{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Part 2: creating and importing image features\n", "\n", "Within GridFix, a \"feature\" defines a two-step transformation on an image and an associated RegionSet. In the first step, each input image may be transformed in a way that is useful for the respective analysis, such as generating a contrast map or edge density image. The resulting feature map (which is stored and processed as a numpy array) is then summarized using the RegionSet so that one feature value per region is returned. \n", "\n", "As an example, the most simple feature object is _LuminanceFeature_, which transforms an RGB image into a greyscale luminance image (using the same process as Matlab's _rgb2gray.m_) and then returns the mean luminance value for each region of interest / grid cell." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Import GridFix toolbox and related modules\n", "\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib as mp\n", "import matplotlib.pyplot as plt\n", "\n", "from gridfix import *" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "__Loading data:__ In oder to be able to create and import various image features, we will first load the same example images used in part 1 and again define a 8-by-6 grid for evaluation. If we leave out all the plotting and example code, that's just two lines of Python code: " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "images = ImageSet('images/tutorial_images.csv', label='tutorial')\n", "grid = GridRegionSet(size=images.size, gridsize=(8,6), label='testgrid')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 1. Creating features from images\n", "\n", "GridFix has some built-in image features than can be used directly on input images. To illustrate, let's create the aforementioned LuminanceFeature for our input images and the previously defined grid. `print()`ing a Feature object will show details on the Feature itself as well as the associated datasets. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Regions:\n", "\t\n", "Images:\n", "\t\n" ] } ], "source": [ "fLum = LuminanceFeature(grid, images)\n", "print(fLum)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "A feature's main result is a vector of feature values whose length depends on the associated RegionSet (i.e., one value per region). In our example, the newly defined feature `fLum` has a length of 48, one mean luminance value for each grid cell. To see the feature values for a feature object, `apply()` it to one of the _imageids_ in the set (as in the previous tutorial, feel free to change the _imageid_ and observe the result):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0.93599965, 0.89392132, 0.79004721, 0.74111512, 0.68258492,\n", " 0.71182295, 0.71191775, 0.7004852 , 0.96326098, 0.86776976,\n", " 0.80081014, 0.72126652, 0.64451908, 0.39865884, 0.36996616,\n", " 0.6401726 , 0.96006093, 0.87535547, 0.76301469, 0.63979265,\n", " 0.58886162, 0.37999733, 0.52692699, 0.53575212, 0.84881097,\n", " 0.75357533, 0.64711389, 0.57149906, 0.45624284, 0.28651496,\n", " 0.48697679, 0.52280946, 0.88639214, 0.83512268, 0.65783505,\n", " 0.49580753, 0.54547947, 0.2634846 , 0.44386838, 0.40858254,\n", " 0.62477865, 0.39145541, 0.37857847, 0.7307231 , 0.52127252,\n", " 0.27579 , 0.52870886, 0.38594558])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_img = '111'\n", "fLum.apply(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The vector of length 48 displayed above will later be incorporated into the GLMM predictor matrix. However, the pure values are not very intuitive when exploring data. Therefore, all Feature() objects support plotting, which will apply the feature to the specified _imageid_ and then (by default) display both the resulting feature map and the accompanying RegionSet, with each region shaded according to it's feature value (normalized to the full display range of 0..255 by default). " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ttVx66aU8+OCDNfevFnVBJbnfZZddxo9//OMaE5La2EWlpHBT4G6O83r+BvMz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\nevVBVdImTJjIxLBxABJbzm5yIj9gr9erEo/vvfceF110EfPmzaOwsJC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fLum.plot(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now that we know how to define a feature based on an ImageSet, let's add another feature, this time investigating the edge density map returned by the Sobel edge detector. Feature values here are defined as the proportion of edges in each region / grid cell (because the edge map is binarized, this is equivalent to the mean of each region): " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fEdge = SobelEdgeFeature(grid, images)\n", "fEdge.plot(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 2. Using pre-generated feature maps" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "GridFix can, of course, also load predefined feature maps directly from image- or MATLAB files, such as the output maps generated by a saliency model or image segmentation algorithm. In this example, we will use saliency maps created by the well-known saliency model by Itti, Koch & Niebur (1998) [2].