I am totally beginner in this field. I started working with neural network for image classification purpose. My question is I loaded one row through panda. now I want to see that image like from which category it is. it has label 0. so how i can convert that pixels values into image.
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import pandas as pd
import seaborn as sns
import cv2
import os
#import deep learning libraries
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
df = pd.read_csv(r"/content/drive/MyDrive/Colab Notebooks/fe/icml_face_data.csv", nrows = 1)
df.pixel
0 70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...
Name: pixel, dtype: object
The above pixel values I am getting now in next step I want to convert this into image.
You can visualise an image using matplotlib (plt.imshow) or seaborn (sns.heatmap). Note that in all cases you'll probably want to change the colour map to something other than the default.
However, it looks like your image is stored in a vector, not a 2D matrix. You can reshape this using numpy (np.reshape) but you will need to know the original dimensions of your image for this transformation. You can use df.pixel.to_numpy() to make a numpy vector.
Related
import nibabel as nib
import numpy as np
import torch
from skimage.transform import radon,iradon
dir = "/hdd1/Data/3D_CT/train/000000098656.nii.gz"
nib_loader = nib.load(dir).get_fdata()
theta = np.linspace(0,180,360)
slices = nib_loader[150,:,:]
rt = radon(slices,theta,circle=True)
print(slices.max())
print(slices.min())
print(rt.max())
print(rt.min())**
slices.max()=1220.0
slices.min()=-1024.0
rt.max()=0.0
rt.min()=-510128.35438634525
radon transform change image scale how to fix the radon transform scale?
i want radon transform image sclae same slices image scale
import rasterio as rio
from rasterio.plot import show
from sklearn import cluster
import matplotlib.pyplot as plt
import numpy as np
import glob
for filepath in glob.iglob('./dengue3/*.tiff'):
elhas_raster = rio.open(filepath)
elhas_arr = elhas_raster.read() # read the opened image
vmin, vmax = np.nanpercentile(elhas_arr, (5,95)) # 5-95% contrast stretch
# create an empty array with same dimension and data type
imgxyb = np.empty((elhas_raster.height, elhas_raster.width, elhas_raster.count), elhas_raster.meta['dtype'])
# loop through the raster's bands to fill the empty array
for band in range(imgxyb.shape[2]):
imgxyb[:,:,band] = elhas_raster.read(band+1)
#print(imgxyb.shape)
# convert to 1d array
img1d=imgxyb[:,:,:7].reshape((imgxyb.shape[0]*imgxyb.shape[1],imgxyb.shape[2]))
#print(img1d.shape)
Above code I am using to read the tiff images in a folder and get the arrays. However, the output is -
ValueError: cannot reshape array of size 6452775 into shape (921825,12)
Images are 12 band. I tried using 12 in place of 7 in the above code, but the code doesnt execute. How do I resolve this? Thank you for your time.
You have changed the size of the index you're trying to reshape, but not the reshape command parameter:
img1d=imgxyb[:,:,:7].reshape((imgxyb.shape[0]*imgxyb.shape[1],imgxyb.shape[2]))
This should be:
img1d=imgxyb[:,:,:7].reshape((imgxyb.shape[0]*imgxyb.shape[1],7))
I want to apply Hough Transform on stock prices (array of numbers).
I read OpenCV and scikit-image docs and examples ,but got nothing how to apply the transformation to the arrays of numbers instead of images.
I created 2D array from data. First dimension is X(simply index of data) and second dimension is close prices.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pywt as wt
from skimage.transform import (hough_line, hough_line_peaks,probabilistic_hough_line)
from matplotlib import cm
path = "22-31May-100Tick.csv"
df = pd.read_csv(path)
y = df.Close.values
x = np.arange(0,len(y),1)
data = []
for i in x:
a = [i,y[i]]
data.append(a)
data = np.array(data)
How is it possible to apply the transformation with OpenCV or sickit-image?
