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Merge pull request #7 from kianmeng/fix-typos
Fix typos
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README.md

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### Verifying the installation
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To verify wether all the modules are working correctly, you can run:
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To verify whether all the modules are working correctly, you can run:
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```python
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#load only the neede functions from the specific module
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#load only the needed functions from the specific module
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import pyaesthetics
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pyaesthetics.utils.runtest()
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```
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## Example
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pyaeshtetics modules can be used one at the time to estimate one specific feature, or they can be automatically called using an automated entrypoint that calls all the available modules at once.
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### Example 1: one single feature (e.g. Brigthness BT601)
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### Example 1: one single feature (e.g. Brightness BT601)
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```python
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#load only the neede functions from the specific module
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#load only the needed functions from the specific module
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from pyaesthetics.brightness import relativeluminance_bt601
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from pyaeshtetics.utils import sRGB2RGB
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import cv2 #to open and handle images
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- Music A., Maerten A., Wagemans J. (2023).*Beautification of images by generative adversarial networks*. Journal of Vision 2023;23(10):14.
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- Liu, Q., Zhu, S., Zhou, X., Liu, F., Becker, B., Kendrick, K. M., & Zhao, W. (2024). Mothers and fathers show different neural synchrony with their children during shared experiences. NeuroImage, 288, 120529.
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### Theses
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### Thesis
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- Gabrieli G. (2018), Using users' physiological response to predict aesthetic experience of websites, Master Degree in Human-Computer Interaction, University of Trento (Italy)
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- Veldhuizen M. (2024), Analyzing the Role of Aesthetic Features in Packaging Designs on Consumer Responses: The Case of Specialty Coffee, Master Degree in Communication Science, Vrije Universiteit (Netherlands)
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docs/bookthings.rst

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In this tutorial we will analyze the visual complexity of two books' cover. We will start from two images taken in the wild, we will remove the background, interpolate the cover, and subsequently evaluate the visual complexity of the two covers.
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Please note that this tutorial uses rembg `https://github.com/danielgatis/rembg <https://github.com/danielgatis/rembg>`_ to remove the backgound.
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Please note that this tutorial uses rembg `https://github.com/danielgatis/rembg <https://github.com/danielgatis/rembg>`_ to remove the background.
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If you are having issues getting rembg working with numpy>=2, please use a nightly version (see `https://github.com/microsoft/onnxruntime/issues/21063 <https://github.com/microsoft/onnxruntime/issues/21063>`_ for more details):
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>>> python -m pip install coloredlogs flatbuffers numpy packaging protobuf sympy

docs/cakedLisa.rst

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Mona Lisa vs Caked Lisa
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=========================
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In May 2022 a man was arrested after he smeared the protective glass of Leonardo's famous panting with a cake.
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In May 2022 a man was arrested after he smeared the protective glass of Leonardo's famous painting with a cake.
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Pictures of the Mona Lisa covered in Cake were taken by visitors of the Louvre Museum, and rapidly published online.
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.. image:: examples/guardianscreen.png
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>>> # Define the path to the different files and folders
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>>> BASEPATH = 'path/to/project/'
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>>> DB = BASEPATH + 'Raw/db.csv' #ratings' databse
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>>> DB = BASEPATH + 'Raw/db.csv' #ratings' database
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>>> PAINTINGS = BASEPATH + 'Raw/paintings/' #folder with paintings' images
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>>> PROCESSED = BASEPATH + 'Processed/' #empty folder for processed data
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docs/html/gettingstarted.html

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<p>If you’d like to estimate a single feature, you can use the specific pair of module/function.
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Below, an example of the estimation of the brightness of an image, using the BT601 method.</p>
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<p>First load the modules you need.</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1">#load only the neede functions from the specific module</span>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1">#load only the needed functions from the specific module</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pyaesthetics.brightness</span> <span class="kn">import</span> <span class="n">relativeluminance_bt601</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pyaeshtetics.utils</span> <span class="kn">import</span> <span class="n">sRGB2RGB</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">cv2</span>

docs/index.rst

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>>> import pyaesthetics
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>>> pyaesthetics.utils.runtest()
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If no error is reported, your installation is sucessful.
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If no error is reported, your installation is successful.
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Updating pyaeshtetics
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***************************
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* Music A., Maerten A., Wagemans J. (2023).Beautification of images by generative adversarial networks. Journal of Vision 2023;23(10):14.
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* Liu, Q., Zhu, S., Zhou, X., Liu, F., Becker, B., Kendrick, K. M., & Zhao, W. (2024). Mothers and fathers show different neural synchrony with their children during shared experiences. NeuroImage, 288, 120529.
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Theses
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Thesis
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*********************
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* Gabrieli G. (2018), Using users' physiological response to predict aesthetic experience of websites, Master Degree in Human-Computer Interaction, University of Trento (Italy)
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* Veldhuizen M. (2024), Analyzing the Role of Aesthetic Features in Packaging Designs on Consumer Responses: The Case of Specialty Coffee, Master Degree in Communication Science, Vrije Universiteit (Netherlands)

paper/paper.md

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# Use in Scientific Publications
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The package has already been utilized in several peer-reviewed scientific publications and theses. The package was used by @bizzego2022dataset and @liu2024mothers to verify that the visual complexity of different visual stimuli was consistent across trials, by @gabrieli2023machine to investigate the effect of various visual features of still images of websites on participants' aesthetic judgments of the websites, by @cianfanelli2023binding to investigate the visual complexity of IAPS images, by @music2023beautification to explore which visual features contribute to the perception of beauty in images, and by @Veldhuizen2024 to investigate which aesthetic features are most important in influencing consumers’ aesthetic judgments of packaging labels.
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The package has already been utilized in several peer-reviewed scientific publications and thesis. The package was used by @bizzego2022dataset and @liu2024mothers to verify that the visual complexity of different visual stimuli was consistent across trials, by @gabrieli2023machine to investigate the effect of various visual features of still images of websites on participants' aesthetic judgments of the websites, by @cianfanelli2023binding to investigate the visual complexity of IAPS images, by @music2023beautification to explore which visual features contribute to the perception of beauty in images, and by @Veldhuizen2024 to investigate which aesthetic features are most important in influencing consumers’ aesthetic judgments of packaging labels.
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# Acknowledgements
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pyaesthetics.egg-info/PKG-INFO

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## Example
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pyaeshtetics modules can be used one at the time to estimate one specific feature, or they can be automatically called using an automated entrypoint that calls all the available modules at once.
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### Example 1: one single feature (e.g. Brigthness BT601)
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### Example 1: one single feature (e.g. Brightness BT601)
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```python
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#load only the neede functions from the specific module
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#load only the needed functions from the specific module
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from pyaesthetics.brightness import relativeLuminance_BT601, sRGB2RGB
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import cv2 #to open and handle images
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import pyaesthetics
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img = "/path/to/image/image.jpg" #path to a sample image
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results = pyaesthetics.analysis.analyzeImage(img, method="complete") #perform all the availabe analysis using standard parameters
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results = pyaesthetics.analysis.analyzeImage(img, method="complete") #perform all the available analysis using standard parameters
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print(results)
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```
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Or for a faster analysis:

pyaesthetics/quadtreedecomposition.py

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blocks divided by the total possible number of blocks is stored in `self.standardized_complexity`.
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Standardized complexity may range from 0 to 1 (with one being the highest complexity possible).
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The minSize parameter can be automatically adjusted to the common divisor of image width and height closest
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to the imputted minSize via the `autoadjust` parameter (default to False). The adjusted `minSize` can be
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to the inputted minSize via the `autoadjust` parameter (default to False). The adjusted `minSize` can be
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obtained (if `autoadjust` = True) by accessing self.minSize.
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"""
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