NumPy defines a new data type called the ndarray or n-dimensional array. Replacements for the standard functions of the math module exist. When the NumPy package is loaded, ndarrays become as much a part of the Python language as standard Python data types such as lists and dictionaries.
In this episode, we will learn how to use skimage functions to apply thresholding to an image. Thresholding is a type of image segmentation, where we change the pixels of an image to make the image easier to analyze. In thresholding, we convert an image from color or grayscale into a binary image, i.e., one that is simply black and white. Most frequently, we use thresholding as a way to select areas of interest of an image, while ignoring the parts we are not concerned with.
We have already done some simple thresholding, in the "Manipulating pixels" section of the Skimage Images episode. In that case, we used a simple NumPy array manipulation to separate the pixels belonging to the root system of a plant from the black background. In this episode, we will learn how to use skimage functions to perform thresholding. Then, we will use the masks returned by these functions to select the parts of an image we are interested in. The syntax of the argument of the array function looks like nested lists of numbers with the level of nesting being equal to the dimensionality of the array – 2 in the above case. The attributeshape returns a tuple which gives the size of the array along each dimension axis.
Consistent with Python indexing, the numbering of successive axes starts at 0, so the size along the zero axis is 2 and the size along the 1 axis is 3. We'll see some examples of how this works in practice below. In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays. NumPy provides many methods to calculate statistics on all array values, or on one of the array axes (for example on the equivalent of rows or columns in two-dimensional arrays).
You can think of them as fast vectorized wrappers for simple functions that take one or more scalar values and produce one or more scalar results. This section addresses basic image manipulation and processing using the core scientific modules NumPy and SciPy. Some of the operations covered by this tutorial may be useful for other kinds of multidimensional array processing than image processing. In particular, the submodulescipy.ndimage provides functions operating on n-dimensional NumPy arrays. The issue is that your mask is not a full array, but just the scalar False, it is probably an optimization for all-unmasked arrays, but ends up producing issues such as this. Which is very different from what you would get if a.mask was a full boolean array with the same shape.
The benefit of the masked array module is that you don't have to modify the shape of the input array, but in my use case I actually just want to throw away the data I don't care about. This is not desirable in cases where you do tensor computations and where the shape of the input must be preserved. Numpy is very convenient for this use case, because it supports using boolean arrays directly as masks. But before we dive into masking with boolean arrays, let's briefly discuss Numpy masked arrays.
To return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy. Give a new shape to the array without changing its data. The 'F' order determines whether the array data should be viewed as in FORTRAN i.e. If you're doing any kind of processing on this data, and want toskip or flag these unwanted entries without just deleting them, you may have to use conditionals or filter your data somehow.
The numpy.ma module provides some of the same funcionality ofNumPy ndarrays with added structure to ensure invalid entries are not used in computation. Arithmetic and comparison operations are supported by masked arrays. As much as possible, invalid entries of a masked array are not processed, meaning that the corresponding data entriesshould be the same before and after the operation. The answer is that by default NumPy iterates the last index in a multi-dimensional array most rapidly and the first index least rapidly while assigning elements to successive memory locations.
Thus, the order in which the elements appear isa, a, a, a in the above example. As we discovered above, this ordering is revealed in the conversion of a multi-dimensional array to a one-dimensional array, and is the same as that used by the C language. The Fortran language uses the opposite convention; the first index is iterated most rapidly. It is possible to make NumPy behave like Fortran in this regard, but special options and methods must be invoked. The binary images produced by thresholding are held in two-dimensional NumPy arrays, since they have only one color value channel.
They are boolean, hence they contain the values 0 and 1 . Now suppose we want to select only the shapes from the image. In other words, we want to leave the pixels belonging to the shapes "on," while turning the rest of the pixels "off," by setting their color channel values to zeros. The skimage library has several different methods of thresholding.
We will start with the simplest version, which involves an important step of human input. Specifically, in this simple, fixed-level thresholding, we have to provide a threshold value t. Basically, this mask assignment behaviour propagates to other parts of numpy, where it causes problems. Maybe mask should always be a list for masked arrays, because it is confusing otherwise . And "ma.view" chould definitely work there, although I can imagine some edge cases.
Note, however, that the structure of an Awkward Array's option type is not always preserved when converting to NumPy masked arrays. Masked arrays can only have missing numbers, not missing lists, so missing lists are expanded into lists of missing numbers. The output is a view of the array as a numpy.ndarray or one of its subclasses, depending on the type of the underlying data at the masked array creation. To extract or not elements from a table, you can use Boolean tables as masks. The idea is to provide a boolean array of the same size as the one for which you want to extract elements under certain conditions.
When the value of the Boolean in the mask is set to True, the corresponding element of the array is returned; otherwise, it is not. Numpy supports these attributes regardless of the dtype but Numba chooses to limit their support to avoid potential user error. The real attribute returns a view of the real part of the complex array and it behaves as an identity function for other numeric dtypes. The imag attribute returns a view of the imaginary part of the complex array and it returns a zero array with the same shape and dtype for other numeric dtypes.
