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Why Use an RTOS?

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Why Use an RTOS? RT Computer and Machine Vision An Overview of MPEG and Image Processing Tips December 12, 2017 Sam Siewert FFMPEG FAQ Read It!! http://ffmpeg.org/faq.html You should know how to Decode Video (recorded from your camera or pre- recorded by someone else) You should know how to Encode Video (to turn in with your labs) Sam Siewert 2 Ffmpeg (avconv) Notes sudo apt-get install ffmpeg ffmpeg -i movie.mpg –ss 30 –t 30 movie%d.ppm –- 30 seconds @ 30 sec ssiewert@ssiewert-VirtualBox:~/a485/media$ ffmpeg -i big_buck_bunny_480p_surround-fix.avi -ss 30 -t 30 bbb%d.ppm ffmpeg version 0.8.6-4:0.8.6-0ubuntu0.12.04.1, Copyright (c) 2000-2013 the Libav developers built on Apr 2 2013 17:02:36 with gcc 4.6.3 Input #0, avi, from 'big_buck_bunny_480p_surround-fix.avi': Duration: 00:09:56.45, start: 0.000000, bitrate: 2957 kb/s Stream #0.0: Video: mpeg4 (Simple Profile), yuv420p, 854x480 [PAR 1:1 DAR 427:240], 24 tbr, 24 tbn, 24 tbc Stream #0.1: Audio: ac3, 48000 Hz, 5.1, s16, 448 kb/s Incompatible pixel format 'yuv420p' for codec 'ppm', auto-selecting format 'rgb24' [buffer @ 0x907700] w:854 h:480 pixfmt:yuv420p [avsink @ 0x9054c0] auto-inserting filter 'auto-inserted scaler 0' between the filter 'src' and the filter 'out' [scale @ 0x905b60] w:854 h:480 fmt:yuv420p -> w:854 h:480 fmt:rgb24 flags:0x4 Output #0, image2, to 'bbb%d.ppm': Metadata: encoder : Lavf53.21.1 Stream #0.0: Video: ppm, rgb24, 854x480 [PAR 1:1 DAR 427:240], q=2-31, 200 kb/s, 90k tbn, 24 tbc Stream mapping: Stream #0.0 -> #0.0 Press ctrl-c to stop encoding ... Last message repeated 719 times -0kB time=29.00 bitrate= -0.0kbits/s frame= 720 fps= 38 q=0.0 Lsize= -0kB time=30.00 bitrate= -0.0kbits/s video:864686kB audio:0kB global headers:0kB muxing overhead -100.000002% ssiewert@ssiewert-VirtualBox:~/a485/media$ Sam Siewert 3 Now with PPM Frames PPM is Simple, but No Compression – Good for CV – http://en.wikipedia.org/wiki/Netpbm_format - Read this! – JPEG, PNG are Compressed – TIFF is an Alternative, but More Complex Sam Siewert 4 Simple Re-encode When Quality is not a Concern, Keep it Simple ffmpeg -f image2 -i bbb%d.ppm bbbtrans.mpg vlc bbbtrans.mpg Sam Siewert 5 Quality Encoding is Tricky Use MPEG4 HQ Settings, Encode 480p, AR=4:3 ffmpeg -f image2 -i bbb%d.ppm -maxrate 20000k - bufsize 32M -s 640x480 -vcodec mpeg4 -qscale 1 bbbtranshq.mp4 Sam Siewert 6 Color and Object Recognition Demo (Revisited) Object Recognition and Tracking Using Color in Real-Time Use Color Models or “Signatures” for Known Objects – General Color Perception and Recognition – Computer Vision – Specific Color Signature Recognition – Machine Vision – Controlled Lighting, Apriori algorithm, not tracking primary colors, but rather centroids of objects with a color signature ECEN 4623/5623 – University of Colorado Boulder Sam Siewert 7 Basic Concepts Single Camera Tilt/Pan Object Tracking – 2 Axes of Rotation – +/- 45 deg Tilt Rotation Servo – +/- 45 deg Pan Rotation Servo – Side or Rear Mounting of Tilt Servo Front View Side View Camera Pan Camera Rotation Tilt Rotation Pan Rotation Tilt Rotation Pan Servo Pan Tilt Servo Servo Tilt Servo Mounting Plane Mounting Plane Sam Siewert 8 Dual Camera Tilt/Pan Tracking Baseline with 2 Fixed Cameras – Pan Serve Pans Entire Baseline – Tilt Servo Tilts Pan Servo – Side Mounting Plate Camera Camera Tilt Rotation Pan Rotation Pan Servo Tilt Servo Mounting Plane Sam Siewert 9 Target Centroid Apply Edge Detection or Enhancement – High Pass Filter PSF Convolution – Point Spread Function – E.g. Edge Enhancement kernel -k/8 -k/8 -k/8 – 9 multiplications at every pixel and accumulate for new pixel value -k/8 k+1 -k/8 – k=0,1,2,3,… (k=0, no change) Raster Processing to Find Edges on Rows -k/8 -k/8 -k/8 Raster Processing to Find Edges on Columns Use Target Shape Characteristics Threshold Filter to Clean up Sharpened Image Chapter 24, “The Scientist and Engineer’s Guide to Digital Signal Processing”, by Steven Smith Sam Siewert 10 Edge Enhancement/Filter RGB Sharpen and Filter Sharpen Balanced Gray Sharpen and Filter Sharpen Sam Siewert 11 Pixel Coordinates Define Image Coordinates to Track Object Centroid Tilt/Pan With Servos to Keep Target Centroid in FOV Center X Frame Origin Reference Target Pixel Address 0,0 Pixel Address 160, 120 w o R l e t x i a P m r 0 o 4 F 2 C S T N Y NTSC 320 Pixel Column Format Sam Siewert 12 Stereo Ranging with Common Tilt/Pan Fixed Camera Baseline Target centroid ∆centroid = (dl + dr) ∆centroid b α θ = f d f ≡ focal − length d L lens R lens b α f f θ L detector R detector dl dr Sam Siewert 13 Pixel to Servo Calibration at Distance Pan Shifted Tilt One Servo Frame Tilt Shifted Frame Increment Pan One Servo Reference Target Increment Pixel Address Pan Shifted 160, 120 Target Pixel w o Address 158, 120 R l e t x i a P m r 0 o 4 F 2 Tilt Shifted Target C Pixel Address S 160, 122 T N NTSC 320 Pixel Column Format Pan 1 Servo Increment and Find Centroid X Pixel Change Tilt 1 Servo Increment and Find Centroid Y Pixel Change Sam Siewert 14 Characterize Camera FOV at Distances FOV Width/Height Linear as a Function of Distance Pixels/Inch Not Necessarily Linear as Function of Distance Use to Calibrate Servo Step Size (Gain) for Target Centering – For Servo Step Sizes of 1 Increment in Tilt/Pan, Camera Will Track Slowly – For Larger Servo Step Sizes May Over-shoot – Determine Deadbands (Servo Limits for Pixel Change Accuracy) CCTV FOV analysis Pixels Per Inch at Distances 50 y = 1.2132x - 1.1711 40 40 30 30 measured FOV 20 20 linear fit 10 10 Pixels Per Inch Per Pixels 0 0 distance from target distance from 0 20 40 0 10 20 30 40 50 FOV width Distance to Target Sam Siewert 15 Finding Centroid In Image Target Known by Color, Shape, or Brightness Raster to Find Target Edges and Max Width and Height Scanline for Symmetric targets Mark Centroid in Image for Easy Debug Noise in Image Will Cause Centroid Error – Use Averaging Pan Right to Move Target Left in Image Tilt Up to Move Target Down in Image Sam Siewert 16 More Tips Consider Controlled Lighting or For Mobile Robots, On-Board Lighting with LEDs Use Frame Grabber ADC Sensitivity Settings to Control Brightness Consider Automatic Calibration Sequences – Place Reference Targets to Set Pixel Step Size as a Function of Servo Step Size – Use Stereo Range Estimation to Determine Distance to Target and Set Tracking Gains for Current Distance When Target is Lost, Go Into Search Mode – Start from Max Tilt/Pan and Raster to Min Tilt/Pan to Find Target – For Search Modes Use Coarser Step Size Consider Small Servo Step Size, But High Frame Rate and Servo Command Rate Be Careful of Processor Overload with Image Processing at 30 FPS Sam Siewert 17 Project Suggestions Target Tracker – Tilt/Pan Camera Target Tracker – Fixed Camera, Tilt/Pan Laser Pointer Stereo Ranging Tracker Stereo Scene Imager – Raster a Scene with Tilt/Pan Laser Pointer – Fixed Camera Ranges to Each Laser Pointer Location – Builds 3-D Scene Map Line Follower Mobile Robot – Downward Camera Keeps Robot On Course – Image Processing Drives Steering Commands – Can Look Upward and Use Laser Pointer to detect Obstacles GPS Coarse Navigation with Close-In Computer Vision Scanners – Image to XY Plot Combine with Robotic Projects for Arm Navigation Sam Siewert 18 Video Media Sam Siewert 19 Embedding Video Codecs Codec = Compression, Decompression Build Your Own – Run Length Encoding – Difference Images – Python Viewer (Displays PPM sequences) – X-Windows Viewer (Displays PPM sequences) Theora/Ogg Open Source Option – http://www.theora.org/ – Stream over Raw TCP to VLC Viewer MJPEG Open Source Option – http://mjpeg.sourceforge.net/ Sam Siewert 20 Notes on Computer Color Encoding RGB, 24-bit, [0-255] for each color band Each Pixel is a 3-D Vector in RGB Space Blue Cyan Magenta White Green Black Red Yellow Sam Siewert 21 YUV/YCrCb RGB An Alternative to RGB is YUV, Where Y is Luminance and CrCb is Chrominance The following 2 sets of formulae are taken from information from Keith Jack's excellent book "Video Demystified" (ISBN 1-878707-09-4). RGB to YUV Conversion (For Computers with RGB [0-255]) – Y = (0.257 * R) + (0.504 * G) + (0.098 * B) + 16 – Cr = V = (0.439 * R) - (0.368 * G) - (0.071 * B) + 128 – Cb = U = -(0.148 * R) - (0.291 * G) + (0.439 * B) + 128 YUV to RGB Conversion – B = 1.164(Y - 16) + 2.018(U - 128) – G = 1.164(Y - 16) - 0.813(V - 128) - 0.391(U - 128) – R = 1.164(Y - 16) + 1.596(V - 128) In both these cases, you have to clamp the output values to keep them in the [0-255] range. Sam Siewert 22 RGB to Grayscale From 24 bits to 8 bits most often Single Color Band from RGB – Not True Grayscale, but Useful for Computer Vision Applications – Some Targets Like a Laser Pointer are Best Seen in Red Band or Green Band Alone GIMP Uses a Conversion to 8-bit Luminance – Y = 0.3R + 0.59G + 0.11B – Defined by equal amounts of color the eye is most sensitive to green, then red, and then blue Sam Siewert 23 R, G, or B band only vs.
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