# Project 2 – Particle System Tracking

For this project, I wanted to work with particle systems and attractors within it.

First, I used a few objects from the computer vision package to track a face from the incoming camera and get points from it with edge detection. I did this with cv.jit.faces to get the bounding box of a face to track, then used cv.jit.canny to extract binary edges, then cv.jit.features2track to collect the easiest points in the window to track. This works best with binary edges because of the contrast between black and white pixels.

I then separated the points into two lists: x-coordinates and y-coordinates, normalized them, and sent them to the particle system subpatcher. There are three modes the user can choose from:

0: the system as a whole attracts to several different points. For example, if there are 50 attractor points, 1/5 of the particles will attract to each point.

1: all particles jump between attractors at banged intervals.

2: particles follow the center of mass of the tracked face.

Once the list of x and y coordinates are sent, I filled a 250-cell (number of particles) matrix with the coordinates (repeatedly, so that an even number of cells has each coordinate). These were then used as another input into the jit.gen objects which calculated the force. A jit.gen object basically transforms every cell in the incoming matrices simultaneously. So a cell in the particle system (inlet 1) could take the corresponding x,y coordinate of it’s attractor in the second matrix (inlet 2).

I used the Amazing Max Stuff Particle System method of calculating new positions through f=m*a. I definitely spent a lot of time trying to understand how jit.gen works, since it is much more efficient at manipulating matrices. I had a lot of trouble figuring out how to use the x,y coordinate matrix of point positions in jit.gen. After understanding that the jit.gen object basically works on every cell of each input matrix, it became much simpler to just gibe the position matrix as another input. Before figuring that out, I tried a lot of different things using swiz and vec that involved pulling the matrices apart in the jit.gen object and trying to put them back together afterwards. This, of course, was very tedious and didn’t work well. I had to find a way to use jit.gen because before that, the patch used way too much processing power and the particles were not moving smoothly. I also attempted some javascript, but found it to be difficult to integrate with max objects.

You can also change a lot of parameters such as speed, level of attraction, rate of position change, etc. throughout the patch.

I originally wanted to have the particles attract to facial points so that you could actually recognize a face, but I think this would take outside hardware or more sophisticated algorithms for facial point detection. Even so, I definitely enjoyed the finished product.

Below I’ve uploaded three videos, one for each mode. When you first open the patch, the video and facial detection automatically start. To start the particle system, you toggle it on manually. For all modes, you can change rate at which the particles attract/retract in the particle system subpatcher. It is set automatically on a metronome.

Mode 0:

In this mode, you can see the particle system as it attracts towards the facial points. Moving your face also moves the system as a whole.

Mode 1:

In this mode, the system jumps between facial points on a metronome.

Mode 2:

In this mode, the system follows the center of mass of the facial points. You can see how they follow my face as I move.

Here’s the code for the patch:

