![]() ![]() We define a function show_selected_images that iterates over all images, calls the above function to filter them based on color and displays them on the screen using imshow. We need to carefully set the threshold value. It’s all based on what is required in the situation at hand and we can modify the values accordingly. ![]() Similarly, on the other hand, if the threshold is too low, then green might not even match images that have dark green in them. If the threshold is too high, we might start seeing blue images in our search. Let’s consider the case where we are trying to find images with color Green. The threshold basically defines how different can the colors of the image and selected color be. If we extract say 5 colors from an image, even if one color matches with the selected color, we select that image. We need to scan through all possibilities. We need to calculate the delta and compare it to the threshold because for each color there are many shades and we cannot always exactly match the selected color with the colors in the image.īy saying green, the user can mean light green, green or dark green. The for loop simply iterates over all the colors retrieved from the image.įor each color, the loop changes it to lab, finds the delta (basically difference) between the selected color and the color in iteration and if the delta is less than the threshold, the image is selected as matching with the color. We use the method rgb2lab to convert the selected color to a format we can compare. We first extract the image colors using our previously defined method get_colors in RGB format. Get some Inspirations from 1800+ skills Logo Design. Finally, to combine paths while reading files from a directory, we import os. To compare colors we first convert them to lab using rgb2lab and then calculate similarity using deltaE_cie76. KMeans algorithm is part of the sklearn's cluster subpackage. To extract the count, we will use Counter from the collections library. We import the basic libraries including matplotlib.pyplot and numpy. The sample_image.jpg was clicked by me and the other 5 images in the folder images were taken from Unsplash. The complete notebook is available at this repository. In this article, I explain how I understood the basics of OpenCV, extracted colors from images using KMeans algorithm and filtered images from a collection of images based on RGB values of colors. I got inspired to actually write the code that can extract colors out of images and filter the images based on those colors. We’ve all seen that we can search online on the basis of certain filters one of which is color. When I came across OpenCV which allows import and manipulation of images in Python, I started to wonder if information could be extracted out of those images using Machine Learning and used in some way. I recently started reading about how I could work with Images in Python. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |