For example, if we measure some structure horizontally in the image and find that it is 10 pixels in length, with a pixel size of 0.5µm, we can deduce that its actual length in reality is (approximately!) 10 × 0.5µm = 5µm. Knowing the pixel size makes it possible to calibrate size measurements. pixel values are not determined by detecting light emitted from little square regions, see Blur & the PSF).Īlternative terms, such as sampling distance, are often used instead of pixel size – and are potentially less misleading.īut ‘pixel size’ is still used commonly in software (including ImageJ), and we will use the term as a useful shorthand. what would happen at their ‘edges’?), and does not really match up to the realities of how images are recorded (i.e. In short, pushing the pixels-are-square model too far leads to confusion in the end (e.g. This slightly murky philosophical distinction is considered in Alvy Ray Smith’s technical memo ( right), the title of which gives a good impression of the central argument. Talking of pixels as having (usually) equal widths and heights makes them sound very square-like, but previously I stated that pixels are not squares – they are just displayed using squares. Whenever the field of view remains the same, the pixel size increases as the number of pixels in the image decreases. show () glue_fig ( 'fig_pixel_size', fig )įig. text ( s / 2, - offset * 1.5, f ' µm/px', pad = 20, fontsize = 'small' ) plt. annotate ( xy = ( 0, - offset ), xytext = ( s, - offset ), ** arrow_args ) plt. shape show_image ( zoom ( im, 2 ** ii, order = 0 ), pos = ( 2, 3, ii + 1 )) color_pixels = ( 0.6, 0.2, 0.2, 0.9 ) color_microns = ( 0.2, 0.2, 0.6, 0.9 ) arrow_args = dict ( xycoords = 'data', arrowprops = dict ( arrowstyle = "", shrinkA = 0, shrinkB = 0, color = color_pixels ), annotation_clip = False, text = "" ) text_args = dict ( fontsize = 'x-small' ) offset = s / 15 plt. shape len_microns = 64 pixel_size = len_microns / len_pixels # Show the image # We need to 'zoom' it to be the same size as the original so that the # arrows line up exactly across figures s = im_orig. copy () # Compute image and pixel sizes len_pixels = im. shape - sz ) // 2 im_orig = im_orig from ansform import downscale_local_mean, resize from scipy.ndimage import zoom im_orig = resize ( im_orig, ( 512, 512 )) fig = create_figure () n_plots = 6 for ii in range ( n_plots ): # Downscale if we need to if ii > 0 : im = downscale_local_mean ( im_orig, ( 2 ** ii, 2 ** ii )) else : im = im_orig. # Crop center of an image to a target shape (sz,sz) im_orig = load_image ( 'sunny_cell.tif' ) sz = 600 r = ( im_orig. The result is that we have two value in µm/px, corresponding to the pixel width and pixel height. We can divide the width and height in physical units (often µm) by the number of pixels along that dimension, as shown in Fig. the width and height of the area that has been imaged. One way to think about this in microscopy is to consider the field of view of an image, i.e. We often need to know the pixel size for our images if our analysis results are be meaningful. The ‘pixel size’ is an idea that helps us translate measurements we make in images to the sizes and positions of things in real life. However, if we don’t get too philosophical about it 1, we intuitively know that the things depicted in our images usually have a size in real life. In one sense, a pixel is just a number: it doesn’t really have a size at all. This chapter explores pixels in more detail, including how they are arranged within an image and how they relate to things in the physical world. If in doubt, you’ll always calculate histograms or other measurements before and after trying out something new, to check whether the pixels have been changed. Hopefully by now you’re appropriately nervous about accidentally changing pixel values and therefore compromising your image’s integrity. append ( './././' ) from helpers import * import numpy as np from matplotlib import pyplot as plt Introduction #
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