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<!DOCTYPE html>
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<title>Pushing the Score</title>
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<div id="header"><h1>
This experiment analyses and measures data from peoples motion and explores how we humans become an extension of technology, adapting ourselves to immaterial environments by seeking information-control, performing sounds.
</h1></div>
<div id="container">
<h2><div class="mobile icon"></div></h2>
<h2><div class="laptop icon"></div></h2>
<h2><div class="tablet icon"></div></h2>
<div id="title">
<h2>PUSHING THE SCORE</h2>
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<div id="buttons"><h2>
<a href="#concept">CONCEPT ⟶</a>
<br>
<a href="#body">BODY ⟶</a>
<br>
<a href="#samples">SAMPLES ⟶</a>
<br></h2>
</div>
<div id="links">
<h2>TGC #3 @DePlayer @XPUB</h2>
</div>
<div id="rotate"><h2>
www.issue.xpub.nl/02/ www.deplayer.nl/
</h2></div>
<!-- Labels -->
<h2><div id="concept"><table>
<td>CONCEPT</td>
</table></div></h2>
<h2><div id="body"><table>
<td>BODY</td>
</table></div></h2>
<h2><div id="samples"><table>
<td>SAMPLES</td>
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<text transform="matrix(1 0 0 1 50.6133 588.7969)"><tspan x="0" y="0" class="st2 st3">Technology is providing us new ways to shape our perception of space, </tspan><tspan x="0" y="23" class="st2 st3">while at the same time it is transforming our bodies into gadgets. This </tspan><tspan x="0" y="46" class="st2 st3">is not only changing our spatial awareness but its also extending our </tspan><tspan x="0" y="69" class="st2 st3">senses beyond given nature. Moreover, control systems that regulate </tspan><tspan x="0" y="92" class="st2 st3">and command specific behaviours can be very practical tools to </tspan><tspan x="0" y="115" class="st2 st3">improve physical functionalities or translate its data. For instance, this </tspan><tspan x="0" y="138" class="st2 st3">experiment employs “Optical Flow” sensor which detects motion from </tspan><tspan x="0" y="161" class="st2 st3">image objects between frames, and “Open Sound Control (OSC)” </tspan><tspan x="0" y="184" class="st2 st3">which enables to exchange and format data from different devices, for </tspan><tspan x="0" y="207" class="st2 st3">instance, from Python to Puredata.</tspan><tspan x="0" y="230" class="st2 st3 st4"> </tspan><tspan x="58.9" y="230" class="st2 st3">Although the unique possibilities to improving human physical </tspan><tspan x="0" y="253" class="st2 st3">or cognitive limitations by plugging a body to an electronic or </tspan><tspan x="0" y="276" class="st2 st3">mechanical device are yet very hypothetical and might extend beyond </tspan><tspan x="0" y="299" class="st2 st3">our imagination, nevertheless technology is continuously transforming </tspan><tspan x="0" y="322" class="st2 st3">the abstract or fictional conception of “cybernetics” to a more realistic </tspan><tspan x="0" y="345" class="st2 st3">evidence. The communication between both automated and living </tspan><tspan x="0" y="368" class="st2 st3">systems is continuously evolving, upgrading and rising up more </tspan><tspan x="0" y="391" class="st2 st3">sophisticated engineered tools that might enable us to increase our </tspan><tspan x="0" y="414" class="st2 st3">knowledge, morphing our perception through deeper experiences.</tspan><tspan x="0" y="437" class="st2 st3 st4"> </tspan><tspan x="58.9" y="437" class="st2 st3">In this experiment, the potential for controlling data through </tspan><tspan x="0" y="460" class="st2 st3">motion on space while becoming independent of physicality, opens up </tspan><tspan x="0" y="483" class="st2 st3">new creative and pragmatic alternatives for facing both technological </tspan><tspan x="0" y="506" class="st2 st3">and communication constraints.</tspan></text>
