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convert .nii to .tif using imwrite, it saves black image insted of the image

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!

Convert a folder of images into a dataset of numpy arrays in Google Colab

I have uploaded the fairface dataset (https://github.com/joojs/fairface) into my google drive and I'm trying to convert the images to a dataset of arrays that I can use in a CNN.
First, I created a list of the files for the validation set. Now I am trying to convert the images to arrays. This is what I am trying, but it says my directory does not exist.
val is the folder of validation images.
import os
from PIL import Image
from numpy import asarray
val_items = os.listdir('/content/val')
train_items = os.listdir('/content/train')
val_img_array = []
# load the image and convert into
# numpy array
for i in range(len(val_items)):
img = Image.open('/content/val/*.jpg')
numpydata = asarray(img)
val_img_array.append(numpydata)
print(val_img_array)
Please give me any guidance you have. Thanks!

You are not importing the drive correctly. Your path should look like this:
Image.open("/content/drive/MyDrive/val/")

How to find a file/ data from a given data set in python- opencv image processing project?

I have a data set of images in an image processing project. I want to input an image and scan through the data set to recognize the given image. What module/ library/ approach( eg: ML) should I use to identify my image in my python- opencv code?

To find exactly the same image, you don't need any kind of ML. The image is just an array of pixels, so you can check if the array of the input image equals that of an image in your dataset.
import glob
import cv2
import numpy as np
# Read in source image (the one you want to match to others in the dataset)
source = cv2.imread('test.jpg')
# Make a list of all the images in the dataset (I assume they are images in a directory)
filelist = glob.glob(r'C:\Users\...\Images\*.JPG')
# Loop through the images, read them in and check if an image is equal to your source
for file in filelist:
img = cv2.imread(file)
if np.array_equal(source, img):
print("%s is the same image as source" %(file))
break

Read mnist images into Tensorflow

I was looking at this Tensorflow tutorial.
In the tutorial the images are magically read like this:
mnist = learn.datasets.load_dataset("mnist")
train_data = mnist.train.images
My images are placed in two directories:
../input/test/
../input/train/
They all have a *.jpg ending.
So how can read them into my program?
I don't think I can use learn.datasets.load_dataset because this seems to take in a specialized dataset structure, while I only have folders with images.

mnist.train.images is essentially a numpy array of shape [55000, 784]. Where, 55000 is the number of images and 784 is the number of pixels in each image (each image is 28x28)
You need to create a similar numpy array from your data in case you want to run this exact code. So, you'll need to iterate over all your images, read image as a numpy array, flatten it and create a matrix of size [num_examples, image_size]
The following code snippet should do it:
import os
import cv2
import numpy as np
def load_data(img_dir):
return np.array([cv2.imread(os.path.join(img_dir, img)).flatten() for img in os.listdir(img_dir) if img.endswith(".jpg")])
A more comprehensive code to enable debugging:
import os
list_of_imgs = []
img_dir = "../input/train/"
for img in os.listdir("."):
img = os.path.join(img_dir, img)
if not img.endswith(".jpg"):
continue
a = cv2.imread(img)
if a is None:
print "Unable to read image", img
continue
list_of_imgs.append(a.flatten())
train_data = np.array(list_of_imgs)
Note:
If your images are not 28x28x1 (B/W images), you will need to change the neural network architecture (defined in cnn_model_fn). The architecture in the tutorial is a toy architecture which only works for simple images like MNIST. Alexnet may be a good place to start for RGB images.

You can check the answers given in How do I convert a directory of jpeg images to TFRecords file in tensorflow?. Easiest way is to use the utility provided by tensor flow :build_image_data.py, which does exactly the thing you want to do.

Get information of multipage tiff image (I;16) with python

I am working with images from a multispectral camera and after doing some processing I have 16 bits multipage image with 6 bands in tiff format. Now, I need to get information of each layer through python code, so I used:
from __future__ import division
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
files = os.listdir('my directory')
for file in files:
I = Image.open(os.path.join('my directory', "image.tif"))
plt.imshow(np.asarray(I))
plt.show()
print (I.size, I.mode, I.format)
band1 = np.asarray(I,dtype=np.uint16)
A = np.asarray(I,dtype=np.float32)
I can read the image but I cannot find the information of the six different layers and actually, I am not sure from which band the values of "A" are. The image is too big to upload it, but I can try to share it if it´s necessary. Thanks.