I have made a numpy array out of data from an image. I want to convert the numpy array into a one-dimensional one.
import numpy as np
import matplotlib.image as img
if __name__ == '__main__':
my_image = img.imread("zebra.jpg")[:,:,0]
width, height = my_image.shape
my_image = np.array(my_image)
img_buffer = my_image.copy()
img_buffer = img_buffer.reshape(width * height)
print str(img_buffer.shape)
The 128x128 image is here.
However, this program prints out (128, 128). I want img_buffer to be a one-dimensional array though. How do I reshape this array? Why won't numpy actually reshape the array into a one-dimensional array?
.reshape returns a new array, rather than reshaping in place.
By the way, you appear to be trying to get a bytestring of the image - you probably want to use my_image.tostring() instead.
reshape doesn't work in place. Your code isn't working because you aren't assigning the value returned by reshape back to img_buffer.
If you want to flatten the array to one dimension, ravel or flatten might be easier options.
>>> img_buffer = img_buffer.ravel()
>>> img_buffer.shape
(16384,)
Otherwise, you'd want to do:
>>> img_buffer = img_buffer.reshape(np.product(img_buffer.shape))
>>> img_buffer.shape
(16384,)
Or, more succinctly:
>>> img_buffer = img_buffer.reshape(-1)
>>> img_buffer.shape
(16384,)
Related
I am using nibabel lib to load data from nii file. I read the document of the lib at http://nipy.org/nibabel/gettingstarted.html, and found that
This information is available without the need to load anything of the main image data into the memory. Of course there is also access to the image data as a NumPy array
This is my code to load the data and it shapes
import nibabel as nib
img = nib.load('example.nii')
data = img.get_data()
data = np.squeeze(data)
data = np.copy(data, order="C")
print data.shape
I got the result
128, 128, 64
What is order of data shape? Is it WidthxHeightxDepth? And my input must arranged as depth, height, width. So I will use input=data.transpose(2,0,1). Is it right? Thanks all
Update: I found that the Numpy will read the image by order Height x Width x Depth as the reference http://www.python-course.eu/images/axis.jpeg
OK, here's my take:
Using scipy.ndimage.imread('img.jpg', mode='RGB'), the resulting array will always have this order: (H, W, D) i.e. (height, width, depth) because of the terminology that numpy uses for ndarrays (axis=0, axis=1, axis=2) or analogously (Y, X, Z) if one would like to visualize in 3 dimensions.
# read image
In [21]: img = scipy.ndimage.imread('suza.jpg', mode='RGB')
# image shape as (H, W, D)
In [22]: img.shape
Out[22]: (634, 1366, 3)
# transpose to shape as (D, H, W)
In [23]: tr_img = img.transpose((-1, 0, 1))
In [23]: tr_img.shape
Out[23]: (3, 634, 1366)
If you consider the img_shape as a tuple,
# index (0, 1, 2)
img_shape = (634, 1366, 3)
# or index (-3, -2, -1)
Choose which one is a convenient way for you to remember.
NOTE: The scipy.ndimage.imread() API has been removed since Scipy 1.2.0. So, it is now recommended to use imageio.imread(), which reads the image and returns Array, a subclass of numpy array, following the same conventions discussed above.
# read image
$ img = imageio.imread('suza.jpg', format='jpg')
# convert the image to a numpy array
$ img_np = np.asarray(img)
PS: It should also be noted that libraries like tensorflow also (almost) follows the same convention as numpy.
tf.image_decode_jpeg() returns:
A Tensor of type uint8. 3-D with shape [height, width, channels]
Is there any way to reverse the sign (postive=negative, negative=positive) of each individual element of a numpy array without iterating through the array?
An easy solution would be to multiple your numpy array with -1.
For example:
data = np.array([1,2,3,4,-1,-2,-3,-4])
print(data)
>> array([1,2,3,4,-1,-2,-3,-4])
data = data * -1
print(data)
>> array([-1,-2,-3,-4, 1,2,3,4]
Get the axis you want and mutliply it by -1.
Exemple :
import numpy as np
arr = np.array([[1,-2],[-3,4]])
arr[0,:] = arr[0,:] *-1
I have a numpy array and I need to cut a partition of it based on an ROI like (x1,y1)(x2,y2). The background color of the numpy array is zero.
I need to crop that part from the first numpy array and then resize the cropped array to (640,480) pixel.
I am new to numpy and I don't have any clue how to do this.
#numpy1: the first numpy array
roi=[(1,2),(3,4)]
It kind of sounds like you want to do some image processing. Therefore, I suggest you to have a look at the OpenCV library. In their Python implementation, images are basically NumPy arrays. So, cropping and resizing become quite easy:
import cv2
import numpy as np
# OpenCV images are NumPy arrays
img = cv2.imread('path/to/your/image.png') # Just use your NumPy array
# instead of loading some image
# Set up ROI [(x1, y1), (x2, y2)]
roi = [(40, 40), (120, 150)]
# ROI cutout of image
cutout = img[roi[0][1]:roi[1][1], roi[0][0]:roi[1][0], :]
# Generate new image from cutout with desired size
new_img = cv2.resize(cutout, (640, 480))
# Just some output for visualization
img = cv2.rectangle(img, roi[0], roi[1], (0, 255, 0), 2)
cv2.imshow('Original image with marked ROI', img)
cv2.imshow('Resized cutout of image', new_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
----------------------------------------
System information
----------------------------------------
Platform: Windows-10-10.0.16299-SP0
Python: 3.8.5
NumPy: 1.19.1
OpenCV: 4.4.0
----------------------------------------
You can crop an array like
array = array[start_x:stop_x, start_y:stop_y]
or in your case
array = array[roi[0][0]:roi[0][1], roi[1][0]:roi[1][1]]
or one of
array = array[slice(*roi[0]), slice(*roi[1])]
array = array[tuple(slice(*r) for r in roi)]
depending on the amount of abstraction and over-engineering that you need.
