*library
there is a mostly known library imported from NumPy and imageio
import NumPy as np
import os
import nibabel as nib
import imageio
// method where I have I write code to convert a nift to png
Method
convert a nift(.nii) image to png image
def nii_to_image(niifile):
filenames = os.listdir(filepath) #read nii folder
slice_trans = []
#filename is the path of nii image
for f in filenames:
#Start reading nii files
img_path = os.path.join(filepath, f)
img = nib.load(img_path) #read nii
img_fdata = img.get_fdata()
fname = f.replace('.nii','')
# Remove the nickname of nii
img_f_path = os.path.join(imgfile, fname)
#Create a folder corresponding to the image of nii
if not os.path.exists(img_f_path):
os.mkdir(img_f_path) #New folder
# to image
(x,y,z) = img.shape
for i in range(z): #x is the sequence of images
silce = img_fdata[i, :, :] #Select which direction the slice can be
imageio.imwrite(os.path.join(img_f_path,'{}.png'.format(i)), silce) #Save image
#main function where fill path was gived
main
if __name__ == '__main__':
filepath = '/content/drive/MyDrive/sem 8/dataset/pr'
imgfile = '/content/drive/MyDrive/sem 8/dataset/propi'
nii_to_image(filepath)
After you load the nifti file as NumPy array as you did, run on every slice (z from img.shape) and then save the array to png.
Make sure that when you run on each slice you save only the existing one (the z_slice_number):
slice = img_fdata[:, :, z_slice_numer]
And to save this slice you can do as follow (or another way from here):
matplotlib.image.imsave('name.png', slice)
Related
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)
I am trying to use this OpenCV class of text detector to find out if I can use it for my project. I am loaging image like this:
src = cv2.imread(img_path)
And then I am trying to use function mentioned before like this:
bbox, confidence = cv2.text_TextDetectorCNN.detect(src)
But this code falls with the following mistake:
TypeError: descriptor 'detect' for 'cv2.text_TextDetectorCNN' objects doesn't apply to a 'numpy.ndarray' object
I have already tried to use image = cv2.cvtColor(src, cv2.COLOR_BGR2RGB, cv2.CV_8UC3) but it doesn't do a thing - image remains an ndarray.
UPD: Attaching whole code and problematic picture.
import os
# borrowed from https://github.com/lengstrom/fast-style-transfer/blob/master/src/utils.py
def list_images(in_path, formats=[".jpg", ".jpeg", ".gif", ".png", ".pgm", ".bmp"]):
img_files = []
for (dirpath, _, filenames) in os.walk(in_path):
for file in filenames:
_, ext = os.path.splitext(file)
ext = str.lower(ext)
if ext in formats:
img_files.append(os.path.join(dirpath, file))
return img_files
def test_accuracy(folder_path):
image_list = list_images(folder_path)
for img_path in image_list:
src = cv2.imread(img_path)
bbox, confidence = cv2.text_TextDetectorCNN.detect(src)
print(img_path, bbox, confidence) # To check format of output
Tested image:
(I've tested 3 different .jpg images, none of them worked)
Question: is there a way to convert ndarray into some OpenCV format that will be accepted by mentioned function?
Your problem is that you don't create an instance of your text detector. See this:
import cv2
import numpy as np
# After downloading the caffemodel and
# textbox.prototxt you have to create
# an instance of your model
textSpotter = cv2.text.TextDetectorCNN_create(
"textbox.prototxt", "TextBoxes_icdar13.caffemodel"
)
src = cv2.imread("1.png")
bbox, confidence = textSpotter.detect(src)
print("HELLO", bbox, confidence) # To check format of output
I have a folder of dicom images and I stored these images in an array and I would like to print them out in a different folder.
I cannot find a method that will write out each of the images like the cv2.imwrite
import pydicom
import skimage, os
import numpy as np
FolderPathName = r'FolderPathName'
slices = [pydicom.read_file(FolderPathName + imagename) for imagename in os.listdir(FolderPathName)]
# Sort the dicom slices in their respective order
slices.sort(key=lambda x: int(x.InstanceNumber))
for x in range(len(slices)):
#write the images in a new folder
Method 1:
In your case,
The answer is ...