For non-numeric dtypes, including all structured/record dtypes, using these attributes will result in a compile-time error. This behavior differs from Numpy's but it is chosen to avoid the potential confusion with field names that overlap these attributes. It is possible for a dataset to have no missing data, yet still have option type, just as it's possible to have a NumPy masked array with no mask.
No data have been altered, nor have the dataset's nodata values been changed. A new band has been added to the dataset to store the valid data mask. By default it is saved to a "sidecar" GeoTIFF alongside the dataset file.
When such a .msk GeoTIFF exists, Rasterio will ignore the nodata metadata values and return mask arrays based on the .msk file. When setting, None will set to a default based on the data type. It's clear that masked arrays are the right solution here.
We cannot represent the missing data without mischaracterizing the evolution of the curve. Individual elements and sets of elements are extracted from an array by indexing. NumPy adopts and extends the indexing methods used in standard Python for strings and lists. NumPy is a module which was created allow efficient numerical calculations on multi-dimensional arrays of numbers from within Python.
It is derived from the merger of two earlier modules named Numeric and Numarray. The actual work is done by calls to routines written in the Fortran and C languages. You may have wondered why we called the return values of the rectangle functionrr and cc?! You may have guessed that r is short for row and c is short for column. However, the rectangle function returns mutiple rows and columns; thus we used a convention of doubling the letter r to rr to indicate that those are multiple values.
In fact it may have even been clearer to name those variablesrows and columns; however this would have been also much longer. Whatever you decide to do, try to stick to some already existing conventions, such that it is easier for other people to understand your code. Often we wish to select only a portion of an image to analyze, and ignore the rest. Creating a rectangular sub-image with slicing, as we did in theskimage Images lesson is one option for simple cases. Another option is to create another special image, of the same size as the original, with white pixels indicating the region to save and black pixels everywhere else. In preparing a mask, we sometimes need to be able to draw a shape – a circle or a rectangle, say – on a black image.
These methods also work with non-boolean arrays, where non-zero elements evaluate to True. One objective of Numba is having a seamless integration with NumPy. NumPy arrays provide an efficient storage method for homogeneous sets of data. Numba excels at generating code that executes on top of NumPy arrays.
If you apply the masking strategies 1-3 from above, it is good to know that the shape of the input array is not preserved, like with numpy.ma. Instead you end up with a list of the preserved elements. This tutorial is for people who have a basic understanding of NumPy and want to understand how masked arrays and the numpy.ma module can be used in practice.
For a complete discussion of creation methods for masked arrays please see section Constructing masked arrays. Suppress the rows and/or columns of a 2-D array that contain masked values. Mask rows and/or columns of a 2D array that contain masked values. The tuple in the argument list of these functions defines the shape of the array.
The arrays are filled with the values indicated by the function names. Here are the binary images produced by the additional thresholding. Note that we have not completely removed the offending white pixels.
However, we have reduced the number of extraneous pixels, which should make the output more accurate. Use Otsu's method of thresholding to create a binary image, where the pixels that were part of the maize plant are white, and everything else is black. The downside of the simple thresholding technique is that we have to make an educated guess about the threshold t by inspecting the histogram. There are also automatic thresholding methods that can determine the threshold automatically for us. It is particularly useful for situations where the grayscale histogram of an image has two peaks that correspond to background and objects of interest.
Remember that grayscale images contain pixel values in the range from 0 to 1, so we are looking for a threshold t in the closed range [0.0, 1.0]. We see in the image that the geometric shapes are "darker" than the white background but there is also some light gray noise on the background. One way to determine a "good" value fort is to look at the grayscale histogram of the image and try to identify what grayscale ranges correspond to the shapes in the image or the background. See Table 4-1 for a short list of standard array creation functions. Since NumPy is focused on numerical computing, the data type, if not specified, will in many cases be float64 . If it is useful to have gaps in the line where the data is missing, then the undesired points can be indicated using a masked array or by setting their values to NaN.
No marker will be drawn where either x or y are masked and, if plotting with a line, it will be broken there. NumPy arrays can be indexed with slices, but also with boolean or integer arrays . I am not sure there is a good way to fix this, except the long plan of revamping everything.
OTOH, we have maybe been a bit hesitant of improving the current masked arrays slowly while allowing small incompatibilities in principle. In principle np.ma.nomask could be its own singleton maybe, and then indexing with it could more reasonably do the correct thing. Since the operation of ndarray and scalar value is the operation of the value of each element and the scalar value, alpha blend can be calculated as follows. Be careful when saving as an image file with Pillow because the data type is cast automatically.
We will index an array C in the following example by using a Boolean mask. It is called fancy indexing, if arrays are indexed by using boolean or integer arrays . This will result in a 1D array with all the non-masked values. However, you said you wanted to keep the band dimentionality. However, bear in mind that every band has to have the same number of non-masked values for this to work.
In general, it can be hard to determine if an Awkward Array is a view or a copy because some operations need to construct RegularArrays. Furthermore, the view-vs-copy behavior can change from one version of Awkward Array to the next. Awkward Arrays with option type are converted to NumPy masked arrays.
Load the image into Python and put its data into an array. Thanks to the PIL loading the Image data and handing it to an array is very simple. We only have to reshape the data as the Image's getdata() method returnes a flattened data set. If the image would not be grayscale but RGB, we would hava a data set three times as long.
