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The RoboCV Workshop

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The RoboCV workshop happened in BITS-Pilani, Goa campus in January 2010. This is the presentation used during the workshop - the complete set.

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The RoboCV Workshop

  1. 1. and (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  2. 2. presents (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  3. 3. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  4. 4. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  5. 5. The word robot originally was supposed to mean a slave It is a machine which performs a variety of tasks, either using manual external control or intelligent automation A manually controlled car or a ASIMOV trying to kick a football are all robots (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  6. 6. ž Robotics is a multi disciplinary field of engineering encompassing the vistas of › Mechanical design › Electronic control › Artificial Intelligence ž It finds it’s uses in all aspects of our life › automated vacuum cleaner › Exploring the ‘Red’ planet › Setting up a human colony there :D (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  7. 7. ROBOTS CONTROL AUTONOMOUS MANUAL APPLICATIONS INDUSTRIAL MEDICAL INTERFACE HARDWARE SOFTWARE INTERLINKED (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  8. 8. Ø Locomotion System Ø Actuators Ø Power Supply System Ø Transmission System Ø Switches Ø Sensory Devices For Feedback Ø Sensor Data Processing Unit (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  9. 9. Ø A mobile robot must have a system to make it move. Ob. Ø This system gives our machine the ability to move forward, backward and take turns Ø It may also provide for climbing up and down Ø Or even flying or floating J (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  10. 10. Ø Each type of locomotion requires different number of degrees of freedom Ø More degrees of freedom means more the number of actuators you will have to use Ø Although one actuator can be used to control more than one degree of freedom (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  11. 11. Ø Wheeled Ø Legged Ø Climbing Ø Flying Ø Floating Ø Snake-Like (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  12. 12. Ø The kind of locomotion most frequently used in robotics at the undergrad level Ø This involves conversion of electrical energy into mechanical energy (mostly using motors) Ø The issue is to control these motors to give the required speed and torque (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  13. 13. Ø We have a simple equation for the constant power delivered to the motor: › P = ζ X ω Ø Note that the torque and angular velocity are inversely proportionally to each other Ø So to increase the speed we have to reduce the torque (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  14. 14. Ø The dc motors available have very high speed of rotation which is generally not needed Ø At high speeds, they lack torque Ø For reduction in speed and increase in “pulling capacity” we use pulley or gear systems (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  15. 15. Ø Differential Drive Ø Dual Differential Drive Ø Car-type Drive Ø Skid-steer Drive Ø Synchronous Drive (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  16. 16. Ø Simplest, easiest to implement and most widely used. Ø It has a free moving wheel in the front accompanied with a left and right wheel. The two wheels are separately powered Ø When the wheels move in the same direction the machine moves in that direction. Ø Turning is achieved by making the wheels oppose each other’s motion, thus generating a couple (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  17. 17. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  18. 18. Ø In-place (zero turning radius) rotation is done by turning the drive wheels at the same rate in the opposite direction Ø Arbitrary motion paths can be implemented by dynamically modifying the angular velocity and/or direction of the drive wheels Ø Total of two motors are required, both of them are responsible for translation and rotational motion (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  19. 19. Ø Simplicity and ease of use makes it the most preferred system by beginners Ø Independent drives makes it difficult for straight line motion. The differences in motors and frictional profile of the two wheels cause them to move with slight turning effect Ø The above drawback must be countered with appropriate feedback system. Suitable for human controlled remote robots (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  20. 20. Ø Uses synchronous rotation of its wheels to achieve motion & turns Ø It is made up of a system of 2 motors. One which drive the wheels and the other turns the wheels in a synchronous fashion Ø The two can be directly mechanically coupled as they always move in the same direction with same speed (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  21. 21. The direction of motion is given by black arrow. The alignment of the machine is shown by red arrow (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  22. 22. Ø The use of separate motors for translation and wheel turning guarantees straight line motion without the need for dynamic feedback control Ø This system is somewhat complex in designing but further use is much simpler (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  23. 23. Ø Actuators, also known as drives, are mechanisms for getting robots to move. Ø Most actuators are powered by pneumatics (air pressure), hydraulics (fluid pressure), or motors (electric current). Ø They are devices which transform an input signal (mainly an electrical signal)) into motion (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  24. 24. Ø Widely used because of their small size and high energy output. Ø Operating voltage: usually 6,12,24V. Ø Speed: 1-20,000 rpm.. Ø Power: P = ζ X ω (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  25. 25. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ØThe stator is the stationary outside part of a motor. Ø The rotor is the inner part which rotates. Ø Red represents a magnet or winding with a north polarization. Ø Green represents a magnet or winding with a south polarization. Ø Opposite, red and green, polarities attract. Ø Commutator contacts are brown and the brushes are dark grey.
  26. 26. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Ø Stator is composed of two or more permanent magnet pole pieces. Ø Rotor composed of windings which are connected to a mechanical commutator. Ø The opposite polarities of the energized winding and the stator magnet attract and the rotor will rotate until it is aligned with the stator. Ø Just as the rotor reaches alignment, the brushes move across the commutator contacts and energize the next winding. Ø A yellow spark shows when the brushes switch to the next winding.
  27. 27. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ØIt is an electric motor that can divide a full rotation into a large number of steps. Ø The motor's position can be controlled precisely, without any feedback mechanism. Ø There are three types: Ø Permanent Magnet Ø Variable Resistance Ø Hybrid type
  28. 28. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Ø Stepper motors work in a similar way to dc motors, but where dc motors have 1 electromagnetic coil to produce movement, stepper motors contain many. Ø Stepper motors are controlled by turning each coil on and off in a sequence. Ø Every time a new coil is energized, the motor rotates a few degrees, called the step angle.
  29. 29. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  30. 30. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Full Step Ø Stepper motors have 200 rotor teeth, or 200 full steps per revolution of the motor shaft. Ø Dividing the 200 steps into the 360º's rotation equals a 1.8º full step angle. Ø Achieved by energizing both windings while reversing the current alternately.
  31. 31. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ØServos operate on the principle of negative feedback, where the control input is compared to the actual position of the mechanical system as measured. ØAny difference between the actual and wanted values (an "error signal") is amplified and used to drive the system in the direction necessary to reduce or eliminate the error ØTheir precision movement makes them ideal for powering legs, controlling rack and pinion steering, to move a sensor around etc.
  32. 32. Ø Suitable power source is needed to run the robots Ø Mobile robots are most suitably powered by batteries Ø The weight and energy capacity of the batteries may become the determinative factor of its performance (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  33. 33. Ø For a manually controlled robot, you can use batteries or voltage eliminators (convert the normal 220V supply to the required DC voltage 12V , 24V etc.) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  34. 34. Ø Gear Ø Belt Pulley Ø Chain Sprocket Ø Rack and Pinion Ø Pick Place Mechanisms (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  35. 35. Ø Gears are the most common means of transmitting power in mechanical engineering Ø Gears form vital elements of mechanisms in many machines such as vehicles, metal tooling machine tools, rolling mills, hoisting etc. Ø In robotics its vital to control actuator speeds and in exercising different degrees of freedom (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  36. 36. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  37. 37. Ø To achieve torque magnification and speed reduction Ø They are analogous to transformers in electrical systems Ø It follows the basic equation: Ø ω1 x r1 = ω2 x r2 Ø Gears are very useful in transferring motion between different dimension (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  38. 38. Ø An arrangement of gears to convert rotational torque to linear motion Ø Same mechanism used to steer wheels using a steering Ø In robotics used extensively in clamping systems (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  39. 39. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  40. 40. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  41. 41. Ø It allows for mechanical power, torque, and speed to be transmitted across axes Ø If the pulleys are of differing diameters, it gives a mechanical advantage Ø In robotics it can be used in lifting loads or speed reduction Ø Also it can be used in a differential drive to interconnect wheels (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  42. 42. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  43. 43. Ø Sprocket is a profiled wheel with teeth that meshes with a chain Ø It is similar to the system found in bicycles Ø It can transfer rotary motion between shafts in cases where gears are unsuitable Ø Can be used over a larger distance Ø Compared to pulleys has lesser slippage due to firm meshing between the chain and sprocket (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  44. 44. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  45. 45. Ø For picking and placing many mechanisms can be used: vHook and pick vClamp and pick vSlide a sheet below and pick vMany other ways vLots of Scope for innovation (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  46. 46. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  47. 47. ž Image Processing is a tool for analyzing image data in all areas of natural science ž It is concerned with extracting data from real-world images ž Differences from computer graphics is that computer graphics makes extensive use of primitives like lines, triangles & points. However no such primitives exist in a real world images. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  48. 48. ž Increasing need to replicate human sensory organs ž Eye (Vision) : The most useful and complex sensory organ (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  49. 49. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  50. 50. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  51. 51. ž Automated visual inspection system Checking of objects for defects visually ž Remote Sensing ž Satellite Image Processing ž Classification (OCR), identification (Handwriting, finger prints) etc. ž Detection and Recognition systems (Facial recognition..etc) ž Biomedical applications (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  52. 52. ž Camera, Scanner or any other image acquisition device ž PC or Workstation or Digital Signal Processor for processing ž Software to run on the hardware platform (Matlab, Open CV etc.) ž Image representation to process the image (usually matrix) and provide spatial relationship ž A particular color space is used to represent the image(c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  53. 53. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Image Acquisition Device (Eg. CCD or CMOS Camera) Image Processor (Eg. PC or DSP) Image Analysis Tool (Eg. Matlab or Open CV) Machine Control Of Hardware through serial or parallel interfacing
  54. 54. ž Using a camera ž Analog cameras ž Digital cameras › CCD and CMOS cameras ž Captures data from a single light receptor at a time ž CCD – Charge Coupled Devices ž CMOS – Complementary MOSFET Sensor based (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  55. 55. ž Digital Cameras › CCD Cameras – High quality, low noise images – Genarates analog signal converted using ADC – Consumes high power › CMOS Cameras – Lesser sensitivity – Poor image quality – Lesser power ž Analogue cameras require grabbing card or TV tuner card to interface with a PC (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  56. 56. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Colored pixels on CCD Chip
  57. 57. ž Matlab ž Open CV (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  58. 58. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  59. 59. ž Two types: Vector and Raster ž Vector images store curve information ž Example: India’s flag ž Three rectangles, one circle and the spokes ž We will not deal with vector images at all (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  60. 60. ž Raster images are different ž They are made up of several dots (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  61. 61. ž If you think about it, your laptop’s display is a raster display ž Also, vector images are high level abstractions ž Vector representations are more complex and used for specific purposes (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  62. 62. ž Raster › Matrix ž Vector › Quadtrees › Chains › Pyramid Of the four, matrix is the most general. The other three are used for special purposes. All these representations must provide for spatial relationships (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  63. 63. ž Computers cannot handle continuous images but only arrays of digital numbers ž So images are represented as 2-D arrays of points (2-D matrix)(Raster Represenatation) ž A point on this 2-D grid (corresponding to the image matrix element) is called PIXEL (picture element) ž It represents the average irradiance over the area of the pixel (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  64. 64. ž Each pixel requires some memory ž Color depth : Amount of memory each pixel requires ž Examples › 1-bit › 8-bit › 32-bit › 64-bit (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  65. 65. ž Pixels are tiny little dots of color you see on your screen, and the smallest possible size any image can get ž When an image is stored, the image file contains information on every single pixel in that image i.e › Pixel Location › Intensity ž The number of pixels used to represent the image digitally is called Resolution ž More the number of pixels used, higher the resolution ž Higher resolution requires more processing power (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  66. 66. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  67. 67. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  68. 68. ž MATLAB stands for MATrix LABoratory, a software developed by Mathworks Inc (www.mathworks.com). MATLAB provides extensive library support for various domains of scientific and engineering computations and simulations (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  69. 69. ž When you click the MATLAB icon (from your desktop or Start>All Programs), you typically see three windows: Command Window, Workspace and Command History. Snapshots of these windows are shown below (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  70. 70. ž This window shows the variables defined by you in current session on MATLAB (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  71. 71. ž Command History stores the list of recently used commands for quick reference (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  72. 72. ž This is where you run your code (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  73. 73. ž This is where you run your code (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  74. 74. ž In MATLAB, variables are stored as matrices (singular: matrix), which could be either an integer, real numbers or even complex numbers ž These matrices bear some resemblance to array data structures (used in computer programming) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  75. 75. ž Let us start with writing simple instructions on MATLAB command window ž To define an integer, ž Type a=4 and hit enter ž >>a=4 ž To avoid seeing the variable, add a semicolon after the instruction ž >>a=4; (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  76. 