import pydicom
import skimage, os
import numpy as np
FolderPathName = r'FolderPathName'
slices = [pydicom.read_file(FolderPathName + imagename) for imagename in os.listdir(FolderPathName)]
# Sort the dicom slices in their respective order
slices.sort(key=lambda x: int(x.InstanceNumber))
jpg_folder = '' # Set your jpg folder
for idx in range(len(slices)):
#write the images in a new folder
jpg_filepath = os.path.join( jpg_folder, "pic-{}.jpg".format(idx) )
np_pixel_array = slices[idx].pixel_array
cv2.imwrite(jpg_filepath, np_pixel_array)
Method 2:
But, there is better way to process dicom files ...
import pydicom
import os
import numpy as np
import cv2
dicom_folder = '' # Set the folder of your dicom files that inclued images
jpg_folder = '' # Set the folder of your output folder for jpg files
# Step 1. prepare your input(.dcm) and output(.jpg) filepath
dcm_jpg_map = {}
for dicom_f in os.listdir(dicom_folder):
dicom_filepath = os.path.join(dicom_folder, dicom_f)
jpg_f = dicom_f.replace('.dcm', '.jpg')
jpg_filepath = os.path.join(jpg_folder,jpg_f)
dcm_jpg_map[dicom_filepath] = jpg_filepath
# Now, dcm_jpg_map is key,value pair of input dcm filepath and output jpg filepath
# Step 2. process your image by input/output information
for dicom_filepath, jpg_filepath in dcm_jpg_map.items():
# convert dicom file into jpg file
dicom = pydicom.read_file(dicom_filepath)
np_pixel_array = dicom.pixel_array
cv2.imwrite(jpg_filepath, np_pixel_array)
In above code,
the Step 1 is focus on file path processing. It's good for your to porting your code into different environment easily.
The Step 2 is major code which focus on any kind of image processing.
I'm working on a The Japanese Female Facial Expression (JAFFE) Database. You can find the database on this link http://www.kasrl.org/jaffe.html.
When I download the database I got a list of pictures. I would like to convert these image files into a CSV file but I'm still new in deep learning and I don't know how. Someone proposed that I work with OpenCV. what should I do?
i have simple example
i hope this help you.
from PIL import Image
import numpy as np
import sys
import os
import csv
def createFileList(myDir, format='.jpg'):
fileList = []
print(myDir)
for root, dirs, files in os.walk(myDir, topdown=False):
for name in files:
if name.endswith(format):
fullName = os.path.join(root, name)
fileList.append(fullName)
return fileList
# load the original image
myFileList = createFileList('path/to/directory/')
for file in fileList:
print(file)
img_file = Image.open(file)
# get original image parameters...
width, height = img_file.size
format = img_file.format
mode = img_file.mode
# Make image Greyscale
img_grey = img_file.convert('L')
value = np.asarray(img_grey.getdata(), dtype=np.int).reshape((img_grey.size[1], img_grey.size[0]))
value = value.flatten()
print(value)
with open("img_pixels.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(value)
Install pillow, numpy, pandas
Convert the image to RGB
plot RGB along with x,y co-ordinates in a pandas Dataframe
Save the dataframe as csv
Sample working code as below
from PIL import Image
from numpy import array, moveaxis, indices, dstack
from pandas import DataFrame
image = Image.open("data.tiff")
pixels = image.convert("RGB")
rgbArray = array(pixels.getdata()).reshape(image.size + (3,))
indicesArray = moveaxis(indices(image.size), 0, 2)
allArray = dstack((indicesArray, rgbArray)).reshape((-1, 5))
df = DataFrame(allArray, columns=["y", "x", "red","green","blue"])
print(df.head())
df.to_csv("data.csv",index=False)
You don't need to write any code, you can just use vips on the command-line on macOS, Linux or Windows.
So, in Terminal (or Command Prompt, if on Windows):
vips im_vips2csv TM.AN1.190.tiff result.csv
will convert the 256x256 greyscale image TM.AN1.190.tiff into a 256 line CSV with 256 entries per line. Simples!
If you want to replace the tab separators by commas, you can do:
tr '\t' , < result.csv > NewFile.csv
I have a folder containing 10K tiff files, how can i import all the files using python so that i can do some predictive modelling.
thanks
NK
Use the following approach:
import os
from PIL import Image
import numpy as np
dirname = 'tiff_folder_path'
final = []
for fname in os.listdir(dirname):
im = Image.open(os.path.join(dirname, fname))
imarray = np.array(im)
final.append(imarray)
final = np.asarray(final) # shape = (60000,28,28)