Multimodal Embeddings With Langchain
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This tutorial covers how to perform Text Embedding and Image Embedding using Multimodal Embedding Model with Langchain.
The Multimodal Embedding Model is a model that can vectorize text as well as image.
In this tutorial, we will create a simple Image Similarity Searching example using Multimodal Embedding Model and Langchain.
[Note]
langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.
%%capture --no-stderr
%pip install langchain-opentutorial# Install required packages
from langchain_opentutorial import package
package.install(
[
"langchain-experimental",
"pillow",
"open_clip_torch",
"scikit-learn",
"numpy",
"requests",
],
verbose=False,
upgrade=False,
)Multimodal embedding is the process of creating a vector that represents an image’s features and context, making it compatible with text search in the same vector space.
Image Similarity Search is a technique that allows you to find images in a database that are similar to a given query (either an image or text describing the image) using vector-based representations.
The process involves converting images or text into embedding vectors that capture their visual or semantic features.
These vectors are then compared using similarity metrics, such as Cosine Similarity or Euclidean Distance, to find the most similar images in the database based on their vector representations.
In this tutorial, example images are provided. These images are copyright-free and cover a variety of topics (e.g., dog, cat, female, male,...) created using SDXL.
The images are located at ./data/for_embed_images.zip.
Create a list containing the image path.
# Unzip to image dataset
import zipfile
import tempfile
import os
def extract_zip_to_tempdir(zip_file_path: str) -> str:
"""
Extract a ZIP file containing .png images to a temporary directory.
Args:
zip_file_path (str): Path to the ZIP file.
Returns:
str: Path to the temporary directory containing the extracted files.
"""
# Create a temporary directory
temp_dir = tempfile.mkdtemp()
# Extract ZIP file contents to the temporary directory
with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
zip_ref.extractall(temp_dir)
return temp_dirfrom PIL import Image
# in colab : "os.getcwd()" replace to "<your-drive-path>"
DATA_ROOT_DIR = os.path.join(
os.getcwd(), "08-EMBEDDING", "data", "for_embed_images.zip"
)
image_temp_dir = extract_zip_to_tempdir(DATA_ROOT_DIR)
image_path_db = [
os.path.join(image_temp_dir, img_n)
for img_n in os.listdir(image_temp_dir)
if img_n.endswith(".png")
]In this tutorial, we use OpenCLIP, which implements OpenAI's CLIP as an open source.
OpenCLIP can be used with Langchain to easily embed Text and Image .
You can load OpenCLIP Embedding model using the Python libraries open_clip_torch and langchain-experimental.
# List of (Model,Checkpoint)
import open_clip
open_clip.list_pretrained()[('RN50', 'openai'),
('RN50', 'yfcc15m'),
('RN50', 'cc12m'),
('RN101', 'openai'),
('RN101', 'yfcc15m'),
('RN50x4', 'openai'),
