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
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)
*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)
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 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)
I want to create an image out of an csv data.
I am reading the csv with:
f = open('file.csv', 'rb')
reader = csv.reader(f)
From here, I want to make a grayscale image that is translating each row of numbers in the list into a line of intensities in an image file.
Not sure what would be useful but here are some details about my csv file:
using floats, columns:315, rows: 144
Thanks
Two steps:
convert the csv file to a numpy array using genfromtxt
From #Andrew on How to read csv into record array in numpy?
from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')
then save the numpy array as an image
from numpy import genfromtxt
from matplotlib import pyplot
from matplotlib.image import imread
my_data = genfromtxt('path to csv', delimiter=',')
matplotlib.image.imsave('path to save image as Ex: output.png', my_data, cmap='gray')
image_1 = imread('path to read image as Ex: output.png')
# plot raw pixel data
pyplot.imshow(image_1)
# show the figure
pyplot.show()
For a very simple solution if you just want to get an impression of what the image would look like you can use the pgm format.
You can create it by writing out the pixels as ascii. The link goes into more detail but the gist is that you have a file of the format:
P2 //which format it is
width height //dimensions
maxValue //the highest value a pixel can have (represents white)
a b c ... //the pixel values (new line needed at the end of each row)
How you get the values out of the CSV should be straightforward, then you could use a function like (untested):
def toFile(array, filename):
f = file(filename, 'w')
f.write("P2\n%d %d\n255\n" %(len(array[1]), len(array))
for i in array:
for j in i:
f.write("%d " %(j))
f.write("\n")
f.close()
I think you could try with glob.glob, what should help
import numpy as np
import glob
import cv2
import csv
Libraries ⬆️; You know what⬇️
image_list = []
for filename in glob.glob(r'C:\your path to\file*.png'): # '*' will count files each by one
#Read
img = cv2.imread(filename)
flattened = img.flatten()
print(flattened) # recommend to avoid duplicates, see files and so on.
#Save
with open('output2.csv', 'ab') as f: #ab is set
np.savetxt(f, flattened, delimiter=",")
Cheers
Also, find an easier method that is making fast and not weight image/csv
image_list = []
with open('train_train_.csv', 'w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter ='-')
for filename in glob.glob(r'C:\your path to\file*.png'):
img = cv2.imread(filename)
image_list.append(img)
csv_writer.writerow(img)
print(img)