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			This utility examines the differences between two images. It produces a visual
			annotation and reports statistics.

			It is optimised for images which come from browser screenshots. It analyses the
			image for vertical motion, and annotates connected regions that have the same
			vertical displacement. Then it highlights any remaining ("residual")
			differences which are not explained by vertical motion on a pixel-by-pixel
			basis.

			## Usage

			```
			./uprightdiff [options] <input-1> <input-2> <output>
			Accepted options are:
			--help Show help message and exit
			--block-size arg Block size for initial search (default 16)
			--window-size arg Initial range for vertical motion detection (default
			200)
			--brush-width arg Brush width when heuristically expanding blocks. A
			higher value gives smoother motion regions. This
			should be an odd number. (default 9)
			--outer-hl-window arg The size of the outer square used for detecting
			isolated small features to highlight. This size
			defines what we mean by "isolated". It should be an
			odd number. (default 21)
			--inner-hl-window arg The size of the inner square used for detecting
			isolated small features to highlight. This size
			defines what we mean by "small". It should be an odd
			number. (default 5)
			--intermediate-dir arg A directory where intermediate images should be
			placed. This is our equivalent of debug or trace
			output.
			-v [ --verbose ] Write progress info to stderr.
			--format arg The output format for statistics, may be text (the
			default), json or none.
			-t [ --log-timestamp ] Annotate progress info with elapsed time.
			```

			If you see an error "libdc1394 error: Failed to initialize libdc1394", this can
			be ignored. OpenCV links to libdc1394 unconditionally, and libdc1394
			unconditionally writes out this error on startup if it does not find any
			cameras, but this utility does not make use of any camera functions. This is
			fixed in OpenCV 3.x (which is not released yet): libdc1394 is now optional.

			## Algorithm description

			Motion is detected by first doing an exhaustive search for motion of blocks,
			16x16 pixels by default. The block size should be large enough so that a single
			block by itself contains identifying features, but not so large that regions of
			motion will be missed. This usually means that it should be similar to the font
			size.

			Unlike similar algorithms used by video compression or robotics, we require an
			exact match for motion search to succeed.

			Then, starting from the block search results, regions with known motion are
			expanded into regions of unknown motion. This is done at the full resolution,
			but with a broad "brush size", defaulting to 9px, which defines a minimum
			region of action. Using a brush size which is similar to or larger than the
			font size prevents motion regions from closely hugging the text.

			Connected regions in the resulting optical flow map are identified by a flood
			fill algorithm. If a region has an area of at least 50px, it is shown in the
			annotation as a contour outline, with a labelled arrow showing the direction and
			magnitude of motion. These labels use a rotating palette of bluish colours.
			Regions smaller than 50px are simply filled with the bluish colour.

			A model of the first image is created, with motion applied. Regions
			where the second image matches the moved image are shown in grey, half faded
			to white.

			Residuals are shown by putting the intensity of the moved model in the red
			channel, and the intensity of the second image in the green channel. If the
			motion search algorithm failed for a given pixel, then the first image is used,
			instead of the moved image. Thus, residual colours appear as follows:

			\| First \| Second \| Result
			\|-------\|--------\|-------
			\| Dark \| Dark \| Dark
			\| Dark \| Light \| Green
			\| Light \| Dark \| Red
			\| Light \| Light \| Yellow

			Since it is hard to spot isolated residual pixels by eye, we finally run a
			search for isolated residual features, and put a yellow circle around any that
			are found. An isolated feature is defined as a location where all of the
			residual pixels in a large outer square are also within a smaller inner square
			at its centre.

			The following statistics are provided:

			* modifiedArea: This is a simple count of the number of pixels for which the
			source does not match the destination (after they have both been expanded to
			the same size).
			* movedArea: The number of pixels for which nonzero motion was detected.
			* residualArea: The number of pixels which differed between the resulting image
			and the second input image.

			By specifying --format=json, these statistics are provided on stdout JSON format,
			e.g.:

			{"modifiedArea":5045596,"movedArea":6081096,"residualArea":78707}

			## Compilation

			Install the dependencies. On Debian/Ubuntu this means:

			`sudo apt-get install build-essential g++ libopencv-highgui-dev libopencv-imgcodecs-dev libboost-program-options-dev`

			On Mac OS X with homebrew:

			`brew install opencv boost`

			Then compile:

			```
			autoreconf --verbose --install --symlink
			./configure
			make
			```

			And optionally install it:

			`make install PREFIX=/usr/local`

			All dependencies (C++11, Boost and OpenCV) are cross-platform, and the code is
			intended to be portable, so it should be possible to compile it on other
			operating systems if necessary. However, a custom makefile will be required.

rIURD/README.md24598d45c203master

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rIURD/README.md
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README.md
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