We can use the OpenAI Contrastive Language-Image Pre-Training (CLIP) Model which is a neural network already trained on a variety of (image, text) pairs. ![]() Here's a quantitative method to determine duplicate and near-duplicate images using the sentence-transformers library which provides an easy way to compute dense vector representations for images. Print('difference : ', hashing1-hashing2) Reduced_image = image.resize((50, 50)).convert('RGB').convert("1")Īnd the code for comparing two image hashing: from PIL import Image The code I use to reduce the image size is this : from PIL import Image The hashing difference score of the pixeled images is even bigger! : 26īelow two more examples of near duplicate image pairs as requested by zen: When pixeld (50x50 pixels), they look like this: The difference between the hashing score of these images is : 24 ![]() This is a sample of a near duplicate image pair: To tackle this, I tried to reduce the pixelation of the near-duplicate images to 50x50 pixel and make them black/white, but I still don't have what I need (small difference score). ![]() As the difference score between their hashing is generally similar to the hashing difference of completely different random images. However, finding near-duplicate and slightly modified images seems to be difficult. The code is working perfectly for finding exact-duplicate images. I am using Perceptual hashing technique to find near-duplicate and exact-duplicate images.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |