I'm using OpenCV's imread function to read my images into Python, for further processing as a NumPy array later in the pipeline. I know that OpenCV uses BGR instead of RGB, and have accounted for it, where required. But one thing that stumps me is why I get these differing outputs for the following scenarios?
Reading an image directly into a single works fine. The plotted image (using matplotlib.pyplot) reproduces my .tiff/.png input correctly.
img_train = cv2.imread('image.png')
plt.imshow(img_train)
plt.show()
When I use cv2.imread in a loop (for reading from a directory of such images - which is my ultimate goal here), I create an array as follows:
files = [f for f in listdir(mypath) if isfile(join(mypath, f))]
img_train = np.empty([len(files), height, width, channel])
for n in range(0, len(files)):
img_train[n] = cv2.imread(join(mypath, files[n]))
plt.imshow(img_train[n])
plt.show()
When I try to cross check and plot the image obtained thus, I get a very different output. Why so? How do I rectify this so that it looks more like my input, like in the first case? Am I reading the arrays correctly in the second case, or is it flawed?
Otherwise, is it something that stems from Matplotlib's plotting function? I do not know how to cross check for this case, though.
Any advice appreciated.
Extremely trivial solution.
np.empty creates an array of dtype float by default.
Changing this to uint8 as in the first case with OpenCV alone worked fine.
Related
I have a problem and don't know how to solve:
I'm learning how to analyze DICOM files with Python and, so,
I got a patient exam, on single patient and one single exam, which is 200 DICOM files all of the size 512x512 each archive representing a different layer of him and I want to turn them into a single archive .npy so I can use in another tutorial that I found online.
Many tutorials try to convert them to jpg or png using opencv first, but I don't want this since I'm not interested in a friendly image to see right now, I need the array. Also, this step screw all the quality of images.
I already know that using:
medical_image = pydicom.read_file(file_path)
image = medical_image.pixel_array
I can grab the path, turn 1 slice in a pixel array and them use it, but the thing is, it doesn't work in a for loop.
The for loop I tried was basically this:
image = [] # to create an empty list
for f in glob.iglob('file_path'):
img = pydicom.dcmread(f)
image.append(img)
It results in a list with all the files. Until here it goes well, but it seems it's not the right way, because I can use the list and can't find the supposed next steps anywhere, not even answers to the errors that I get in this part, (so I concluded it was wrong)
The following code snippet allows to read DICOM files from a folder dir_path and to store them into a list. Actually, the list does not consist of the raw DICOM files, but is filled with NumPy arrays of Hounsfield units (by using the apply_modality_lut function).
import os
from pathlib import Path
import pydicom
from pydicom.pixel_data_handlers import apply_modality_lut
dir_path = r"path\to\dicom\files"
dicom_set = []
for root, _, filenames in os.walk(dir_path):
for filename in filenames:
dcm_path = Path(root, filename)
if dcm_path.suffix == ".dcm":
try:
dicom = pydicom.dcmread(dcm_path, force=True)
except IOError as e:
print(f"Can't import {dcm_path.stem}")
else:
hu = apply_modality_lut(dicom.pixel_array, dicom)
dicom_set.append(hu)
You were well on your way. You just have to build up a volume from the individual slices that you read in. This code snippet will create a pixelVolume of dimension 512x512x200 if your data is as advertised.
import dicom
import numpy
images = [] # to create an empty list
# Read all of the DICOM images from file_path into list "images"
for f in glob.iglob('file_path'):
image = pydicom.dcmread(f)
images.append(image)
# Use the first image to determine the number of rows and columns
repImage = images[0]
rows=int(repImage.Rows)
cols=int(repImage.Columns)
slices=len(images)
# This tuple represents the dimensions of the pixel volume
volumeDims = (rows, cols, slices)
# allocate storage for the pixel volume
pixelVolume = numpy.zeros(volumeDims, dtype=repImage.pixel_array.dtype)
# fill in the pixel volume one slice at a time
for image in images:
pixelVolume[:,:,i] = image.pixel_array
#Use pixelVolume to do something interesting
I don't know if you are a DICOM expert or a DICOM novice, but I am just accepting your claim that your 200 images make sense when interpreted as a volume. There are many ways that this may fail. The slices may not be in expected order. There may be multiple series in your study. But I am guessing you have a "nice" DICOM dataset, maybe used for tutorials, and that this code will help you take a step forward.
I'm fairly new to Python, and I have been trying to recreate a working IDL program to Python, but I'm stuck and keep getting errors. I haven't been able to find a solution yet.
The program requires 4 FITS files in total (img and correctional images dark, flat1, flat2). The operations are as follows:
flat12 = (flat1 + flat2)/2
img1 = (img - dark)/flat12
The said files have dimensions (1024,1024,1). I have resized them to (1024,1024) to be able to even use im_show() function.
I have also tried using cv2.add(), but I get this:
TypeError: Expected Ptr for argument 'src1'
Is there any workaround for this? Thanks in advance.
To read your FITS files use astropy.io.fits: http://docs.astropy.org/en/latest/io/fits/index.html
This will give you Numpy arrays (and FITS headers if needed, there are different ways to do this, as explained in the documentation), so you could do something like:
>>> from astropy.io import fits
>>> img = fits.getdata('image.fits', ext=0) # extension number depends on your FITS files
>>> dark = fits.getdata('dark.fits') # by default it reads the first "data" extension
>>> darksub = img - dark
>>> fits.writeto('out.fits', darksub) # save output
If your data has an extra dimension, as shown with the (1024,1024,1) shape, and if you want to remove that axis, you can use the normal Numpy array slicing syntax: darksub = img[0] - dark[0].
