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Articles related to "image"


Detecting Face Features with Python

  • Today we are going to learn how to work with images to detect faces and to extract facial features such as the eyes, nose, mouth, etc.
  • In the past, we have covered before how to work with OpenCV to detect shapes in images, but today we will take it to a new level by introducing DLib, and abstracting face features from an image.
  • So far we did pretty well at finding the face, but we still need some work to extract all the features (landmarks).
  • So far DLib has been pretty magical in the way it works, with just a few lines of code we could achieve a lot, and now we have a whole new problem, would it continue to be as easy?
  • Turns out DLib offers a function called shape_predictor() that will do all the magic for us but with a caveat, it needs a pre-trained model to work.

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Slideio: a new python library for reading medical images.

  • Many bio-image formats support 3 and 4 dimensions (volumes and time series).
  • It allows reading of an arbitrary region of an image at an arbitrary scale with minimal memory and computational resources.
  • Slideio library is designed to read medical images using their internal structure to make the process as performant as possible.
  • A Slide object contains at least one Scene object which is a continuous raster region (2D image, volume, time-series, etc).
  • Method read_block retrieves pixel values of continuous regions.
  • Normally it is not possible to read the whole image at the original scale because of the large size.
  • retrieves the whole image and scales it to 500 pixels width picture.
  • The code snippet below reads a rectangle region from the image and down-scales it to a 500 pixels width picture.
  • Slideio supports 2D slides as well as 3D data sets and time series.

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Neural Supersampling for Real-Time Rendering

  • Example rendering attributes used as input to the neural supersampling method — color, depth, and dense motion vectors — rendered at a low resolution.
  • At inference time, our neural network takes as input the rendering attributes (color, depth map and dense motion vectors per frame) of both current and multiple previous frames, rendered at a low resolution.
  • From top to bottom shows the rendered low-resolution color input, the 16x supersampling result by the introduced method, and the target high-resolution image rendered offline.
  • From left to right shows the rendered low-resolution color input, the 16x supersampling result by the introduced method, and the target high-resolution image rendered offline.

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Slideio: a new python library for reading medical images.

  • Many bio-image formats support 3 and 4 dimensions (volumes and time series).
  • It allows reading of an arbitrary region of an image at an arbitrary scale with minimal memory and computational resources.
  • Slideio library is designed to read medical images using their internal structure to make the process as performant as possible.
  • A Slide object contains at least one Scene object which is a continuous raster region (2D image, volume, time-series, etc).
  • Method read_block retrieves pixel values of continuous regions.
  • Normally it is not possible to read the whole image at the original scale because of the large size.
  • retrieves the whole image and scales it to 500 pixels width picture.
  • The code snippet below reads a rectangle region from the image and down-scales it to a 500 pixels width picture.
  • Slideio supports 2D slides as well as 3D data sets and time series.

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When data is messy

  • It turns out that most of the tench pictures the neural net had seen were of people holding the fish as a trophy.
  • It’s figured out about dramatic stage lighting and human forms, but many of its images don’t contain anything that remotely resembles a microphone.
  • This week Vinay Prabhu and Abeba Birhane pointed out major problems with another dataset, 80 Million Tiny Images, which scraped images and automatically assigned tags to them with the help of another neural net trained on internet text.
  • This is not just a problem with bad data, but with a system where major research groups can release datasets with such huge issues with offensive language and lack of consent.
  • Like the algorithm that upscaled Obama into a white man, ImageNet is the product of a machine learning community where there’s a huge lack of diversity.

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Slideio: a new python library for reading medical images.

  • Many bio-image formats support 3 and 4 dimensions (volumes and time series).
  • It allows reading of an arbitrary region of an image at an arbitrary scale with minimal memory and computational resources.
  • Slideio library is designed to read medical images using their internal structure to make the process as performant as possible.
  • A Slide object contains at least one Scene object which is a continuous raster region (2D image, volume, time-series, etc).
  • Method read_block retrieves pixel values of continuous regions.
  • Normally it is not possible to read the whole image at the original scale because of the large size.
  • retrieves the whole image and scales it to 500 pixels width picture.
  • The code snippet below reads a rectangle region from the image and down-scales it to a 500 pixels width picture.
  • Slideio supports 2D slides as well as 3D data sets and time series.

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Getting Started with OpenCV

  • Cool, we got to read and display our image with OpenCV and got a peek at how to convert GBR colors into RGB to display them inline with Matplolib.
  • Note that I’ve also defined the colormap in .imshow as ‘Greys’; That parameter will be ignored when we plot RGB images but will be helpful later on when we draw the individual dimensions of the arrays.
  • We can tell by looking at the first map that the intensity of blue is higher in the ground than it is in the building, and we can see with the saturation plot that the values around the skateboard are higher than in other parts of the image.
  • We explored how to load and display our pictures, how to convert the array to different color formats, and how to access, modify, and filter the dimensions.

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Slideio: a new python library for reading medical images.

  • Many bio-image formats support 3 and 4 dimensions (volumes and time series).
  • It allows reading of an arbitrary region of an image at an arbitrary scale with minimal memory and computational resources.
  • Slideio library is designed to read medical images using their internal structure to make the process as performant as possible.
  • A Slide object contains at least one Scene object which is a continuous raster region (2D image, volume, time-series, etc).
  • Method read_block retrieves pixel values of continuous regions.
  • Normally it is not possible to read the whole image at the original scale because of the large size.
  • retrieves the whole image and scales it to 500 pixels width picture.
  • The code snippet below reads a rectangle region from the image and down-scales it to a 500 pixels width picture.
  • Slideio supports 2D slides as well as 3D data sets and time series.

save | comments | report | share on


CLAHE and Thresholding in Python

  • In this article, let’s talk about histogram equalization and image thresholding.
  • Histogram equalization is one of the tools we have for image pre-processing and it makes image thresholding or segmentation tasks easier.
  • The reason we need histogram equalization is that when we collect images that are washed out or images with low contrast, we can stretch the histogram to span the entire range.
  • So let's look at the histogram and use the equalization to stretch the histogram to threshold it.
  • Therefore performing the global equalization might not work very well on your image, In those cases, we can use Adaptive Histogram Equalization or also know as CLAHE (Contrast Limiting Adaptive Histogram Equalization).
  • But it still has a lot of noise, Let’s see how thresholding works out to get better results.
  • Before getting started on thresholding we need to look at the histogram of the CLAHE image.

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CLAHE and Thresholding in Python

  • In this article, let’s talk about histogram equalization and image thresholding.
  • Histogram equalization is one of the tools we have for image pre-processing and it makes image thresholding or segmentation tasks easier.
  • The reason we need histogram equalization is that when we collect images that are washed out or images with low contrast, we can stretch the histogram to span the entire range.
  • So let's look at the histogram and use the equalization to stretch the histogram to threshold it.
  • Therefore performing the global equalization might not work very well on your image, In those cases, we can use Adaptive Histogram Equalization or also know as CLAHE (Contrast Limiting Adaptive Histogram Equalization).
  • But it still has a lot of noise, Let’s see how thresholding works out to get better results.
  • Before getting started on thresholding we need to look at the histogram of the CLAHE image.

save | comments | report | share on