I recommend using slicing and skimage. skimage.transform.resize is what you need.
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import resize
image = data.camera()
crop = image[10:100, 10:100]
crop = resize(crop, (640, 480))
plt.imshow(crop)
More about slicing, pls see here.
Details on skimage, see here
I'd like to segment my image using numpy's label and then based on the number of indices found in each label remove those which satisfy my criteria. For example if an image with regions in it that I'd segmented were created like this and segmented using scipy's label:
from numpy import ones, zeros
from numpy.random import random_integers
from scipy.ndimage import label
image = zeros((512, 512), dtype='int')
regionator = ones((11, 11), dtype='int')
xs = random_integers(5, 506, size=500)
ys = random_integers(5, 506, size=500)
for x, y in zip(xs, ys):
image[x-5:x+6, y-5:y+6] = regionator
labels, n_labels = label(image)
Now I'd like to retrieve the indices for each region which has a size greater than 121 pixels (or one regionator size). I'd then like to take those indices and set them to zero so they are no longer part of the labeled image. What is the most efficient way to accomplish this task?
Essentially something similar to MATLAB's regionprops or utilizing IDL's reverse_indices output from its histogram function.
I would use bincount and threshold the result to make a lookup table:
import numpy as np
threshold = 121
size = np.bincount(labels.ravel())
keep_labels = size <= threshold
# Make sure the background is left as 0/False
keep_labels[0] = 0
filtered_labels = keep_labels[labels]
On the last above I index the array keep_labels with the array labels. This is called advanced indexing in numpy and it requires that labels be an integer array. Numpy then uses the elements of labels as indices to keep_labels and produces an array the same shape as labels.
Here's what I've found to work for me so far with good performance even for large datasets.
Using the get indices process taken from here I've come to this:
from numpy import argsort, histogram, reshape, where
import bisect
h = histogram(labels, bins=n_labels)
h_inds = where(h[0] > 121)[0]
labels_f = labels.flatten()
sortedind = argsort(labels_f)
sorted_labels_f = labels_f[sortedind]
inds = []
for i in range(1, len(h_inds)):
i1 = bisect.bisect_left(sorted_labels_f, h[1][h_inds[i]])
i2 = bisect.bisect_right(sorted_labels_f, h[1][h_inds[i]])
inds.extend(sortedind[i1:i2])
# Now get rid of all of those indices that were part of a label
# larger than 121 pixels
labels_f[inds] = 0
filtered_labels = reshape(labels_f, (512, 512))
I'm trying to plot a histogram with matplotlib.
I need to convert my one-line 2D Array
[[1,2,3,4]] # shape is (1,4)
into a 1D Array
[1,2,3,4] # shape is (4,)
How can I do this?
Adding ravel as another alternative for future searchers. From the docs,
It is equivalent to reshape(-1, order=order).
Since the array is 1xN, all of the following are equivalent:
arr1d = np.ravel(arr2d)
arr1d = arr2d.ravel()
arr1d = arr2d.flatten()
arr1d = np.reshape(arr2d, -1)
arr1d = arr2d.reshape(-1)
arr1d = arr2d[0, :]
You can directly index the column:
>>> import numpy as np
>>> x2 = np.array([[1,2,3,4]])
>>> x2.shape
(1, 4)
>>> x1 = x2[0,:]
>>> x1
array([1, 2, 3, 4])
>>> x1.shape
(4,)
Or you can use squeeze:
>>> xs = np.squeeze(x2)
>>> xs
array([1, 2, 3, 4])
>>> xs.shape
(4,)
reshape will do the trick.
There's also a more specific function, flatten, that appears to do exactly what you want.
the answer provided by mtrw does the trick for an array that actually only has one line like this one, however if you have a 2d array, with values in two dimension you can convert it as follows
a = np.array([[1,2,3],[4,5,6]])
From here you can find the shape of the array with np.shape and find the product of that with np.product this now results in the number of elements. If you now use np.reshape() to reshape the array to one length of the total number of element you will have a solution that always works.
np.reshape(a, np.product(a.shape))
>>> array([1, 2, 3, 4, 5, 6])
Use numpy.flat
import numpy as np
import matplotlib.pyplot as plt
a = np.array([[1,0,0,1],
[2,0,1,0]])
plt.hist(a.flat, [0,1,2,3])
The flat property returns a 1D iterator over your 2D array. This method generalizes to any number of rows (or dimensions). For large arrays it can be much more efficient than making a flattened copy.