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``` This entry was posted in Uncategorized on December 10, 2018 by egiuse@andrew.cmu.edu. ```
``` Project 1 – Vertex Extrusion Objects For this project, I started with the simple idea of vertex extrusion after seeing a tutorial on youtube. I used incoming video for both the object itself and the texture, and rendered using jit.gl.mesh. The jit.gl.handle object allows the viewer to visually spin and change the view based on mouse control. You can change the shape of the object being rendered as well. I then created multiple of these objects and set them with changing rotation, position, and scale using jit.mo functions like we’ve done in class. The default starts with 2 objects, but every time you clap the number of objects doubles until you reach 64. At this point the number of objects goes back to 2 because jit.multiple can’t handle a huge number of objects. You can also manually change how much space there is between the objects and the position of the camera (default is 2 for the y coordinate). I then used a motion detection patch to change the rate of scaling for each object as well as sliding between MIDI frequency notes based on your motion. 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This entry was posted in Uncategorized on November 7, 2018 by egiuse@andrew.cmu.edu. Assignment 4 – pfft~ For this assignment, I created an fft~ subpatch that uses delay and feedback to change the input signal. I then used this in a patch that uses the resulting sound to change the matrix values for an audio-visual reactive patch using jitter. The patch also takes audio input from the built-in microphone, and uses both this and a loaded sound file. Here’s a video: And heres the code for the full patch: <pre><code> ———-begin_max5_patcher———- 2265.3oc2ZssjiZqE8Y2eET9wS0sKjDhKmmb9.xWPpTtjwxzZFYDAv8kjJy2 9YKI.C1fC1FRO0YlpArDRZuWZeW7WOsX4V0G7hkN+WmeyYwh+5oEKLMoaXQ0 uWr7.6iXIqv7ZKk723xC7Rd9OV9rsewNSOpse6ELotwziGDoRdoYTnSMpNVd dqGX4emmWQEu369ryKdgvEhObA6AWPX3B7KnKemeuZX1Yp7yLtcjKEokKa5s ZROVvMTWXUyEkeJMu+xZBMiUF+pHMYSNOtzNSDDcErTdgX8MDNTey2akqyuq Gye+zS5KOORDKk+N.M0KWI+CyprbGKtWDD2KBh6EAcuEthh8Mbkqq9FIPeEi uSl5.unfkvufqTY7z93JzMvUng1fOs6NNFNvrMh7oS.COvtHaWu6hH+aPO.O D+VHRRYxkO27zsw+jvHCK6GLA7+1ikkp91Z8Q2gJ+Er5VVZxMxdAAF4YTn4F 1q95Tt8BF5xUNHW2dXbZzTHS+.LtGwXWhNKJxwRNKuOt16KPSNHv0t6RmSIY J9KRRNLBY4n+Mjj6UT18KRTthyqzgCPOlI5iG1x6Sl0K5DIjyLAurgmx1ZoO 2IvRs1F88v99FUXBwHWScmCOTwpio.C67BEhipOvg16dOsW.v6Zwd8rEGz+0 ITrwAEVqY3JIgn4.Jx1uu7GNvkWXG2ITuTbbqgR5yANcJzHtK21TWrUnvpZP HODTTpRRj793PxDoTfFU732BqicCmOCgE6yjrO+gyZoRk4zqFwMr0OhX2tC6 BdQVbfZTChZ4uqdVXuw2sAHVXHaXkk4BvqlMQtEMvA.PrBQb4wTglB.sWups 0EK2qjR06IR0VlrjeHS0ZSW2a9AVZYrJWSSBvaY6dOn1Y4.yzurtYUtHQ.7r jmlT9pkQPgQggqhnDDlBPQoH96EMXwogzPAHLXCT+uv5WIS.ny.DxebjIEke 1CsTJf3ZJy4ZkaCgTIBYkglbyIuIJ.RowdhCEg6Si6VhT18NRXk3YjWPzvGx jw0Swm9+CY36gLVU8vj5T6Az6dwquIJWEykxsRU726aeuI5p8pzxTvVqoqeI WnMPX6IW89433kldhUx2E6r5VDRq4bOKtiUacaEh+jaUoV4da9xkhhNNyOA4 .AXIHp+ULh6aLd4iMxjQDiLIweZshm6rsGjt+Lxbm0bS7BqBdyZklNGAtnkv Nv.q7e3rVK+3TDyjbjqyZMc6rWpXkDryZvyVJ2DvmCwY8NwA8NUu93Bmh5S. j0FKY0UL4lvMqMKRvr.bYNGTqxTEBsuihdvAxjHxzBHt0.dLXPj4VX3E94MC Axo4L+56EZaz4EUNDQ0t8XYYsZdQqgngquoLSTvyMMIRsMQZZJmC9xpFOsoU VNP3k.UeL2xReDVWfqp3AxSOJLjhsQXi6o5I7TD9cplpqsDqtm3VXONQaB0Z OuwKutjhhzrbdAOsjcQP.636YGkka52lW29OyNY2N60v7hkI4hcpTScMaC05 lqWNHNGakFosYFyajxx5Yv1.2FnyBfIOVrkkq2IpBGG2DXiRI61Uy3j78kUc mIRSOCEKUYC2IDF1qWYraUPmGt1ba5oXywTaua.8xxM5fT69dPvgUJpcm9OX oBP+gqCayvttMcZSI40h3bHh0N7qsm25omcfPbLuwUoaagA30EY0BQKa1k2I Rf.F61VIKonaKWnSCMUEyGj8jNJVIvEcegNmvRaMx1lz5z90Lscoeg+iy52X RGzp1uQekhdv3JF1j+MY1e.D5pV9vndK7yoH0WT6DXFPNv0PgH0Y89b9e3Dt B7XVjw46.4E7PXIcPrD8EhkHe+UP5UUGWk81WJXhVgOAlDxPno2OmnoqMKJa 4GPgQeQv42T.dRzNLGB+vChejubM6pR5FQ+xEFcWQOILhFBKI+TJKVgkTzbp YaI8A.FR3c.LtOfGgHS7gD25qc.tX0gC7zxKlNQ5N9Gsh+rCtbJjLoHcHewF BR2e+.Vg5Xdb8VasaUmtDGDbQoHsIB0e6D.5L1MralJnijJz1JbvyEU3cKTA ZtnBxsPEy1NBdrTg+fTQSsJusp8dSA2ZK8aUnqMo9bsHcOEN7jWI0hdqlCxa 9pWZUnRF6pTzbUylD4pCP5khLI2YcIKOgaxxzI93V9mzdX4fo3nGtihwXs85 GZc2DQmQ.wjV7qrL.Qr2zfA7rthV5nEa9acrRpxAyEZG35+PNqyUPxw7O97O 0mjsNMQ8myjyZ1QH+TPAKFTnV2BgcVmojetQWfBXzM0ASqNapRVaXr8mzxUk 65TngvAJt57TyLj87wBr4KAPf86TKZpOaveBOZPDxqhmmuiFTKg9tJWty4Ut Tp.wGKWqk7L0XEHI8yZwsMuxR2Y6hmyJ3arRqtl+2WEWukC+g7O9cFbGxNU. nG1dxG3d0wMyY23jr.549Aqf0K7+E0182.t9ZEQzEabiccBGw5DXi1nFLy2Y qjJZ1WX8I9z5kZVY2GZk0eoc+iKM0aB.W5X1EqImGak7FCbRmhUBOhUxaRVI 2wrOgmfUxKZrnG5QWI5XPunofm92RffDMRSHO7BMFNZBVG7XrOLElG5l66.K DlLG1.G0RiP8uzOlgezXTAn8ux34ekw3uLlFi62Qq67uzCsUSdrkdLprm4jb p.7QszyR7EiZo8lEA7NxOCrSeokKavpmcn2503rC69rC59xC4d3C397C21TS Fa.2mEfbSMTdWOSNGTophL84Ma3p5SZtKPny7vj2vFQplGpvDSJNmtfZ8AAz 9fpAf4WUorX0oLA1lrWHklo77pCUmEwxjb1NgtHp0E1o400qLNxEEo+PrVQb iBc8rOAMg5PHUiBUkaQEYd5xYuGtd5C8wAdT8jFF3RCCsOE5QNe5gjbpN56f VmeaVtJSk27M.rhD079PNYMbVspvoRn0gGcQ9tQQFzEgBoHyStDB0qKXykbc 0l6L1fPBjBkdDAQg9DySgdHLNn6X2lzYXQAATTTE9.