76. ž Similarly to define a 2x2 matrix, the instruction in MATLAB is written as ž >> b=[ 1 2; 3 4]; ž If you are familiar with operations on matrix, you can find the determinant or the inverse of the matrix. ž >> determin= det(b) ž >> d=inv(b) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  77. 77. ž Images as we have already seen are stored as matrices ž So now we try to see this for real on MATLAB ž We shall also look into the basic commands provided by MATLAB’s Image Processing Toolbox (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  78. 78. ž Once you have started MATLAB, type the following in the Command Window ž >> im=imread(‘sample.jpg'); ž This command stores the file image file ‘sample.jpg’ in a variable called ‘im’ ž It takes this file from the Current- Directory specified ž Else, entire path of file should be mentioned (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  79. 79. ž You can display the image in another window by using imshow command ž >>figure,imshow(im); ž This pops up another window (called as figure window), and displays the image ‘im’ (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  80. 80. ž The ‘imview’ command can also be used in order toview the image ž imview(im); ž Difference is that in this case you can see specific pixel values just by moving the cursor over the image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  81. 81. ž To know the breadth and height of the image, use the size function, ž >>s=size(im); ž The size function basically gives the size of any array in MATLAB ž Here we get the size of the IMAGE ARRAY (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  82. 82. ž Now that we have our image stored in a variable we can observe and understand the following: ž How pixels are stored? ž What does the values given by each pixel indicate? ž What is Image Resolution? (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  83. 83. ž Have a look at the values stored ž Say the first block of 10 x 10 ž >>im(1:10,1:10); ž Or Say view the pixel range 50:150 on both axis ž >> figure,imshow(im(50:150,50:150)); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  84. 84. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  85. 85. ž 1-bit = BLACK or WHITE ž 8-bit = 28 different shades ž 24-bit = 224 different shades ž 64-bit images – High end displays ž Used in HDRI, storing extra information per pixel, etc (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  86. 86. ž This is another name for 1-bit images ž Each pixel is either White or Black ž Technically, this is a black & white image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  87. 87. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  88. 88. ž Another name for 8-bit images ž Each pixel can be one of 256 different shades of gray ž These images are popularly called Black & White. Though, this is technically wrong. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  89. 89. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  90. 90. ž Again, each pixel gets 8 bits ž But each of the 256 values maps to a color in a predefined “palette” ž If required, you can have different bit depths (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  91. 91. ž We won’t be dealing with indexed images (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  92. 92. ž 8-bits is too less for all the different shades of colors we see ž So 24-bits is generally used for color images ž Thus each pixel can have one of 224 unique colors (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  93. 93. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  94. 94. ž Now, a new problem arises: ž How do you manage so many different shades? ž Programmers would go nuts ž Then came along the idea of color spaces (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  95. 95. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  96. 96. ž A color space can be thought of as a way to manage millions of colors ž Eliminates memorization, and increases predictability ž Common color spaces: › RGB › HSV › YCrCb or YUV › YIQ (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  97. 97. ž You’ve probably used this already (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  98. 98. ž Each pixel stores 3 bytes of data ž The 24-bits are divided into three 8-bit values ž The three are: Red, Green and Blue i.e the primary colours ž Mixing of primary colours in right proportions gives any particular colour ž Each pixel has these 3 values (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  99. 99. ž 1 byte = 8 bits can store a value between 0-255 ž We get pixel data in the form RGB values with each varying from 0-255 ž That is how displays work ž So there are 3 grayscale channels (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  100. 100. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  101. 101. ž Advantages: › Intuitive › Very widely used ž Disadvantages: › Image processing is relatively tough (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  102. 102. ž HSV makes image processing easier ž Again, 24 bits = three 8-bit values or 3 channels ž The 3 channels are: › Hue › Saturation (Shade of Colour) › Value (Intensity) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  103. 103. ž The Hue is the tint of color used › It represents the colour of the pixel (Eg. Red Green Yellow etc) ž The Saturation is the “amount” of that tint › It represents the intensity of the colour (Eg. Dark red and light red) ž The Value is the “intensity” of that pixel › It represents the intensity of brightness of the colour (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  104. 104. ž RGB image converted to HSV (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha RGB HUE SATURATION VALUE
  105. 105. ž Advantages: › The color at a pixel depends on a single value › Illumination independent ž Disadvantages: › Something (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  106. 106. ž Intuitively RGB might seem to be the simpler and better colour space to deal with ž Though HSV has its own advantages especially in colour thresholding ž As the colour at each pixel depends on a single hue value it is very useful in separating out blobs of specific colours even when there are huge light variations ž Thus it is very useful in processing real images taken from camera as there is a large amount of intensity variation in this case ž Hence, ideal for robotics applications (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  107. 107. ž Widely used in digital video ž Has three 8-bit channels: › Y Component: – Gives luminance or intensity › Cr Component: – It is the RED component minus a reference value › Cb Component: – It is the BLUE component minus a reference value ž Hence Cr and Cb components represent the colour called “Color Difference Components” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  108. 108. ž Advantages: › Used in video processing › Gives you a 2-D colour space hence helps in closer distinguishing of colours ž Disadvantages: (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  109. 109. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  110. 110. ž The camera returns images in a certain color space ž You might want to convert to different color spaces to process it ž Colour space conversions can take place between RGB to any other colour space and vice versa (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  111. 111. ž Since cameras usually input images in rgb ž We would like to convert these images into HSV or YCrCb ž Conversions: › RGB->HSV › HSV->RGB › RGB->YCrCb › YCrCb->RGB (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  112. 112. ž RGB -> HSV (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  113. 113. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ž HSV RGB YCrCb
  114. 114. ž >>h = rgb2hsv(im) ž This converts the RGB image to HSV ž The new colour space components can be seen using ž >> imview(h) ž >> imview(h(:,:,1)) “—HUE—” ž >> imview(h(:,:,2)) “—Saturation— ” ž >> imview(h(:,:,3)) “—Value—” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  115. 115. ž >>R = hsv2rgb(im) ž This converts the HSV image to RGB ž The new colour space components can be seen using ž >> imview(R) ž >> imview(R(:,:,1)) “—Red—” ž >> imview(R(:,:,2)) “—Green—” ž >> imview(R(:,:,3)) “—Blue—” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  116. 116. ž >> Y = rgb2ycbcr(im); ž This converts the RGB image to YCbCr ž The new colour space components can be seen using ž >> imview(Y) ž >> imview(Y(:,:,1)) “—Luminance—” ž >> imview(Y(:,:,2)) “—Differenced Blue—” ž >> imview(Y(:,:,3)) “—Differenced Red—” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  117. 117. ž >> R = ycbcr2rgb(im); ž This converts the YCbCr image to RGB ž The new colour space components can be seen using ž >> imview(R) ž >> imview(R(:,:,1)) “—Red—” ž >> imview(R(:,:,2)) “—Green—” ž >> imview(R(:,:,3)) “—Blue—” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  118. 118. ž Formulae for conversion are very complex ž But the best thing is, you don’t need to remember these formulae ž Matlab and OpenCV have built-in functions for these transformations :-) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  119. 119. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  120. 120. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  121. 121. ž OpenCV is a collection of many functions that help in image processing ž You can use OpenCV in C/C++, .net languages, Java, Python, etc as well ž We will only discuss OpenCV in C/C++ (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  122. 122. ž It is blazingly fast ž Quite simple to use and learn ž Has functions for machine learning, image processing, and GUI creation (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  123. 123. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  124. 124. ž Download the latest OpenCV package from http://sourceforge.net/projects/opencv / ž Install the package, and note where you installed it (like C:Program FilesOpenCV) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  125. 125. ž Now, we need to tell Microsoft Visual Studio that we’ve installed OpenCV ž So, we tell it where to find the OpenCV header files ž Start Microsoft Visual Studio 2008 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  126. 126. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  127. 127. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha 1 2
  128. 128. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Type these paths into the list
  129. 129. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Type these paths into the list
  130. 130. ž Right now, Visual Studio knows where to find the OpenCV include files and library files ž Now we create a new project (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  131. 131. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  132. 132. ž Accept all default settings in the project ž You’ll end up with an empty project with a single file (like Mybot.cpp) ž Open this file, we’ll write some code now (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  133. 133. ž Add the following at the top of the code #include <cv.h> #include <highgui.h> ž This piece of code includes necessary OpenCV functionality (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  134. 134. ž Now, we get to the main() function int main() { ž The main function is where for program execution begins (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  135. 135. ž Next, we load an image IplImage* img = cvLoadImage("C:hello.jpg"); ž The IplImage is a data type, like int, char, etc (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  136. 136. ž Comes built-into OpenCV ž Any image in OpenCV is stored as an IplImage thingy ž It is a “structure” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  137. 137. ž Opens filename and returns it as an IplImage structure ž Supported formats: › Windows bitmaps - BMP, DIB › JPEG files - JPEG, JPG, JPE › Portable Network Graphics - PNG › Portable image format - PBM, PGM, PPM › Sun rasters - SR, RAS › TIFF files - TIFF, TIF › OpenEXR HDR images - EXR › JPEG 2000 images - jp2 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  138. 138. ž Now we show this image in a window cvNamedWindow("myfirstwindow"); cvShowImage("myfirstwindow", img); ž This uses some HighGUI functions (comes along with OpenCV) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  139. 139. ž Creates a window with the caption title ž This is a HighGUI function ž You can add controls to each window as well (track bars, buttons, etc) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  140. 140. ž Shows img in the window with caption title ž If no such window exists, nothing happens (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  141. 141. ž Finally, we wait for an input, release and exit cvWaitKey(0); cvReleaseImage(&img); return 0; } (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  142. 142. ž Waits for time milliseconds, and returns whatever key is pressed ž If time=0, waits till eternity ž Here, we’ve used it to keep the windows from vanishing immediately (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  143. 143. ž Erases img from the RAM ž Get rid of an image as soon as possible. RAM is precious J ž Note that you send the address of the image (&img) and not just the image (img) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  144. 144. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  145. 145. ž Right now, Visual Studio knows where OpenCV is ž But it does not know, whether to use OpenCV or not ž We need to tell this explicitly (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  146. 146. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  147. 147. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  148. 148. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  149. 149. ž Got errors? › Check if the syntax is correct › Copy all DLL files in *OpenCVbin into C:WindowsSystem32 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  150. 150. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  151. 151. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  152. 152. ž src is the original image ž dst is the destination ž code is one of the follow: › CV_BGR2HSV › CV_RGB2HSV › CV_RGB2YCrCb › CV_HSV2RGB › CV_<src_space>2<dst_space> (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  153. 153. ž src should be a valid image. Or an error will pop up ž dst should be a valid image, i.e. you need a blank image of the same size ž code should be valid (check the OpenCV documentation for that) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  154. 154. ž Allocates memory for an image of size size, with bits bits/pixel and chan number of channels ž Used for creating a blank image ž Use cvSize(width, height) to specify the size (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  155. 155. ž Example: › IplImage* blankImg = cvCreateImage(cvSize(640, 480), 8, 3); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  156. 156. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  157. 157. ž Wired › Motor Driving module › Interface with PC (Parallel/Serial) ž Wireless › The Motor-driving module › The Wireless Receiver Circuit › The Wireless Transmitter Circuit › Interface with PC (Parallel/Serial) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  158. 158. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  159. 159. ž IC 7805 Voltage Regulator ž L293D Motor Driver ž MCT2E Opto-Coupler ž Parallel Port Male-Connector ž RF-RX Connector (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  160. 160. ž It’s a three terminal linear 5 volt regulator used to supply the board and other peripherals ž Prescribed input voltage to this component is about 7-9 Volts (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  161. 161. ž Voltage fluctuations can be controlled by using low pass filter capacitors across output and input ž Higher input voltage can be applied if heatsink is provided (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  162. 162. ž Used to control Dc and Stepper Motors ž Uses a H-Bridge which is an electronic switching circuit that can reverse direction of current ž It’s a Dual-H bridge ž Basically used to convert a low voltage input into a high voltage output to drive the motor or any other component ž Eg: Microcontrollerà Motor Driverà Motor (5 Volts) (12 Volts) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  163. 163. ž Different Motor Driver ICs › L293D – 600mA Current Rating – Dual H-bridge (Dc and Stepper Motors) › L298N – 1 Amp Current Rating – Dual H-bridge (Dc and Stepper Motors) › L297-L298 (Coupled) – For stepper motor overdriving – Dual H-bridge (Dc and Stepper Motors) – 2 Ics in parallel › ULN2003/ULN2803 – 500mA Current Rating – For unipolar stepper motors (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  164. 164. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  165. 165. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  166. 166. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  167. 167. ž Output Current: › 600 mA ž Output Voltage › Wide Range › 4.5 V – 36 V (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  168. 168. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  169. 169. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ž There are many situations where signals and data need to be transferred from one subsystem to another within a piece of electronics ž Relays are too bulky as they are electromechanical in nature and at the same time give lesser efficiency ž In these cases an electronic component called Optocoupler is used
  170. 170. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ž They are generally used when the 2 subsystems are at largely different voltages ž These use a beam of light to transmit the signals or data across an electrical barrier, and achieve excellent isolation
  171. 171. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ž In our circuit, Opto-isolator (MCT2E) is used to ensure electrical isolation between motors and the PC parallel port during wired connection ž The Viz-Board has four such chips to isolate the four data lines (pin 2, pin 3, pin 4, pin 5) coming out of the parallel port
  172. 172. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  173. 173. ž Along with the Viz-Board 2 extensions have been provided i.e › The Rf Transmitter Module › The Rf Reciever Module (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Transmitt er Receive r
