You have an image with data type uint8 or uint16, and you want to convert it to a floating-point data type so that you can do the math on the image that you couldn’t do before.
In NumPy, there is a special function called astype() that can be used to explicitly convert the data type of an array. The syntax for using this function is as follows:
new_array = old_array.astype('float32')
What is image data?
Image data is a collection of pixels that together form an image. Pixels are the smallest units of an image and are usually square in shape. Each pixel has a specific color, which is determined by the amount of light that is passing through it. The more light that hits a pixel, the brighter it will appear.
What is a float?
Data type float is a number with a decimal point or a number in exponential form. For example, 3.14159, -6.02e23, and 1.0e-12 are floating point numbers. “2” is not a floating point number because it does not have a decimal place or an exponential component.
Floating point numbers are often used for scientific calculations where very large and very small numbers are needed. They are also used for financial calculations where roundoff errors can be very costly.
Why can’t image data be converted to a float?
This is because the pixel values in an image are typically integers that represent the brightness of a particular color in the image. However, when you try to convert these values to a float, the decimal points are lost and you are left with whole numbers.
How to convert image data to a float
One way to convert image data to a float is to use the Pillow library. The Pillow library provides a function called Image.open() that returns an Image object. The Image object has a method called getdata() that returns the image data as a tuple.
We can use the tuple() function to convert the data to a list, and then use the map() function to convert the list of integers to a list of floats. Finally, we can use the numpy.array() function to convert the list of floats to a numpy array.
Here is an example:
import numpy as np
from PIL import Image
Open an image file (.jpg or .png)
img = Image.open(‘sample-image.jpg’)
Get data as tuple
data = img.getdata()
Convert tuple to list
data = list(data)
Convert list of integers to list of floats # Use map() if you’re using Python 3 # Use itertools.imap() if you’re using Python 2 from itertools import imap data = imap(float, data)
Convert list of floats to numpy array arr = np.array(data)
This method is to load the image using the PIL library, then convert it to a NumPy float array with the proper shape (height, width, channels) using the float() method.
If you have an image that is already in a NumPy float array with the proper shape, you can simply call float() on it to convert it.
Here is an example:
This method is most accurate when converting between integer and float data types, but can also be used when converting between other data types.
First, convert the image to an array using the to_a method. Then, use the map method to convert each element in the array to a float. Finally, use the flatten method to flatten the array into a single dimension.
This will return an array of floats that represent the image data.
In conclusion, we found that image data cannot be converted to float. We also found that this is a common issue when working with images and data. We recommend that you avoid this issue by using the correct data type for your images.