Demo-Spatial Transformations

docs/examples/spatial_transformation/SpatialTransformations.jl

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+# ---
+# title: Cropping, Resizing and Rescaling
+# cover: assets/lighthouse.png
+# author: Ashwani Rathee
+# date: 2020-11-24
+# ---
+
+# This demonstration shows how to use cropping,resizing and rescaling operations on an 
+# image in Julia using ImageTransformations.jl
+
+using ImageCore, ImageShow, ImageTransformations, TestImages, OffsetArrays, ImageShow
+## load an example image
+img_source = testimage("lighthouse")  
+
+# ## Cropping Operation
+
+# Cropping is one of the most basic photo manipulation processes, and it is carried out to 
+# remove an unwanted object or irrelevant noise from the periphery of a photograph, to 
+# change its aspect ratio, or to improve the overall composition.
+
+# Let's first check the size of the image
+
+img_size = size(img_source)
+
+# Output is `(512,768)` which stands means `img_source` is `512` in height and `768` in width.
+# In Julia, images as multidimensional arrays are stored in column-major order, which means that this first index corresponds to the
+# vertical axis (column) and the second to the horizontal axis (row).
+#
+# !!! tip
+#     An related issue about the memory order is the indexing performance, see [Performance Tips](https://docs.julialang.org/en/v1/manual/performance-tips/#man-performance-column-major) for more details.
+
+# Let's crop the image from sides by 1/8 of img_source each side and leave it as it is from
+# top to bottom.
+
+# Easiest way to do this is indexing: `img_source[y1:y2, x1:x2]`
+
+# Region of Interest: [y1, y2] sets the range for y-axis and [x1, x2] sets the range for
+# x-axis of source image.
+
+img_cropped = @view img_source[ :,floor(Int, 1/8*img_size[2]) : floor(Int, 7/8*img_size[2])]
+
+# Let's see the size of the cropped image:
+
+size(img_cropped)
+
+# We can also do size-preserved cropping of the image by replacing the contents of image to white pixels or transparent pixels using PaddedView:
+
+img_padded = PaddedView(ARGB(0, 0, 0, 0), OffsetArray(img_cropped, OffsetArrays.Origin(1, floor(Int, 1/8*img_size[2]))), axes(img_source))
+
+# ## Resizing Operation
+
+# Resizing is a method to resize an image to a given specific output image shape. It is
+# different from rescaling as in rescaling we use a scaling factor to manipulate the image.
+
+img_square = imresize(img_source, (400, 400));
+img_small = imresize(img_source, ratio=1/4);
+img_medium = imresize(img_small, size(img_small).*2);
+mosaicview(img_source, img_square, img_small, img_medium; nrow=1)
+
+# ## Rescaling
+
+# Rescale operation resizes an image by a given scaling factor. The scaling factor can 
+# either be a single floating point value, or multiple values - one along each axis. 
+# Image scaling is the process of changing the size of an image while preserving the 
+# original aspect ratio.
+
+# ### Rescaling by percentage
+
+percentage_scale = 0.6
+new_size = trunc.(Int, size(img_source) .* percentage_scale)
+img_rescaled = imresize(img_source, new_size);
+mosaicview(img_source, img_rescaled; nrow=1)
+
+# We calculated new size by estimating the size of frame by multiplying size by scale and
+# then truncated it to Int format.
+
+# ### Rescaling to a specific dimension
+
+new_width = 200
+percentage_scale = new_width / size(img_source,2);
+new_size = trunc.(Int, size(img_source) .* percentage_scale);
+img_rescaled = imresize(img_source, new_size);
+mosaicview(img_source, img_rescaled; nrow=1)
+
+# We have updated our scale by percentage solution to calculate scale-percentage 
+# dynamically based on a change in one of the dimensions.
+# Remember: `size(sourceimage) == (height, width)
+
+# ### Rescaling by two-fold using restrict function
+
+rescaled_both = restrict(img_source); # both side
+rescaled_height = restrict(img_source, 1); # height
+rescaled_width = restrict(img_source, 2); # width
+mosaicview(img_source, rescaled_both, rescaled_height, rescaled_width; nrow=1)