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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **coloranalysis**\ncoloranalysis is a python package for calculating the percentage of area covered by one or more colors in an image.\n\n### **Prerequisites**\nnumpy, opencv and matplotlib are required to execute coloranalysis, you can download them using the following commands:\n```\npip install numpy\npip install opencv-python\npip install matplotlib\n```\n\n### **Installing**\nYou can either clone or download this repository, or use this command:\n```\npip install coloranalysis\n```\n\n### **Usage**\n\nSee [this notebook](https://github.com/sravyadhulipala/coloranalysis/blob/master/example/colorAreasExample.ipynb) for an example program on how to use this package. However, reading this document entirely is recommended.\n\nLet us consider this image of a rainbow. \n\n\u003cimg src=https://github.com/sravyadhulipala/coloranalysis/blob/master/example/IPTestRainbow.jpg width=\"400\" height=\"200\"\u003e\n\nTo know the area covered by red color, or the area covered by multiple colors in the image, we should get the HEX codes using a [colorpicker.](https://imagecolorpicker.com/)\n\nIn the above image, HEX codes of all colors are: [\"#FE0000\", \"#FD6400\", \"#FFFF02\", \"#008101\", \"#0000FE\", \"#4B0081\", \"#BC31FD\"]\n\nImport colorArea, the class that calculates the area of the colors we want, as follows. \n```\nfrom coloranalysis.colors import colorAreas\n```\n*colorAreas takes no arguments.*\n\n**getArea** \n\n*returns* a list of the percentages of area covered by the given colors.\n\n*arguments* \n\n- **hexColours**: A list of strings representing the HEX codes.\n- **path**: A string specifying the path of the image.\n- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.\n\n*hexColours - colour with a 'u'*\n\nFor a digital image as above, the recommended *diff* value is 10. While the recommended *diff* value for images of real-life objects is 30-50. \nFor more information on HSV color space, see [this link.](https://www.linuxtopia.org/online_books/graphics_tools/gimp_advanced_guide/gimp_guide_node51.html)\n\n**detectColor**\n\n*returns* a tuple with 'mask' and 'result' representing the pixels that match a single color\n\n*arguments*\n- **colour**: Values of the color in HSV color space, in the range H[0-360], S[0-100], V[0-100]\n- **img**: A numpy array returned by cv2.imread()\n- **hsv_img**: A numpy array returned by cv2.cvtColor()\n- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.\n\n*colour - colour with a 'u'*\n\n'mask' and 'result' can be used to visualize the presence of the color in the given image\n\n**convertHEXColours**\n\n*returns* a tuple with two lists representing values of the colors in HSV colorspace and RGB colorspace respectively.\n\n*arguments* \n- **hexColours**: A list of strings representing the HEX codes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsravyadhulipala%2Fcoloranalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsravyadhulipala%2Fcoloranalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsravyadhulipala%2Fcoloranalysis/lists"}