{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Image to features\n", "\n", "Read an image with opencv."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n)\n", "        a += \"    \";\n", "    return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render 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anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## OpenCV"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["imgt_61.jpg (1, 30000)\n", "imgt_66.jpg (1, 30000)\n"]}, {"data": {"text/plain": ["(2, 30000)"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["import numpy as np\n", "import argparse\n", "import cv2\n", "from numpy import matrix\n", "import os\n", "from functools import reduce\n", "\n", "# create NumPy arrays from the boundaries\n", "lower = np.array([0, 0, 0], dtype = \"uint8\")\n", "upper = np.array([100, 115, 240], dtype = \"uint8\")\n", "\n", "i=0\n", "List= []\n", "folder = \"tomates\"\n", "\n", "if not os.path.exists(folder):\n", "    raise FileNotFoundError(os.path.abspath(folder))\n", "\n", "for element in os.listdir(folder):\n", "    i+=1\n", "    # load the image\n", "    image = cv2.imread(os.path.join(folder, element))\n", "    res = cv2.resize(image,(100, 100), interpolation = cv2.INTER_CUBIC)\n", "\n", "    # find the colors within the specified boundaries and apply the mask\n", "    mask = cv2.inRange(res, lower, upper)\n", "    output = cv2.bitwise_and(res, res, mask = mask)\n", "    shape = output.shape\n", "    nb = reduce(lambda a,b: a*b, shape)\n", "    mat = output.reshape((1, nb))\n", "    List.append(mat)\n", "    print(element, mat.shape)\n", "\n", "bigmat = np.vstack(List)\n", "bigmat.shape"]}, {"cell_type": "code", "execution_count": 4, "metadata": {"ExecuteTime": {"end_time": "2016-10-29T14:20:52.157733", "start_time": "2016-10-29T14:20:51.439058"}}, "outputs": [{"data": {"image/jpeg": 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\n", "text/plain": ["<IPython.core.display.Image object>"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["from pyquickhelper.helpgen import NbImage\n", "NbImage(\"tomates/imgt_61.jpg\")"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0"}}, "nbformat": 4, "nbformat_minor": 2}