Thank you
I'm having some difficulties trying to convert a NumPy array to R data set. I have a 2D image of 2362 x 2163 on gray scale. I have to import the gray scale value of each pixel to do some statistical analysis. First, this is what I imported for the process:
import cv2
import numpy as np
from skimage import img_as_ubyte
from skimage import data
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
from pandas import DataFrame
pandas2ri.activate()
First I import the image using cv2 as a NumPy array and just in case I converted the array to values between 0 and 255 (256 gray levels).
xchest = cv2.imread("/home/user/xchest.tif", cv2.IMREAD_GRAYSCALE)
xchestsk = img_as_ubyte(xchest)
The output of:
type(xchestsk)
is:
<class 'numpy.ndarray'>
I visually checked the array an as expected is something like:
[[8, 8, 9, ... 200, 234, 245]...[250, 234, 134, ... 67, 8, 8]]
I need all that pixel information on a simple data set that I can use and analyze on RStudio. I tried with:
xchest_R = DataFrame(xchestsk)
xchest_R = r.data('xchestsk')
r.assign("test", xchest_R)
r("save(test, file='/home/user/xchest.gzip', compress=TRUE)")
But when I load it on R:
> load("/home/user/xchest.gzip")
I just get a value: test > "xchestsk"
Like if I just imported a string.
I tried with:
np.save("/home/user/xchest.npy", xchestsk)
But when I try to import it on R with:
> library(RcppCNPy)
> xchest_R <- npyLoad("/home/user/xchest.npy", "integer")
RStudio crashes and I have to restart the session.
Finally, I tried converting the NumPy array to a CSV file:
np.savetxt("/home/eera5607/xchest.csv", xchestsk, delimiter=",")
But when I import it to R:
> xchest_data = read.csv(file="/home/eera5607/xchest.csv", header=FALSE, sep=",")
I can't do simple statistical analysis like:
> mean(xchest_data)
Because I get this warning:
> argument is not numeric or logical: returning NA
I tried converting the data to one variable and 5000000+ points with:
xchest_list = xchestsk.tolist()
xchest_ov = []
for list in xchest_list:
xchest_ov += list
Then I converted the xchest_ov list to CSV but I get the same warning in RStudio.
What I need is to import all those values, if possible, keeping the matrix structure (but it is not necessary, at least import the pixel values as a regular R data set) to which I can apply some statistical analysis. I know I can do some analysis directly on Python but I would like this data on RStudio. I have very little knowledge in this topics. I'm a radiologist and this is the first time I'm working with R.
I'm working with the Mnist data set, in order to learn about Machine learning, and as for now I'm trying to display the first digit in the Mnist data set as an image, and I have encountered a problem.
I have a matrix with the dimensions 784x10000, where each column is a digit in the data set. I have created the matrix myself, because the Mnist data set came in the form of a text file, which in itself caused me quite a lot of problems, but that's a question for itself.
The MN_train matrix below, is my large 784x10000 matrix. So what I'm trying to do below, is to fill up a 28x28 matrix, in order to display my image.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
grey = np.zeros(shape=(28,28))
k = 0
for l in range(28):
for p in range(28):
grey[p,l]=MN_train[k,0]
k = k + 1
print grey
plt.show(grey)
But when I try to display the image, I get the following error:
The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
Followed by a image plot that does not look like the number five, as I would expect.
Is there something I have overlooked, or does this tell me that my manipulation of the text file, in order to construct the MN_train matrix, has resulted in an error?
The error you get is because you supply the array to show. show accepts only a single boolean argument hold=True or False.
In order to create an image plot, you need to use imshow.
plt.imshow(grey)
plt.show() # <- no argument here
Also note that the loop is rather inefficient. You may just reshape the input column array.
The complete code would then look like
import numpy as np
import matplotlib.pyplot as plt
MN_train = np.loadtxt( ... )
grey = MN_train[:,0].reshape((28,28))
plt.imshow(grey)
plt.show()