  174. 174. ž Radio frequency modules are used for data transmission wirelessly at a certain frequency ž It sends and receives radio waves of a particular frequency and a decoder and encoder IC is provided to encode and decode this information ž Wireless transmission takes place at a particular frequency Eg. 315Mhz ž Theses modules might be single or dual frequency (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  175. 175. ž Antenna is recommended on both of them - just connect any piece of 23 cm long to the Antenna pin ž The kit has a dual frequency RF module with frequencies 315/434 Mhz (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  176. 176. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  177. 177. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  178. 178. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  179. 179. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
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  181. 181. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  182. 182. ž The encoder IC encodes the parallel port data and sends it to the RF transmitter module for wireless transmission ž They are capable of encoding information which consists of N address bits and (12-N) data bits (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  183. 183. ž The HT12E Encoder IC has 8 address bits and 4 data bits ž A DIP-Switch can be used to set or unset the address bits A0-A7 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  184. 184. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A0-A7—Address Bits AD8-AD11—Data Bits
  185. 185. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A0-A7—Address Bits AD8-AD11—Data Bits
  186. 186. ž The decoder IC decodes the RF transmitter data and sends it to the parallel port for wireless transmission ž They are capable of encoding information which consists of N address bits and (12-N) data bits (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  187. 187. ž The HT12D Decoder IC has 8 address bits and 4 data bits ž A DIP-Switch can be used to set or unset the address bits A0-A7 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  188. 188. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A0-A7—Address Bits D8-D11—Data Bits
  189. 189. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A0-A7—Address Bits D8-D11—Data Bits
  190. 190. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  191. 191. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  192. 192. ž Serial Port ž Parallel Port ž USB (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  193. 193. ž Data is transferred serially i.e packets are sent one after the other through a single port (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  194. 194. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  195. 195. ž Data is transferred in parallel through different data pins at the same time ž Communication is pretty fast ž Found in old printer ports (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  196. 196. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  197. 197. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha 25th pin : Ground 2nd-12th pin : I/O lines
  198. 198. ž Parallel port is faster than serial ž A mass of data can be transmitted at the same time through parallel ports ž Though parallel and serial ports are not found these days in laptops ž Desktops and old laptops have these ports (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  199. 199. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Direct Output from parallel port Output from motor driver
  200. 200. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  201. 201. Camera, object and source positions (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  202. 202. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Image sampling and quantization
  203. 203. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Continuous image projected on an array sensor Result of image sampling and quantization
  204. 204. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Sampling: Digitizing the coordinate values (spatial resolution) Quantization: Digitizing the amplitude values (intensity levels)
  205. 205. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha • 1 bit /pixel • B bits/pixel –2B gray levels –1 byte = 8 bits –> 256 levels –2 possible values –2 gray levels -> 0 or 1 (binary image)
  206. 206. ž All this sampling and quantization puts in extra noise on the image! ž Noise can be reduced by › Using hardware › Using software: filters (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  207. 207. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  208. 208. ž Why do we need to enhance images? ž Why filter images? (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  209. 209. ž Large amounts of external disturbances in real images ž Due to different factors like changing lighting and other real-time effects ž To improve quality of a captured image to make it easier to process the image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  210. 210. ž First step in most IP applications ž Used to remove noise in the input image ž To remove motion blur from an image ž Enhancing the edges of an image to make it appear sharper (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  211. 211. ž Generally used types Of Filtering › Averaging Filter › Mean Filter › Median Filter › Gaussian Smoothing › Histogram Equalization (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  212. 212. ž The Averaging filter is used to sharpen the images by taking average over a number of images ž It eliminates noise by assuming that different snaps of the same image have different noise patterns (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  213. 213. ž Noise is gaussian in nature i.e follows a gaussian curve ž Hence, summing up noises infinite times approaches zero (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  214. 214. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  215. 215. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  216. 216. ž This is extremely useful for satellites that take intergalactic photographs ž The images are extremely faint, and there is more noise than the image itself ž Millions of pictures are taken, and averaged to get a clear picture (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  217. 217. ž The Mean is used to soften an image by averaging surrounding pixel values (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Center pixel = (22+77+48+150+77+158+0+77+219)/9
  218. 218. ž The center pixel would be changed from 77 to 92 as that is the mean value of all surrounding pixels ž This filter is often used to smooth images prior to processing ž It can be used to reduce pixel flicker due to overhead fluorescent lights (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  219. 219. ž This replaces each pixel value by the median of its neighbors, i.e. the value such that 50% of the values in the neighborhood are above, and 50% are below ž This can be difficult and costly to implement due to the need for sorting of the values ž However, this method is generally very good at preserving edges (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  220. 220. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  221. 221. ž Its performance is particularly good for removing short noise ž The median is calculated by first sorting all the pixel values from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value ž If the neighborhood under consideration contains an even number of pixels, the average of the two middle pixel values is used (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  222. 222. ž Used to `blur' images and remove detail and noise ž The effect of Gaussian smoothing is to blur an image ž The Gaussian outputs a `weighted average' of each pixel's neighborhood, with the average weighted more towards the value of the central pixels (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  223. 223. ž A Gaussian provides gentler smoothing and preserves edges better than a similarly sized mean filter (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Before Blurring After Blurring
  224. 224. ž It is very useful in contrast enhancement ž Especially to eliminate noise due to changing lighting conditions etc ž Transforms the values in an intensity image so that the histogram of the output image approximately matches a specified histogram (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  225. 225. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  226. 226. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Filters and histograms
  227. 227. ž ‘Imfilter’ function is used for creating different kinds of filters In MATLAB ž B = imfilter(A,H,’option’) filters the multidimensional array A with the multidimensional filter H ž The array A can be a nonsparse numeric array of any class and dimension ž The result B has the same size and class as A (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  228. 228. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha ž Options in imfilter ž Convolution is same as correlation except that the h matrix is inverted before applying the filter
