{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Script pour interpreter les résultats du benchmark" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib \n", "%matplotlib notebook\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "import cv2\n", "import json\n", "from random import randint\n", "from math import cos,sin,atan,sqrt\n", "from glob import glob" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def carte(matched,positions,vt,meta):\n", " \n", " fresque = cv2.imread(meta['base_dir']+'fresque_small{}.ppm'.format(meta['fresque_id']))\n", " \n", " fig,ax = plt.subplots()\n", " ax.imshow(fresque)\n", " for i in range(len(matched)):\n", " if(matched[i]==1):\n", " #ax.arrow(vt[i][0],vt[i][1],p[i][0]-vt[i][0],p[i][1]-vt[i][1])\n", " ax.plot([vt[i][0],p[i][0]],[vt[i][1],p[i][1]],marker='D',color='red')\n", " ax.plot([vt[i][0]],[vt[i][1]],marker='D',color='green')\n", " fig.show()\n", " \n", "def correlation(matched, position, vt, d, meta):\n", " \n", " fig,ax = plt.subplots()\n", " for i in range(len(matched)):\n", " if matched[i] == 1:\n", " frag = cv2.imread('{}/fragments/fresque{}/frag_bench_{:05}.ppm'.format(meta['base_dir'],meta['fresque_id'],i))\n", " ax.scatter(frag.shape[1],frag.shape[0],s=d[i]*2,alpha=0.5)\n", " ax.set_xlabel(\"Width\")\n", " ax.set_ylabel('Height')\n", " ax.set_title(\"Erreur de placement en fonction de la hauteur et la largeur des fragments.\")\n", " fig.show()\n", " \n", "def distance_vecteur(matched,p,v):\n", " \n", " fig, ax = plt.subplots()\n", " for i in range(len(matched)):\n", " if matched[i] == 1:\n", " vecteur = (v[i][0]-p[i][0],v[i][1]-p[i][1])\n", " #print('{}:{} {}:{}'.format(v[i][0], v[i][1], vecteur[0], vecteur[1]))\n", " #ax.arrow(0,0,vecteur[0],vecteur[1])\n", " ax.scatter(vecteur[0],vecteur[1],s = (vecteur[0]**2+vecteur[1]**2)**0.5)\n", " \n", " ax.set_xlabel(\"W\")\n", " ax.set_ylabel('H')\n", " ax.set_title(\"Vecteur d'erreur de placement.\")\n", " fig.show()\n", " \n", "def correl_pos_err(matched,p,d):\n", " \n", " fig, ax = plt.subplots()\n", " for i in range(len(matched)):\n", " if matched[i] == 1:\n", " ax.scatter(p[i][0],p[i][1],s = d[i]*10)\n", " \n", " ax.set_xlabel(\"W\")\n", " ax.set_ylabel('H')\n", " ax.set_title(\"Erreur de placement en fonction de l'emplacement.\")\n", " plt.gca().invert_yaxis()\n", " fig.show()\n", "\n", "def dist_dist(m,p,d):\n", " \n", " ab,ordo = [],[]\n", " fig,ax = plt.subplots()\n", " \n", " for i in range(len(m)):\n", " if m[i] == 1:\n", " ax.scatter(sqrt(p[i][0]**2+p[i][1]**2),d[i])\n", " ab.append(sqrt(p[i][0]**2+p[i][1]**2))\n", " ordo.append(d[i])\n", " ax.set_xlabel(\"Distance à l'origine\")\n", " ax.set_ylabel(\"Erreur, distance à la verité terrain\")\n", " ax.set_title(\"Erreur en fonction de la distance à l'origine\")\n", " fig.savefig(\"correl_post_backtrack.pdf\")\n", " fig.show()\n", " \n", " \n", " A = np.vstack([ab, np.ones(len(ab))]).T\n", " print(A.shape)\n", " m, c = np.linalg.lstsq(A, np.array(ordo), rcond=None)[0]\n", " print(\"m: {}, c:{}\".format(m,c))\n", " \n", "def rectification(pos,m,c):\n", " npos = [0,0,pos[2]]\n", " err = m*sqrt(pos[0]**2+pos[1]**2)+c\n", " alpha = atan(pos[0]/pos[1])\n", " \n", " npos[0] = pos[0] + sin(alpha)*err\n", " npos[1] = pos[1] + cos(alpha)*err\n", " \n", " return(npos)\n", "\n", "def barchart(d):\n", " fig, ax = plt.subplots()\n", " \n", " intd = [int(dist) for dist in d if(dist>0)]\n", " \n", " bars = [intd.count(i) for i in range(max(intd)+1)]\n", " ab = np.arange(0,len(bars),1)\n", " \n", " ax.bar(ab,bars)\n", " \n", " fig.show()\n", " \n", "def