+LhR7IUi3oSDpNgaXE OOgtklpHUyVlBD92O8+f99WHp ———–end_max5_patcher———– </code></pre>   This entry was posted in Assignments on October 19, 2018 by egiuse@andrew.cmu.edu. Assignment 3 – Multiconvolves For this assignment, I took a portion of Queen’s “Under Pressure” and convolved it with 4 different IR. Here they are: Popping a balloon above water, and recording it with a Zoom recorder underwater. https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/10/underwater.wav Popping a balloon in the racquetball courts. https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/10/racquetball_court.wav The sound of bacon frying. https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/10/bacon.wav The sound of horses galloping. https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/10/horses.wav   I then created a patch where as the song plays, the signal first is by itself, then is convolved with each of the four IR’s, then multiconvolved with 2 IR’s, and finally with all 4 at once. Here’s a video of the patch:   and here’s the code: <pre><code> ———-begin_max5_patcher———- 5747.3oc6ct0aiiik.94T+JLL1mVjJMuJRMXdX2GWfcvBLuMnPiBJ1JIpaaI 2xxUppaL0u8khjRVxwRh1QxVV4zEZGacijGcN7xg76v+5S2M+wjuGtc9r+1r uL6t69qOc2c5Ckef6r+9t4qC99hUAa0W170ga2F7b376MmKK76Y5iuML72mw X9HTwoh2sNJdUXl99H6OXxtrhihsGcSP1hWhhe9qogKxL4FOJ6Az8yHb+7+H n5ePd.M6Ws2SzRc5l73u8YLFWjnlGd1O1DZdLymWdCay9wJ8QmOO+.+6O8o7 Ot2wx8i6xxRhOZYCe5kMxCbUwAoKaDVwmGsrg7arr8XP7y8U4KN7UUx8lWqa lErZ0ITpQcVpoXTd4k19aTjX9aKVUe1go1Biszb27mhVE9svzsQpWS6ye2MO XylJG9tJ2RtH32RzOHw8kGJJ1bHZ4gRC+VTw8yKOZPppDloJd6RM4uuK8lu+ wjrLLMdWjNqXNn5kwmJdfEBEiVtxrI+OXlumVaW3sWrndw87pjE+d3xJxWkx vlv3n3MogaCiyBxr4txSuL7ofcqx95SIwYai9ScFDqD1G67OErHrwaNNXsoz 8emFETnIb27mSiVlDmmIpIqyObQx8kYXttTwqVXzWQbvlibypW7J4RCmbqpP ta6iAo4uJdznQPJNYVRxp5mp79VE9Tl8zahhiOPJlkro4SlF87KsbuOlnN45 1d15yr8q6hMm8qJisrutM3a0k1YJKLq0W8G+2ChiVGjElEYdEPPkmLLNPUPe Y6hzDk4Y0xq4Le6HmYoRKdQ3qQKydQmPUUFTWdzlBkn4kukWF8b31r5GKK34 s0OxarPUGZ2iVqzulEtdyJUon9ETqcmpljUqmp1wOn9JcEQklaGV4Dp9Ida0 xMUIE1pypsConhOK0eOndJJoZVn4FeZPNkezEIqWqLgeywihWF98J0dXqQ2V QxYJ0VE8svGVllroZJ8sfzRq7xK3Kzes50rLbgpBs7J0NTHdrFENhbmzgbm3 KMs2iM0DpE8dsH522nu8YlpJCYJENiEvg4l29t49279QYYt7qAYYoQpV6C2+ ssUjrUDs4RtU6BSdp5oqd9p4pUIwOeDoLlTULW6N19RRZVWuXpcGEkNVCmOJ tZMj6ulREqpe8xpsQtrZaHOSmtHtqsgl.ZamtxF4lQYq9.Sp2K1EqBCRarwB xY2XAwWqFgE5FMnhC6Raycl+Dau3FWNYM2NA4j3pKmNgJuvWzJuj9DsvjRbs tKDeJzRI5Tq7Bp4pYcHIZuaUbwdjcCYOdIMFEHyH2MCXvIiQxTvXjAFi8lwn .Y75CxUiQzU2Xrl2BqKjdb2SOEl9yY+O+yO+O8BTs8WO6tJJVYRtyLfWR+2y dJxzGClVl5apfi2rzzu8dZ7zpjfrbatZ9Z8pKY+eOhjsuFZj0qGLyPivcpO5 6MtjfENT4XhvejrKc1Vk52xsydILM7Ljen1keTkYL0W8erbgmoaubsDkzZqB yupxk+9eeFMc4LU0tY+XlwOrydJMY8rWxx1729ke40We8grWBeIZ61GRRe9W 5SK5NDnVGCQQjJxx7YOnIYo27qoQ5xvUA+XFuESyyuSald7y70VldztLLo7V MLGA0nsc11vLu92HzJo3Di.qSIEw+ppyj1tX3rclc9DLJUsRfnJCGeux5kLU s2p.gNl6hw+4Oym8Bd+agQ7XOHxmiRluwRqS4DtU4z1HUmmWMUkV14JwcgEZ jHrB+ykAK94YHPPcKPTZOLltEKtWwmMJP3yuLCa94fn3e153l0WwANxxQgB2 MsDouYh07M+pEoh26Z.xV0n6q9s7+urenqhxmRypIdRZT0IQu5iWMZ1WKjT0 d6WYh2qTX0cNY+GUWTGWiwpadqJNkwpenpxQGrNpgyuVovje9OK1O6xu8ZBz 5sdMdEQwQY4q0fBIZUIXsKbm5BKsZXWWeCnZAeFV8O8fytel4mDynfM+jVb1 .yuYEmNX.ZHqRK+DJtrkeJo69BwtY7tv.MDXomtlaFw3SObmhLL+1zGBCj7S nFiFuh7qydahY2Lxu06VkEsHI9aIq9V3OU1vrY+YXZRiBQViBQlaMaZG1a2t EjO+TaU7Me6BHZ2nFc7is3D0y2uUF4kmVYiZT8H9sL334c4k8n3rq8vieJWb 8SU6F+WA6VFkjuBGm8G6BCieX8F5YzpgaxvB2VIIc1Vgi5bsY3ZWrgyeSujL tgxgtH8Xv1nEYpNCjmHJCKV0NVL+ojUqRd84UIOFrJesnkbXGXTWQ55f3rEI o4hhCVIkkKlSS0m4IU0dGk22wmiTEyUgwOaVXceYFGSnzG3HpOR01+bUEF+9 1ZE+82VYNBm6dv7+SV8x1DodI0RF6O1ErJJ6GMj2xW3fayRCUOCamol8qs2y n8qSybu50vBzSq4je9iqcuMYW5hBc.qVxr5pPKUYKUounH8kxZvplCa015Ty CTGyCjgLOPFAYh7Ag6VlvaHyE3SIWfGpbg8g2ctfOz4Bjq4hA6MBaDXkxcUR fF3LgSJE3ALSj2kXmxEzg78AlOFxETmUK7GRCDmaBgMjuQP9NlMzqO5ALeHb NeLjFqkO8NyG9dCZ1v0Fz7EC5aEWkF5kgz.lOXNmOFz5uPt1kK7vZz5ZMX3g TZ36MFpMWa.3bt33c5Z+vTziirgwoT5X60+vLbyxjrbPmGMKmO96EIqRzK9r vzsgG0293CFV7iOquG6HoMWVtWWErhu4iO1coFT+px67XCusXD0yeNMXYT8U vQN9PkIJ5AhOB66kmdTjuDwLeScn2lx16DWbqRtpok7qWxHDl4aTDhQZ5NIU Jo09n90pF4uEVuClSf4aRS1jjVNr1Gn90tucYIkE37yWYfqUjeUQTTo27ORh CVnFOcEkl6qnolpdXGiBKia8plLGSSuTex3xl+UXtiE9L1U0pfEKBiyp89RR 3XQtnVHwRrVwh6yY72pmTkiyunI4j6iPxAtf9ZNfqyVmDmrcSNjnCpATMICV Jkj7KjHkXqhLGSo3NL7TVZRhOips4DRNWX9FgfIiVqOWqsntkmR8ASEHsZij H7n5aUoHoD.iGCw6a2.v4ZclGtJb8g2tPh7wlhMBQvRyShh8j16+8acXeFEO ft7M4ecNH0ZbNoEX1Riu13qcODteZeSi8Ew+ujnLY2NKMXwerKL6wSK..zRX Of4YV6dX6Rl2u8H..8zi6ASv.DfI9WnpBTO6FF9BXRDDe.f3C.De.ZYZ0LUZ L.quayZJkHXtEg.jyGWQ.fqAeKFLyvHri.tP+HBIKyfjGF4JFPX.Q1FWAODs 3CaBsRNPkmXBvjm3jg6GOAijDWR0LNgeZpYSgHVhbpR9YKKDGU2nU82b4L0e VUyOHNqIgcpBKho+D9cV4+0eMZdkorCw2SYmAxSfxt2Ekcl3wGUh+nSYmvSa 8wM3XOYnri2+FgVIkG5FhxN9.Dx3XrGDpphjx7gExAJ65Tf46qoriQj.kccJ sLUE6trZpCYmAt.toCS.icU0Q7j.hcWGD67.D6trH1wqiX2PzJUkl0ymxpxg avESGD53CEBcFe8xQ7oMBcCk7ySpIIuT9MkPnavclBGeZNSweLasd.vgD0+Z E3vyF9K6P9sN.n24M7VlsPNph+cA1BeOxPqu5.zB+vgV38iq.8ucHSH2lE+C hZmWt.8+fAjoKq.a4jGpmwAfSrw.c.iChy7GC7ZLRP8x0bwfRTjqbMMjvl4J pYC5aCW46ZH0KAP5AF1AF1G2jaOJXXeTPyu6zW1bkESW7KZeizD4iP841oIi a1vi7rKxzNXu.cSseZxrq8.W1OMKi26WosSyGCVjDOamZLeouFjEl1OR.JlU Dsv3GMnsWSDvAxZzzmXVgOX6pS2LWxdDIvVCvVCvVy0fsFgA1MSnB1AuxIFY v0LTaJoHCNCBGEKd2VaJoWi8+JprnERfHoFcQNS2FBAC.I0S6YeFA5GDdjv. 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It was pretty fun and funky to make this. This entry was posted in Assignments on October 3, 2018 by egiuse@andrew.cmu.edu. Assignment 2 – Remix For this assignment, I thought it would be cool to make a remix of a song and see what was possible in Max using the skills I’ve learned so far this semester. I chose the song Good Times Roll – GRiZ x Big Gigantic, and created a patch from the first minute of it or so. It was definitely pretty entertaining, and I’m sure I could incorporate more techniques and higher-level uses of timeshifting in the future! For this time, I used timeshifting and feedback in subpatches for different parts of the song, here’s one of them: This particular subpatch uses a metronome and a ramp to slide between different delay times and also feedback into the system. I used individual subpatches to distort the signal for any interval in the song that I wanted, and also created a couple sounds in garage band to add just for fun. You can adjust delay and feedback for each subpatch as well. Here’s the link to the zip folder containing the necessary audio files: https://drive.google.com/open?id=1wOce5NGWziuwbO1C7FJ6Ug_lHwaL0sea Here’s a recording of the patch: and here’s the actual patch: .gist table { margin-bottom: 0; } Thanks for reading! It was definitely a lot of fun to make this! This entry was posted in Assignments on September 19, 2018 by egiuse@andrew.cmu.edu. Assignment 1 – Layered Vocals For this project I took a song (Mr. Blue Sky by Electric Light Orchestra), isolated the vocals through Audacity and then layered this new vocals track on to create a new “mix”. I then repeated this process by isolating the vocals from the new mix and again layering it on. I did this 30 times. I was curious to see how feasible it is for a program like Audacity to separate vocals from everything else, and use the program as a feedback system to create new mixes. To create mix #1, I separated the vocals and instrumentals individually and then layered them together. Original recording by Electric Light Orchestra: https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/09/mr_blue_sky.mp3   Mix #1 https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/09/mix_1.mp3   Mix #10 https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/09/mix_10.mp3   Mix #30 https://courses.ideate.cmu.edu/18-090/f2018/wp-content/uploads/2018/09/mix_30.mp3   I have never worked with sound media or used any sort of audio software before, so I had no idea what to expect. You can clearly hear how the original signal is destroyed, particularly at around 1:02. The results were certainly interesting, and I’m sure that continuing to isolate and layer the vocals would eventually produce something quite different! This entry was posted in Assignments on September 5, 2018 by egiuse@andrew.cmu.edu. ```