! pip install pandas scikit-learn scikit-image statsmodels requests dash Load repository and create a virtual environment for google colab, for local machine follow the instruction mentioned in the repository.
Code Implementation of Image Crop Analysis:įrom llections import PatchCollection More details regarding this research can be found here. It contains 4073 images of individual labels along with gender identity and ethnic group as recorded on wikidata. WikiCelb consists of images of individual Wikipedia pages obtained through the wikidata query service.
The model is trained by using WikiData Query Service consisting of images and labels of celebrities in wikidata. This is done by cropping only one dimension. Otherwise, the cropping algorithm tries to ensure that the most salient point is within the crop with the desired aspect ratio.If the saliency map is almost symmetric horizontally, then a center crop is performed irrespective of the aspect ratio.This is repeated for each aspect ratio to show the image on multiple devices. The image and the coordinates of the most salient point and the desired aspect ratio are passed as an input to a cropping algorithm.For a given image, the image is discretized into a grid of points, and each grid point is associated with a saliency score predicted by the model.The Twitter algorithm finds the most salient point, and then a set of heuristics is used to create a suitable center crop around that point for a given aspect ratio.
Twitter’s image cropping algorithm relies on a machine learning model trained to predict saliency. After having a saliency score, the model selects the crop by trying to centre it around the most salient point, shifting to stay within the original image. The saliency score is meant to capture the importance of each region of the image. A saliency map aims to simplify and change the representation of an image into something more meaningful and easier to analyze. In computer vision, a saliency map is an image that shows each pixel’s unique quality. The system employees supervised machine learning model on existing saliency maps to predict saliency score over any given input image. The developers of this system focused on an automated image cropping system that automatically crops images that users submit on Twitter to show image previews with respect to different aspect ratios across multiple devices such as a tablet, mobile phones, and desktop.