Plot image python

Plot image python DEFAULT

Introduction

Matplotlib is one of the most widely used data visualization libraries in Python. Matplotlib plots and visualizations are commonly shared with others, be it through papers or online.

In this article, we'll take a look at how to save a plot/graph as an image file using Matplotlib.

Creating a Plot

Let's first create a simple plot:

Here, we've plotted a sine function, starting at and ending at with a step of . Running this code yields:

Now, let's take a look at how we can save this figure as an image.

Save Plot as Image in Matplotlib

In the previous example, we've generated the plot via the function, passing in the data we'd like to visualize.

This plot is generated, but isn't shown to us, unless we call the function. The function, as the name suggests, shows the generated plot to the user in a window.

Once generated, we can also save this figure/plot as a file - using the function:

Now, when we run the code, instead of a window popping up with the plot, we've got a file () in our project's directory.

This file contains the exact same image we'd be shown in the window:

It's worth noting that the function isn't unique to the instance. You can also use it on a object:

The function has a mandatory argument. Here, we've specified the filename and format.

Additionally, it accepts other options, such as , , , , etc.

We'll go over some popular options in the proceeding sections.

Setting Image DPI

The DPI parameter defines the number of dots (pixels) per inch. This is essentially the resolution of the image we're producing. Let's test out a couple of different options:

This results in three new image files on our local machine, each with a different DPI:

The default value is .

Save Transparent Image with Matplotlib

The argument can be used to create a plot with a transparent background. This is useful if you'll use the plot image in a presentation, on a paper or would like to present it in a custom design setting:

If we put this image on a dark background, it'll result in:

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Changing Plot Colors

You can change the face color by using the argument. It accepts a and defaults to .

Let's change it to :

This results in:

Setting Image Border Box

The argument accepts a string and specifies the border around the box we're plotting. If we'd like to set it to be , i.e. to crop around the box as much as possible, we can set the argument to :

This results in a tigthly packed box. This is easier to visualize if we color the face with a different color for reference:

Conclusion

In this tutorial, we've gone over several ways to save the plot as an image file using Matplotlib.

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Sours: https://stackabuse.com/save-plot-as-image-with-matplotlib/

Image tutorial

In [4]: img=mpimg.imread('stinkbug.png')Out[4]:array([[[ 0.40784314, 0.40784314, 0.40784314], [ 0.40784314, 0.40784314, 0.40784314], [ 0.40784314, 0.40784314, 0.40784314], ..., [ 0.42745098, 0.42745098, 0.42745098], [ 0.42745098, 0.42745098, 0.42745098], [ 0.42745098, 0.42745098, 0.42745098]], [[ 0.41176471, 0.41176471, 0.41176471], [ 0.41176471, 0.41176471, 0.41176471], [ 0.41176471, 0.41176471, 0.41176471], ..., [ 0.42745098, 0.42745098, 0.42745098], [ 0.42745098, 0.42745098, 0.42745098], [ 0.42745098, 0.42745098, 0.42745098]], [[ 0.41960785, 0.41960785, 0.41960785], [ 0.41568628, 0.41568628, 0.41568628], [ 0.41568628, 0.41568628, 0.41568628], ..., [ 0.43137255, 0.43137255, 0.43137255], [ 0.43137255, 0.43137255, 0.43137255], [ 0.43137255, 0.43137255, 0.43137255]], ..., [[ 0.43921569, 0.43921569, 0.43921569], [ 0.43529412, 0.43529412, 0.43529412], [ 0.43137255, 0.43137255, 0.43137255], ..., [ 0.45490196, 0.45490196, 0.45490196], [ 0.4509804 , 0.4509804 , 0.4509804 ], [ 0.4509804 , 0.4509804 , 0.4509804 ]], [[ 0.44313726, 0.44313726, 0.44313726], [ 0.44313726, 0.44313726, 0.44313726], [ 0.43921569, 0.43921569, 0.43921569], ..., [ 0.4509804 , 0.4509804 , 0.4509804 ], [ 0.44705883, 0.44705883, 0.44705883], [ 0.44705883, 0.44705883, 0.44705883]], [[ 0.44313726, 0.44313726, 0.44313726], [ 0.4509804 , 0.4509804 , 0.4509804 ], [ 0.4509804 , 0.4509804 , 0.4509804 ], ..., [ 0.44705883, 0.44705883, 0.44705883], [ 0.44705883, 0.44705883, 0.44705883], [ 0.44313726, 0.44313726, 0.44313726]]], dtype=float32)
Sours: http://omz-software.com/pythonista/matplotlib/users/image_tutorial.html
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Working with Images in Python using Matplotlib

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

Working with Images in Python using Matplotlib

The module in library is used for working with images in Python. The module also includes two useful methods which are which is used to read images and which is used to display the image.

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Below are some examples which illustrate various operations on images using library:

Example 1: In this example, the program reads an image using the and displays that image using .

 

 

Output:

Example 2: The below program reads an image and then represents the image in array.

 

 

Output:

[[[0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]] [[0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]] [[0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]] ... [[0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]] [[0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]] [[0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] [0.03529412 0.52156866 0.28235295] ... [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648] [0.05490196 0.6156863 0.34117648]]]

Example 3: Here, the shape of the image is which represents (height, width, mode) of the image, for colored image mode value is from 0 to 2 and for black and white image mode value is 0 and 1 only. In the output image, only the mode of the image is modified.

 

 

 

 

Output:

(225, 225, 3)

Example 4: In the below program, all the parameters of the shape of the image are modified. Here the height of the image is 150 pixels (displaying from the 50th pixel), width is 100 pixels (displaying from the 100th pixel) and mode value is 1.

 

 

 

 

Output:

(225, 225, 3)

Example 5: Here, none of the parameters are modified. So, the original image is displayed.

 

 

 

 

Output:

(225, 225, 3)




Sours: https://www.geeksforgeeks.org/working-with-images-in-python-using-matplotlib/
The Numpy Stack in Python - Lecture 21: Plotting Images

How to Display an OpenCV image in Python with Matplotlib?

The OpenCV module is an open-source computer vision and machine learning software library. It is a huge open-source library for computer vision, machine learning, and image processing. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as numpy which is a highly optimized library for numerical operations, then the number of weapons increases in your Arsenal i.e whatever operations one can do in Numpy can be combined with OpenCV.

First, let’s look at how to display images using OpenCV:

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Now there is one function called cv2.imread() which will take the path of an image as an argument. Using this function you will read that particular image and simply display it using the cv2.imshow() function. 

Python3

 

 

Output:

DIsplay image using OpenCV

Now let’s jump into displaying the images with Matplotlib module. It is an amazing visualization library in Python for 2D plots of arrays. The Matplotlib module is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

We are doing minor changes to the above code to display our image with Matplotlib module. 

Python3

 

 

 

Output:

image plot with Matplotlib

One can also display gray scale OpenCV images with Matplotlib module for that you just need to convert colored image into a gray scale image.

Python3

 

 

 

 

Output:

Display grayscale image plot with Matplotlib

This is how we can display OpenCV images in python with Matplotlib module.


Sours: https://www.geeksforgeeks.org/how-to-display-an-opencv-image-in-python-with-matplotlib/

Image python plot

Plotting images and contours¶

For the example in the following page we start from the example introduced in Initializing axes with world coordinates.

Plotting images as bitmaps or contours should be done via the usual matplotlib methods such as or . For example, continuing from the example in Initializing axes with world coordinates, you can do:

ax.imshow(hdu.data,vmin=-2.e-5,vmax=2.e-4,origin='lower')

(png, svg, pdf)

../../_images/images_contours-2.png

and we can also add contours corresponding to the same image using:

importnumpyasnpax.contour(hdu.data,levels=np.logspace(-4.7,-3.,10),colors='white',alpha=0.5)

(png, svg, pdf)

../../_images/images_contours-3.png

To show contours for an image in a different coordinate system, see Overplotting markers and artists.

Note

If you like using the pyplot interface, you can also call and instead of and .

Sours: https://docs.astropy.org/en/stable/visualization/wcsaxes/images_contours.html
OpenCV Python Tutorial For Beginners 26 - Understanding image Histograms using OpenCV Python

How to Save a Plot to a File Using Matplotlib

[Matplotlib](https://matplotlib.org/ is a powerful two-dimensional plotting library for the Python language. Matplotlib is capable of creating all manner of graphs, plots, charts, histograms, and much more.

In most cases, matplotlib will simply output the chart to your viewport when the method is invoked, but we’ll briefly explore how to save a matplotlib creation to an actual file on disk.

Using matplotlib

While the feature-list of matplotlib is nearly limitless, we’ll quickly go over how to use the library to generate a basic chart for your own testing purposes.

Like all Python libraries, you’ll need to begin by installing matplotlib. We won’t go through the installation process here, but there’s plenty of information in the official documentation.

Once installed, import the library. You’ll likely also want to import the sub-library, which is what you’ll generally be using to generate your charts and plots when using matplotlib.

Now to create and display a simple chart, we’ll first use the method and pass in a few arrays of numbers for our values. For this example, we’ll plot the number of books read over the span of a few months.

We can also add a few axis labels:

Finally, we can display the chart by calling :

The savefig Method

With a simple chart under our belts, now we can opt to output the chart to a file instead of displaying it (or both if desired), by using the method.

The method requires a filename be specified as the first argument. This filename can be a full path and as seen above, can also include a particular file extension if desired. If no extension is provided, the configuration value of is used instead.

Additional savefig Options

In addition to the basic functionality of saving the chart to a file, also has a number of useful optional arguments.

  • can be used to set the resolution of the file to a numeric value.
  • can be set to , which causes the background of the chart to be transparent.
  • can be set to alter the size of the bounding box (whitespace) around the output image. In most cases, if no bounding box is desired, using is ideal.
  • If is set to , then the option specifies the amount of padding around the image.

There are a handful of additional options for specific occasions, but overall this should get you started with easily generating image file outputs from your matplotlib charts.

Sours: https://chartio.com/resources/tutorials/how-to-save-a-plot-to-a-file-using-matplotlib/

You will also be interested:

Image tutorial

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Sours: https://matplotlib.org/stable/tutorials/introductory/images.html


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