\n", "\n", "For external feature maps, typically each map corresponds to a single image from the fixation dataset. Therefore, an intuitive way to represent a collection of feature maps is as another ImageSet object (internally, ImageSets are a collection of high-precision floating point matrices). The following example shows a way to load files for an ImageSet that is different from the example in part 1: here, we load a folder containing .mat files and explicitly specify the _imageids_ to assign to each image in the folder. The attribute `mat_var='IKN98'` tells the software to load the _IKN98_ matrix from the specified MATLAB file, allowing to store multiple input maps in the same file:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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gGum5smn+GWBlflEGy1ZYtm0pQFjdHE4fDlMfKOvaY9i7lgBUPW4rDyWYzuEK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\n/J/ExbaoD+Bia9Q3bKOgdkw+z/acuq8xhanPK+1TwbS1CBX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ids = ['112', '67', '6', '52', '37', '106', '129', '9', '107', '97', '58', '111', '85', '149', '150']\n", "maps = ImageSet('maps', imageids=ids, mat_var='IKN98')\n", "maps.plot(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now that the IKN98 feature maps have been loaded, we can create a feature to assign each region (grid cell) a unique value based on the input feature map. The corresponding Feature object is called __MapFeature__. This feature object does not transform the input map in any way and simply applies a statistical function to each region. The function to use can be specified in the `stat=` parameter (default is `stat=np.mean` to return the mean feature value per region). All numpy statistics functions work, such as `mean`, `std`, `median` and so on, but this feature will also accept any other function that can process an array and return a scalar. In our example, the following code creates a MapFeature which returns the mean saliency map value in each grid cell:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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v0rLLVaAmJiY2TTGhsQb/ssfw56V10vrA8ZlJbe/G1jsIgs20TgAG/bNu5c/d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\ngG7rewE5P7/6vG2Oesk9Ytf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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fIKN = MapFeature(grid, maps, stat=np.mean)\n", "fIKN.plot(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 3. A special case: the Central Viewing Bias \"feature\"\n", "\n", "When viewing a naturalistic scene on a computer screen, human observers exhibit a strong bias towards the center of the image, even when the center of the image does not even contain the most salient image features or the most information [3]. Even though the assumption of higher fixation probabilities for more central regions is not technically a feature of the input image, central viewing bias can be modeled in GridFix as a special `Feature()` object, the __CentralBiasFeature__. This object type returns the distance between image center and each region's center of gravity (CoG) as its feature values, using one of the following models of central viewing bias: \n", "\n", "- Euclidean distance in pixels\n", "- Gaussian distance measure as proposed in [4], with adjustable $\\sigma^2$ and $\\nu$ parameters \n", "- Manhattan- / Taxicab metric (see https://en.wikipedia.org/wiki/Taxicab_geometry)\n", "\n", "A CentralBiasFeature can be defined and plotted like any other feature (note that only some variants create an actual feature map, which is not needed for e.g. euclidean distance). For this example, we will use the \"gaussian\" distance measure, keeping the default parameters suggested by Clarke and Tatler in [3]. As always, you can try other values and observe how they change the feature map." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Regions:\n", "\t\n", "Images:\n", "\t\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fCent = CentralBiasFeature(grid, images, measure='gaussian', sig2=0.23, nu=0.45)\n", "print(fCent)\n", "fCent.plot(example_img)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 4. Concluding Remarks\n", "\n", "This concludes the second part of our GridFix tutorial. Since we now know how to create and import the image features we want to compare to our participants' fixation data, the next step in this tutorial will explain how to combine all these puzzle pieces into a GLMM predictor matrix and run the resulting model in R. " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## References\n", "\n", "[1] Nuthmann, A., & Einhäuser, W. (2015). A new approach to modeling the influence of image features on fixation selection in scenes. Annals of the New York Academy of Sciences, 1339(1), 82-96. http://dx.doi.org/10.1111/nyas.12705\n", "\n", "[2] Itti, L., Koch, C. & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence (11), 1254-1259. http://dx.doi.org/10.1109/34.730558\n", "\n", "[3] Tatler, B. W. (2007). The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. J Vis 7(14), 411-417. http://dx.doi.org/10.1167/7.14.4\n", "\n", "[4] Clarke, A. D. F. & Tatler, B. W. (2014). Deriving an appropriate baseline for describing fixation behaviour. Vision Res 102, 41-51. http://dx.doi.org/10.1016/j.visres.2014.06.016" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }