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Loading and Saving Images Loading and Saving Images To saved the image to disc in the same directory you started from . That was the “print working directory” command. You should be able to navigate to that location on your computer and see a file called my-image.png. SimpleCV offers support for the image formats Windows bitmap (bmp), portable image formats (pbm, pgm, ppm) and Sun raster (sr, ras). With help of plugins (you need to specify to use them if you build yourself the library, nevertheless in the packages we ship present by default) you may also load image formats like JPEG (jpeg, jpg, jpe), JPEG 2000 (jp2 - codenamed in the CMake as Jasper), TIFF files (tiff, tif) and portable network graphics (png). Furthermore, OpenEXR is also a possibility. First thing we are going to do is load an image: >>> logo=Image("simplecv") That line of code will load the SimpleCV logo into memory. Now we want to see it: >>> logo.show() What you should see is: Now let’s save the logo: >>> logo.save("my-image.png") That saved the image to disc in the same directory you started python from you can always type: >>> pwd '/home/xamox/Code/simplecv-examples' That was the “print working directory” command. You should be able to navigate to that location on your computer and see a file called my-image.png. You can also specify saving images such as: >>> logo.save("path/to/img.png") To load an image, specify the file path in the constructor: >>> my_image = Image(“path/to/image.jpg”) Getting an Image from the Camera As long as your camera driver is supported then you shouldn’t have a problem. This means you should be able to open your webcam software in other software it should more than likely work with SimpleCV. To load the camera just type: >>> cam=Camera() Then to grab an image from the Camera we type: >>> img=cam.getImage() We now have an image loaded into memory and just as before if we want to display it, we just type: >>> img.show() You can also save it, etc. Note: There is a list of supported web cams on the wiki. You can once again run help if you don’t know what to do with that image. >>> helpcam Note: Remember to type ‘q’ to quit the interactive help mode in the SimpleCV shell. Image Manipulation Now that we can easily load and save images. It’s time to start doing some image processing with them. Let’s make our picture a thumbnail: >>> thumbnail=img.scale(90,90) >>> thumbnail.show() This will show a scaled down version of the image. Now let’s erode the picture some: >>> eroded=img.erode() >>> eroded.show() It should look almost as the picture was made of ink and had water spilled on it. Let’s crop a section of the image out: >>> cropped=img.crop(100,100,50,50) >>> cropped.show() What that did is went from the coordinate in (X,Y), which is (0,0) and is the top left corner of the picture. We basically said move to coordinates (100,100) in the (X,Y) and crop a picture from that which is 50 pixels by 50 pixels. Now you maybe asking how are you suppose to know what parameters to put into the crop() function, or even that the crop function exist. Well remember good old help? It also works for functions. So since image is an Image, than you can type ‘help img’, and get the help for that. To learn what crop needs as input. >>> helpimg.crop Features Features are things you are looking for in the picture. They can be blobs, corners, lines, etc. Features are sometimes referred to as a fidicual in computer vision. These features are something that is measureable, and something that makes images unique. Features are something like when comparing things like fruit. In this case our features could be the shape and the color, amongst others. What features are in SimpleCV is an abstract representation of that. You take your image, then perform a function on it, and get back features or another image with them applied. The crop example is a case where an image is returned after we perform something to do. In a simple example we will use the famous “lenna” image, and find corners in the picture. >>> img=Image("lenna") >>> img.findCorners() Color Did you notice in the previous example how we could change the color when we draw? Well there are times when we want to detect color as well. This is represented in SimpleCV as an object. Why? Well typically color is used in the common format Red- Green-Blue (RGB). This can also be represented as as ‘tuple’ in SimpleCV like (R,G,B). Each of those color channels have a value between 0 and 255. So the color black is: (0,0,0), the color white is (255,255,255). As you can imagine the RGB for the color Red is the red channel all the way up and the others with no value. So red is (255,0,0). But, this would get hard to remember after time, or for instance, how do you make orange? Well orange is red and yellow mixed, but