Skip to main content

import - How to Flatten and stack Efficiently 50000 Images



I have 50000 grayscale images with 56*56 pixels each one. I need to flatten images and stakck it in the same array with dimensions 50000*3136 after that export the file as .CSV. I am doing this code which load all images in the memory which can cause a problem if we have not enough memory space.


imagesToArray[pathsrc_, pathdes_] := Module[{filesList, data}, (
(* pathsrc_ : list of images path *)
(* pathdes_ : CSV file path*)

filesList = FileNames["*.png", pathsrc];
Print["Number of images :", Length@filesList];
data =
Table[Flatten[ImageData[Import[filesList[[i]]]]], {i, 1,
Length@filesList}];

Export[pathdes, data];
data
)]

Is there any alternative to do it faster without memory problem?



Answer



You can combine @nikie's approach above with the use of a faster built-in import function.


If you can forgo all the behind-the-scene checking that Import does, you can try using the built-in function that Import ultimately calls to read in a PNG file, which is Image`ImportExportDump`ImageReadPNG. I found that out by using Trace@Import[somePNGfile] and wading through quite a lot of output. Incidentally, the counterpart to this function also exists, i.e. ImageWritePNG, but it requires multiple arguments and I haven't figured that one out yet; however, Export is already quite a bit zippier than Import at least for PNG files, so the need is less pronounced there.


It returns a list containing the image imported. To get each image, you can use e.g.


First@Image`ImportExportDump`ImageReadPNG[pathToImage]


It is at least 10x faster than using Import, and quite possibly more.


For instance, let's generate 200 small PNGs of the kind you are working with:


MapIndexed[
Export["images\\" <> ToString[First@#2] <> ".png", #1] &,
Image /@ RandomReal[{0, 1}, {200, 56, 56}]
];

Now let's compare timings:


First@Image`ImportExportDump`ImageReadPNG["images\\" <> ToString[#] <> ".png"] & /@ 

Range[1, 200]; // AbsoluteTiming

Import["images\\" <> ToString[#] <> ".png"] & /@ Range[1, 200]; // AbsoluteTiming

(* Out:
{0.221601, Null}
{15.5932, Null}
*)

That's a 70x speedup!



Comments

Popular posts from this blog

plotting - Filling between two spheres in SphericalPlot3D

Manipulate[ SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, Mesh -> None, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], {n, 0, 1}] I cant' seem to be able to make a filling between two spheres. I've already tried the obvious Filling -> {1 -> {2}} but Mathematica doesn't seem to like that option. Is there any easy way around this or ... Answer There is no built-in filling in SphericalPlot3D . One option is to use ParametricPlot3D to draw the surfaces between the two shells: Manipulate[ Show[SphericalPlot3D[{1, 2 - n}, {θ, 0, Pi}, {ϕ, 0, 1.5 Pi}, PlotPoints -> 15, PlotRange -> {-2.2, 2.2}], ParametricPlot3D[{ r {Sin[t] Cos[1.5 Pi], Sin[t] Sin[1.5 Pi], Cos[t]}, r {Sin[t] Cos[0 Pi], Sin[t] Sin[0 Pi], Cos[t]}}, {r, 1, 2 - n}, {t, 0, Pi}, PlotStyle -> Yellow, Mesh -> {2, 15}]], {n, 0, 1}]

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1.