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| from colordescriptor import ColorDescriptor | |
| from CLIP import CLIPImageEncoder | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from datasets import * | |
| dataset = load_dataset("huggan/CelebA-faces") | |
| candidate_subset = dataset["train"].select(range(10)) # This is a small CBIR app! :D | |
| def emb_dataset(dataset): | |
| # This function might need to be split up, to reduce start-up time of app | |
| # It could also use batches to increase speed | |
| # If indexes are saved in files, this is all not really necessary | |
| ## Color Embeddings | |
| cd = ColorDescriptor((8, 12, 3)) | |
| dataset_with_embeddings = dataset.map(lambda row: {'color_embeddings': cd.describe(row["image"])}) # we assume that dataset has a column 'image' | |
| ## CLIP Embeddings | |
| clip_model = CLIPImageEncoder() | |
| dataset_with_embeddings = dataset_with_embeddings.map(lambda row: {'clip_embeddings': clip_model.encode_image(row["image"])}) | |
| # Add index | |
| dataset_with_embeddings.add_faiss_index(column='color_embeddings') | |
| dataset_with_embeddings.add_faiss_index(column='clip_embeddings') | |
| print(dataset_with_embeddings) | |
| return dataset_with_embeddings | |
| dataset_with_embeddings = emb_dataset(candidate_subset) | |
| # Main function, to find similar images | |
| # TODO: allow different descriptor/embedding functions | |
| # TODO: implement different distance measures | |
| def get_neighbors(query_image, selected_descriptor, top_k=5): | |
| """Returns the top k nearest examples to the query image. | |
| Args: | |
| query_image: A PIL object representing the query image. | |
| top_k: An integer representing the number of nearest examples to return. | |
| Returns: | |
| A list of the top_k most similar images as PIL objects. | |
| """ | |
| if "Color Descriptor" in selected_descriptor: | |
| cd = ColorDescriptor((8, 12, 3)) | |
| qi_embedding = cd.describe(query_image) | |
| qi_np = np.array(qi_embedding) | |
| scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples( | |
| 'color_embeddings', qi_np, k=top_k) | |
| images = retrieved_examples['image'] #retrieved images is a dict, with images and embeddings | |
| return images | |
| if "CLIP" in selected_descriptor: | |
| clip_model = CLIPImageEncoder() | |
| qi_embedding = clip_model.encode_image(query_image) | |
| scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples( | |
| 'clip_embeddings', qi_embedding, k=top_k) | |
| images = retrieved_examples['image'] | |
| return images | |
| else: | |
| print("This descriptor is not yet supported :(") | |
| return [] | |
| # Define the Gradio Interface | |
| iface = gr.Interface( | |
| fn=get_neighbors, | |
| inputs=[ | |
| gr.Image(type="pil", label="Your Image"), | |
| gr.CheckboxGroup(["Color Descriptor", "LBP", "CLIP"], label="Descriptor method?"), | |
| ], | |
| outputs=gr.Gallery(), | |
| title="Image Similarity Gallery", | |
| description="Upload an image and get similar images", | |
| allow_flagging="never" | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch() | |