I've got a problem. I'm trying create image from binary data which I got from hyperspectral camera. The file which I have is in BSQ uint16 format. From the documentation I found out that images contained in the file (.dat) have a resolution of 1024x1024 and there are 24 images in total. The whole thing is to form a kind of "cube" which I want use in the future to creat multi-layered orthomosaic.
I would also like to add that I am completely new in python but I try to be up to date with everything I need. I hope that everything what I have written is clear and uderstandable.
At first I tried to use Numpy liblary to creating 3D array but ended up with an arrangement of random pixels.
from PIL import Image
import numpy as np
file=open('Sequence 1_000021.dat','rb')
myarray=np.fromfile(file,dtype=np.uint16)
print('Size of new array',":", len(myarray))
con_array=np.reshape(myarray,(24,1024,1024),'C')
naPIL=Image.fromarray(con_array[1,:,:])
naPIL.save('naPIL.tiff')
The result: enter image description here
Example of image which I want to achieve (thumbnail): enter image description here
As suspected it's just byte order, I get a sensible looking image when running the following code in a Jupyter notebook:
import numpy as np
from PIL import Image
# open as big-endian, convert to native order, then reshape as appropriate
raw = np.fromfile(
'./Sequence 1_000021.dat', dtype='>u2'
).astype('uint16').reshape((24, 1024, 1024))
# display inline
Image.fromarray(raw[1,:,:])
Related
I want to convert .nii images to .tif to train my model using U-Net.
1-I looped through all images in the folder.
2-I looped through all slices within each image.
3-I saved each slice as .tif.
The training images are converted successfully. However, the labels (masks) are all saved as black images. I want to successfully convert those masks from .nii to .tif, but I don't know how. I read that it could be something with brightness, but I didn't get the idea clearly, so I couldn't solve the problem until now.
The only reason for this conversion is to be able to train my model. Feel free to suggest a better idea, if anyone can share a way to feed the network with the .nii format directly.
import nibabel as nib
import matplotlib.pyplot as plt
import imageio
import numpy as np
import glob
import os
import nibabel as nib
import numpy as np
from tifffile import imsave
import tifffile as tiff
for filepath in glob.iglob('data/Task04_Hippocampus/labelsTr/*.nii.gz'):
a = nib.load(filepath).get_fdata()
a = a.astype('int8')
base = Path(filepath).stem
base = re.sub('.nii', '', base)
x,y,z = a.shape
for i in range(0,z):
newimage = a[:, :, i]
imageio.imwrite('data/Task04_Hippocampus/masks/'+base+'_'+str(i)+'.tif', newimage)
Unless you absolutely have to use TIFF, I would strongly suggest using the NiFTI format for a number of important reasons:
Image values are often not arbitrary. For example, in CT images the values correspond to x-ray attenuation (check out this Wikipedia page). TIFF, which is likely to scale the values in some way, is not suitable for this.
NIfTI also contains a header which has crucial geometric information needed to correctly interpret the image, such as the resolution, slice thickness, and direction.
You can directly extract a numpy.ndarray from NIfTI images using SimpleITK. Here is a code snippet:
import SimpleITK as sitk
import numpy as np
img = sitk.ReadImage("your_image.nii")
arr = sitk.GetArrayFromImage(img)
slice_0 = arr[0,:,:] # this is a 2D axial slice as a np.ndarray
As an aside: the reason the images where you stored your masks look black is because in NIfTI format labels have a value of 1 (and background is 0). If you directly convert to TIFF, a value of 1 is very close to black when interpreted as an RGB value - another reason to avoid TIFF!
I want to convert a float32 image into uint8 image in Python.
I tried using the following code, but the output image only has values like 2 and 3 so the image is practically black.
gen_samples[0] * 255).round().astype(np.uint8)
When I try displaying the float32 image I get a blackish/greyish image where I can somewhat make out the required image.
Normalize the array to 0..1 first.
Assuming gen_samples is the image matrix:
arr_min = np.min(gen_samples)
arr_max = np.max(gen_samples)
gen_samples = (gen_samples - arr_min) / (arr_max - arr_min)
Since you tagged the question using scikit-image you are probably using skimage. In this case img_as_ubyte should do the trick:
from skimage.util import image_as_ubyte
img = img_as_ubyte(gen_samples[0])
Further, since you tagged the question with imageio-python I'll assume that the image data actually comes from some (binary) image format rather than being generated in the script. In this case, you can often use the backend that does the decoding and do the conversion while the image is being loaded. This is, however, specific to the format being used, so for a more specific answer you would have to provide more insight into where your images are coming from.
I am trying to read czi format images, But because they need a lot of memmory I tried reading them in memmap file.
Here is the code I used>
import czifile as czi
fileName = "Zimt3.czi"
# read file to binary
file = czi.CziFile(fileName)
imageArr = file.asarray(out="/media/my drive/Temp/temp.bin")
Now imageArr is a variable with dimensons of (9,3,29584,68084,1) in memmap. These are high resolution microscopic images from Carl Zeiss device.
Here is an screenshot of more specifications.
I think this means that imageArr contains 9 images with the dimention of (29584,68084,3)
But I cant extract this kind of numpy array to visualize as an image.
Can you please help me convert (9,3,29584,68084,1) in memmap to (29584,68084,3) images please.
It looks like a very large file. If you just want to visualize it, you can use slideio python package (http://slideio.com). It makes use of internal image pyramids. You can read the image partially with high resolution or the whole image with low resolution. The code below rescales the image so that the width of the delivered raster will be 500 pixels (the height is computed to keep the image size ratio).
import slideio
import matplotlib.pyplot as plt
slide = slideio.open_slidei(file_path="Zimt3.czi",driver_id="CZI")
scene = slide.get_scene(0)
block = scene.read_block(size=(500,0))
plt.imshow(scene.read_block())
Be aware that matplotlib can display images if they have 1 or 3 channels. A CZI file can have an arbitrary number of channels. In this case you have to select what channels you want to display:
block = scene.read_block(size=(500,0), channel_indices=[0,2,5])
Another problem with visualization can be if your file is a 3 or 4D image. In this case, slideio returns 3d or 4d numpy array. Matplotlib cannot display 3d or 4d images. You will need to look for a specific visualization package or select a z-slice and/or time-frame:
block = scene.read_block(size=(500,0), channel_indices=[0,2,5], slices=(0,1), frames=(1,2))
For more details see the package documentation.
I was curious about image processing with python, so I found this great library imageio,
I tried to manipulate the pixels of a picture and save them in a new file,
but i had some problems with the loops
this is what the code looks like
enter image description here
and this the error that i Got !
IndexError: index 3507 is out of bounds for axis 0 with size 3507
the code :
# -*- coding: iso-8859-1 -*-
import imageio
import numpy as np
im = imageio.imread("JAFFRE009a.png")
taille=im.shape #taille is a tuple (Width,Height)
print taille # (4961,3507)
matrice_pixels=open("matrice.txt",'w')
for i in range(taille[1]):
line=""
for j in range(taille[0]):
line+=repr(im[i][j])
matrice_pixels.write(line+'\n')
matrice_pixels.close()
Because your image doesn't have squarred shape, reshape it before you go through your loop
EDIT
We can iterate through each row/column position and save to a file as below.It will take very long time depending upon file size.
Instead of writing your own function, you may want to take advantage of inbuilt binary save (which is more efficient) as
np.save('matrix.py', np_array)
You can load this file as np array and manipulate
Or as a text file using np.save [ will take longer ]
np.save('matrix.txt', np_array)
Working Code:
import imageio
import numpy as np
im = imageio.imread("9v9zU.png")
matrice_pixels=open("matric.txt","wb")
nx,ny = im.shape
for i in range(nx):
line=""
for j in range(ny):
line+=repr(im[i][j])
matrice_pixels.write(line+'\n')
matrice_pixels.close()
#Save as Binary data
np.save('matrix1.npy', im)
#Save as Human readable data
np.savetxt('matrix1.txt', im)
Alternately, you may want to look into off the shelf libraries that will do what you are intending to do.
For e.g. This SO link discusses how to remove section of the picture based upon its color using PIL library.
Also , in future, please DO NOT post a picture of your code. Copy/pase to SO window so that we can copy and modify. In this case I had write everything down line by line to test(thankfully code was not that long).
I have a list called w (size: 784), which I outputted to a png greyscale image:
import matplotlib.pyplot as plt
tmp = 1/(1+np.exp(-10*w/w.max()))
plt.imshow(tmp.reshape(28,28),cmap="gray")
plt.draw()
plt.savefig("final_weight_vector")
Now I want to read the png image back to be a vector.
The solutions I found so far:
First:
import matplotlib.image as mpimg
img=mpimg.imread('final_weight_vector.png')
but img appears to not be greyscale, because its dimensions turend out to be (600, 800, 4).
Second:
reading the file as RGB and converting to greyscale:
im = Image.open('final_weight_vector.png').convert('LA')
However, I couldn't find how to iterate over im so I have no idea as to what's inside. Further, I am not sure the output of im will have the exact same values as the original w.
Help please?
The problem is that what you saved is probably a plot of the 28x28 image, not the image itself.
To be sure, please preview the image. I bet it is 600x800, not 28x28. I also suppose it contains many additional elements, like axes and padding.
If you want to store your array in a loadable format, you may use numpy.save() (and numpy.load() to load it).
You may also use PIL to save your array as image (e.g. using something similar to: http://code.activestate.com/recipes/577591-conversion-of-pil-image-and-numpy-array/)