diff --git a/pyrobolearn/datasets/README.md b/pyrobolearn/datasets/README.md new file mode 100644 index 0000000..e69de29 diff --git a/pyrobolearn/datasets/parsers/3d/README.md b/pyrobolearn/datasets/parsers/3d/README.md new file mode 100644 index 0000000..1f59f1b --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/README.md @@ -0,0 +1,54 @@ +## 3D datasets + +This repo contains a brief description of 3D datasets that I could find online. It also contains the Python code to load, parse, and plot some of these datasets. + + +### List of datasets + +Here is a non-exhaustive list of datasets that could be used: + +* [**TUM Kitchen**: "The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition", 2009](https://ias.in.tum.de/dokuwiki/software/kitchen-activity-data) + * scenarios: "everyday manipulation activities in a natural kitchen environment" + * labeled segmented data: 51 DOF articulated human model, 3D joint positions, videos and labels +* [**CMU-MMAC**: "Guide to the Carnegie Mellon University Multimodal Activity database", 2009](http://kitchen.cs.cmu.edu/) + * scenarios: human activity of subjects performing the tasks involved in cooking and food preparation +* [**HumanEva**: "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion", 2010](http://humaneva.is.tue.mpg.de/) + * 6 scenarios: walking, jogging, gesturing, throwing and catching a ball, boxing, combo (=walking+jogging+balancing) + * nb of subjects: 4 + * data: 2D and 3D pose estimation +* [**MPII Cooking**: "A database for fine grained activity detection of cooking activities", 2012](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/human-activity-recognition/mpii-cooking-activities-dataset/) +Datasets + * nb of subjects: 12 + * 65 fine-grained cooking activities: background activity, change temperature, cut apart, cut dice, cut in, cut off ends, cut out inside, cut slices, cut stripes, dry, fill water from tap, grate, lid: put on, lid: remove, mix, move from X to Y, open egg, open tin, open/close cupboard, open/close drawer, open/close fridge, open/close oven, package X, peel, plug in/out, pour, pull out, puree, put in bowl, put in pan/pot, put on bread/dough, put on cutting-board, put on plate, read, remove from package, rip open, scratch off, screw close, screw open, shake, smell, spice, spread, squeeze, stamp, stir, strew, take & put in cupboard, take & put in drawer, take & put in fridge, take & put in oven, take & put in spice holder, take ingredient apart, take out from cupboard, take out from drawer, take out from fridge, take out from oven, take out from spice holder, taste, throw in garbage, unroll dough, wash hands, wash objects, whisk, wipe clean + * labeled and segmented data: 2D body joint positions +* [**J-HMDB**: "Joint-annotated Human Motion Data Base", 2013](http://jhmdb.is.tue.mpg.de/) +Datasets + * 21 scenarios: brush hair, catch, clap, climb stairs, golf, jump, kick ball, pick, pour, pull-up, push, run, shoot +ball, shoot bow, shoot gun, sit, stand, swing baseball, throw, walk, wave + * labeled and segmented data: 36-55 clips per action class with each clip containing 15-40 frames + 2D joint positions. 10 body parts connected by 13 joints (shoulder, elbow, wrist, hip, knee, ankle, neck) and two landmarks (face and belly). +* [**Human 3.6M**: "Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments", 2014](http://vision.imar.ro/human3.6m/description.php) + * 3.6 million 3D human poses and corresponding images + * nb of subjects: 11 + * 15 scenarios: direction, discussion, eating, activitie while seated, greeting, taking photo, posing, making purchases, smoking, waiting, walking, sitting on chair, talking on the phone, walking dog, walking together + * data: 3D joint positions and joint angles +* [**UTD-MHAD**: "UTD-MHAD: A Multimodal Dataset for Human Action Recognition utilizing a Depth Camera and a Wearable Inertial Sensor", 2015](http://www.utdallas.edu/~kehtar/UTD-MHAD.html) + * nb of subjects: 8 + * 27 different actions: (1) right arm swipe to the left, (2) right arm swipe to the right, (3) right hand wave, (4) two hand front clap, (5) right arm throw, (6) cross arms in the chest, (7) basketball shoot, (8) right hand draw x, (9) right hand draw circle (clockwise), (10) right hand draw circle (counter clockwise), (11) draw triangle, (12) bowling (right hand), (13) front boxing, (14) baseball swing from right, (15) tennis right hand forehand swing, (16) arm curl (two arms), (17) tennis serve, (18) two hand