I had 4 RGB images in an array of shape (4,32,32,3). Then I divided these images in 16 equal 8x8 blocks (64 represents the 8x8 block), decoded_imgs.shape = (4,16,64,3) , 3 represents the colour channels. After some processing now I want to reshape them again as (4,32,32,3) by placing each block to its original location. I have tried by using reshape function but it destroys the image data.
data_new = decoded_imgs.transpose(0,2,1,3).reshape(4,32,32,3)
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I have a dataset which comprises of the binary data of pixelated 50x50 images. The array shape is (50, 50, 90245). I want to reach 50x50 pixels of each of the 90245 images. How can I slice the array?
If data is the variable storing the image data, and i is the index of the image you want to access, then you can do:
data[:,:,i]
to get the desired image data.
If data is the variable storing the image data, and i is the index of the image you want to access, then you can do as #BrokenBenchmark suggested. In case you want a (50,50,1) 3D array as the output, you could do:
data[:,:,i:i+1]
to get the image as a 3D array.
Edit1: If you reshaped your data matrix to be of shape (90245,50,50), you can get the ith image by doing data[i,:,:] or just data[i] to get a (50,50) image. Similarly, to get a (1,50,50) image, you could do data[i:i+1,:,:] or just data[i:i+1].
Edit2: To reshape the array, you could use the swapaxes() function in numpy.
I have a 480x640 image and I am cropping to 195x195.
The original 480x640 has a corresponding (68,2) array which can be used to further process the image. Since I cropped the image, how am can I rescale the (68,2) array values to correspond to the new cropped image?
I tried to do something along the line:
shape = shape*((195*195)/(480*640))
but it was no use. N.B. shape is the (68,2) array
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 have an issue with expanding the size of the Sklearn digit dataset digits from 8*8 to 32*32 pixels.
My approach is to take the 8*8 array and then flatten and expand it. That is, enlarge from 64 to 1024 pixels in total. Therefore I simply want to multiply the values along each row 16 times:
create a new array (=newfeatures) with 1024 NaN values.
Replace every 16. value of the newfeatures array with the values of the original array, that is (0=0),(16=1),(32=2),(...),(1008=64).
3.Replace the remaining NaN values with dropna(ffill) to "expand" the original image to a 32*32 pixels image.
Therefore I use the following code:
#Load in the training dataset
digits=datasets.load_digits()
features=digits.data
targets=digits.target
#Plot original digit
ax[0].imshow(features[0].reshape((8,8)))
#Expand 8*8 image to a 32*32 image (64 to 1024)
newfeatures=np.ndarray((1797,16*len(features[0])))
newfeatures[:]=np.NaN
newfeatures=pd.DataFrame(newfeatures)
for row in range(1797):
for i in range(0,63):
newfeatures.iloc[row,16*i]=features[row][i]
newfeatures.fillna(method="ffill",axis=1,inplace=True)
#Plot expanded image with 32*32 pixels
ax[1].imshow(newfeatures.values[0].reshape((32,32)))
As you can see, the result is not as expected
you can use skimage's resize as shown below
from skimage import transform
new_features = np.array(list
(map
(lambda img: transform.resize(
img.reshape(8,8),#old shape
(32, 32), #new shape
mode='constant',
#flatten the resized image
preserve_range=True).ravel(),
features)))
new_features shape will be (1797, 1024) and displaying the first image will show
Based on the above solution I think the following is a little bit more neater way:
from skimage import transform
newfeatures=[transform.resize(features[i].reshape(8,8),(32,32))for i in
range(len(features))]
plt.imshow(newfeatures[0].reshape((32,32)))
I have an image that has 7 bands with 6938x752 pixels in each band. I want to do some processing on it so am using a python module called RIOS that deals with the reading and writing of the image to let the user focus on the processing. It reads the image in as a numpy array in blocks so that the processing is more efficient than reading in the whole image.
It reads the image in as a numpy array with the shape (7, 200, 200). I want to process the data by pixel so that I have the information from each band for each pixel. Is there a way to index the array so that for I can process just the 7 values (one for each band) for each pixel within image?
The only little code I can provide here is the function I use to read in, process and write the data.
def MASKimage(info, inputs, outputs):
inputs.image1 = inputs.image1.astype(numpy.float32) # Read the image in as an array with shape (7, 200, 200)
process = * Do some processing on the array which is the 7 values pixel by pixel * # has shape (1,7)
outputs.outimage = process
You can try using np.transpose and np.reshape:
inputs.image1 = input.images1.transpose((1,2,0)).reshape(200*200,7)
You can just iterate through the image with a single loop to do your processing as each element represents a pixel with 7 bands.