = []\n",
        "        \n",
        "        for i in range(4,len(data_list),4):\n",
        "          \n",
        "            newPipe = game.getRandomPipe()\n",
        "            game_state.upperPipes.append(newPipe[0])\n",
        "            game_state.upperPipes[int((i-4)/4)]['x'] = int(float(data_list[i]))# + SCREENWIDTH /2)\n",
        "            game_state.upperPipes[int((i-4)/4)]['y'] = int(float(data_list[i+1]))# + SCREENWIDTH /2)\n",
        "\n",
        "            game_state.lowerPipes.append(newPipe[1])\n",
        "            game_state.lowerPipes[int((i-4)/4)]['x'] = int(float(data_list[i+2]))# + SCREENWIDTH /2)\n",
        "            game_state.lowerPipes[int((i-4)/4)]['y'] = int(float(data_list[i+3]))# + SCREENWIDTH /2)\n",
        "        \n",
        "        # draw sprites\n",
        "        SCREEN.blit(IMAGES['background'], (0,0))\n",
        "\n",
        "        for uPipe, lPipe in zip(game_state.upperPipes, game_state.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'], (game_state.basex, BASEY))\n",
        "        # print score so player overlaps the score\n",
        "        # showScore(self.score)\n",
        "        SCREEN.blit(IMAGES['player'][game_state.playerIndex],\n",
        "                    (game_state.playerx, game_state.playery))\n",
        "\n",
        "        image_data = pygame.surfarray.array3d(SCREEN)\n",
        "        #IPython.display.display(PIL.Image.fromarray(image_data))\n",
        "        \n",
        "        #running = False\n",
        "    #print(type(image_data))\n",
        "    print(\"image_data\" ,image_data)\n",
        "    print(\"image_data.shape: \", image_data.shape)\n",
        "    IPython.display.display(PIL.Image.fromarray(image_data))\n",
        "    #plt.show()      \n",
        "                       \n",
        "    #print(\"bird_down: \", bird_down)      \n",
        "    file.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RoY9XL6ERYaG",
        "outputId": "d9d2f07e-16a1-423f-9cd9-27bc80f18c47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "file= \"data.txt\" #file path or a file in the current folder\n",
        "Image_Generator(file)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_data [[[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]\n",
            "\n",
            " [[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]\n",
            "\n",
            " [[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]\n",
            "\n",
            " [[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]\n",
            "\n",
            " [[  0   0   0]\n",
            "  [  0   0   0]\n",
            "  [  0   0   0]\n",
            "  ...\n",
            "  [222 216 149]\n",
            "  [222 216 149]\n",
            "  [222 216 149]]]\n",
            "image_data.shape:  (288, 512, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x288 at 0x24F6CE472B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bd04oRFiRKJc",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}