push, (19) right hand knock on door, (20) right hand catch an object, (21) right hand pick up and throw, (22) jogging in place, (23) walking in place, (24) sit to stand, (25) stand to sit, (26) forward lunge (left foot forward), (27) squat (two arms stretch out). + * labeled and segmented data: 861 data sequences, 3D skeleton joint positions +* [**G3D**: "G3D: A Gaming Action Dataset and Real Time Action Recognition Evaluation Framework", 2012](http://dipersec.king.ac.uk/G3D/index.html) + * nb of subjects: 10 + * 20 gaming actions: punch right, punch left, kick right, kick left, defend, golf swing, tennis swing forehand, tennis swing backhand, tennis serve, throw bowling ball, aim and fire gun, walk, run, jump, climb, crouch, steer a car, wave, flap and clap + * data: 3D joint positions +* [**CMU-Datasets**](mocap.cs.cmu.edu/search.php) + * dataset: very diverse, contains data such as sport activities (soccer, dancing, etc), everyday movements/activities, locomotion (walk/run, jump, etc), interaction with environment and other agents, human subjects imitating animal behaviors (such as snake, dog, chicken, etc), stretching, finegrained movements, and so on. + * data: 3D joint positions and videos. +* Others: MSRC-12 Kinect Gesture Datase, MPI HDM05 Motion Capture Database, MSR Action3D Database, VGG Human Pose Estimation datasets, etc. + + + +### Python Code + +The python code to load, parse, and plot (using animations) has been implemented for the following datasets: + +* UTD-MHAD: see `utd-mhad.ipynb` +* CMU-Datasets: see `cmu.ipynb` diff --git a/pyrobolearn/datasets/parsers/3d/cmu.ipynb b/pyrobolearn/datasets/parsers/3d/cmu.ipynb new file mode 100644 index 0000000..ae3fef6 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/cmu.ipynb @@ -0,0 +1,1769 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [CMU-Datasets](mocap.cs.cmu.edu/search.php)\n", + "\n", + "* dataset: very diverse, contains data such as sport activities (soccer, dancing, etc), everyday movements/activities, locomotion (walk/run, jump, etc), interaction with environment and other agents, human subjects imitating animal behaviors (such as snake, dog, chicken, etc), stretching, finegrained movements, and so on.\n", + "* data: 3D joint positions and videos.\n", + "\n", + "The CMU datasets is composed of '.asf/.amc' files. More info about the this file format can be found on this [link](http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html).\n", + "\n", + "To parse these files, we will need the `cgkit` module: http://cgkit.sourceforge.net/\n", + "\n", + "The relevant documentation can be found here: http://cgkit.sourceforge.net/doc2/asfamc.html\n", + "\n", + "Clone and install the following github repo: https://github.com/VanushVaswani/amcparser" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib.animation as animation\n", + "\n", + "from IPython.display import HTML\n", + "\n", + "#%matplotlib inline\n", + "%matplotlib notebook\n", + "\n", + "from amcparser.skeleton import Skeleton\n", + "from amcparser.motion import SkelMotion" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "PATH = 'data/CMU'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 343/343 [00:00<00:00, 585.49it/s] | 51/343 [00:00<00:00, 509.01it/s]\n" + ] + } + ], + "source": [ + "# Define some variables\n", + "links = ['upperback', 'rfoot', 'lradius', 'lthumb', 'rradius', 'lwrist', 'rhand', 'rhumerus',\n", + " 'lowerneck', 'lhand', 'ltibia', 'head', 'rwrist', 'lfingers', 'rtoes', 'lowerback',\n", + " 'lhipjoint', 'rhipjoint', 'lclavicle', 'upperneck', 'rfemur', 'thorax', 'rclavicle',\n", + " 'rtibia', 'lfoot', 'lhumerus', 'rthumb', 'rfingers', 'root', 'ltoes', 'lfemur']\n", + "Nl = len(links)\n", + "\n", + "# File paths\n", + "skeleton_filename = PATH+'/02.asf'\n", + "motion_filename = PATH+'/02_01.amc'\n", + "#video_filename = PATH+'/02_01.avi'\n", + "framerate = 120.