('RN50x16', 'openai'),
('RN50x64', 'openai'),
('ViT-B-32', 'openai'),
('ViT-B-32', 'laion400m_e31'),
('ViT-B-32', 'laion400m_e32'),
('ViT-B-32', 'laion2b_e16'),
('ViT-B-32', 'laion2b_s34b_b79k'),
('ViT-B-32', 'datacomp_xl_s13b_b90k'),
('ViT-B-32', 'datacomp_m_s128m_b4k'),
('ViT-B-32', 'commonpool_m_clip_s128m_b4k'),
('ViT-B-32', 'commonpool_m_laion_s128m_b4k'),
('ViT-B-32', 'commonpool_m_image_s128m_b4k'),
('ViT-B-32', 'commonpool_m_text_s128m_b4k'),
('ViT-B-32', 'commonpool_m_basic_s128m_b4k'),
('ViT-B-32', 'commonpool_m_s128m_b4k'),
('ViT-B-32', 'datacomp_s_s13m_b4k'),
('ViT-B-32', 'commonpool_s_clip_s13m_b4k'),
('ViT-B-32', 'commonpool_s_laion_s13m_b4k'),
('ViT-B-32', 'commonpool_s_image_s13m_b4k'),
('ViT-B-32', 'commonpool_s_text_s13m_b4k'),
('ViT-B-32', 'commonpool_s_basic_s13m_b4k'),
('ViT-B-32', 'commonpool_s_s13m_b4k'),
('ViT-B-32', 'metaclip_400m'),
('ViT-B-32', 'metaclip_fullcc'),
('ViT-B-32-256', 'datacomp_s34b_b86k'),
('ViT-B-16', 'openai'),
('ViT-B-16', 'laion400m_e31'),
('ViT-B-16', 'laion400m_e32'),
('ViT-B-16', 'laion2b_s34b_b88k'),
('ViT-B-16', 'datacomp_xl_s13b_b90k'),
('ViT-B-16', 'datacomp_l_s1b_b8k'),
('ViT-B-16', 'commonpool_l_clip_s1b_b8k'),
('ViT-B-16', 'commonpool_l_laion_s1b_b8k'),
('ViT-B-16', 'commonpool_l_image_s1b_b8k'),
('ViT-B-16', 'commonpool_l_text_s1b_b8k'),
('ViT-B-16', 'commonpool_l_basic_s1b_b8k'),
('ViT-B-16', 'commonpool_l_s1b_b8k'),
('ViT-B-16', 'dfn2b'),
('ViT-B-16', 'metaclip_400m'),
('ViT-B-16', 'metaclip_fullcc'),
('ViT-B-16-plus-240', 'laion400m_e31'),
('ViT-B-16-plus-240', 'laion400m_e32'),
('ViT-L-14', 'openai'),
('ViT-L-14', 'laion400m_e31'),
('ViT-L-14', 'laion400m_e32'),
('ViT-L-14', 'laion2b_s32b_b82k'),
('ViT-L-14', 'datacomp_xl_s13b_b90k'),
('ViT-L-14', 'commonpool_xl_clip_s13b_b90k'),
('ViT-L-14', 'commonpool_xl_laion_s13b_b90k'),
('ViT-L-14', 'commonpool_xl_s13b_b90k'),
('ViT-L-14', 'metaclip_400m'),
('ViT-L-14', 'metaclip_fullcc'),
('ViT-L-14', 'dfn2b'),
('ViT-L-14-336', 'openai'),
('ViT-H-14', 'laion2b_s32b_b79k'),
('ViT-H-14', 'metaclip_fullcc'),
('ViT-H-14', 'dfn5b'),
('ViT-H-14-378', 'dfn5b'),
('ViT-g-14', 'laion2b_s12b_b42k'),
('ViT-g-14', 'laion2b_s34b_b88k'),
('ViT-bigG-14', 'laion2b_s39b_b160k'),
('ViT-bigG-14', 'metaclip_fullcc'),
('roberta-ViT-B-32', 'laion2b_s12b_b32k'),
('xlm-roberta-base-ViT-B-32', 'laion5b_s13b_b90k'),
('xlm-roberta-large-ViT-H-14', 'frozen_laion5b_s13b_b90k'),
('convnext_base', 'laion400m_s13b_b51k'),
('convnext_base_w', 'laion2b_s13b_b82k'),
('convnext_base_w', 'laion2b_s13b_b82k_augreg'),
('convnext_base_w', 'laion_aesthetic_s13b_b82k'),
('convnext_base_w_320', 'laion_aesthetic_s13b_b82k'),
('convnext_base_w_320', 'laion_aesthetic_s13b_b82k_augreg'),
('convnext_large_d', 'laion2b_s26b_b102k_augreg'),
('convnext_large_d_320', 'laion2b_s29b_b131k_ft'),
('convnext_large_d_320', 'laion2b_s29b_b131k_ft_soup'),
('convnext_xxlarge', 'laion2b_s34b_b82k_augreg'),
('convnext_xxlarge', 'laion2b_s34b_b82k_augreg_rewind'),
('convnext_xxlarge', 'laion2b_s34b_b82k_augreg_soup'),
('coca_ViT-B-32', 'laion2b_s13b_b90k'),
('coca_ViT-B-32', 'mscoco_finetuned_laion2b_s13b_b90k'),
('coca_ViT-L-14', 'laion2b_s13b_b90k'),
('coca_ViT-L-14', 'mscoco_finetuned_laion2b_s13b_b90k'),
('EVA01-g-14', 'laion400m_s11b_b41k'),
('EVA01-g-14-plus', 'merged2b_s11b_b114k'),
('EVA02-B-16', 'merged2b_s8b_b131k'),
('EVA02-L-14', 'merged2b_s4b_b131k'),
('EVA02-L-14-336', 'merged2b_s6b_b61k'),
('EVA02-E-14', 'laion2b_s4b_b115k'),
('EVA02-E-14-plus', 'laion2b_s9b_b144k'),
('ViT-B-16-SigLIP', 'webli'),
('ViT-B-16-SigLIP-256', 'webli'),
('ViT-B-16-SigLIP-i18n-256', 'webli'),
('ViT-B-16-SigLIP-384', 'webli'),
('ViT-B-16-SigLIP-512', 'webli'),
('ViT-L-16-SigLIP-256', 'webli'),
('ViT-L-16-SigLIP-384', 'webli'),
('ViT-SO400M-14-SigLIP', 'webli'),
('ViT-SO400M-16-SigLIP-i18n-256', 'webli'),
('ViT-SO400M-14-SigLIP-378', 'webli'),
('ViT-SO400M-14-SigLIP-384', 'webli'),
('ViT-L-14-CLIPA', 'datacomp1b'),
('ViT-L-14-CLIPA-336', 'datacomp1b'),
('ViT-H-14-CLIPA', 'datacomp1b'),
('ViT-H-14-CLIPA-336', 'laion2b'),
('ViT-H-14-CLIPA-336', 'datacomp1b'),
('ViT-bigG-14-CLIPA', 'datacomp1b'),
('ViT-bigG-14-CLIPA-336', 'datacomp1b'),
('nllb-clip-base', 'v1'),
('nllb-clip-large', 'v1'),
('nllb-clip-base-siglip', 'v1'),
('nllb-clip-base-siglip', 'mrl'),
('nllb-clip-large-siglip', 'v1'),
('nllb-clip-large-siglip', 'mrl'),
('MobileCLIP-S1', 'datacompdr'),
('MobileCLIP-S2', 'datacompdr'),
('MobileCLIP-B', 'datacompdr'),
('MobileCLIP-B', 'datacompdr_lt'),
('ViTamin-S', 'datacomp1b'),
('ViTamin-S-LTT', 'datacomp1b'),
('ViTamin-B', 'datacomp1b'),
('ViTamin-B-LTT', 'datacomp1b'),
('ViTamin-L', 'datacomp1b'),
('ViTamin-L-256', 'datacomp1b'),
('ViTamin-L-336', 'datacomp1b'),
('ViTamin-L-384', 'datacomp1b'),
('ViTamin-L2', 'datacomp1b'),
('ViTamin-L2-256', 'datacomp1b'),
('ViTamin-L2-336', 'datacomp1b'),
('ViTamin-L2-384', 'datacomp1b'),
('ViTamin-XL-256', 'datacomp1b'),
('ViTamin-XL-336', 'datacomp1b'),
('ViTamin-XL-384', 'datacomp1b'),
('RN50-quickgelu', 'openai'),
('RN50-quickgelu', 'yfcc15m'),
('RN50-quickgelu', 'cc12m'),
('RN101-quickgelu', 'openai'),
('RN101-quickgelu', 'yfcc15m'),
('RN50x4-quickgelu', 'openai'),
('RN50x16-quickgelu', 'openai'),
('RN50x64-quickgelu', 'openai'),
('ViT-B-32-quickgelu', 'openai'),
('ViT-B-32-quickgelu', 'laion400m_e31'),
('ViT-B-32-quickgelu', 'laion400m_e32'),
('ViT-B-32-quickgelu', 'metaclip_400m'),
('ViT-B-32-quickgelu', 'metaclip_fullcc'),
('ViT-B-16-quickgelu', 'openai'),
('ViT-B-16-quickgelu', 'dfn2b'),
('ViT-B-16-quickgelu', 'metaclip_400m'),
('ViT-B-16-quickgelu', 'metaclip_fullcc'),
('ViT-L-14-quickgelu', 'openai'),
('ViT-L-14-quickgelu', 'metaclip_400m'),
('ViT-L-14-quickgelu', 'metaclip_fullcc'),
('ViT-L-14-quickgelu', 'dfn2b'),
('ViT-L-14-336-quickgelu', 'openai'),
('ViT-H-14-quickgelu', 'metaclip_fullcc'),
('ViT-H-14-quickgelu', 'dfn5b'),
('ViT-H-14-378-quickgelu', 'dfn5b'),
('ViT-bigG-14-quickgelu', 'metaclip_fullcc')]# model and checkpoint setting
MODEL = "ViT-g-14"
CHECKPOINT = "laion2b_s34b_b88k"# model load
from langchain_experimental.open_clip import OpenCLIPEmbeddings
clip_embedding = OpenCLIPEmbeddings(
model_name=MODEL,
checkpoint=CHECKPOINT,
)
# Show Model Architecture
clip_embeddingOpenCLIPEmbeddings(model=CLIP(
(visual): VisionTransformer(
(conv1): Conv2d(3, 1408, kernel_size=(14, 14), stride=(14, 14), bias=False)
(patch_dropout): Identity()
(ln_pre): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)
(transformer): Transformer(
(resblocks): ModuleList(
(0-39): 40 x ResidualAttentionBlock(
(ln_1): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=1408, out_features=1408, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=1408, out_features=6144, bias=True)
(gelu): GELU(approximate='none')
(c_proj): Linear(in_features=6144, out_features=1408, bias=True)
)
(ls_2): Identity()
)
)
)
(ln_post): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)
)
(transformer): Transformer(
(resblocks): ModuleList(
(0-23): 24 x ResidualAttentionBlock(
(ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
)
(ls_1): Identity()
(ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(mlp): Sequential(
(c_fc): Linear(in_features=1024, out_features=4096, bias=True)
(gelu): GELU(approximate='none')
(c_proj): Linear(in_features=4096, out_features=1024, bias=True)
)
(ls_2): Identity()
)
)
)
(token_embedding): Embedding(49408, 1024)
(ln_final): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
), preprocess=Compose(
Resize(size=224, interpolation=bicubic, max_size=None, antialias=True)
CenterCrop(size=(224, 224))
ToTensor()
Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
), tokenizer=, model_name='ViT-g-14', checkpoint='laion2b_s34b_b88k')# Embedding Images
# It takes a very long time on Colab. I recommend using a smaller model instead.
image_vector_db = clip_embedding.embed_image(image_path_db)# Preprocessing for Image Similarity Search
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import normalize
import numpy as np
def combine_images_vertically(images: list) -> Image.Image:
"""
Combine the given image path list vertically and save it as a single image.
Args:
images (list): PIL Image List to combine.
Returns:
combined_image (Image.Image): A single image with all input images combined vertically.
"""
# Extract dimensions of each image
widths, heights = zip(*(img.size for img in images))
# Calculate the dimensions of the combined image
max_width = max(widths)
total_height = sum(heights)
# Create a blank canvas with the calculated dimensions
combined_image = Image.new("RGB", (max_width, total_height))
# Paste each image onto the canvas one below the other
y_offset = 0
for img in images:
combined_image.paste(img, (0, y_offset))
y_offset += img.height
return combined_imageImage Similarity Search with Text finds the image in the image dataset that most relates to a given text query.
We will use cosine similarity for calculation of similarity.
Because cosine similarity is commonly used in image similarity search.
Text Query Embedding
Calculate the similarity between the Text Query Embedding Vector and the Image Embedding Vector
Get similar images
"""
OpenCLIP Embedding must explain the image in detail
to produce meaningful results of similarity in the future.
"""
text_query = "a cat with green eyes,pink flowers,tree"# Text Query Embedding
text_query_embedding_vector = clip_embedding.embed_query(text_query)# Get Similarity
similarity = cosine_similarity(
normalize(np.array(text_query_embedding_vector).reshape(1, -1)),
normalize(np.array(image_vector_db)),
)
sorted_idx = similarity.argsort()[0][::-1]for i, idx in enumerate(sorted_idx):
# window : \\
# mac, linux : /
file_name = image_path_db[idx].split("\\")[-1] # default window
print(f"Similarity Rank{i+1} : {similarity[0][idx]:.3f} | {file_name}")Similarity Rank1 : 0.297 | cat_00005_.png
Similarity Rank2 : 0.287 | cat_00007_.png
Similarity Rank3 : 0.283 | cat_00014_.png
Similarity Rank4 : 0.279 | cat_00006_.png
Similarity Rank5 : 0.272 | cat_00016_.png
Similarity Rank6 : 0.265 | cat_00008_.png
Similarity Rank7 : 0.260 | cat_00015_.png
Similarity Rank8 : 0.255 | cat_00013_.png
Similarity Rank9 : 0.186 | dog_00006_.png
Similarity Rank10 : 0.167 | cat_00002_.png
Similarity Rank11 : 0.164 | dog_00008_.png
Similarity Rank12 : 0.152 | cat_00003_.png
Similarity Rank13 : 0.150 | tree_00002_.png
Similarity Rank14 : 0.149 | cat_00004_.png
Similarity Rank15 : 0.146 | cat_00001_.png
Similarity Rank16 : 0.145 | dog_00009_.png
Similarity Rank17 : 0.141 | cat_00010_.png
Similarity Rank18 : 0.140 | tree_00004_.png
Similarity Rank19 : 0.140 | tree_00003_.png
Similarity Rank20 : 0.136 | dog_00011_.png
Similarity Rank21 : 0.133 | dog_00010_.png
Similarity Rank22 : 0.127 | dog_00002_.png
Similarity Rank23 : 0.126 | dog_00007_.png
Similarity Rank24 : 0.116 | cat_00012_.png
Similarity Rank25 : 0.116 | tree_00008_.png
Similarity Rank26 : 0.115 | tree_00011_.png
Similarity Rank27 : 0.114 | dog_00005_.png
Similarity Rank28 : 0.110 | dog_00012_.png
Similarity Rank29 : 0.108 | cat_00009_.png
Similarity Rank30 : 0.108 | dog_00001_.png
Similarity Rank31 : 0.107 | cat_00011_.png
Similarity Rank32 : 0.107 | tree_00001_.png
Similarity Rank33 : 0.100 | dog_00004_.png
Similarity Rank34 : 0.097 | tree_00013_.png
Similarity Rank35 : 0.096 | dog_00013_.png
Similarity Rank36 : 0.094 | tree_00006_.png
Similarity Rank37 : 0.093 | tree_00007_.png
Similarity Rank38 : 0.092 | dog_00016_.png
Similarity Rank39 : 0.092 | dog_00014_.png
Similarity Rank40 : 0.091 | tree_00015_.png
Similarity Rank41 : 0.089 | female_00014_.png
Similarity Rank42 : 0.082 | dog_00003_.png
Similarity Rank43 : 0.080 | tree_00005_.png
Similarity Rank44 : 0.078 | dog_00015_.png
Similarity Rank45 : 0.073 | male_00005_.png
Similarity Rank46 : 0.071 | tree_00010_.png
Similarity Rank47 : 0.062 | female_00012_.png
Similarity Rank48 : 0.060 | female_00015_.png
Similarity Rank49 : 0.060 | female_00016_.png
Similarity Rank50 : 0.054 | cow_00006_.png
Similarity Rank51 : 0.051 | tree_00014_.png
Similarity Rank52 : 0.048 | tree_00012_.png
Similarity Rank53 : 0.047 | female_00013_.png
Similarity Rank54 : 0.046 | male_00016_.png
Similarity Rank55 : 0.044 | tree_00016_.png
Similarity Rank56 : 0.037 | cow_00008_.png
Similarity Rank57 : 0.037 | cow_00004_.png
Similarity Rank58 : 0.037 | male_00007_.png
Similarity Rank59 : 0.036 | male_00008_.png
Similarity Rank60 : 0.035 | cow_00007_.png
Similarity Rank61 : 0.034 | female_00002_.png
Similarity Rank62 : 0.034 | female_00003_.png
Similarity Rank63 : 0.032 | tree_00009_.png
Similarity Rank64 : 0.031 | cow_00002_.png
Similarity Rank65 : 0.026 | cow_00003_.png
Similarity Rank66 : 0.024 | female_00010_.png
Similarity Rank67 : 0.023 | cow_00001_.png
Similarity Rank68 : 0.022 | cow_00005_.png
Similarity Rank69 : 0.011 | female_00004_.png
Similarity Rank70 : 0.011 | female_00001_.png
Similarity Rank71 : 0.011 | cow_00012_.png
Similarity Rank72 : 0.009 | male_00006_.png
Similarity Rank73 : 0.007 | female_00009_.png
Similarity Rank74 : 0.006 | cow_00015_.png
Similarity Rank75 : 0.006 | cow_00016_.png
Similarity Rank76 : 0.005 | female_00011_.png
Similarity Rank77 : 0.003 | cow_00009_.png
Similarity Rank78 : -0.002 | cow_00014_.png
Similarity Rank79 : -0.004 | car_00012_.png
Similarity Rank80 : -0.005 | cow_00011_.png
Similarity Rank81 : -0.006 | car_00011_.png
Similarity Rank82 : -0.009 | cow_00013_.png
Similarity Rank83 : -0.011 | male_00009_.png
Similarity Rank84 : -0.011 | male_00004_.png
Similarity Rank85 : -0.014 | cow_00010_.png
Similarity Rank86 : -0.017 | female_00006_.png
Similarity Rank87 : -0.018 | car_00009_.png
Similarity Rank88 : -0.022 | car_00015_.png
Similarity Rank89 : -0.023 | car_00013_.png
Similarity Rank90 : -0.024 | car_00010_.png
Similarity Rank91 : -0.027 | male_00010_.png
Similarity Rank92 : -0.031 | male_00003_.png
Similarity Rank93 : -0.032 | car_00008_.png
Similarity Rank94 : -0.035 | car_00007_.png
Similarity Rank95 : -0.035 | car_00005_.png
Similarity Rank96 : -0.038 | car_00016_.png
Similarity Rank97 : -0.038 | male_00002_.png
Similarity Rank98 : -0.039 | male_00015_.png
Similarity Rank99 : -0.044 | female_00008_.png
Similarity Rank100 : -0.045 | car_00004_.png
Similarity Rank101 : -0.048 | car_00002_.png
Similarity Rank102 : -0.049 | female_00005_.png
Similarity Rank103 : -0.056 | car_00014_.png
Similarity Rank104 : -0.059 | car_00001_.png
Similarity Rank105 : -0.061 | car_00006_.png
Similarity Rank106 : -0.064 | female_00007_.png
Similarity Rank107 : -0.065 | male_00012_.png
Similarity Rank108 : -0.075 | car_00003_.png
Similarity Rank109 : -0.084 | male_00001_.png
Similarity Rank110 : -0.086 | male_00011_.png
Similarity Rank111 : -0.109 | male_00013_.png# Display the top 5 similar images
top5 = sorted_idx[:5]
comb_imgs = combine_images_vertically([Image.open(image_path_db[idx]) for idx in top5])
comb_imgsImage Similarity Search with Image finds the image in the image dataset that most relates to a given image query.
We will use cosine similarity for calculation of similarity.
Because cosine Similarity is commonly used in image similarity search.
Image Query Embedding
Calculate the similarity between the Image Query Embedding Vector and the Image Embedding Vector
Get similar images
# query image url
import requests
from io import BytesIO
def load_image_from_url(url: str, resolution: int = 512) -> Image.Image:
"""
Load an image from a URL and return it as a PIL Image object.
Args:
url (str): The URL of the image.
Returns:
Image.Image: The loaded PIL Image object.
"""
response = requests.get(url)
response.raise_for_status() # Raise an error for failed requests
image = Image.open(BytesIO(response.content))
image = image.resize((resolution, resolution), resample=Image.Resampling.LANCZOS)
return imageimg_url = "https://i.pinimg.com/736x/85/0a/ca/850aca7cd77c110e99ab20862aef14cf.jpg"
image_query = load_image_from_url(img_url)
image_querydef save_image_to_tempfile(url: str) -> str:
"""
Download an image from a URL and save it to a temporary file.
Args:
url (str): The URL of the image.
Returns:
str: The file path to the saved image.
"""
response = requests.get(url)
# Raise an error for failed requests
response.raise_for_status()
# Create a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
temp_file.write(response.content)
# Close the file to allow other processes to access it
temp_file.close()
return temp_file.name# Image Embedding
# Save Temp file used image url
image_query_url = save_image_to_tempfile(img_url)
# Image Embedding
image_query_vector = clip_embedding.embed_image([image_query_url])
# Remove Temp file
os.remove(image_query_url)similarity = cosine_similarity(
normalize(np.array(image_query_vector)),
normalize(np.array(image_vector_db)),
)
sorted_idx = similarity.argsort()[0][::-1]for i, idx in enumerate(sorted_idx):
# window : \\
# mac, linux : /
file_name = image_path_db[idx].split("\\")[-1] # default window
print(f"Similarity Rank{i+1} : {similarity[0][idx]:.3f} | {file_name}")Similarity Rank1 : 0.442 | car_00009_.png
Similarity Rank2 : 0.434 | car_00010_.png
Similarity Rank3 : 0.428 | car_00011_.png
Similarity Rank4 : 0.414 | car_00012_.png
Similarity Rank5 : 0.401 | car_00013_.png
Similarity Rank6 : 0.394 | car_00001_.png
Similarity Rank7 : 0.385 | car_00015_.png
Similarity Rank8 : 0.380 | car_00002_.png
Similarity Rank9 : 0.371 | car_00016_.png
Similarity Rank10 : 0.370 | car_00006_.png
Similarity Rank11 : 0.365 | car_00014_.png
Similarity Rank12 : 0.357 | car_00003_.png
Similarity Rank13 : 0.353 | car_00005_.png
Similarity Rank14 : 0.337 | car_00004_.png
Similarity Rank15 : 0.335 | car_00008_.png
Similarity Rank16 : 0.326 | car_00007_.png
Similarity Rank17 : 0.318 | male_00004_.png
Similarity Rank18 : 0.305 | male_00003_.png
Similarity Rank19 : 0.289 | male_00001_.png
Similarity Rank20 : 0.288 | male_00013_.png
Similarity Rank21 : 0.284 | male_00012_.png
Similarity Rank22 : 0.278 | male_00007_.png
Similarity Rank23 : 0.277 | male_00006_.png
Similarity Rank24 : 0.272 | male_00010_.png
Similarity Rank25 : 0.271 | cow_00012_.png
Similarity Rank26 : 0.271 | male_00002_.png
Similarity Rank27 : 0.267 | cow_00007_.png
Similarity Rank28 : 0.260 | cat_00003_.png
Similarity Rank29 : 0.255 | male_00008_.png
Similarity Rank30 : 0.255 | cat_00009_.png
Similarity Rank31 : 0.252 | cat_00012_.png
Similarity Rank32 : 0.248 | male_00016_.png
Similarity Rank33 : 0.239 | male_00005_.png
Similarity Rank34 : 0.238 | cat_00010_.png
Similarity Rank35 : 0.238 | dog_00002_.png
Similarity Rank36 : 0.236 | cow_00010_.png
Similarity Rank37 : 0.233 | cow_00016_.png
Similarity Rank38 : 0.229 | cow_00005_.png
Similarity Rank39 : 0.228 | cat_00002_.png
Similarity Rank40 : 0.226 | cow_00009_.png
Similarity Rank41 : 0.225 | male_00011_.png
Similarity Rank42 : 0.225 | male_00015_.png
Similarity Rank43 : 0.221 | male_00009_.png
Similarity Rank44 : 0.220 | tree_00005_.png
Similarity Rank45 : 0.218 | cow_00006_.png
Similarity Rank46 : 0.215 | cow_00015_.png
Similarity Rank47 : 0.211 | cat_00006_.png
Similarity Rank48 : 0.208 | cow_00008_.png
Similarity Rank49 : 0.207 | cow_00004_.png