  229. 229. ž h = ones(5,5) / 25; ž imsmooth = imfilter(im,h); ž Here a mean filter is implemented using the appropriate ‘h’ matrix ž imshow(im), title('Original Image'); ž figure, imshow(imsmooth), title('Filtered Image') (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  230. 230. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  231. 231. ž FSPECIAL is used to create predefined filters ž h = FSPECIAL(TYPE); ž FSPECIAL returns h as a computational molecule, which is the appropriate form to use with imfilter (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  232. 232. ž FSPECIAL is used to create predefined filters ž h = FSPECIAL(TYPE); ž FSPECIAL returns h as a computational molecule, which is the appropriate form to use with imfilter (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  233. 233. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  234. 234. ž The process of adjusting intensity values can be done automatically by the histeq function ž >>im = imread('pout.tif'); ž >>jm = histeq(im); ž >>imshow(jm) ž >>figure, imhist(jm,64) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  235. 235. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Original Image
  236. 236. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Histogram Equalized Image
  237. 237. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  238. 238. ž Things aren’t as simple as they were in Matlab ž C/C++ needs a bit of syntax and formalities (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  239. 239. ž We’ll try doing the following right now › Gaussian filter › Median filter › Bilateral filter › Simple blur › Averaging filter (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  240. 240. ž Start Microsoft Visual Studio 2008 ž I assume you have OpenCV installed (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  241. 241. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  242. 242. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  243. 243. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  244. 244. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  245. 245. #include <cv.h> #include <highgui.h> (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  246. 246. #include <cv.h> #include <highgui.h> int main() { (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  247. 247. #include <cv.h> #include <highgui.h> int main() { IplImage* img = cvLoadImage(“C:noisy.jpg”); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  248. 248. #include <cv.h> #include <highgui.h> int main() { IplImage* img = cvLoadImage(“C:noisy.jpg”); IplImage* imgBlur = cvCreateImage(cvGetSize(img), 8, 3); IplImage* imgGaussian = cvCreateImage(cvGetSize (img), 8, 3); IplImage* imgMedian = cvCreateImage(cvGetSize (img), 8, 3); IplImage* imgBilateral = cvCreateImage(cvGetSize (img), 8, 3); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  249. 249. cvSmooth(img, imgBlur, CV_BLUR, 3, 3); cvSmooth(img, imgGaussian, CV_GAUSSIAN, 3, 3); cvSmooth(img, imgMedian, CV_MEDIAN, 3, 3); cvSmooth(img, imgBilateral, CV_BILATERAL, 3, 3); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  250. 250. cvNamedWindow(“original”); cvNamedWindow(“blur”); cvNamedWindow(“gaussian”); cvNamedWindow(“median”); cvNamedWindow(“bilateral”); cvShowImage(“original”, img); cvShowImage(“blur”, imgBlur); cvShowImage(“gaussian”, imgGaussian); cvShowImage(“median”, imgMedian); cvShowImage(“bilateral”, imgBilateral); cvWaitKey(0); return 0; } (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  251. 251. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  252. 252. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  253. 253. ž Blur: The plain simple Photoshop blur ž Gaussian: The best result (preserved edges and smoothed out noise) ž Median: Nothing special ž Bilateral: Got rid of some noise, but preserved edges to a greater extend (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  254. 254. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  255. 255. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  256. 256. ž Your OpenCV installation comes with detailed documentation ž *OpenCVdocsindex.html ž Scroll down, and you’ll see OpenCV Reference Manuals (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  257. 257. ž Try looking up cvSmooth in the CV Reference Manual (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  258. 258. ž Now try looking up cvEqualizeHist (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  259. 259. ž There are no built-in functions for this ž So, we’ll code it ourselves ž And this will be a good exercise for getting better at OpenCV (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  260. 260. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  261. 261. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  262. 262. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  263. 263. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  264. 264. #include <cv.h> #include <highgui.h> (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  265. 265. #include <cv.h> #include <highgui.h> int main() { (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  266. 266. #include <cv.h> #include <highgui.h> int main() { IplImage* imgRed[25]; IplImage* imgGreen[25]; IplImage* imgBlue[25]; Holds the R, G and B channels separately for each of the 25 images (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  267. 267. IplImage* imgBlue[25]; for(int i=0;i<25;i++) { IplImage* img; char filename[150]; sprintf(filename, "%d.jpg", (i+1)); img = cvLoadImage(filename); • Generate the strings “1.jpg”, “2.jpg”, etc and store them into filename • Load the image filename (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  268. 268. img = cvLoadImage(filename); imgRed[i] = cvCreateImage(cvGetSize(img), 8, 1); imgGreen[i] = cvCreateImage(cvGetSize(img), 8, 1); imgBlue[i] = cvCreateImage(cvGetSize(img), 8, 1); • Allocate memory for each component of image i (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  269. 269. imgBlue[i] = cvCreateImage(cvGetSize(img), 8, 1); cvSplit(img, imgBlue[i], imgGreen[i], imgRed[i], NULL); cvReleaseImage(&img); } • Split img into constituent channels • Note the order: B G R • Release img (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  270. 270. ž We created 75 grayscale images: 25 for red, 25 for green and 25 for blues ž Loaded 25 color images in the loop ž Split each image, and stored in an appropriate grayscale image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  271. 271. CvSize imgSize = cvGetSize(imgRed[0]); IplImage* imgResultRed = cvCreateImage(imgSize, 8, 1); IplImage* imgResultGreen = cvCreateImage(imgSize, 8, 1); IplImage* imgResultBlue = cvCreateImage(imgSize, 8, 1); IplImage* imgResult = cvCreateImage(imgSize, 8, 3); • This will hold the final, filtered image • It will be a combination of the grayscale channels imgResultRed, imgResultGreen and imgResultBlue (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  272. 272. IplImage* imgResult = cvCreateImage(imgSize, 8, 3); for(int y=0;y<imgSize.height;y++) { for(int x=0;x<imgSize.width;x++) { • Two loops to take us through the entire image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  273. 273. for(int x=0;x<imgSize.width;x++) { int theSumRed=0; int theSumGreen=0; int theSumBlue=0; for(int i=0;i<25;i++) { • To figure out the average, we need to find the numerator (the sum) over all 25 images (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  274. 274. for(int i=0;i<25;i++) { theSumRed+=cvGetReal2D(imgRed[i], y, x); theSumGreen+=cvGetReal2D(imgGreen[i], y, x); theSumBlue+=cvGetReal2D(imgBlue[i], y, x); } • To figure out the average, we need to find the numerator (the sum) over all 25 images (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  275. 275. theSumRed = (float)theSumRed/25.0f; theSumGreen = (float)theSumGreen/25.0f; theSumBlue = (float)theSumBlue/25.0f; cvSetReal2D(imgResultRed, y, x, theSumRed); cvSetReal2D(imgResultGreen, y, x, theSumGreen); cvSetReal2D(imgResultBlue, y, x, theSumBlue); } } • Once we have the sum, we divide by 25 and set the appropriate pixels (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  276. 276. cvMerge(imgResultBlue, imgResultGreen, imgResultRed, NULL, imgResult); cvNamedWindow("averaged"); cvShowImage("averaged", imgResult); cvWaitKey(0); return 0; } • Merge the three channels, and display the image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  277. 277. ž cvLoadImage always loads as BGR ž cvSplit to get the individual channels ž cvMerge to combine individual channels into a color image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  278. 278. ž IplImage to store any image in OpenCV ž cvCreateImage to allocate memory ž cvReleaseImage to erase an image from the RAM (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  279. 279. ž cvWaitKey to get a keypress within certain milliseconds ž cvNamedWindow to create a window ž cvShowImage to show an image in a window (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  280. 280. ž cvGetReal2D to get value at a pixel in grayscale images ž cvSetReal2D to set the value at a pixel ž CvSize to store an image’s size (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  281. 281. ž you can always refer to the OpenCV documentation (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  282. 282. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  283. 283. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  284. 284. ž The process of extracting image components that are useful in representation of image for some particular purpose ž Basic morphological operations are: › Dilation › Erosion (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  285. 285. ž The operation that grows or thickens objects in a binary image ž The specific manner of thickening is controlled by a shape referred to as “structuring element” (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  286. 286. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Structuring Element Binary Image
  287. 287. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Dilated Image
  288. 288. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  289. 289. ž Erosion shrink or thins objects in a binary image ž The manner of shrinkage is controlled by the structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  290. 290. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Structuring Element Binary Image
  291. 291. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Eroded Image
  292. 292. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  293. 293. ž In practical image processing dilation and erosion are performed in various combinations ž An image can undergo a series for diltions and erosion using the same or different structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  294. 294. ž In practical image processing dilation and erosion are performed in various combinations ž An image can undergo a series for diltions and erosion using the same or different structuring element ž Two Common Kinds: › Morphological Opening › Morphological Closing (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  295. 295. ž It is basically one erosion followed by one dilation by the same structuring element ž They are used to smooth object contours, break thin connections and remove thin protrusions (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  296. 296. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A—Image B—Structuring element
  297. 297. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  298. 298. ž It is basically one dilation followed by one erosion by the same structuring element ž They are used to smooth object contours like opening ž But unlike opening they generally join narrow breaks, fill long thin gulfs and fills holes smaller than the structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  299. 299. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha A—Image B—Structuring element
  300. 300. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  301. 301. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  302. 302. ž Used to generate a structuring element ž >>se=strel(shape,parameters) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  303. 303. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  304. 304. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  305. 305. ž Dilation in matlab is done using the following command: ž >>bw2=imdilate(bw,st) ž Bw = Original image ž St = Structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  306. 306. ž Erosion in matlab is done using the following command: ž >>bw2=imerode(bw,st) ž Bw = Original image ž St = Structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  307. 307. ž Opening in matlab is done using the following command: ž >>bw2=imopen(bw,st) ž Bw = Original image ž St = Structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  308. 308. ž Closing in matlab is done using the following command: ž >>bw2=imclose(bw,st) ž Bw = Original image ž St = Structuring element (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  309. 309. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  310. 310. ž cvErode(src, dst) ž cvDilate(src, dst) ž Opening & closing: use the appropriate sequence (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  311. 311. ž By default, OpenCV uses the zero structuring element (all are zeros) ž You can explicitly specify your structuring element as well ž Check the OpenCV Documentation for more information (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  312. 312. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  313. 313. ž Computers can manipulate images very efficiently ž But, comprehending an image with millions of colors is tough ž Solution: Figure out interesting regions, and process them (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  314. 314. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  315. 315. ž Each pixel is checked for its value ž If it lies within a range, it is marked as “interesting” (or made white) ž Otherwise, it’s made black ž Figuring out the range depends on lighting, color, texture, etc (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  316. 316. ž Demo thresholdRGB ž Demo thresholdHSV (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  317. 317. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  318. 318. ž MATLAB provides a facility to execute multiple command statements with a single command. This is done by writing a .m file ž Goto File > New > M-file ž For example, the graythresh function can be manually written as a m-file as: (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  319. 319. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  320. 320. ž Observe that, comments (in green) can be written after the symbol ‘%’. A commented statement is not considered for execution ž M-files become a very handy utility for writing lengthy programs and can be saved and edited, as and when required ž We shall now see, how to define your own functions in MATLAB. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  321. 321. ž Functions help in writing organized code with minimum repetition of logic ž Instead of rewriting the instruction set every time, you can define a function ž Syntax: ž Create an m-file and the top most statement of the file should be the function header ž function [return values] = function- name(arguments) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  322. 322. ž The inbuilt graythresh function in matlab is used for thresholding of grayscale images ž It uses the Otsu’s Method Of thresholding ž A sample thresholding opreation has been shown in the next slide (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  323. 323. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Image thresholded for the colour blue
  324. 324. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha The Real thing J
  325. 325. ž Thresholding of a grayscale image can be done in MATLAB using the following commands: ž >> level=graythresh(imGRAY); ž >> imBW = im2bw(imGRAY,level); ž >> imview(imBW); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  326. 326. ž The graythresh command basically gives an idea as to what exactly the threshold value should be ž Graythresh returns a value that lies in the range 0-1 ž This gives the level of threshold which is obtained by a complex method called the Otsu’s Method of Thresholding (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  327. 327. ž This level can be converted into pixel value by multiplying by 255 ž Lets say, level=.4 ž Then threshold value for the grayscale image is: ž 0.4 x 255 =102 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  328. 328. ž What this indicates is that for the given image the values below 102 have to be converted to 0 and values from 103-255 to the value 1 ž Conversion from grayscale to binary image is done using the function: ž >>imBW = im2bw(imGRAY,level); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  329. 329. ž Here level is the threshold level obtained from graythresh function ž This function converts pixel intensities between 0 to level to zero intensity (black) and between level+1 to 255 to maximum (white) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  330. 330. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  331. 331. ž In order to threshold an RGB colour image using the graythresh function, the following have to be done: › Conversion of the RGB image into its 3 grayscale components › Subtracting each of these components from the other 2 to get the pure colour intensities › Finding level for each of the grayscale using graythresh › Thresholding the image using imbw and the level (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  332. 332. ž Commands: ž Im=Imread(‘rgb.jpg’); ž R = im(:,:,1); --Red ž G = im(:,:,2); --Green ž B = im(:,:,3); --Blue (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  333. 333. ž Ronly=R-B-G; --Pure RED ž Gonly=G-R-B; --Pure GREEN ž Bonly=B-G-R; --Pure BLUE (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  334. 334. ž Levelr=graythresh(Ronly); ž Levelg=graythresh(Gonly); ž Levelb=graythresh(Bonly); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  335. 