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CVBpy 14.1
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CVBpy 14.1
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Common Vision Blox Minos module for Python. More...
Classes | |
| class | Classifier |
| Load a saved classifier from a file. More... | |
| class | ClassifierFactory |
| Learner object that creates a classifier from an image list. More... | |
| class | ClassifierModelInfo |
| Information about a Minos classifier model. More... | |
| class | FilterOrder |
| Order of the butterworth_low_pass() or butterworth_high_pass() filter to be applied. More... | |
| class | KernelSize |
| Available kernel sizes for the filter functions exported by the Minos library. More... | |
| class | LearnParameters |
| The set of parameters, which controls, how a classifier is being learned from a training set. More... | |
| class | ReadMode |
| Options for the read functions. More... | |
| class | SearchMode |
| Different modes for the search calls, that return a single result. More... | |
| class | SearchResult |
| Search result returned by Minos. More... | |
| class | TrainingSet |
| Load a saved training set from a file or create an empty training set. More... | |
Functions | |
| cvb.Image | butterworth_high_pass (cvb.Image image, float gain, int offset, float cut_off, int order) |
| Apply a Butterworth high pass filter to the image. More... | |
| cvb.Image | butterworth_low_pass (cvb.Image image, float cut_off, int order) |
| Apply a Butterworth low pass filter to the image. More... | |
| cvb.Image | dilate (cvb.Image image) |
| Apply a 3 x 3 dilation filter to the input image. More... | |
| cvb.Image | edge (cvb.Image image, int kernel_size) |
| Apply an edge filter to the input image. More... | |
| cvb.Image | erode (cvb.Image image) |
| Apply a 3 x 3 erosion filter to the input image. More... | |
| cvb.Image | laplace (cvb.Image image) |
| Apply a 3 x 3 Laplace filter to the input image. More... | |
| cvb.Image | low_pass (cvb.Image image, int kernel_size) |
| Apply a low pass filter to the input image. More... | |
| cvb.Image | pyramid (cvb.Image image, int kernel_size) |
| Apply a pyramid filter to the input image. More... | |
| List[cvb.minos.SearchResult] | search_all_correlations (cvb.ImagePlane image_plane, cvb.ImagePlane template_plane, float threshold, int radius, Optional[cvb.Area2D] aoi, Optional[float] density=1.0) |
| Find all correlation matches of the template_plane in the image_plane aoi. More... | |
| cvb.minos.SearchResult | search_correlation (cvb.ImagePlane image_plane, cvb.ImagePlane template_plane, Optional[cvb.Area2D] aoi, Optional[float] density=1.0) |
| Find the best correlation match of the template_plane in the image_plane aoi with sub-pixel accuracy. More... | |
| cvb.Image | sharpen (cvb.Image image) |
| Apply a 3 x 3 sharpen filter to the input image. More... | |
| cvb.Image | user_filter (cvb.Image image, int kernel_size, List[float] kernel) |
| Apply a user-defined filter to the input image. More... | |
| cvb.Image butterworth_high_pass | ( | cvb.Image | image, |
| float | gain, | ||
| int | offset, | ||
| float | cut_off, | ||
| int | order | ||
| ) |
Apply a Butterworth high pass filter to the image.
Underflow and overflow gray values are truncated to 0 and 255 respectively.
image : cvb.Image Image to be filtered.
gain : float Gain to be applied to the frequency response.
offset : int Offset to be applied to the frequency response.
cut_off : float Cut off parameter of the Butterworth algorithm.
order : int Order of the Butterworth filter to be used (see cvb.minos.FilterOrder).
cvb.Image The filtered image.
Apply a Butterworth low pass filter to the image.
Underflow and overflow gray values are truncated to 0 and 255 respectively.
image : cvb.Image Image to be filtered.
cut_off : float Cut off parameter of the Butterworth algorithm.
order : int Order of the Butterworth filter to be used (see cvb.minos.FilterOrder).
cvb.Image The filtered image.
Apply an edge filter to the input image.
Edge filters are available with kernel sizes 2 x 2 and 3 x 3.
image : cvb.Image Image to be filtered.
kernel_size : int Kernel size to be used (see cvb.minos.KernelSize).
cvb.Image The filtered image.
Apply a low pass filter to the input image.
Low pass filters are available with kernel sizes 2 x 2, 3 x 3 and 5 x 5.
image : cvb.Image Image to be filtered.
kernel_size : int Kernel size to be used (see cvb.minos.KernelSize).
cvb.Image The filtered image.
Apply a pyramid filter to the input image.
Pyramid filters are available with kernel sizes 3 x 3, 4 x 4 and 5 x 5.
image : cvb.Image Image to be filtered.
kernel_size : int Kernel size to be used (see cvb.minos.KernelSize).
cvb.Image The filtered image.
| List[cvb.minos.SearchResult] search_all_correlations | ( | cvb.ImagePlane | image_plane, |
| cvb.ImagePlane | template_plane, | ||
| float | threshold, | ||
| int | radius, | ||
| Optional[cvb.Area2D] | aoi, | ||
| Optional[float] | density = 1.0 |
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| ) |
Find all correlation matches of the template_plane in the image_plane aoi.
If the number of results exceeds 32767, then the result list will be truncated at that number. The order of the results depends on the scan direction defined by the aoi (left/top to right/bottom without aoi).
image_plane : cvb.ImagePlane Image plane in which to look for correlation matches.
template_plane : cvb.ImagePlane Template with which to look for correlation matches.
threshold : float Minimum correlation for the results to be reported.
radius : int Minimum distance between two positive results.
aoi : Optional[cvb.Area2D] Area in which to look for correlation matches (default entire image).
density : Optional[float] Scan density with which to look for correlation matches where 1.0 means scanning all pixels.
List[cvb.minos.SearchResult] Extracted matches.
| cvb.minos.SearchResult search_correlation | ( | cvb.ImagePlane | image_plane, |
| cvb.ImagePlane | template_plane, | ||
| Optional[cvb.Area2D] | aoi, | ||
| Optional[float] | density = 1.0 |
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| ) |
Find the best correlation match of the template_plane in the image_plane aoi with sub-pixel accuracy.
The amount of sub pixel accuracy that may be achieved depends on the size of the template.
image_plane : cvb.ImagePlane Image plane in which to look for correlation matches.
template_plane : cvb.ImagePlane Template with which to look for correlation matches.
aoi : Optional[cvb.Area2D] Area in which to look for correlation matches (default entire image).
density : Optional[float] Scan density with which to look for correlation matches where 1.0 means scanning all pixels.
cvb.minos.SearchResult Best result that was detected (empty SearchResult if no match was found).
Apply a user-defined filter to the input image.
The convolution kernel may have the size 2 x 2, 3 x 3 or 5 x 5. The filter coefficients need to be specified as a list of float values passed as the kernel argument. Please note that although the kernel elements are of type float, the filter will only work on and only create output data with 8 bits per pixel. Overflow and under-flow values will be truncated to 0 and 255 respectively. If too many kernel coefficients are given then any surplus coefficients will be ignored. The kernel coefficients should be passed as a 1D array with width * height elements starting in the top left corner of the filter mask and listing the elements line by line.
image : cvb.Image Image to be filtered.
kernel_size : int Kernel size to be used (see cvb.minos.KernelSize).
kernel : List[float] Kernel coefficients; the number of coefficients, that are needed depending on the kernel_size.
cvb.Image The filtered image.