Similarity Rank50 : 0.204 | tree_00016_.png
Similarity Rank51 : 0.203 | dog_00008_.png
Similarity Rank52 : 0.200 | cow_00003_.png
Similarity Rank53 : 0.199 | dog_00004_.png
Similarity Rank54 : 0.199 | tree_00008_.png
Similarity Rank55 : 0.198 | cow_00014_.png
Similarity Rank56 : 0.198 | tree_00006_.png
Similarity Rank57 : 0.197 | tree_00014_.png
Similarity Rank58 : 0.195 | tree_00002_.png
Similarity Rank59 : 0.195 | cow_00013_.png
Similarity Rank60 : 0.193 | cat_00016_.png
Similarity Rank61 : 0.193 | female_00014_.png
Similarity Rank62 : 0.192 | dog_00005_.png
Similarity Rank63 : 0.192 | dog_00001_.png
Similarity Rank64 : 0.192 | cat_00013_.png
Similarity Rank65 : 0.191 | tree_00004_.png
Similarity Rank66 : 0.191 | tree_00007_.png
Similarity Rank67 : 0.190 | female_00002_.png
Similarity Rank68 : 0.189 | tree_00015_.png
Similarity Rank69 : 0.185 | dog_00007_.png
Similarity Rank70 : 0.183 | female_00007_.png
Similarity Rank71 : 0.180 | cat_00007_.png
Similarity Rank72 : 0.180 | tree_00010_.png
Similarity Rank73 : 0.179 | cow_00001_.png
Similarity Rank74 : 0.178 | tree_00003_.png
Similarity Rank75 : 0.178 | tree_00012_.png
Similarity Rank76 : 0.177 | cat_00008_.png
Similarity Rank77 : 0.176 | female_00003_.png
Similarity Rank78 : 0.175 | tree_00009_.png
Similarity Rank79 : 0.175 | female_00015_.png
Similarity Rank80 : 0.174 | cat_00011_.png
Similarity Rank81 : 0.174 | tree_00011_.png
Similarity Rank82 : 0.174 | female_00005_.png
Similarity Rank83 : 0.173 | dog_00012_.png
Similarity Rank84 : 0.170 | tree_00013_.png
Similarity Rank85 : 0.169 | cat_00005_.png
Similarity Rank86 : 0.168 | cow_00002_.png
Similarity Rank87 : 0.168 | tree_00001_.png
Similarity Rank88 : 0.167 | female_00011_.png
Similarity Rank89 : 0.166 | female_00009_.png
Similarity Rank90 : 0.164 | dog_00009_.png
Similarity Rank91 : 0.164 | cat_00015_.png
Similarity Rank92 : 0.163 | dog_00010_.png
Similarity Rank93 : 0.162 | female_00004_.png
Similarity Rank94 : 0.162 | female_00008_.png
Similarity Rank95 : 0.162 | female_00016_.png
Similarity Rank96 : 0.162 | female_00006_.png
Similarity Rank97 : 0.156 | cow_00011_.png
Similarity Rank98 : 0.155 | dog_00011_.png
Similarity Rank99 : 0.153 | cat_00014_.png
Similarity Rank100 : 0.153 | dog_00006_.png
Similarity Rank101 : 0.149 | female_00012_.png
Similarity Rank102 : 0.148 | female_00001_.png
Similarity Rank103 : 0.145 | female_00010_.png
Similarity Rank104 : 0.134 | cat_00001_.png
Similarity Rank105 : 0.133 | dog_00016_.png
Similarity Rank106 : 0.131 | dog_00003_.png
Similarity Rank107 : 0.130 | dog_00015_.png
Similarity Rank108 : 0.129 | female_00013_.png
Similarity Rank109 : 0.128 | dog_00013_.png
Similarity Rank110 : 0.123 | cat_00004_.png
Similarity Rank111 : 0.119 | dog_00014_.png# Display the top 5 similar images
top5 = sorted_idx[:5]
comb_imgs = combine_images_vertically([Image.open(image_path_db[idx]) for idx in top5])
comb_imgs# Clean up temporary directory manually if needed
import shutil
shutil.rmtree(image_temp_dir)Set up the environment. You may refer to for more details.
You can checkout the for more details.