\n", + "\n", + "# Create skeleton and motion\n", + "skel = Skeleton(skeleton_filename)\n", + "skelMot = SkelMotion(skel, motion_filename, (1./framerate))\n", + "\n", + "# Compute all the trajectories\n", + "skelMot.traverse(None, 0, -1)\n", + "\n", + "# Data\n", + "X_TO = np.array([skel.bones[jnt].xyz_data for jnt in ['head', 'upperneck', 'lowerneck',\n", + " 'upperback', 'thorax', 'lowerback', 'root']])\n", + "X_RA = np.array([skel.bones[jnt].xyz_data for jnt in ['rclavicle', 'rhumerus', 'rradius',\n", + " 'rwrist', 'rhand', 'rthumb', 'rfingers']])\n", + "X_LA = np.array([skel.bones[jnt].xyz_data for jnt in ['lclavicle', 'lhumerus', 'lradius',\n", + " 'lwrist', 'lhand', 'lthumb', 'lfingers']])\n", + "X_RL = np.array([skel.bones[jnt].xyz_data for jnt in ['rhipjoint', 'rfemur', 'rtibia', 'rfoot', 'rtoes']])\n", + "X_LL = np.array([skel.bones[jnt].xyz_data for jnt in ['lhipjoint', 'lfemur', 'ltibia', 'lfoot', 'ltoes']])\n", + "X = np.vstack((X_TO, X_RA, X_LA, X_RL, X_LL))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\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": [ + "# Plot trajectory profiles\n", + "plt.figure()\n", + "plt.subplot(2,2,1)\n", + "plt.title('X')\n", + "plt.plot(range(T), X[11,:,2] - X[6,:,2])\n", + "\n", + "plt.subplot(2,2,2)\n", + "plt.title('Y')\n", + "plt.plot(range(T), X[11,:,0] - X[6,:,0])\n", + "\n", + "plt.subplot(2,2,3)\n", + "plt.title('Z')\n", + "plt.plot(range(T), X[11,:,1] - X[6,:,1])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf b/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf new file mode 100644 index 0000000..c2c869f --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf @@ -0,0 +1,338 @@ +# AST/ASF file generated using VICON BodyLanguage +# ----------------------------------------------- +:version 1.10 +:name VICON +:units + mass 1.0 + length 0.45 + angle deg +:documentation + .ast/.asf automatically generated from VICON data using + VICON BodyBuilder and BodyLanguage model FoxedUp or BRILLIANT.MOD +:root + order TX TY TZ RX RY RZ + axis XYZ + position 0 0 0 + orientation 0 0 0 +:bonedata + begin + id 1 + name lhipjoint + direction 0.655637 -0.713449 0.247245 + length 2.52691 + axis 0 0 0 XYZ + end + begin + id 2 + name lfemur + direction 0.34202 -0.939693 0 + length 7.59371 + axis 0 0 20 XYZ + dof rx ry rz + limits (-160.0 20.0) + (-70.0 70.0) + (-60.0 70.0) + end + begin + id 3 + name ltibia + direction 0.34202 -0.939693 0 + length 7.28717 + axis 0 0 20 XYZ + dof rx + limits (-10.0 170.0) + end + begin + id 4 + name lfoot + direction 0.0886837 -0.243657 0.965798 + length 2.2218 + axis -90 7.62852e-016 20 XYZ + dof rx rz + limits (-45.0 90.0) + (-70.0 20.0) + end + begin + id 5 + name ltoes + direction 1.53547e-011 -4.22004e-011 1 + length 1.11249 + axis -90 7.62852e-016 20 XYZ + dof rx + limits (-90.0 20.0) + end + begin + id 6 + name rhipjoint + direction -0.645062 -0.722004 0.250209 + length 2.49697 + axis 0 0 0 XYZ + end + begin + id 7 + name rfemur + direction -0.34202 -0.939693 0 + length 7.58734 + axis 0 0 -20 XYZ + dof rx ry rz + limits (-160.0 20.0) + (-70.0 70.0) + (-70.0 60.0) + end + begin + id 8 + name rtibia + direction -0.34202 -0.939693 0 + length 7.21538 + axis 0 0 -20 XYZ + dof rx + limits (-10.0 170.0) + end + begin + id 9 + name rfoot + direction -0.102907 -0.282735 0.953662 + length 2.23735 + axis -90 -7.62852e-016 -20 XYZ + dof rx rz + limits (-45.0 90.0) + (-20.0 70.0) + end + begin + id 10 + name rtoes + direction -1.5354e-011 -4.21751e-011 1 + length 1.11569 + axis -90 -7.62852e-016 -20 XYZ + dof rx + limits (-90.0 20.0) + end + begin + id 11 + name lowerback + direction 0.00952301 0.997604 -0.0685217 + length 2.05943 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 12 + name upperback + direction 0.00494399 0.999577 -0.0286701 + length 2.06523 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 13 + name thorax + direction 0.000729686 0.999992 0.00388603 + length 2.06807 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 14 + name lowerneck + direction 0.00452882 0.995459 0.0950816 + length 1.57426 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 15 + name upperneck + direction 0.021922 0.997711 -0.0639762 + length 1.56399 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 16 + name head + direction 0.008024 0.999444 -0.0323691 + length 1.6265 + axis 0 0 0 XYZ + dof rx ry rz + limits (-20.0 45.0) + (-30.0 30.0) + (-30.0 30.0) + end + begin + id 17 + name lclavicle + direction 0.967826 0.247107 -0.0474446 + length 3.6598 + axis 0 0 0 XYZ + dof ry rz + limits (-20.0 10.0) + (0.0 20.0) + end + begin + id 18 + name lhumerus + direction 1 -4.48971e-011 -2.90544e-027 + length 4.86513 + axis 180 -30 -90 XYZ + dof rx ry rz + limits (-60.0 90.0) + (-90.0 90.0) + (-90.0 90.0) + end + begin + id 19 + name lradius + direction 1 -4.48958e-011 1.79638e-026 + length 3.35554 + axis 180 -30 -90 XYZ + dof rx + limits (-10.0 170.0) + end + begin + id 20 + name lwrist + direction 1 -4.48952e-011 3.34345e-026 + length 1.67777 + axis -2.22354e-014 90 90 XYZ + dof ry + limits (-180.0 0.0) + end + begin + id 21 + name lhand + direction 1 -4.49055e-011 6.6869e-026 + length 0.661175 + axis -4.26681e-014 90 90 XYZ + dof rx rz + limits (-90.0 90.0) + (-45.0 45.0) + end + begin + id 22 + name lfingers + direction 1 -4.48731e-011 1.33738e-025 + length 0.533057 + axis -8.53362e-014 90 90 XYZ + dof rx + limits (0.0 90.0) + end + begin + id 23 + name lthumb + direction 0.707107 -6.35005e-011 0.707107 + length 0.765366 + axis -90 45 2.85299e-015 XYZ + dof rx rz + limits (-45.0 45.0) + (-45.0 45.0) + end + begin + id 24 + name rclavicle + direction -0.973174 0.211421 -0.0907389 + length 3.59444 + axis 0 0 0 XYZ + dof ry rz + limits (-10.0 20.0) + (-20.0 0.0) + end + begin + id 25 + name rhumerus + direction -1 -4.48964e-011 1.56311e-027 + length 5.02649 + axis 180 30 90 XYZ + dof rx ry rz + limits (-90.0 60.0) + (-90.0 90.0) + (-90.0 90.0) + end + begin + id 26 + name rradius + direction -1 -4.48948e-011 2.01724e-026 + length 3.36431 + axis 180 30 90 XYZ + dof rx + limits (-10.0 170.0) + end + begin + id 27 + name rwrist + direction -1 -4.49008e-011 3.34345e-026 + length 1.68216 + axis -2.22354e-014 -90 -90 XYZ + dof ry + limits (-180.0 0.0) + end + begin + id 28 + name rhand + direction -1 -4.49013e-011 6.6869e-026 + length 0.730406 + axis -4.26681e-014 -90 -90 XYZ + dof rx rz + limits (-90.0 90.0) + (-45.0 45.0) + end + begin + id 29 + name rfingers + direction -1 -4.48867e-011 1.33738e-025 + length 0.588872 + axis -8.53362e-014 -90 -90 XYZ + dof rx + limits (0.0 90.0) + end + begin + id 30 + name rthumb + direction -0.707107 -6.34907e-011 0.707107 + length 0.845506 + axis -90 -45 -2.85299e-015 XYZ + dof rx rz + limits (-45.0 45.0) + (-45.0 45.0) + end +:hierarchy + begin + root lhipjoint rhipjoint lowerback + lhipjoint lfemur + lfemur ltibia + ltibia lfoot + lfoot ltoes + rhipjoint rfemur + rfemur rtibia + rtibia rfoot + rfoot rtoes + lowerback upperback + upperback thorax + thorax lowerneck lclavicle rclavicle + lowerneck upperneck + upperneck head + lclavicle lhumerus + lhumerus lradius + lradius lwrist + lwrist lhand lthumb + lhand lfingers + rclavicle rhumerus + rhumerus rradius + rradius rwrist + rwrist rhand rthumb + rhand rfingers + end diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc b/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc new file mode 100644 index 0000000..c19815a --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc @@ -0,0 +1,10293 @@ +#!OML:ASF H:\Terrain\Patient Classification 1\Takeo\Monday\TakeoMonday.ASF +:FULLY-SPECIFIED +:DEGREES +1 +root 10.4194 16.7048 -30.1003 -2.48972 -9.82194 -3.00914 +lowerback 7.86573 1.57272 6.3571 +upperback 3.47255 2.55226 1.65684 +thorax -0.599593 1.31944 -2.05527 +lowerneck -12.4901 4.06706 1.03438 +upperneck 1.61784 5.61246 -6.40733 +head 2.97226 2.54741 -1.92752 +rclavicle -1.6201e-014 -1.43125e-014 +rhumerus -9.40273 14.8795 -82.1974 +rradius 46.2257 +rwrist -16.7798 +rhand -25.9429 18.5743 +rfingers 7.12502 +rthumb 0.600913 -11.4046 +lclavicle -1.6201e-014 -1.43125e-014 +lhumerus -51.9812 32.3861 64.939 +lradius 20.3824 +lwrist 12.631 +lhand -21.8909 22.8474 +lfingers 7.12502 +lthumb 4.5141 52.7793 +rfemur 16.1777 1.96704 29.9133 +rtibia 11.5302 +rfoot -15.9687 0.925564 +rtoes 4.33359 +lfemur -28.0191 -1.02078 -20.1783 +ltibia 22.3977 +lfoot -12.1104 13.478 +ltoes -10.2227 +2 +root 10.4117 16.684 -29.9168 -2.69729 -10.0082 -2.83243 +lowerback 7.44596 1.65374 6.09331 +upperback 3.91254 2.58553 1.67171 +thorax 0.0784557 1.35992 -1.86912 +lowerneck -13.0403 4.1508 1.03655 +upperneck 1.41954 5.71695 -6.43796 +head 3.01316 2.59206 -1.92353 +rclavicle -3.06128e-014 -7.95139e-016 +rhumerus -8.66985 14.6305 -81.9822 +rradius 46.032 +rwrist -16.5037 +rhand -25.3579 18.1254 +rfingers 7.12502 +rthumb 1.16593 -11.8512 +lclavicle -3.06128e-014 -7.95139e-016 +lhumerus -51.4595 32.9736 65.2094 +lradius 20.3072 +lwrist 12.3811 +lhand -21.901 23.0332 +lfingers 7.12502 +lthumb 4.50436 52.9653 +rfemur 17.2048 2.06921 29.6756 +rtibia 11.2385 +rfoot -15.6726 0.950482 +rtoes 2.11338 +lfemur -27.7269 -0.953135 -20.5822 +ltibia 22.4395 +lfoot -12.3785 13.6867 +ltoes -8.45391 +3 +root 10.4037 16.6749 -29.7319 -2.73684 -10.1104 -2.67905 +lowerback 7.31519 1.74924 5.93159 +upperback 3.96845 2.68906 1.61541 +thorax 0.211031 1.41449 -1.83734 +lowerneck -13.1188 4.14188 1.06949 +upperneck 1.08186 5.66983 -6.25219 +head 2.90215 2.582 -1.8696 +rclavicle 3.97569e-015 -1.03368e-014 +rhumerus -8.50177 14.3985 -81.9469 +rradius 45.6568 +rwrist -16.2262 +rhand -25.4816 18.2851 +rfingers 7.12502 +rthumb 1.04638 -11.6921 +lclavicle 3.97569e-015 -1.03368e-014 +lhumerus -51.3593 33.2859 65.0346 +lradius 20.0295 +lwrist 11.6134 +lhand -21.8378 22.427 +lfingers 7.12502 +lthumb 4.56542 52.3578 +rfemur 17.7116 2.03566 29.3786 +rtibia 11.6107 +rfoot -15.695 1.22609 +rtoes 0.922151 +lfemur -27.6281 -0.810148 -21.0041 +ltibia 22.6869 +lfoot -12.2625 13.6748 +ltoes -9.11447 +4 +root 10.3951 16.6696 -29.54 -2.6529 -10.1914 -2.43014 +lowerback 7.19363 1.82368 5.74365 +upperback 3.81888 2.77415 1.4497 +thorax 0.127573 1.4515 -1.90802 +lowerneck -12.9178 4.12991 1.31882 +upperneck 0.706102 5.58519 -6.09066 +head 2.73437 2.55894 -1.86943 +rclavicle 1.37658e-014 0 +rhumerus -8.68107 14.2787 -82.1702 +rradius 45.2994 +rwrist -16.0827 +rhand -25.8239 18.565 +rfingers 7.12502 +rthumb 0.715839 -11.4136 +lclavicle 1.37658e-014 0 +lhumerus -51.4118 33.3294 64.7565 +lradius 19.8149 +lwrist 11.1137 +lhand -21.9155 21.714 +lfingers 7.12502 +lthumb 4.49033 51.6464 +rfemur 18.2149 2.09324 28.9948 +rtibia 11.7512 +rfoot -15.3617 1.42214 +rtoes 0.396563 +lfemur -27.6637 -0.391122 -21.6105 +ltibia 23.2309 +lfoot -11.8145 13.2795 +ltoes -11.1406 +5 +root 10.3841 16.6488 -29.3447 -2.50765 -10.3526 -2.46894 +lowerback 6.93084 1.85711 5.71898 +upperback 3.70601 2.80336 1.40701 +thorax 0.158931 1.46518 -1.93228 +lowerneck -12.7095 4.17378 1.546 +upperneck 0.38896 5.58739 -5.9898 +head 2.59153 2.57436 -1.87971 +rclavicle -2.04251e-014 -7.15625e-015 +rhumerus -8.88558 14.3777 -82.4627 +rradius 45.0282 +rwrist -16.0025 +rhand -26.022 18.4229 +rfingers 7.12502 +rthumb 0.524504 -11.5562 +lclavicle -2.04251e-014 -7.15625e-015 +lhumerus -51.3723 33.2687 64.7988 +lradius 19.8026 +lwrist 11.3747 +lhand -21.8573 21.6385 +lfingers 7.12502 +lthumb 4.54656 51.5698 +rfemur 18.5238 2.05559 28.8937 +rtibia 12.2574 +rfoot -15.0286 1.70983 +rtoes -0.597689 +lfemur -27.6786 -0.161279 -21.8039 +ltibia 24.1117 +lfoot -11.5185 12.8466 +ltoes -11.8881 +6 +root 10.3736 16.6153 -29.1487 -2.27833 -10.4567 -2.74192 +lowerback 6.39393 1.89875 5.83274 +upperback 3.70818 2.82625 1.52352 +thorax 0.460678 1.48492 -1.85437 +lowerneck -12.6363 4.16435 1.46386 +upperneck 0.109727 5.56187 -5.76511 +head 2.46892 2.57522 -1.79365 +rclavicle -9.39258e-015 -3.33958e-014 +rhumerus -8.94255 14.3995 -82.5464 +rradius 44.7054 +rwrist -16.0208 +rhand -26.1876 18.2668 +rfingers 7.12502 +rthumb 0.36451 -11.7126 +lclavicle -9.39258e-015 -3.33958e-014 +lhumerus -51.2401 33.4019 64.9731 +lradius 19.7523 +lwrist 11.4084 +lhand -21.7171 21.9356 +lfingers 7.12502 +lthumb 4.68197 51.8639 +rfemur 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-2.36002 -10.2713 -2.44292 +lowerback 5.94446 1.79342 4.97885 +upperback 3.73693 2.61242 1.65782 +thorax 0.673863 1.36867 -1.21366 +lowerneck -12.6639 4.23843 0.871191 +upperneck -0.503805 5.68322 -4.98816 +head 2.21473 2.66958 -1.42777 +rclavicle 1.9133e-014 1.31198e-014 +rhumerus -9.04388 14.801 -82.5439 +rradius 43.7541 +rwrist -16.5425 +rhand -25.8764 18.1624 +rfingers 7.12502 +rthumb 0.665108 -11.8163 +lclavicle 1.9133e-014 1.31198e-014 +lhumerus -51.1759 33.8174 65.2761 +lradius 19.8353 +lwrist 11.135 +lhand -21.4038 22.1568 +lfingers 7.12502 +lthumb 4.98449 52.0782 +rfemur 19.7277 2.06085 28.7127 +rtibia 13.9784 +rfoot -15.0329 1.75063 +rtoes -8.99435 +lfemur -27.7763 -0.479807 -22.3193 +ltibia 27.6883 +lfoot -9.30166 13.1214 +ltoes -11.1841 +9 +root 10.3343 16.5753 -28.5503 -2.28592 -10.2581 -2.54086 +lowerback 5.87195 1.73346 5.11106 +upperback 3.56428 2.54204 1.52084 +thorax 0.544173 1.33202 -1.42625 +lowerneck -12.8389 4.28669 0.757051 +upperneck -0.501307 5.7362 -4.30349 +head 2.21631 2.71548 -1.17916 +rclavicle -1.33186e-014 -1.03368e-014 +rhumerus -9.46188 14.6013 -83.1637 +rradius 43.0615 +rwrist -16.2744 +rhand -25.7951 18.6118 +rfingers 7.12502 +rthumb 0.743616 -11.3667 +lclavicle -1.33186e-014 -1.03368e-014 +lhumerus -50.921 33.424 65.3926 +lradius 19.7381 +lwrist 11.3958 +lhand -21.7051 22.6165 +lfingers 7.12502 +lthumb 4.69349 52.5445 +rfemur 19.9706 2.02266 28.6945 +rtibia 14.6633 +rfoot -14.231 1.88766 +rtoes -11.8154 +lfemur -27.9258 -0.751252 -22.2513 +ltibia 29.0729 +lfoot -8.74962 14.2092 +ltoes -10.7542 +10 +root 10.323 16.5366 -28.3523 -2.23404 -10.2098 -2.46793 +lowerback 5.65422 1.7393 4.99464 +upperback 3.45035 2.53558 1.41083 +thorax 0.546233 1.32679 -1.469 +lowerneck -12.567 4.24422 0.659075 +upperneck -0.942763 5.65924 -3.82541 +head 1.99407 2.70184 -1.02124 +rclavicle -1.70955e-014 -8.34896e-015 +rhumerus -9.9406 14.2338 -83.6317 +rradius 42.4407 +rwrist -15.9523 +rhand -25.841 19.0222 +rfingers 7.12502 +rthumb 0.699333 -10.9564 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-7.23079e-015 -1.27222e-014 +rhumerus -63.7887 -34.3173 -47.1111 +rradius 9.93192 +rwrist 23.4686 +rhand -19.5534 13.502 +rfingers 7.12502 +rthumb 6.77091 -16.3697 +lclavicle -7.23079e-015 -1.27222e-014 +lhumerus -32.0358 -3.35783 91.4085 +lradius 65.228 +lwrist 25.6386 +lhand -25.7351 12.7292 +lfingers 7.12502 +lthumb 0.801561 42.7075 +rfemur -23.2433 6.73071 25.0009 +rtibia 27.7761 +rfoot -4.35066 -15.1615 +rtoes -10.693 +lfemur 16.9276 -2.85303 -22.5749 +ltibia 27.7778 +lfoot -13.6748 -0.618019 +ltoes -24.1466 \ No newline at end of file diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt b/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt new file mode 100644 index 0000000..3733e52 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt @@ -0,0 +1,3 @@ +# CMU Dataset + +* webpage (link to download the dataset): mocap.cs.cmu.edu/search.php diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt b/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt new file mode 100644 index 0000000..3cf8c36 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt @@ -0,0 +1,15 @@ + + Subject #2 (various expressions and human behaviors) + +02_01 02_01.tvd 02_01.c3d 02_01.amc 02_01.avi walk +02_02 02_02.tvd 02_02.c3d 02_02.amc 02_02.mpg 02_02.avi walk +02_03 02_03.tvd 02_03.c3d 02_03.amc 02_03.mpg 02_03.avi run/jog +02_04 02_04.tvd 02_04.c3d 02_04.amc 02_04.mpg 02_04.avi jump, balance +02_05 02_05.tvd 02_05.c3d 02_05.amc 02_05.mpg 02_05.avi punch/strike +02_06 02_06.tvd 02_06.c3d 02_06.amc 02_06.mpg 02_06.avi bend over, scoop up, rise, lift arm +02_07 02_07.tvd 02_07.c3d 02_07.amc 02_07.mpg 02_07.avi swordplay +02_08 02_08.tvd 02_08.c3d 02_08.amc 02_08.mpg 02_08.avi swordplay +02_09 02_09.tvd 02_09.c3d 02_09.amc 02_09.mpg 02_09.avi swordplay +02_10 02_10.tvd 02_10.c3d 02_10.amc 02_10.mpg 02_10.avi wash self + + diff --git a/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt b/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt new file mode 100644 index 0000000..074d121 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt @@ -0,0 +1,24 @@ +The skeleton joint order in UTD-MAD dataset: + +1. head; +2. shoulder_center; +3. spine; +4. hip_center; +5. left_shoulder; +6. left_elbow; +7. left_wrist; +8. left_hand; +9. right_shoulder; +10. right_elbow; +11. right_wrist; +12. right_hand; +13. left_hip; +14. left_knee; +15. left_ankle; +16. left_foot; +17. right_hip; +18. right_knee; +19. right_ankle; +20. right_foot; + +Each skeleton data is a 20 x 3 x num_frame matrix. Each row of a skeleton frame corresponds to three spatial coordinates of a joint. \ No newline at end of file diff --git a/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt b/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt new file mode 100644 index 0000000..624a110 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt @@ -0,0 +1,3 @@ +# UTD-MHAD Dataset + +* webpage (link to download the dataset): http://www.utdallas.edu/~kehtar/UTD-MHAD.html diff --git a/pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png b/pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png new file mode 100644 index 0000000..e2a0f97 Binary files /dev/null and b/pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png differ diff --git a/pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png b/pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png new file mode 100644 index 0000000..8301016 Binary files /dev/null and b/pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png differ diff --git a/pyrobolearn/datasets/parsers/3d/mocap_parser.py b/pyrobolearn/datasets/parsers/3d/mocap_parser.py new file mode 100644 index 0000000..9ec4976 --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/mocap_parser.py @@ -0,0 +1,230 @@ +import numpy as np +from scipy.interpolate import CubicSpline +import matplotlib.pyplot as plt + +class MocapParser(object): + + def __init__(self, filename): + """ + Parser for motion capture. By default, if the data is described in Cartesian space, the x-axis should + be pointing in front of the human, the y-axis on his/her left, and z-axis upward. + :param filename: + """ + self.filename = filename + self.num_samples = 0 + self.joint_names = [] + self.link_names = [] + self.marker_names = [] + + self.data = self.loadFile(filename) + + + def loadFile(self, filename): + raise NotImplementedError("loadFile is not implemented.") + + def interpolate(self, data, method='cubic', axis=-1) + """ + Interpolate the Mocap data such that it is between 0 and 1, along the given axis. + + :param data: mocap data + :param method: 'linear', 'cubic', 'hermite' interpolation + :param axis: The axis on which to interpolate. The length should be equal to the number of samples in the + mocap data + :return: Interpolator - function that given the time [0,1] will give the corresponding data + """ + self.num_samples = data.shape[axis] + x = np.linspace(0., 1., self.num_samples) + interpolator = CubicSpline(x, self.data, axis=axis) + return interpolator + + def getMarkerName(self, marker_idx=None): + if marker_idx is None: + return self.getMarkerNames() + else: + return self.marker_names[marker_idx] + + def getMarkerNames(self): + return self.marker_names + + def getJointName(self, joint_idx=None): + if joint_idx is None: + return self.getJointNames() + else: + return self.joint_names[joint_idx] + + def getJointNames(self): + return self.joint_names + + def getLinkName(self, link_idx=None): + if link_idx is None: + return self.getLinkNames() + else: + return self.link_names[link_idx] + + def getLinkNames(self): + return self.link_names + + def getMarkerPosition(self, marker_idx=None): + if marker_idx is None: + return self.getMarkerPositions() + else: + pass + + def getMarkerPositions(self): + pass + + def getJointPosition(self, joint_idx=None): + if joint_idx is None: + return self.getJointPositions() + else: + pass + + def getJointPositions(self): + pass + + def getJointVelocity(self, joint_idx=None): + if joint_idx is None: + return self.getJointVelocities() + else: + pass + + def getJointVelocities(self): + pass + + def getLinkPosition(self, link_idx=None): + if link_idx is None: + return self.getLinkPositions() + else: + pass + + def getLinkPositions(self): + pass + + def getLinkVelocity(self, link_idx=None): + if link_idx is None: + return self.getLinkVelocities() + else: + pass + + def getLinkVelocities(self): + pass + + def getLinkOrientation(self, link_idx=None): + if link_idx is None: + return self.getLinkOrientations() + else: + pass + + def getLinkOrientations(self): + pass + + def getLinkAngularVelocity(self, link_idx=None): + if link_idx is None: + return self.getLinkAngularVelocities() + else: + pass + + def getLinkAngularVelocities(self): + pass + + + ## Plotting ## + def plot3d(self, ax=None): + pass + + def plotJointProfile(self, ax=None, joint_idx=None, pos=True, vel=True, acc=True): + pass + + def plotLinkProfile(self, ax=None, link_idx=None, pos=True, vel=True, acc=True, wrt='world'): + pass + + def plotMarkerProfile(self, ax=None, link_idx=None, pos=True, vel=True, acc=True, wrt='world'): + pass + + def animate3d(self, ax=None, title=None): + pass + + + + +from amcparser.skeleton import Skeleton +from amcparser.motion import SkelMotion + +class CMUMocapParser(MocapParser): + + def __init__(self, skeleton_filename, motion_filename, skeleton_scale=1.0): + super(CMUMocapParser, self).__init__(motion_filename) + self.joint_names = ['head', 'upperneck', 'lowerneck', 'upperback', 'thorax', 'lowerback', 'root', # Spine + 'rclavicle', 'rhumerus', 'rradius', 'rwrist', 'rhand', 'rthumb', 'rfingers', # Right arm + 'lclavicle', 'lhumerus', 'lradius', 'lwrist', 'lhand', 'lthumb', 'lfingers', # Left arm + 'rhipjoint', 'rfemur', 'rtibia', 'rfoot', 'rtoes', # Right leg + 'lhipjoint', 'lfemur', 'ltibia', 'lfoot', 'ltoes'] # Left leg + self.link_names = self.joint_names + self.marker_names = self.joint_names + self.base_name = 'root' + + # Load skeleton + self.skeleton = Skeleton(skeleton_filename, scale=skeleton_scale) + + def loadFile(self, filename, framerate=120.): + self.skeleton_motion = SkelMotion(self.skeleton, filename, (1./framerate)) + # compute trajectories + #self.data = self.skeleton_motion.traverse(bone, start, end) + self.data = self.skeleton_motion.traverse(None, 0, -1) + # make sure that given axis + + def animate3d(self, ax=None, title=None): + if ax is None: + fig = plt.figure() + ax = fig.gca(projection='3d') + + # Rescaling such that the skeleton it is in the right proportion and at the middle + xmin, xmax = X[..., 2].min(), X[..., 2].max() + ymin, ymax = X[..., 0].min(), X[..., 0].max() + zmin, zmax = X[..., 1].min(), X[..., 1].max() + x_len, y_len, z_len = (xmax - xmin), (ymax - ymin), (zmax - zmin) + max_len = max([x_len, y_len, z_len]) + xmin, xmax = xmin + (x_len - max_len) / 2., xmin + (x_len + max_len) / 2. + ymin, ymax = ymin + (y_len - max_len) / 2., ymin + (y_len + max_len) / 2. + zmin, zmax = zmin + (z_len - max_len) / 2., zmin + (z_len + max_len) / 2. + + # Plot trajectories + x, y, z = skel.bones['rhand'].xyz_data.T + T = len(x) + + def init(): + ax.set_title('movement') + ax.set_xlabel('x') + ax.set_xlim(xmin, xmax) + ax.set_ylabel('y') + ax.set_ylim(ymin, ymax) + ax.set_zlabel('z') + ax.set_zlim(zmin, zmax) + # ax.scatter(x[0], y[0], z[0], marker='o') + return fig, + + def animate(i): + # ax.view_init(elev=10., azim=i) + ax.scatter(x[i], y[i], z[i], marker='o') + return fig, + + def animate_skeleton(i): + ax.clear() + init() + # ax.scatter(X[:,2,i], X[:,0,i], X[:,1,i], marker='o', c='b') + for d in [X_TO, X_RA, X_LA, X_RL, X_LL]: + ax.plot(d[:, i, 2], d[:, i, 0], d[:, i, 1], marker='o', c='b') + return [fig] # fig, + + # Animate + # anim = animation.FuncAnimation(fig, animate, init_func=init, + # frames=T, interval=20, blit=True) + anim = animation.FuncAnimation(fig, animate_skeleton, init_func=init, + frames=T, interval=20, blit=False) + + plt.show() + + +# Test +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb b/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb new file mode 100644 index 0000000..b0ebacb --- /dev/null +++ b/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb @@ -0,0 +1,967 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [UTD-MHAD: \"UTD-MHAD: A Multimodal Dataset for Human Action Recognition utilizing a Depth Camera and a Wearable Inertial Sensor\", 2015](http://www.utdallas.edu/~kehtar/UTD-MHAD.html)\n", + "\n", + "* nb of subjects: 8\n", + "* 27 different actions: (1) right arm swipe to the left, (2) right arm swipe to the right, (3) right hand wave, (4) two hand front clap, (5) right arm throw, (6) cross arms in the chest, (7) basketball shoot, (8) right hand draw x, (9) right hand draw circle (clockwise), (10) right hand draw circle (counter clockwise), (11) draw triangle, (12) bowling (right hand), (13) front boxing, (14) baseball swing from right, (15) tennis right hand forehand swing, (16) arm curl (two arms), (17) tennis serve, (18) two hand push, (19) right hand knock on door, (20) right hand catch an object, (21) right hand pick up and throw, (22) jogging in place, (23) walking in place, (24) sit to stand, (25) stand to sit, (26) forward lunge (left foot forward), (27) squat (two arms stretch out).\n", + "* labeled and segmented data: 861 data sequences, 3D skeleton joint positions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import scipy.io as sio\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib.animation as animation\n", + "import glob\n", + "import re\n", + "\n", + "from IPython.display import HTML\n", + "\n", + "#%matplotlib inline\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "PATH = 'data/UTD/*.mat'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nb of demos: 861\n", + "Min T: 41\n", + "Max T: 125\n" + ] + } + ], + "source": [ + "# Definition of some variables\n", + "id_to_jnt = ['head', 'shoulder_center', 'spine', 'hip_center', 'left_shoulder', 'left_elbow', 'left_wrist',\n", + " 'left_hand', 'right_shoulder', 'right_elbow', 'right_wrist', 'right_hand', 'left_hip', 'left_knee',\n", + " 'left_ankle', 'left_foot', 'right_hip', 'right_knee', 'right_ankle', 'right_foot']\n", + "jnt_to_id = {name: idx for idx,name in enumerate(id_to_jnt)}\n", + "id_to_action = ['Swipe left', 'Swipe right', 'Wave', 'Clap', 'Throw', 'Arm cross', 'Basketball shoot', \n", + " 'Draw X', 'Draw circle (clockwise)', 'Draw circle (counter clockwise)',\n", + " 'Draw triangle', 'Bowling', 'Boxing', 'Baseball swing', 'Tennis swing',\n", + " 'Arm curl', 'Tennis serve', 'Push', 'Knock', 'Catch', 'Pickup and throw',\n", + " 'Jog', 'Walk', 'Sit to stand', 'Stand to sit', 'Lunge', 'Squat']\n", + "action_to_id = {name: idx for idx,name in enumerate(id_to_action)}\n", + "Nj = len(id_to_jnt) # 20\n", + "\n", + "# Load all the data\n", + "files = glob.glob(PATH)\n", + "data = [sio.loadmat(f)['d_skel'] for f in files] # list of shape Njx3xT\n", + "N = len(data)\n", + "print(\"Nb of demos: %d\" % len(data))\n", + "Ts = np.array([d.shape[2] for d in data])\n", + "print(\"Min T: %d\" % np.min(Ts))\n", + "print(\"Max T: %d\" % np.max(Ts))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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. 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