what would the tuple be? Luckily SimpleCV has tried to make this much easier with built in maps for these tuple values. To confirm this just type: >>> Color.BLACK >>> (0,0,0) As you can see the color define is basically a map to that tuple color, but just makes it easier for us to remember. You can also get the list of colors by using the help command: >>> helpColor Help on class Color in module SimpleCV.Color: ColorCurve is a color spline class for performing color correction. It can take as parameters a SciPy Univariate spline, or an array with at least 4 point pairs. Either of these must map in a 255x255 space. The curve can then be used in the applyRGBCurve, applyHSVCurve, and applyInstensityCurve functions: >>> img=Image("lenna") >>> clr=ColorCurve([[0,0],[100,120],[180,230],[255,255]]) >>> img.applyIntensityCurve(clr) A color map takes a start and end point in 3D space and lets you map a range of values to it. Using the colormap like an array gives you the mapped color. This is useful for color coding elements by an attribute: Example for Understanding the Concept only Program to write an image in PGM format PGM stands for Portable Gray Map. Saving a 2D array in C as images in PNG, JPG or other formats would need a lot of effort to encode the data in the specified format before writing to a file. However, Netpbm format offers a simple solution with easy portability. A Netpbm format is any graphics format used and defined by the Netpbm project. The portable pixmap format (PPM), the portable graymap format (PGM) and the portable bitmap format (PBM) are image file formats designed to be easily exchanged between platforms. Netpbm is an open-source package of graphics programs and a programming library. It is used mainly in the Unix world, where one can find it included in all major open-source operating system distributions, but also works on macOS, and others. It also works under Microsoft Windows. Each file starts with a two-byte magic number (in ASCII) that identifies the type of file it is (PBM, PGM, and PPM) and its encoding (ASCII or binary). The magic number is a capital P followed by a single-digit number. The ASCII formats allow for human readability and easy transfer to other platforms; the binary formats are more efficient in file size but may have native byte-order issues. In the binary formats, PBM uses 1 bit per pixel, PGM uses 8 bits per pixel, and PPM uses 24 bits per pixel: 8 for red, 8 for green, 8 for blue. The PGM and PPM formats (both ASCII and binary versions) have an additional parameter for the maximum value (numbers of grey between black and white) after the X and Y dimensions and before the actual pixel data. Black is 0 and max value is white. There is a newline character at the end of each line. How to write PGM files? The File format is as follows: 1. Magic Number “P2” 2. Whitespace (blanks, TABs, CRs, LFs). 3. A width, formatted as ASCII characters in decimal. 4. Whitespace. 5. A height, again in ASCII decimal. 6. Whitespace. 7. The maximum gray value, again in ASCII decimal. 8. Whitespace. 9. width X height gray values, each in ASCII decimal, between 0 and the specified maximum value, separated by whitespace, in raster format, from top to bottom. filter_none brightness_4 // C program to read a BMP Image and // write the same into a PGM Image file #include <stdio.h> voidmain() { inti, j, temp = 0; intwidth = 13, height = 13; // Suppose the 2D Array to be converted to Image is as given below intimage[13][13] = { { 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15 }, { 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31}, { 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47}, { 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63}, { 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79}, { 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95 }, { 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111}, { 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127}, { 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143}, { 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159}, { 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175}, { 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191}, { 207, 207, 207, 207, 207, 207, 207, 207, 207, 207, 207, 207, 207} }; FILE* pgmimg; pgmimg = fopen("pgmimg.pgm", "wb"); // Writing Magic Number to the File fprintf(pgmimg, "P2\n"); // Writing Width and Height fprintf(pgmimg, "%d %d\n", width, height); // Writing the maximum gray value fprintf(pgmimg, "255\n"); intcount = 0; for(i = 0; i < height; i++) { for(j = 0; j < width; j++) { temp = image[i][j]; // Writing the gray values in the 2D array to the file fprintf(pgmimg, "%d ", temp); } fprintf(pgmimg, "\n"); } fclose(pgmimg); } Steps to compile and execute the code in Ubuntu 1.
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