Image_GeneratorV5.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_GeneratorV5.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": "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ML-RayCast-Experiments'...\n",
            "remote: Enumerating objects: 73, done.\u001b[K\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": {
        "id": "nR2rN0ieyYQ5",
        "colab_type": "code",
        "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": {
        "id": "BqsexcF4qXfE",
        "colab_type": "code",
        "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": 4,
      "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": "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": 8,
      "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": "F-j-vysiwaMd",
        "colab_type": "code",
        "outputId": "92e3abef-a8fa-4c77-9210-45e2a623f1f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "%pip install pygame"
      ],
      "execution_count": 6,
      "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 71.9MB/s \n",
            "\u001b[?25hInstalling collected packages: pygame\n",
            "Successfully installed pygame-1.9.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGMYTHT4OLy4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1967b3f9-db8f-4438-f88f-3a2b216e69b1"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pygame 1.9.6\n",
            "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epVQmcWzpGRU",
        "colab_type": "code",
        "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",
        "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": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[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",
              "         [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]]], dtype=uint8), 0.1, False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "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",
        "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(pygame.display.get_surface())\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": {
        "id": "RoY9XL6ERYaG",
        "colab_type": "code",
        "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": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_data [[[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
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            " [[0 0 0]\n",
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            "\n",
            " ...\n",
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            "image_data.shape:  (288, 512, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x288 at 0x7F242C850C50>"
            ]
          },
          "metadata": {
            "tags": []
          }
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}