335. ž Rthresh=im2bw (Ronly,levelR); ž Gthresh=im2bw(Gonly,levelG); ž Bthresh=im2bw(Bonly,levelB); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  336. 336. ž Using a manually designed thresh_tool function to adjust the levels as required ž To get a feel of how levels vary (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  337. 337. s=size(im); temp=im; thresh=128; for i=1:s(1,1) for j=1:s(1,2) if temp(i,j)<thresh temp(i,j)=0; else temp(i,j)=255; end end end imview(temp); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  338. 338. ž Splitting of HSV image into components ž Using the Hue channel and thresholding it for different values ž Since the hue value of a single colour is constant it is relatively simple to threshold and gives better accuracy (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  339. 339. ž Splitting of HSV image into components ž Using the Hue channel and thresholding it for different values ž Since the hue value of a single colour is constant it is relatively simple to threshold and gives better accuracy (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  340. 340. function [temp] = ht(im,level1,level2) s=size(im); temp=im; for i=1:s(1,1) for j=1:s(1,2) if (temp(i,j)<level2 & temp(i,j)>level1) temp(i,j)=1; else temp(i,j)=0; end end end imview(temp); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  341. 341. ž To this function we give the input arguments as the upper and lower bounds of the threshold levels ž These levels can be obtained by having a look at the range of hue values for the particular colour (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  342. 342. Now that you know the basics (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  343. 343. ž cvThreshold(src, dst, threshold, max, type) ž type: › CV_THRESH_BINARY › CV_THRESH_BINARY_INV › And several others (check documentation) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  344. 344. ž cvInRangeS(src, scalarLower, scalarUpper, dst); ž scalarLower = cvScalar(chan1, chan2, chan3, chan4); ž scalarUpper = cvScalar(chan1, chan2, chan3, chan4); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  345. 345. ž Ultra basics: motors, drives, etc ž Digital image representation ž Color spaces ž Inter-conversion of color spaces ž Electronics ž Filtering ž Thresholding ž Morphology (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  346. 346. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  347. 347. ž After thresholding, we get a binary image ž We want useable information like centers, outlines, etc ž There geometrical properties can be found using many methods. We’ll talk about moments and contours only. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  348. 348. ž Moments are a mathematical concept ž ∑ ∑intensity*xxorder*yyorder (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  349. 349. ž Consider xorder=0 and yorder=0 for a binary image ž So you’re just summing up pixel values ž This means, you’re calculating the area of the white pixels (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  350. 350. ž Now consider xorder=1 and yorder=0 for a binary image ž You sum only those x which are white ž So you’re calculating the numerator of an average (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  351. 351. ž The number of points where the pixel is white is the area of the image ž So, dividing this particular moment (xorder=1, yorder=0) by the earlier example (xorder=0, yorder=0) gives the average x ž This is the x coordinate of the centroid of the blob (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  352. 352. ž Similarly, for xorder=0 and yorder=1, you’ll get the y coordinate (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  353. 353. ž The order of a moment = xorder+yorder ž So, the area is a zero order moment ž The centroid coordinate = a first order moment / the zero order moment (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  354. 354. ž There are entire books written on this topic ž You can find complex geometrical properties, like the eccentricity of an ellipse, radius of curvature of objects, etc ž Also check for Hu invariants if you’re interested (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  355. 355. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Centroid Area etc
  356. 356. ž These are pixels of an image that are conencted to each other forming separate blobs in an image ž They can be seperated out and labelled (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  357. 357. ž >>L = bwlabel(BW,n) ž Returns a matrix L, of the same size as BW, containing labels for the connected objects in BW ž n can have a value of either 4 or 8, where 4 specifies 4-connected objects and 8 specifies 8-connected objects; if the argument is omitted, it defaults to 8 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  358. 358. ž >>L = bwlabel(BW,n) ž Returns a matrix L, of the same size as BW, containing labels for the connected objects in BW ž n can have a value of either 4 or 8, where 4 specifies 4-connected objects and 8 specifies 8-connected objects; if the argument is omitted, it defaults to 8 (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  359. 359. ž STATS = regionprops(L,properties) ž Measures a set of properties for each labeled region in the label matrix L ž The set of elements of L equal to 1 corresponds to region 1; the set of elements of L equal to 2 corresponds to region 2; and so on (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  360. 360. ž 'Area'– The actual number of pixels in the region ž 'Centroid'-- The center of mass of the region. Note that the first element of Centroid is the horizontal coordinate (or x-coordinate) of the center of mass, and the second element is the vertical coordinate (or y-coordinate) ž 'Orientation' -- Scalar; the angle (in degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region. This property is supported only for 2-D input label matrices (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  361. 361. ž BW = imread('text.png'); ž L = bwlabel(BW); ž stats = regionprops(L,'all'); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  362. 362. ž Label into an RGB image for better vizualization ž RGB = label2rgb(L) (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  363. 363. ž Binary area open remove small objects ž BW2 = bwareaopen(BW,P) ž Removes from a binary image all connected components (objects) that have fewer than P pixels, producing another binary image, BW2. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  364. 364. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  365. 365. ž OpenCV supports functions to calculate moments upto order 3 CvMoments *moments = (CvMoments*)malloc(sizeof (CvMoments)); cvMoments(img, moments, 1); (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  366. 366. ž cvGetSpatialMoment(moments, xorder, yorder) ž cvGetCentralMoment(moments, xorder, yorder) ž Central = spatial/area (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  367. 367. ž Example (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  368. 368. ž For robotics purposes, moments are fine till have one single object ž If we have multiple objects in the same binary image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  369. 369. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  370. 370. ž You can think of contours as an approximation of a binary image (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  371. 371. ž You get polygonal approximation of each connected area (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  372. 372. ž The output you get for the previous binary image is: › Four “chains” of points › Each chain can have any number of points › In our case, each chain has four points (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  373. 373. (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha Contour plotting
  374. 374. ž Contour plot of an image im can be made in MATLAB using the command: ž im = imread(‘img.jpg'); ž imcontour(im,level) ž Level=number of equally spaced contour levels ž if level is not given it will choose automatically (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  375. 375. ž OpenCV linked lists to store the “chains” ž We’ll see some code to find out the squares in the thresholded image you saw (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  376. 376. CvSeq* contours; CvSeq* result; CvMemStorage *storage = cvCreateMemStorage(0); • The chains are stored in contours • result is a temporary variable • storage is for temporary memory allocation (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha
  377. 377. cvFindContours(img, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0)); • img is a grayscale thresholded image • storage is for temporary storage • All chains found would be stored in the contours sequence • The rest of the parameters are usually kept at these values • Check the OpenCV documentation for details information about the last four variables (c) 2009-2010 Electronics & Robotics Club, BITS-Pilani, Goa | Ajusal Sugathan & Utkarsh Sinha

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