Develop Reference

DICOM image is not read correctly

I have a dicom file from which I read images. The images I read, however, has incorrect colormap. Ideally, the image should look like:
However, the following code only gives me
If I only take the red component, I get the image below, which is not correct and cannot be adjusted to the ideal result in any colormap I tried.
or
root = tk.Tk()
root.withdraw()
path = filedialog.askopenfilename()
ds = dicom.dcmread(path, force = True) # reads a file data set
video = ds.pixel_array #reads a sequence of RGB images
plt.imsave(some_path, video[0], format='png') #gives image [2]
What have I done wrong?

This really looks like YCbCr data, is the Photometric Interpretation something like YBR_FULL? If so then as mentioned in the documentation you need to apply a colour space conversion, which in pydicom is:
from pydicom import dcmread
from pydicom.pixel_data_handlers import convert_color_space
ds = dcmread(...)
rgb = convert_color_space(ds.pixel_array, "YBR_FULL", "RGB")

Stitching multiple pngs into a h5 image h5py

I created an model in blender. From here I took 2d slices through the y-plane of that model leading to the following.
600 png files each corresponding to a ylocation i.e y=0, y=0.1 etc
Each png file has a resolution of 500 x 600.
I am now trying to merge the 600 pngs into a h5 file using python before loading the .h5 into some software. I find that each individual png file is read fine and looks great. However when I look at the final 3d image there is some stretching of the image, and im not sure how this is being created.
The images are resized (from 600x600 to 500x600, but I have checked and this is not the cause of the stretching). I would like to know why I am introducing such stretching in other planes (not y-plane).
Here is my code, please note that there is some work in progress here, hence why I append the dataset to a list (this is to be used for later code)
from PIL import Image
import sys
import os
import h5py
import numpy as np
import cv2
from datetime import datetime
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(dir_path + '//..//..')
Xlen=500
Ylen=600
Zlen=600
directory=dir_path+"/LowPolyA21/"
for filename in os.listdir(directory):
if fnmatch.fnmatch(filename, '*.png'):
image = Image.open(directory+filename)
new_image = image.resize((Zlen, Xlen))
new_image.save(directory+filename)
dataset = np.zeros((Xlen, Zlen, Ylen), np.float)
# traverse all the pictures under the specified address
cnt_num = 0
img_list = sorted(os.listdir(directory))
os.chdir(directory)
for img in (img_list):
if img.endswith(".png"):
gray_img = cv2.imread(img, 0)
dataset[:, :, cnt_num] = gray_img
cnt_num += 1
dataset[dataset == 0] = -1
dataset=dataset.swapaxes(1,2)
datasetlist=[]
datasetlist.append(dataset)
dz_dy_dz = (float(0.001),float(0.001),float(0.001))
for j in range(Xlen):
for k in range(Ylen):
for l in range(Zlen):
if datasetlist[i][j,k,l]>1:
datasetlist[i][j,k,l]=1
now = datetime.now()
timestamp = now.strftime("%d%m%Y_%H%M%S%f")
out_h5_path='voxelA_'+timestamp+'_flipped'
out_h5_path2='voxelA_'+timestamp+'_flipped.h5'
with h5py.File(out_h5_path2, 'w') as f:
f.attrs['dx_dy_dz'] = dz_dy_dz
f['data'] = datasetlist[i] # Write data to the file's primary key data below
Example of image without stretching (in y-plane)
Example of image with stretching (in x-plane)

How to convert 1D array of pixels to image in python?

This is with respect to the FER2013 dataset. The data consists of 48x48 pixel grayscale images of faces. The CSV file contains three columns as (emotion, pixels, Usage), where Usage has any of three value - training, PrivateTest and PublicTest. I want to read the array of pixels, convert them into an image and save them in the respective folder named as per their Usage type.
I need python code that can do the above.
Following is my code
import pandas as pd
import numpy as np
from PIL import Image
df=pd.read_csv("fer2013.csv")
for rows in df:
arr=np.array(df['pixels'])
print(arr)
print(arr.shape)
img = Image.fromarray(arr.reshape(48,48), 'L')
img.save("dataset/df['Usage']/img.jpg", "JPEG")
The above code shows error:
cannot reshape array of size 35887 into shape (48,48).

In case there are any doubts (because I have been working with the FER dataset):
import pandas as pd
import numpy as np
from PIL import Image
df = pd.read_csv('fer2013.csv')
for image_pixels in df.iloc[1:,1]: #column 2 has the pixels. Row 1 is column name.
image_string = image_pixels.split(' ') #pixels are separated by spaces.
image_data = np.asarray(image_string, dtype=np.uint8).reshape(48,48)
img = Image.fromarray(image_data) #final image

How to read multiple images in python

I want read 100 colour images and use them for further processing. Suppose one image size is 256x 256 by reading it in python openCV its size is (256,256,3). I now want to read 100 images and after reading I have to get the size as (100,256,256,3).

You could do something like this, supposing that your images are named like 0.png to 99.png:
import numpy as np
result=np.empty((100,256,256,3))
for i in range (100):
result[i,:,:,:] = cv2.imread('{}.png'.format(i),1)

## your image names
#fnames = sorted(glob.glob("images/*.png"))
## read and stack
img = np.stack([cv2.imread(fname) for fname in fnames])

Create Numpy array of images

I have some (950) 150x150x3 .jpg image files that I want to read into an Numpy array.
Following is my code:
X_data = []
files = glob.glob ("*.jpg")
for myFile in files:
image = cv2.imread (myFile)
X_data.append (image)
print('X_data shape:', np.array(X_data).shape)
The output is (950, 150). Please let me know why the list is not getting converted to np.array correctly and whether there is a better way to create the array of images.
Of what I have read, appending to numpy arrays is easier done through python lists and then converting them to arrays.
EDIT: Some more information (if it helps), image.shape returns (150,150,3) correctly.

I tested your code. It works fine for me with output
('X_data shape:', (4, 617, 1021, 3))
however, all images were exactly the same dimension.
When I add another image with different extents I have this output:
('X_data shape:', (5,))
So I'd recommend checking the sizes and the same number of channels (as in are really all images coloured images)? Also you should check if either all images (or none) have alpha channels (see #Gughan Ravikumar's comment)
If only the number of channels vary (i.e. some images are grey), then force loading all into the color format with:
image = cv2.imread (myFile, cv2.IMREAD_COLOR)
EDIT:
I used the very code from the question, only replaced with a directory of mine (and "*.PNG"):
import cv2
import glob
import numpy as np
X_data = []
files = glob.glob ("C:/Users/xxx/Desktop/asdf/*.PNG")
for myFile in files:
print(myFile)
image = cv2.imread (myFile)
X_data.append (image)
print('X_data shape:', np.array(X_data).shape)

Appending images in a list and then converting it into a numpy array, is not working for me. I have a large dataset and RAM gets crashed every time I attempt it. Rather I append the numpy array, but this has its own cons. Appending into list and then converting into np array is space complex, but appending a numpy array is time complex. If you are patient enough, this will take care of RAM crasing problems.
def imagetensor(imagedir):
for i, im in tqdm(enumerate(os.listdir(imagedir))):
image= Image.open(im)
image= image.convert('HSV')
if i == 0:
images= np.expand_dims(np.array(image, dtype= float)/255, axis= 0)
else:
image= np.expand_dims(np.array(image, dtype= float)/255, axis= 0)
images= np.append(images, image, axis= 0)
return images
I am looking for better implementations that can take care of both space and time. Please comment if someone has a better idea.

Here is a solution for images that have certain special Unicode characters, or if we are working with PNGs with a transparency layer, which are two cases that I had to handle with my dataset. In addition, if there are any images that aren't of the desired resolution, they will not be added to the Numpy array. This uses the Pillow package instead of cv2.
resolution = 150
import glob
import numpy as np
from PIL import Image
X_data = []
files = glob.glob(r"D:\Pictures\*.png")
for my_file in files:
print(my_file)
image = Image.open(my_file).convert('RGB')
image = np.array(image)
if image is None or image.shape != (resolution, resolution, 3):
print(f'This image is bad: {myFile} {image.shape if image is not None else "None"}')
else:
X_data.append(image)
print('X_data shape:', np.array(X_data).shape)
# If you have 950 150x150 images, this would print 'X_data shape: (950, 150, 150, 3)'
If you aren't using Python 3.6+, you can replace the r-string with a regular string (except with \\ instead of \, if you're using Windows), and the f-string with regular string interpolation.

Your definition for the .JPG frame that will be put into a matrix of the same size should should be x, y, R, G, B, A. "A" is not used, but it does take up 8 bits at the end of each pixel.