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Technology is providing us new ways to shape our perception of space, while at the same time it is transforming our bodies into gadgets. This is not only changing our spatial awareness but it’s also extending our senses beyond given nature. Moreover, control systems that regulate and command specific behaviours can be very practical tools to improve physical functionalities or translate its data. For instance, this experiment employs “Optical Flow” sensor which detects motion from image objects between frames, and “Open Sound Control (OSC)” which enables to exchange and format data from different devices, for instance, from Python to Puredata. Although the unique possibilities to improving human physical or cognitive limitations by plugging a body to an electronic or mechanical device are yet very hypothetical and might extend beyond our imagination, nevertheless technology is continuously transforming the abstract or fictional conception of “cybernetics” to a more realistic evidence. The communication between both automated and living systems is continuously evolving, upgrading and rising up more sophisticated engineered tools that might enable us to increase our knowledge, morphing our perception through deeper experiences. In this experiment, the potential for controlling data through motion on space while becoming independent of physicality, opens up new creative and pragmatic alternatives for facing both technological and communication constraints.
By exploring the connection between motion and sound, experiments have been performed through different software and tools, which has strengthen substantially the following additional material in this project. For instance, Kinect sensor and Synapse, which receives input data from Kinect and sends it out to Ableton or Max MSP, have been tested out. Similarly, motion detection was together explored with “color detection” in Puredata, which brought up more interesting alternatives. Sound recording and feedback loop was further tested with this method, though mechanically it was hardly accurate. Finally with “Optical Flow”, this work was reconfigured with a wider sense for interacting with data.
This body analyses human motion on space and detects it using “Opitcal Flow” in “Python”, using a series of predesigned multidirectional interpreters. These interpreters are made up of a series of points (intersections), forming a grid which intersects with movement. This is detected in form of numeric values, which are automatically transmitted and formatted to a graphic array in Puredata. This array arrange these values and generates a polygonal waveform based on these received coordinates (which numbers ranges between "x", having values from 0 to 10, and "y" from -1 to 1). This activates an “oscillator” object which defines the frequency of the tone, together with “metro” object, which time spans its duration in miliseconds, consequently iterating the audio (re-writting it in the display). The intersections and the graphic array (together with the entire Puredata patch) become an interactive notation system, while people become the instrument/tool that triggers it.

OPTICAL FLOW

#!/usr/bin/env python
import numpy as np
import cv2, math
import video

help_message = '''
USAGE: opt_flow.py []

Keys:
1 - toggle HSV flow visualization
2 - toggle glitch
'''
# def draw_flow(img, flow, step=4): # size grid
# h, w = img.shape[:2]
# y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
# fx, fy = flow[y,x].T
# lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
# lines = np.int32(lines + 0.5)
# vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
# cv2.polylines(vis, lines, 0, (0, 0, 255)) # BGR
# for (x1, y1), (x2, y2) in lines:
# cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
# return vis

import OSC
# from pythonosc import osc_message_builder
# from pythonosc import udp_client
import time

def send_flow0(img, flow, step=4): # size grid
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
fx, fy = flow[y,x].T
#print "fx, fy", fx, fy
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

flines = []
for (x1, y1), (x2, y2) in lines:
# print ("y1", y1)
if (x1 == 38 or x1 == 46 or x1 == 54 or x1 == 62 or x1 == 70 or x1 == 78 or x1 == 86 or x1 == 94 or x1 == 102 or x1 == 110 or x1 == 118) and y1 in range(38, 90, 8):
flines.append(((x1,y1),(x2,y2)))
normx = x1 / 8 - 4
normy = 1 - ((y1 / 8 - 4) / 3.0)
dx = x2-x1
dy = y2 - y1
m = int(math.sqrt( (dx*dx) + (dy*dy) ))
if m>2:
print ("dot", (normx, normy))
msg = OSC.OSCMessage() msg.setAddress("/dot")
#msg.append(dx)
#msg.append(dy)
#msg.append(m)
msg.append(normx)
msg.append(normy)
client.send(msg)
# client.send_message("/franc", m)

flines = np.int32(flines) cv2.polylines(vis, flines, 0, (0, 40, 255)) # BGR for (x1, y1), (x2, y2) in flines: cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1) return vis flines = np.int32(flines) cv2.polylines(vis, flines, 0, (0, 40, 255)) # BGR for (x1, y1), (x2, y2) in flines: cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1) return vis

# cv2.rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]])

if __name__ == '__main__':
import sys
print help_message
try: fn = sys.argv[1]
except: fn = 0


# connect to pd
# Init OSC
client = OSC.OSCClient()
client.connect(('127.0.0.1', 9001))
# first argument is the IP of the host, second argument is the port to use
#data="hello"
# client = udp_client.SimpleUDPClient("127.0.0.1", 9001)
# connect camera
# cam = video.create_capture(fn)
cam = video.create_capture("0:size=160x120")
#canvas size in pixels

ret, prev = cam.read()
prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
cur_glitch = prev.copy()

while True:
# print "GRAB FRAME"
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prevgray, gray, 0.5, 3, 15, 3, 5, 1.2, 0)
prevgray = gray

cv2.imshow('flow', send_flow0(gray, flow))

ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
cv2.destroyAllWindows()
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PUSHING THE SCORE

CONCEPT

BODY

SAMPLES