barchart_dif(d,nd):\n", " fig, ax = plt.subplots()\n", " \n", " intd = [int(dist) for dist in d if(dist>0)]\n", " intnd = [int(dist) for dist in nd if(dist>0)]\n", " \n", " bars = [intd.count(i) for i in range(max(intd)+1)]\n", " nbars = [intnd.count(i) for i in range(max(intnd)+1)]\n", " \n", " ab = np.arange(0,len(bars),1)\n", " nab = np.arange(0,len(nbars),1)\n", " \n", " offset = 0.25\n", " width = 0.25\n", " \n", " ax.bar(ab,bars,width=width,color='teal',label='Erreurs')\n", " ax.bar(nab-offset,nbars,color='turquoise',width=width,label=\"Erreurs aprés rectification\")\n", " ax.set_xlabel(\"Erreur de placement (px)\")\n", " ax.set_ylabel(\"Nombre de fragments\")\n", " ax.set_title(\"Répartition des fragments en fonction de l'erreur de placement.\")\n", " ax.legend(loc='best')\n", " \n", " fig.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results_filenames = './results_bench/results_bench_f*_*0001'\n", "results_paths = glob(results_filenames)\n", "ab = np.arange(0,len(results_paths),1)\n", "\n", "\n", "maxi,mini,moyennes,ets = [], [], [], []\n", "\n", "for path in results_paths:\n", " with open(path,'r') as f:\n", " res = json.loads(f.readline())\n", " \n", " m = res['matched']\n", " p = res['positions']\n", " v = res['vt']\n", " d = [np.linalg.norm((v[i][0]-p[i][0],v[i][1]-p[i][1])) if m[i]==1 else 0 for i in range(len(v))]\n", " maxi.append(np.max(d))\n", " mini.append(np.min(d))\n", " moyennes.append(np.average(d))\n", " ets.append(np.std(d))\n", "\n", " \n", "print(len(ab),len(maxi))\n", "fig,ax = plt.subplots(1,1)\n", "ax.plot(ab,mini,label=\"Minimum\")\n", "ax.plot(ab,maxi,label=\"Maximum\")\n", "ax.plot(ab,moyennes,label=\"Moyenne\")\n", "ax.plot(ab,ets,label=\"Ecart Type\")\n", "\n", "ax.legend(loc='best')\n", "ax.set_xlabel(\"Fresque\")\n", "ax.set_ylabel(\"Pixel\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] }, { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: './results_bench/results_random_color__f0_*_0008'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult_filename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfresque_id\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[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\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[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\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 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './results_bench/results_random_color__f0_*_0008'" ] } ], "source": [ "results_filenames = './results_bench/results_random_color__f*_*_0008'\n", "result_filename = './results_bench/results_random_color__f{}_*_0008'\n", "results_paths = glob(results_filenames)\n", "\n", "fresque_ids = []\n", "ets = []\n", "dists = []\n", "asso = []\n", "\n", "\n", "print(len(results_paths))\n", "\n", "for fresque_id in range(len(results_paths)):\n", " path = result_filename.format(fresque_id)\n", " \n", " with open(path,'r') as f:\n", " res = json.loads(f.readline())\n", " \n", " m = res['matched']\n", " d = res['distances']\n", " p = res['positions']\n", " v = res['vt']\n", " npos = []\n", "\n", " for i in range(len(m)):\n", " if m[i] == 1:\n", " #print(p[i])\n", " pos = rectification(p[i],0.0073,0.2225)\n", " #print(\"-> {}\".format(pos))\n", " npos.append(pos)\n", " else:\n", " npos.append(-1)\n", " \n", " fresque_ids.append(fresque_id)\n", " ets.append(round(np.std(nd),3))\n", " dists.append(round(np.average(nd),3))\n", " asso.append(np.average(m))\n" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }