I'm currently working in a project which handles .nii files from neuro images. I converted that file into 80 .png files. Now I need to combine those 80 .png into .nii files again.
Please help.
Thanks.
The strict answer is no, you can't do it. Because png files do not contain those information needed for NIfTI file.
However if you don't care whether the coordinate and left-right information is correct or not, you could generate a fake nii file. You can read your png files (I suppose they have the same dimension) using a for loop:
for i = 1:numberOfPNG_file
img(:,:,i) = imread(png_Files{i});
end
The you can use the Matlab NIfTI tool to create nii file:
nii = nii_tool('init', img);
nii_tool('save', nii, 'my_nii.nii');
Hope this helps
%step 1: get the names of the files
files=dir('*.png');
file_names={files.name}';
%step 2: sort the files
%extract the numbers
%Here, the format of the name shoul be enterd and %d should replate the
%number, this is so that the files will be load in the right order
filenum = cellfun(#(x)sscanf(x,'%d.png'), file_names);
% sort them, and get the sorting order
[~,Sidx] = sort(filenum) ;
% use to this sorting order to sort the filenames
SortedFilenames = file_names(Sidx);
%step 3: combine images to single matrix:
%get number of files
num_of_files=numel(SortedFilenames);
for i=1:num_of_files
nifti_mat(:,:,i)=imread(SortedFilenames{i});
end
%step 4: conver to nifti and save:
filename='here_goes_the_name_of_the_file';
niftiwrite(nifti_mat,filename);
Related
Please Help me.
I'm new to python and I want to load all jpg images in 500 folders (each folder has up to 100 jpg images).
I also have a CSV file that contains labels for folders, I want to tell python to consider every folder label for all jpg images in that folder.
example file name: folder_name[.....].jpg
Each file has the same folder name, except the [], which is different for each file.
How can I tell python no matter what it is []??
I would appreciate any help.
train = pd.read_csv("COAD_CMS_label_train.csv")`
train_image = []
for i in tqdm(range(train.shape[0])):
img = image.load_img('tiles/'+train['folder_name'][i]+' [*]'.astype('str')+'.jpg', target_size=(256,256,3),
grayscale=False,)
example for labels:
folder_name,Label
TCGA-A6-2683-01Z-00-DX1.0dfc5d0a-68f4-45e1-a879-0428313c6dbc,CMS2
TCGA-F4-6459-01Z-00-DX1.80a78213-1137-4521-9d60-ac64813dec4c,CMS4
TCGA-A6-6653-01Z-00-DX1.e130666d-2681-4382-9e7a-4a4d27cb77a4,CMS1
You could do something like this which will result in an array of all files with the jpg extension, where parent_dir is the parent directory which contains all the subdirectories with the images.
images_files = glob.iglob(f'{parent_dir}/*.jpg', recursive=True)
Hope that helps.
Good luck
So I have this bit of code, which clips out a shapefile of a tree out of a Lidar Pointcloud. When doing this for a single shapefile it works well.
What I want to do: I have 180 individual tree shapefiles and want to clip every file out of the same pointcloud and save it as a individual .las file.
So in the end I should have 180 .las files. E.g. Input_shp: Tree11.shp -> Output_las: Tree11.las
I am sure that there is a way to do all of this at once. I just dont know how to select all shapefiles and save the output to 180 individual .las files.
Im really new to Python and any help would be appreciated.
I already tried to get this with placeholders (.format()) but couldnt really get anywhere.
from WBT.whitebox_tools import WhiteboxTools
wbt = WhiteboxTools()
wbt.work_dir = "/home/david/Documents/Masterarbeit/Pycrown/Individual Trees/"
wbt.clip_lidar_to_polygon(i="Pointcloud_to_clip.las", polygons="tree_11.shp", output="Tree11.las")
I don't have the plugin you are using, but you may be looking for this code snippet:
from WBT.whitebox_tools import WhiteboxTools
wbt = WhiteboxTools()
workDir = "/home/david/Documents/Masterarbeit/Pycrown/Individual Trees/"
wbt.work_dir = workDir
# If you want to select all the files in your work dir you can use the following.
# though you may need to make it absolute, depending on where you run this:
filesInFolder = os.listDir(workDir)
numberOfShapeFiles = len([_ for _ in filesInFolder if _.endswith('.shp')])
# assume shape files start at 0 and end at n-1
# loop over all your shape files.
for fileNumber in range(numberOfShapeFiles):
wbt.clip_lidar_to_polygon(
i="Pointcloud_to_clip.las",
polygons=f"tree_{fileNumber}.shp",
output=f"Tree{fileNumber}.las"
)
This makes use of python format string templates.
Along with the os.listdir function.
