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Color histogram

Color histogram with a mask (25 min)

We can also apply a mask to the images we apply the color histogram process to, in the same way we did for grayscale histograms. Consider this image of a well plate, where various chemical sensors have been applied to water and various concentrations of hydrochloric acid and sodium hydroxide:

Well plate image

Suppose we are interested in the color histogram of one of the sensors in the well plate image, specifically, the seventh well from the left in the topmost row, which shows Erythrosin B reacting with water.

Use ImageJ to find the center of that well and the radius (in pixels) of the well. Then, navigate to the Desktop/workshops/image-processing/05-creating-histograms directory, and edit the ColorHistogramMask.py program.

Guided by the comments in the ColorHistogramMask.py program, create a circular mask to select only the desired well. Then, use that mask to apply the color histogram operation to that well. When you execute the program on the plate-01.tif image, your program should display maskedImg, which will look like this:

Masked well plate

And, the program should produce a color histogram that looks like this:

Well plate histogram

Solution

Histograms for the morphometrics challenge (10 min - optional, not included in timing)

Using the grayscale and color histogram programs we developed in this episode, create histograms for the bacteria colonies in the Desktop/workshops/image-processing/10-challenges directory. Save the histograms for later use.

Key Points

We can load images in grayscale by passing the as_gray=True parameter to the skimage.io.imread() function.

We can create histograms of images with the np.histogram function.

We can separate the RGB channels of an image using slicing operations.

We can display histograms using the matplotlib pyplot figure(), title(), xlabel(), ylabel(), xlim(), plot(), and show() functions.

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