Otherwise in the example above it will produce and save a (1024,1024,1) image.
I have corresponding masks to the images that I want to segment.
I put the images in one folder and their corresponding masks in another folder.
I'm trying to apply those masks or multiply them by the images using two for loops in python to get the segmented images.
I'm using the code below:
def ImageSegmentation():
SegmentedImages = []
for img_path in os.listdir('C:/Users/mab/Desktop/images/'):
img=io.imread('C:/Users/mab/Desktop/data/'+img_path)
for img_path2 in os.listdir('C:/Users/mab/Desktop/masks/'):
Mask = io.imread('C:/Users/mab/Desktop/masks/'+img_path2)
[indx, indy] = np.where(Mask==0)
Color_Masked = img.copy()
Color_Masked[indx,indy] = 0
matplotlib.image.imsave('C:/Users/mab/Desktop/SegmentedImages/'+img_path2,Color_Masked)
segs.append(Color_Masked)
return np.vstack(Color_Masked)
This code works when I try it for a single image and a single mask (without the folders and loops).
However, when I try to loop over the images and masks I have in the two folders, I get output images that are segmented by the wrong mask (not their corresponding mask).
I can't segment each single image alone without looping because I have more than 500 Images and their masks.
I don't know what I'm missing or placing wrong in this code and how can I fix it? Also, is there an easier way to get the segmented images?
Unless I have grossly misunderstood, you just need something like this:
import glob
filelist = glob.glob('C:/Users/mab/Desktop/images/*.png')
for i in filelist:
mask = i.replace("images","masks")
print(i,mask)
On my iMac, that sort of thing produces:
/Users/mark/StackOverflow/images/b.png /Users/mark/StackOverflow/masks/b.png
/Users/mark/StackOverflow/images/a.png /Users/mark/StackOverflow/masks/a.png
I am looking to store pixel values from satellite imagery into an array. I've been using
np.empty((image_width, image_length)
and it worked for smaller subsets of an image, but when using it on the entire image (3858 x 3743) the code terminates very quickly and all I get is an array of zeros.
I load the image values into the array using a loop and opening the image with gdal
img = gdal.Open(os.path.join(fn + "\{0}".format(fname))).ReadAsArray()
but when I include print img_array I end up with just zeros.
I have tried almost every single dtype that I could find in the numpy documentation but keep getting the same result.
Is numpy unable to load this many values or is there a way to optimize the array?
I am working with 8-bit tiff images that contain NDVI (decimal) values.
Thanks
Not certain what type of images you are trying to read, but in the case of radarsat-2 images you can the following:
dataset = gdal.Open("RADARSAT_2_CALIB:SIGMA0:" + inpath + "product.xml")
S_HH = dataset.GetRasterBand(1).ReadAsArray()
S_VV = dataset.GetRasterBand(2).ReadAsArray()
# gets the intensity (Intensity = re**2+imag**2), and amplitude = sqrt(Intensity)
self.image_HH_I = numpy.real(S_HH)**2+numpy.imag(S_HH)**2
self.image_VV_I = numpy.real(S_VV)**2+numpy.imag(S_VV)**2
But that is specifically for that type of images (in this case each image contains several bands, so i need to read in each band separately with GetRasterBand(i), and than do ReadAsArray() If there is a specific GDAL driver for the type of images you want to read in, life gets very easy
If you give some more info on the type of images you want to read in, i can maybe help more specifically
Edit: did you try something like this ? (not sure if that will work on tiff, or how many bits the header is, hence the something:)
A=open(filename,"r")
B=numpy.fromfile(A,dtype='uint8')[something:].reshape(3858,3743)
C=B*1.0
A.close()
Edit: The problem is solved when using 64bit python instead of 32bit, due to memory errors at 2Gb when using the 32bit python version.
I'm looking to create a function for converting a QImage into OpenCV's (CV2) Mat format from within the PyQt.
How do I do this? My input images I've been working with so far are PNGs (either RGB or RGBA) that were loaded in as a QImage.
Ultimately, I want to take two QImages and use the matchTemplate function to find one image in the other, so if there is a better way to do that than I'm finding now, I'm open to that as well. But being able to convert back and forth between the two easily would be ideal.
Thanks for your help,
After much searching on here, I found a gem that got me a working solution. I derived much of my code from this answer to another question: https://stackoverflow.com/a/11399959/1988561
The key challenge I had was in how to correctly use the pointer. The big thing I think I was missing was the setsize function.
Here's my imports:
import cv2
import numpy as np
Here's my function:
def convertQImageToMat(incomingImage):
''' Converts a QImage into an opencv MAT format '''
incomingImage = incomingImage.convertToFormat(4)
width = incomingImage.width()
height = incomingImage.height()
ptr = incomingImage.bits()
ptr.setsize(incomingImage.byteCount())
arr = np.array(ptr).reshape(height, width, 4) # Copies the data
return arr
I tried the answer given above, but couldn't get the expected thing. I tried this crude method where i saved the image using the save() method of the QImage class and then used the image file to read it in cv2
Here is a sample code
def qimg2cv(q_img):
q_img.save('temp.png', 'png')
mat = cv2.imread('temp.png')
return mat
You could delete the temporary image file generated once you are done with the file.
This may not be the right method to do the work, but still does the required job.