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eyes:logics:testimage [2017/06/09 17:37] jschlie1 created |
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| ====== TestImage ====== | ====== TestImage ====== | ||
| - | THIS position should be used for a brief introduction (max 2 sentences!) of the logic, stating WHAT and WHY it is doing something. | + | or the analysis of image processing routines one often needs simulated - pseudoexperimental - images with known properties (e.g. angular orientation or the structural state of the particle image), but additional noise or random orientations. Test Image allows to create such images. |
| ===== Usage ===== | ===== Usage ===== | ||
| - | Here, a general/generic description of HOW the logic is USED should be given. Try to be as general as possible, but also mention prerequisites, restrictions, advantages, requirements which are specific of this logic. Basically everything the user needs to know to successfully use this logic. | + | This logic creates simulated pseudoexperimental images. It can randomly add noise and shift and rotate images. The logic does not generate an image (like a disk) on its own without an input; for this purpose use the logic: [[:eyes:logics:createimage]]. |
| ===== Example ==== | ===== Example ==== | ||
| - | Here, a very specific example should be given/described. In the future, this can be supported by screenshots etc.. For the moment, give an example easy enough for the user to understand, but specific enough to elaborate why a given parameter is a good set for this very situation. | + | FIXME |
| - | ===== Modes/Processes ===== | + | ===== Parameters and I/O ===== |
| - | ==== thisIsTheNameOfMode1 ==== | + | ^ Parameters ^ Description ^ |
| - | Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences. | + | | copy | How many copies of the input images are generated. Each copy of the image undergoes different noise addition, shifting and rotation from the specified randomization range. | |
| - | + | | Noise Filter High Freq | Gaussian high frequency cut-off for the applied noise (between 0 to 1). | | |
| - | |< 100% 30% >| | + | | Noise Filter Low Freq | Gaussian low frequency cut-off for the applied noise (between 0 to 1). | |
| - | ^ Parameters ^ Description ^ | + | | Noise Filter Transmission | Transmission below the High Freq noise Gaussian filter (between 0 to 1).| |
| - | | Some changeable parameter | Description of this parameter | | + | | inputSigma | Standard deviation (sigma) of the input image's grey values. The input images are not really normalized, since the grey value mean remains unchanged, yet the standard deviation (sigma) of the image can be adjusted with this option.| |
| - | | -> and its sub-parameter | more description | | + | | noiseMean | Mean of the noise.| |
| - | | Next main parameter | and more more more | | + | | noiseSigma | Standard deviation of the noise.| |
| - | | -> and its sub-parameter | ... descriptions | | + | | noiseType | gauss: Gaussian noise; uniform: Uniform noise. | |
| - | + | | randomRotHigh | Upper boundary for random image in-plane rotation angle (degrees). | | |
| - | |< 100% 30% >| | + | | randomRotLow | Lower boundary for random image in-plane rotation angle (degrees). | |
| - | ^ Input ^ Description ^ | + | | randomTransX | Maximum shifting in X direction for random image shifting (pixels). | |
| - | | FirstInput | Input Description 1 | | + | | randomTransY | Maximum shifting in Y direction for random image shifting (pixels). | |
| - | | SecondInput | Input Description 2 | | + | | seed | Random seed. If set as 0, the result would be totally random, which means not reproducible at all. In case that the result shall be reproducible, choose a number unequal 0, and use the same number next time. | |
| - | | //ThirdInput// | Input Description 3: Optional Input in Italic | | + | | snr | Signal to noise ratio. Notice that if it is set as 0 or 1, no noise would be added. | |
| - | + | ||
| - | |< 100% 30% >| | + | |
| - | ^ Output ^ Description ^ | + | |
| - | | FirstOutput | Output Description | | + | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ New/Changed Header Values ^ Description ^ | ^ New/Changed Header Values ^ Description ^ | ||
| - | | headerValue1 | what does it say? how is it changed? | | + | | rndRotation | In-plane rotation applied to the image. | |
| - | | headerValue2 | what does it say? how is it changed? | | + | | rndXShift | X shift applied to the image. | |
| - | | headerValue3 | what does it say? how is it changed? | | + | | rndYShift | Y shift applied to the image. | |
| - | | headerValue4 | what does it say? how is it changed? | | + | |
| - | + | ||
| - | ==== thisIsTheNameOfMode2 ==== | + | |
| - | Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences. | + | |
| - | + | ||
| - | |< 100% 30% >| | + | |
| - | ^ Parameters ^ Description ^ | + | |
| - | | Some changeable parameter | Description of this parameter | | + | |
| - | | -> and its sub-parameter | more description | | + | |
| - | | Next main parameter | and more more more | | + | |
| - | | -> and its sub-parameter | ... descriptions | | + | |
| - | + | ||
| - | |< 100% 30% >| | + | |
| - | ^ Input ^ Description ^ | + | |
| - | | FirstInput | Input Description 1 | | + | |
| - | | SecondImput | Input Description 2 | | + | |
| - | | ThridImput | Input Description 1 | | + | |
| - | + | ||
| - | |< 100% 30% >| | + | |
| - | ^ Output ^ Description ^ | + | |
| - | | FirstOutput | Output Description | | + | |
| - | + | ||
| - | |< 100% 30% >| | + | |
| - | ^ New/Changed Header Values ^ Description ^ | + | |
| - | | headerValue1 | what does it say? how is it changed? | | + | |
| - | | headerValue2 | what does it say? how is it changed? | | + | |
| - | | headerValue3 | what does it say? how is it changed? | | + | |
| - | | headerValue4 | what does it say? how is it changed? | | + | |
| - | + | ||
| - | ===== Concept ===== | + | |
| - | In this paragraph, the "HOW a logic works under the hood" and WHY someone should use it can be elaborated with higher detail. Describes a scenario in an image processing workflow where this logic can be used to solve the resulting problem. Also, wikipages, publications or anything else describing the theory behind an algorithm should be linked here, if applicable. | + | |