I am working on DICOM images, I have 5 scans(folders) each scan contain multiple images, after working some preprocessing on the images, I want to save the processed images in a single file using "np.save", I have the code below that save each folder in a separate file:
data_path = 'E:/jupyter/test/LIDC-IDRI/'
patients_data = os.listdir(data_path)
for pd in range(len(patients_data)):
full_path = load_scan(data_path + patients_data[pd])
after_pixel_hu = get_pixels_hu(full_path)
after_resample, spacing = resample(after_pixel_hu, full_path, [1,1,1])
np.save(output_path + "images_of_%s_patient.npy" % (patients_data[pd]), after_resample)
load_scan is a function for loading(reading) DICOM files, what I want to do with this code is to save all processed images in a single file, not in five files, can anyone tell me how to do that, please?
The first thing to notice is that you are using %s with patients_data[pd]. I assume patients_data is a list of the names of the patients, which means you are constructing a different output path for each patient - you are asking numpy to save each of your processed images to a new location.
Secondly, .npy is probably not the file type you want to use for your purposes, as it does not handle appending data. You probably want to pick a different file type, and then np.save() to the same file path each time.
Edit: Regarding file type, a pdf may be your best option, where you can make each of your images a separate page.
This is the code I have and it works for single images:
Loading images and apply the encoding
from face_recognition.face_recognition_cli import image_files_in_folder
Image1 = face_recognition.load_image_file("Folder/Image1.jpg")
Image_encoding1 = face_recognition.face_encodings(Image1)
Image2 = face_recognition.load_image_file("Folder/Image2.jpg")
Image_encoding2 = face_recognition.face_encodings(Image2)
Face encodings are stored in the first array, after column_stack we have to resize
Encodings_For_File = np.column_stack(([Image_encoding1[0]],
[Image_encoding2[0]]))
Encodings_For_File.resize((2, 128))
Convert array to pandas dataframe and write to csv
Encodings_For_File_Panda = pd.DataFrame(Encodings_For_File)
Encodings_For_File_Panda.to_csv("Celebrity_Face_Encoding.csv")
How do I loop over the images in 'Folder' and extract the encoding into a csv file? I have to do this with many images and cannot do it manually. I tried several approaches, but none a working for me. Cv2 can be used instead of load_image_file?
Try this
Note: I am assuming you dont need to specify folder path before file name in your command. This code will show you how to iterate over the directory to list files and process them
import os
from face_recognition.face_recognition_cli import image_files_in_folder
my_dir = 'folder/path/' # Folder where all your image files reside. Ensure it ends with '/
encoding_for_file = [] # Create an empty list for saving encoded files
for i in os.listdir(my_dir): # Loop over the folder to list individual files
image = my_dir + i
image = face_recognition.load_image_file(image) # Run your load command
image_encoding = face_recognition.face_encodings(image) # Run your encoding command
encoding_for_file.append(image_encoding[0]) # Append the results to encoding_for_file list
encoding_for_file.resize((2, 128)) # Resize using your command
You can then convert to pandas and export to csv. Let me know how it goes
I have a series of kmz files (1000+) within one folder that I have generated of each polygon of a feature class and a corresponding image (all images are in a separate folder). These kmz's are automatically generated from the attribute tables of my shapefiles from arcGIS. Within each kmz file I have a link to an image that corresponds to that feature as such:
<tr>
<td>Preview</td>
<td>G:\Temp\Figures\Ovr0.png</td>
</tr>
At the moment each image is but a tabular text referencing an image in the directory /Temp/Figures. What id like is to convert all those texts into links something along the lines of
<img src="file:///G:/Temp/Figures/Ovr0.png" width = 750 height=500/>
Given the large volume of files it would be ideal if this could be done within python, simplekml? On another note - at some stage I would like to share a few of these kmz files and therefore I was wondering if the best solution was to subdivide each kmz and image pair into their own respective directories and rezip the kmz file somehow?
I have managed to solve my problem by iterating each kmz and image and using the zipfile module to read the contents, rewrite the doc.kml and rezipping the files into a kmz. At the moment the images are placed after the < body >in the kmz but a more complex argument could be written with re I presume.
If there is a more efficient method please let me know...
def edit_kmz(kmz,output,image):
##Read the doc.kml file in the kmz and rewrite the doc.kml file
zf = zipfile.ZipFile(kmz)
temp = r'tempfolder\doc.kml'
for line in zf.read("doc.kml").split("\n"):
with open(temp,'a') as wf: #Create the doc.kml
if "</body>" in line:
wf.write("</body>\n<img src='files/Ovr0.png' width = 750 height=500</img>\n")
else:
wf.write('%s\n'%(line))
zf.close()
##Rezip the file
zf = zipfile.ZipFile(output,'a')
zf.write(image,arcname='files/Ovr0.png') ##Relative Path to the Image
zf.write(temp,arcname='doc.kml') ##Add revised doc.kml file
zf.close()