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<text transform="matrix(1 0 0 1 696.9243 1346.5609)"><tspan x="0" y="0" class="st7 st3">This body analyses human motion on space and detects it using </tspan><tspan x="0" y="23" class="st7 st3">“Opitcal Flow” in “Python”, using a series of predesigned </tspan><tspan x="0" y="46" class="st7 st3">multidirectional interpreters. These interpreters are made up of a </tspan><tspan x="0" y="69" class="st7 st3">series of points (intersections), forming a grid which intersects with </tspan><tspan x="0" y="92" class="st7 st3">movement. This is detected in form of numeric values, which are </tspan><tspan x="0" y="115" class="st7 st3">automatically transmitted and formatted to a graphic array in Puredata. </tspan><tspan x="0" y="138" class="st7 st3 st8"> </tspan><tspan x="86.1" y="138" class="st7 st3">This array arrange these values and generates a polygonal </tspan><tspan x="0" y="161" class="st7 st3">waveform based on these received coordinates (which numbers ranges </tspan><tspan x="0" y="184" class="st7 st3">between &quot;x&quot;, having values from 0 to 10, and &quot;y&quot; from -1 to 1). This </tspan><tspan x="0" y="207" class="st7 st3">activates an “oscillator” object which defines the frequency of the tone, </tspan><tspan x="0" y="230" class="st7 st3">together with “metro” object, which time spans its duration in </tspan><tspan x="0" y="253" class="st7 st3">miliseconds, consequently iterating the audio (re-writting it in the </tspan><tspan x="0" y="276" class="st7 st3">display).</tspan><tspan x="0" y="299" class="st9 st3 st10"> </tspan><tspan x="86.1" y="299" class="st9 st11" style="text-decoration: underline;">The intersections and the graphic array </tspan><tspan x="0" y="322" class="st9 st11" style="text-decoration: underline;">(together with the entire Puredata patch) become </tspan><tspan x="0" y="345" class="st9 st11" style="text-decoration: underline;">an interactive notation system, while people </tspan><tspan x="0" y="368" class="st9 st11" style="text-decoration: underline;">become the instrument/tool that triggers it</tspan><tspan x="464.5" y="368" class="st9 st11">. </tspan></text>
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<text transform="matrix(1 0 0 1 680.1709 1841.7874)" class="st23 st24 st25">OPTICAL FLOW</text>
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<text transform="matrix(1 0 0 1 680.1709 1881.6899)"><tspan x="0" y="0" class="st23 st9 st25">#!/usr/bin/env python</tspan><tspan x="0" y="21.4" class="st23 st9 st25">import numpy as np</tspan><tspan x="0" y="32.1" class="st23 st9 st25">import cv2, math</tspan><tspan x="0" y="42.8" class="st23 st9 st25">import video</tspan><tspan x="0" y="64.2" class="st23 st9 st25">help_message = &apos;&apos;&apos;</tspan><tspan x="0" y="74.9" class="st23 st9 st25">USAGE: opt_flow.py [&lt;video_source&gt;]</tspan><tspan x="0" y="96.3" class="st23 st9 st25">Keys:</tspan><tspan x="0" y="107" class="st23 st9 st25"> 1 - toggle HSV flow visualization</tspan><tspan x="0" y="117.7" class="st23 st9 st25"> 2 - toggle glitch</tspan><tspan x="0" y="139.1" class="st23 st9 st25">&apos;&apos;&apos;</tspan><tspan x="0" y="160.5" class="st23 st9 st25"># def draw_flow(img, flow, step=4): # size grid </tspan><tspan x="0" y="171.2" class="st23 st9 st25"># h, w = img.shape[:2]</tspan><tspan x="0" y="181.9" class="st23 st9 st25"># y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)</tspan><tspan x="0" y="192.6" class="st23 st9 st25"># fx, fy = flow[y,x].T</tspan><tspan x="0" y="203.3" class="st23 st9 st25"># lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)</tspan><tspan x="0" y="214" class="st23 st9 st25"># lines = np.int32(lines + 0.5)</tspan><tspan x="0" y="224.7" class="st23 st9 st25"># vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)</tspan><tspan x="0" y="235.4" class="st23 st9 st25"># cv2.polylines(vis, lines, 0, (0, 0, 255)) # BGR</tspan><tspan x="0" y="246.1" class="st23 st9 st25"># for (x1, y1), (x2, y2) in lines:</tspan><tspan x="0" y="256.8" class="st23 st9 st25"># cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)</tspan><tspan x="0" y="267.5" class="st23 st9 st25"># return vis</tspan><tspan x="0" y="288.9" class="st23 st9 st25">import OSC</tspan><tspan x="0" y="299.6" class="st23 st9 st25"># from pythonosc import osc_message_builder</tspan><tspan x="0" y="310.3" class="st23 st9 st25"># from pythonosc import udp_client</tspan><tspan x="0" y="321" class="st23 st9 st25">import time</tspan><tspan x="0" y="342.4" class="st23 st9 st25">def send_flow0(img, flow, step=4): # size grid </tspan><tspan x="0" y="353.1" class="st23 st9 st25"> h, w = img.shape[:2]</tspan><tspan x="0" y="363.8" class="st23 st9 st25"> y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)</tspan><tspan x="0" y="374.5" class="st23 st9 st25"> fx, fy = flow[y,x].T</tspan><tspan x="0" y="385.2" class="st23 st9 st25"> #print &quot;fx, fy&quot;, fx, fy</tspan><tspan x="0" y="395.9" class="st23 st9 st25"> lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)</tspan><tspan x="0" y="406.6" class="st23 st9 st25"> lines = np.int32(lines + 0.5)</tspan><tspan x="0" y="417.3" class="st23 st9 st25"> vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)</tspan><tspan x="0" y="449.4" class="st23 st9 st25"> flines = []</tspan><tspan x="0" y="460.1" class="st23 st9 st25"> for (x1, y1), (x2, y2) in lines:</tspan><tspan x="0" y="470.8" class="st23 st9 st25"> # print (&quot;y1&quot;, y1)</tspan><tspan x="0" y="481.5" class="st23 st9 st25"> if (x1 == 38 or x1 == 46 or x1 == 54 or x1 == 62 or x1 == 70 or x1 == 78 or x1 == 86 </tspan><tspan x="0" y="492.2" class="st23 st9 st25">or x1 == 94 or x1 == 102 or x1 == 110 or x1 == 118) and y1 in range(38, 90, 8):</tspan><tspan x="0" y="502.9" class="st23 st9 st25"> flines.append(((x1,y1),(x2,y2)))</tspan><tspan x="0" y="513.6" class="st23 st9 st25"> normx = x1 / 8 - 4</tspan><tspan x="0" y="524.3" class="st23 st9 st25"> normy = 1 - ((y1 / 8 - 4) / 3.0)</tspan><tspan x="0" y="535" class="st23 st9 st25"> dx = x2-x1</tspan><tspan x="0" y="545.7" class="st23 st9 st25"> dy = y2 - y1</tspan><tspan x="0" y="556.4" class="st23 st9 st25"> m = int(math.sqrt( (dx*dx) + (dy*dy) ))</tspan><tspan x="0" y="567.1" class="st23 st9 st25"> if m&gt;2:</tspan><tspan x="0" y="577.8" class="st23 st9 st25">
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<text transform="matrix(1 0 0 1 50.6133 1259.8176)"><tspan x="0" y="0" class="st2 st3">By exploring the connection between motion and sound, experiments </tspan><tspan x="0" y="23" class="st2 st3">have been performed through different software and tools, which has </tspan><tspan x="0" y="46" class="st2 st3">strengthen substantially the following additional material in this </tspan><tspan x="0" y="69" class="st2 st3">project. For instance, Kinect sensor and Synapse, which receives input </tspan><tspan x="0" y="92" class="st2 st3">data from Kinect and sends it out to Ableton or Max MSP, have been </tspan><tspan x="0" y="115" class="st2 st3">tested out. Similarly, motion detection was together explored with </tspan><tspan x="0" y="138" class="st2 st3">“color detection” in Puredata, which brought up more interesting </tspan><tspan x="0" y="161" class="st2 st3">alternatives. Sound recording and feedback loop was further tested with </tspan><tspan x="0" y="184" class="st2 st3">this method, though mechanically it was hardly accurate. Finally with </tspan><tspan x="0" y="207" class="st2 st3">“Optical Flow”, this work was reconfigured with a wider sense for </tspan><tspan x="0" y="230" class="st2 st3">interacting with data.</tspan></text>
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