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Implementing and applying image degradation function to Python


Implementing and applying image degradation function to Python

By : lemniscate
Date : November 18 2020, 03:49 PM
Hope this helps I found the error I made in the end. I made a silly mistake by implementing H(u,v) instead of the correct H(u-M/2,v-N/2).
code :


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Image processing degradation function with matlab

Image processing degradation function with matlab


By : user3756680
Date : March 29 2020, 07:55 AM
it helps some times Look imfilter function from Image Processing Toolbox. If this function is not appropriate for you then you can use fft2 and ifft2 functions to do forward and backward Fourier transform.
Responsive, transparent CSS image caption with graceful degradation?

Responsive, transparent CSS image caption with graceful degradation?


By : Shalvi Singh
Date : March 29 2020, 07:55 AM
help you fix your problem I modified your CSS slightly. The main changes were adding position: relative; to the parent element and position: absolute; to the caption.
CSS:
code :
section {
    overflow: auto;
}

section figure {
    float: left;
    clear: both;

    position: relative;
    overflow: auto;

    margin: 0 auto;
    padding: 30px 0 0 0;
    font-size: 15px;
}

section figure img {
    vertical-align: bottom;
}

section figure figcaption {
    position: absolute;
    bottom: 0;
    left: 0;
    right: 0;

    background: rgba(0,0,0,0.7);
    text-align: center;
    color: #fff; 
    padding: 10px;
}


section {
    padding-bottom: 30px;
    background: #ccc;

}
jquery function applying css to image

jquery function applying css to image


By : user3359427
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Your code looks a little more complicated that necessary:
1) Note that jQuery [1] will be the second image because it starts counting at 0. It will also be a DOM object instead of a jQuery one, so you will not be able to use jQuery methods on it (like you are trying to do).
code :
$("div.featured-card-box img")[1]
$("div.featured-card-box img:eq(1)").hide();
Applying a filter on an image with Python

Applying a filter on an image with Python


By : user2217501
Date : March 29 2020, 07:55 AM
it should still fix some issue img is likely an 8-bit unsigned integer. Convolving with the Laplace mask as you do, output values are likely to exceed the valid range of [0,255]. When assigning, for example, a -1 to such an image, the value written will be 254. That leads to an output as shown in the question.
With this particular filter, it is important to convert the image to a signed type first, for example a 16-bit signed integer or a floating-point type.
code :
img = img.astype(np.int16)
A weird image modification after applying scipy.ndimage.zoom function to a mri segmentation image

A weird image modification after applying scipy.ndimage.zoom function to a mri segmentation image


By : user3486657
Date : March 29 2020, 07:55 AM
like below fixes the issue For the fact that you get values different from 0 & 1, I am not too sure because I cannot manage to reproduce, but potentially setting order=0 in the call to zoom could solve it.
Then, for your images comparison, I am pretty sure you are not comparing comparable images. When you call zoom, the boundaries of your image stay the same, but the number of sample points changes. As a consequence, in the case of a matrix representation, the shape of the matrix changes, in your case from (150, 418, 406) to (160, 192, 128). As a result, accessing the 80th element along the first axis of the original image does not map to accessing the 80th element along the first axis of the newly generated image. It would actually be closer to 80 / 150 * 160, which is roughly 85. As an example, see the snippet and figure below. Notice how the images in the first row are comparable, but not the ones in the second row. Applying the correction I just mentioned, images in the third row become comparable.
code :
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import zoom

image = np.random.default_rng(0).integers(2, size=(100, 25, 25))
imageZ = zoom(image, zoom=(1.2, 0.8, 0.8), order=0)
fig, axarr = plt.subplots(nrows=3, ncols=2, figsize=(8, 12))
axarr[0, 0].imshow(image[-1, :, :], cmap='binary')
axarr[0, 1].imshow(imageZ[-1, :, :], cmap='binary')
axarr[1, 0].imshow(image[70, :, :], cmap='binary')
axarr[1, 1].imshow(imageZ[70, :, :], cmap='binary')
axarr[2, 0].imshow(image[70, :, :], cmap='binary')
axarr[2, 1].imshow(imageZ[84, :, :], cmap='binary')
fig.savefig('so.png', bbox_inches='tight', dpi=300)
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