added documentation
This commit is contained in:
Guillaume Raffy
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3c8f14f34b
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57c594cf4d
12
Makefile
12
Makefile
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@ -13,15 +13,13 @@ PACKAGE_FILE_PATH=$(shell pwd)/lipase-$(LIPASE_VERSION).zip
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.PHONY: all
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all: test doc
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.PHONY:
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doc: doc/lipase.pdf
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.PHONY: doc
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doc:
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cd doc; make
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doc/lipase.pdf: doc/lipase.tex
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cd doc; latexmk
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.PHONY:
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.PHONY: clean_doc
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clean_doc:
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cd doc; rm lipase.pdf lipase.aux lipase.fls lipase.log lipase.fdb_latexmk lipase.dvi;
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cd doc; make clean
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$(BUILD_ROOT_PATH)/jars/Lib/fr.univ-rennes1.ipr.lipase.lib.jar: $(LIB_SRC_FILES)
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@ -0,0 +1,29 @@
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RAW_IMAGES_ROOT?=/home/graffy/work/lipase/raw-images
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lipase.pdf: lipase.tex graphics
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pdflatex lipase.tex
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# command used to convert traps sequences in the visible into png with maximum contrast
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TRAPS_VISIBLE_CONVERTER=convert -contrast-stretch 2%x1% -resize 25%
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GENERATED_GRAPHICS=\
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000000_DM300_nofilter_vis_000.png \
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000019_DM300_nofilter_vis_000.png \
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000039_DM300_nofilter_vis_000.png
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.PHONY: graphics
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graphics: $(GENERATED_GRAPHICS)
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000000_DM300_nofilter_vis_000.png : $(RAW_IMAGES_ROOT)/res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0/img_000000000_DM300_nofilter_vis_000.tif
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$(TRAPS_VISIBLE_CONVERTER) "$<" "$@"
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000019_DM300_nofilter_vis_000.png : $(RAW_IMAGES_ROOT)/res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0/img_000000019_DM300_nofilter_vis_000.tif
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$(TRAPS_VISIBLE_CONVERTER) "$<" "$@"
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graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000039_DM300_nofilter_vis_000.png : $(RAW_IMAGES_ROOT)/res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0/img_000000039_DM300_nofilter_vis_000.tif
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$(TRAPS_VISIBLE_CONVERTER) "$<" "$@"
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.PHONY: clean
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clean:
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rm -f $(GENERATED_GRAPHICS)
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rm -f lipase.pdf lipase.aux lipase.fls lipase.log lipase.fdb_latexmk lipase.dvi;
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@ -0,0 +1 @@
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the purpose of this file is to make sure the empty graphics directory can be part of the git repository (workaround to the fact that git doesn't version empty directories)
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@ -1,14 +1,26 @@
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\documentclass[a4paper]{article}
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\documentclass[a4paper, 10pt]{article}
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\usepackage[utf8]{inputenc}
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\usepackage{graphicx}
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\usepackage{subcaption}
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\usepackage[htt]{hyphenat} % allow hyphen inside texttt to avoid overfull hbox warnings
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\usepackage[english, french]{babel}
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\usepackage[margin=0.5in]{geometry} % default margins are too big for my taste: too much wasted space http://kb.mit.edu/confluence/pages/viewpage.action?pageId=3907057
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\usepackage{amsmath} % provides underset
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\hyphenation{tu-yau}
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\title{lipase}
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\author{Guillaume Raffy \and Véronique Vié }
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\begin{document}
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\selectlanguage{english}
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\maketitle
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\section{catalog images}
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image prefix :
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\selectlanguage{french}
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\begin{description}
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\item[AF]
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\item[blé] coupes de blé
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@ -45,5 +57,46 @@
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\item[cin2] autre échantillon similaire à cin1
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\item[cond5678] condition non réalistes
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\end{description}
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\selectlanguage{english}
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\section{computing background image for trap sequences}
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Trap sequences show traps at fixed positions with particles that move over time, as shown in figure \ref{fig:trap_sequence1}. In order to detect the particles, we can subtract from each image a background image, which is an image of the scene without any particle.
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If we suppose that particles are moving fast enough, we can estimate this background image $B$, as :
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\begin{equation}
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B(x,y) = \underset{t\in {1 \ldots T_{max}}}{\mathrm{median}} \{I(x,y,t)\}
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\end{equation}
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where $I(x,y,t)$ is the value of the input sequence at time $t$ and on pixel position $(x,y)$ and $T_{max}$ is the number of frames in the sequence.
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\begin{figure}
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\centering
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\begin{subfigure}[b]{0.3\textwidth}
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\includegraphics[width=1.0\textwidth]{graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000000_DM300_nofilter_vis_000.png}
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%\includegraphics[width=\textwidth]{1.png}
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\caption{Frame 0}
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%\label{fig:1}
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\end{subfigure}
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~
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\begin{subfigure}[b]{0.3\textwidth}
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\includegraphics[width=1.0\textwidth]{graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000019_DM300_nofilter_vis_000.png}
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%\includegraphics[width=\textwidth]{1.png}
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\caption{Frame 19}
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%\label{fig:1}
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\end{subfigure}
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~
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\begin{subfigure}[b]{0.3\textwidth}
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\includegraphics[width=1.0\textwidth]{graphics/res_soleil2018_GGH_GGH_2018_cin2_phiG_I_327_vis_-40_1_Pos0_img_000000039_DM300_nofilter_vis_000.png}
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%\includegraphics[width=\textwidth]{1.png}
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\caption{Frame 39}
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%\label{fig:1}
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\end{subfigure}
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\caption{Example of trap sequence (\texttt{res\_soleil2018/GGH/GGH\_2018\_cin2\_phiG\_I\_327\_vis\_-40\_1/Pos0})}
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\label{fig:trap_sequence1}
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\end{figure}
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\end{document}
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@ -0,0 +1,19 @@
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## 12/03/2020
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- graffy : investigations on how to compute white for sequences containing traps:
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- `telemos.WhiteEstimator` is not suitable for a sequence containing traps, as the resulting white image displays. `telemos.WhiteEstimator` is expecting the input images to only contain small particles.
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- supposing that particles move in the sequences containing trap, the white image can be computed by
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## 17/03/2020
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- graffy
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- I made some manual tests for extracting background of `res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0`. For this sequence, the median image provides a worse background image than the last frame of the sequence, which contains nearly no particle. So for now we'll use the last frame as a background for the moment, but we have to find a better background estimator than just the median image (an idea is for example to keep for each pixel the value that is the most frequent in the sequence)
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- Some manual tests on the sequence `res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0` showed that subtracting the background image makes particles more obviusly visible, as expected. Applying a threshold allows to isolate the particles, but estimating the surface ofd particles from that will be tricky, as only the border of particles have a value which is very different from the background. Of course a fill hole operator could fill rthe particles but this can be fragile as the borders of particles are not always clearly visible on all its surrounding.
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- a more promising solution for detecting particles would be to compute for each pixel :
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- the radial profile around the pixel
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- the radial variance profile around the pixel
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then, particle are the pixels for which
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- the radial variance is low (this means that there's a radial symmetry around the pixel)
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- and the radial profile shows a peak. The position of the peak gives the radius of the particle
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- In order to efficiently compute these radial profile and radial variance profiles on a whole image, convolutions methods can provide dramatic speedups. This technique has already been used in https://subversion.ipr.univ-rennes1.fr/repos/main/projects/antipode (`texori.py`). Unfortunately, I can't reuse this code as it makes extensive used of `numpy` (and imagej's jython doesn't support `numpy`)
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