I'm would like to go from an image filename to a list of coordinates of the white pixels in the image.
I know it involves PIL. I have tried using Image.load() but this doesn't help because the output is not indexable (to use in a for loop).
You can dump an image as a numpy array and manipulate the pixel values that way.
from PIL import Image
import numpy as np
im=Image.open("someimage.png")
pixels=np.asarray(im.getdata())
npixels,bpp=pixels.shape
This will give you an array whose dimensions will depend on how many bands you have per pixel (bpp above) and the number of rows times the number of columns in the image -- shape will give you the size of the resulting array. Once you have the pixel values, it ought to be straightforward to filter out those whose values are 255
To convert a numpy array back to an image use:
im=Image.fromarray(pixels)
Related
Using the following code, PIL easily returns an array of single pixel values from an image. Not sure what the term for it is; but instead of a 3d array (RGB), it simplifies each pixel into one of 256 values.
from PIL import Image
im = Image.open(image_path, 'r')
pixel_values = list(im.getdata())
The question is, how can I edit pixels on an image with this same method? I believe the default arg for the putpixel method expects a 3d array (RGB), and if I only give one value; it only ranges over shades of black.
im.putpixel((x, y), value)
im.show()
I would like to be able to substitute integers (0-255) in for value and have access to the wider spectrum of discrete colors.
Is this possible? Seems like it should already be a built in method.
I have array are which is 50000x32x32. arr[i] stores the i-th grayscale image.
I want to compute the mean image of these images. I tried the following code(I got this code from stack overflow itself). This code was actually meant for RGB images.
I know, these changes of mine have a lot of mistakes, Apologies.
import os, numpy, PIL
from PIL import Image
# Access all PNG files in directory
allfiles=os.listdir(os.getcwd())
imlist=arr
N=len(imlist)
# Assuming all images are the same size, get dimensions of first image
w,h=Image.fromarray(imlist[0]).size
# Create a numpy array of floats to store the average (assume RGB images)
brr=numpy.zeros((h,w),numpy.float)
# Build up average pixel intensities, casting each image as an array of floats
for im in imlist:
imarr=numpy.array(Image.fromarray(im),dtype=numpy.float)
brr=brr+imarr/N
# Round values in array and cast as 8-bit integer
brr=numpy.array(numpy.round(arr),dtype=numpy.uint8)
# Generate, save and preview final image
out=Image.fromarray(brr,mode="L")
out.save("Average.png")
out.show()
Once you have your 5000 × 32 × 32 array, you can compute the mean image by using np.mean() with axis=0 (the first axis, which contains the collection of images). Let's make some random data:
import numpy as np
images = np.random.random((5000, 32, 32))
Now we can compute the mean image:
mean_image = images.mean(axis=0)
We can look at it with:
import matplotlib.pyplot as plt
plt.imshow(mean_image)
Which looks something like:
I have a greyscale image, represented by a 2D array of integers, shape (1000, 1000).
I then use sklearn.feature_extraction.image.extract_patches_2d() to generate an array of 3x3 'patches' from this image, resulting in an array of shape (1000000, 3, 3), as there are 1 million 3x3 arrays for each pixel value in the original image.
I reshape this to (1000, 1000, 3, 3), which is a 1000x1000 array of 3x3 arrays, one 3x3 array for each pixel in the original image.
I now want to effectively subtract the 2D array from the 4D array. I have already found a method to do this, but I would like to make one using vectorisation.
I currently iterate through each pixel and subtract the value there from the 3x3 array at the same index. This is a little bit slow.
This is what currently loads images, formats the arrays before hand, and then performs this subtraction.
from PIL import Image, ImageOps
from skimage import io
from sklearn.feature_extraction import image
import numpy
jitter = 1
patchsize = (jitter*2)+1
#load image as greyscale image using PIL
original = load_image_greyscale(filename)
#create a padded version of the image so that 1000x1000 patches are made
#instead of 998x998
padded = numpy.asarray(ImageOps.expand(original,jitter))
#extract these 3x3 patches using sklearn
patches = image.extract_patches_2d(padded,(patchsize,patchsize))
#convert image to numpy array
pixel_array = numpy.asarray(original)
#then reshape the array of patches so it matches array_image
patch_array = numpy.reshape(patches, (pixel_array.shape[0],pixel_array.shape[1],patchsize,patchsize))
#create a copy for results
patch_array_copy = numpy.copy(patch_array)
#iterate over each 3x3 array in the patch array and subtract the pixel value
#at the same index in the pixel array
for x in range(pixel_array.shape[0]):
for y in range(pixel_array.shape[1]):
patch_array_copy[x,y] = patch_array[x,y] - pixel_array[x,y]
I would like a way to perform the final step in the for loop using matrix operations.
I would also like to extend this at some point to work with RGB images, effectively making it a subtraction of an array with shape(1000,1000,3) from an array with shape(1000,1000,3,3,3). But i'm trying to go one step at a time here.
Any help or tips or suggestions or links to helpful resources would be greatly appreciated.
I am trying to convert an image into an array of pixels.
Here is my current code.
im = Image.open("beeleg.png")
pixels = im.load()
im.getdata() # doesn't work
print(pixels # doesn't work
Ideally, my end goal is to convert the image into a vector of just pixels, so for instance if I have an image of dimensions 100x100, then I want a vector of dimensions 1x10000, where each value is between [0, 255]. Then, divide each of the values in the array by 256 and add a bias of 1 in the front of the vector. However, I am not able to proceed with all this without being able to obtain an array. How to proceed?
Scipy's ndimage library is generally the go-to library for working with pixels as data (arrays). You can load an image from file (most common formats supported) using scipy.ndimage.imread into a numpy array which can be easily reshaped and mathematically operated on. The mode keyword can be used to specify a colorspace transformation upon load (convert an RGB image to black and white). In your case you asked for single color pixels from 0-255 (8bit grayscale) so you would use mode='L'. See The Documentation for usage / more useful functions.
If use OpenCV, gray=cv2.imread(image,0) will return a grayscale image with n rows x m cols single channel numpy array. rows, cols = gray.shape will return the height and width of the image.
I want to do some image processing using Python.
Is there a simple way to import .png image as a matrix of greyscale/RGB values (possibly using PIL)?
scipy.misc.imread() will return a Numpy array, which is handy for lots of things.
Up till now no one told about matplotlib.image:
import matplotlib.image as img
image = img.imread(file_name)
Now the image would be a 3D numpy array
print image.shape
Would be something like: (317, 504, 3)
scipy.misc.imread() is deprecated now. We can use imageio.imread instead of that to read it as a Numpy array
im.load in PIL returns a matrix-like object.
you can use PyGame image and use PixelArray to access the pixeldata
Definitely try
from matplotlib.image import imread
image = imread(filename)
The filename preferably has to be an .jpg image.
And then, try
image.shape
This would return :
for a black and white or grayscale image
An (n,n) matrix where n represents the dimension of the images (pixels) and values inside the matrix range from 0 to 255.
Typically 0 is taken to be black, and 255 is taken to be white. 128 tends to be grey!
For color or RGB image
It will render a tensor of 3 channels. Each channel is an (n,n) matrix where each entry represents the respectively the level of Red, Green or Blue at the actual location inside the image.