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https://github.com/haofanwang/natural-language-joint-query-search

Search photos on Unsplash based on OpenAI's CLIP model, support search with joint image+text queries and attention visualization.
https://github.com/haofanwang/natural-language-joint-query-search

attention clip computer-vision image-retrieval image-search multi-modal-search unsplash visualizations

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Search photos on Unsplash based on OpenAI's CLIP model, support search with joint image+text queries and attention visualization.

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# natural-language-joint-query-search



In the project, we support multiple types of query search including text-image, image-image, text2-image, and text+image-image. In order to analyze the result of retrieved images, we also support visualization of text attention. The attention of image will be supported soon!



## Colab Demo



Search photos on Unsplash, support for joint image+text queries search.



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haofanwang/natural-language-joint-query-search/blob/main/colab/unsplash_image_search.ipynb)



Attention visualization of CLIP.



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haofanwang/natural-language-joint-query-search/blob/main/colab/clip_attention.ipynb)



## Usage



We follow the same environment as the [CLIP](https://github.com/openai/CLIP) project:



```bash

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0

$ pip install ftfy regex tqdm

```



To visualize the attention of CLIP, we slightly modify the code of CLIP as mention [here](https://github.com/openai/CLIP/issues/18), so you don't have to install CLIP via official command. An open-sourced visualization tool is used in our project, you need to clone it into this repo.



```bash

$ git clone https://github.com/shashwattrivedi/Attention_visualizer.git

```



Download the pre-extracted image id and features of Unsplash dataset from [Google Drive](https://drive.google.com/drive/folders/1WQmedVCDIQKA2R33dkS1f980YsJXRZ-q) or just run the following commands, and put them under unsplash-dataset dir, details can be found in [natural-language-image-search](https://github.com/haltakov/natural-language-image-search) project.



```bash

from pathlib import Path



# Create a folder for the precomputed features

!mkdir unsplash-dataset



# Download from Github Releases

if not Path('unsplash-dataset/photo_ids.csv').exists():

  !wget https://github.com/haltakov/natural-language-image-search/releases/download/1.0.0/photo_ids.csv -O unsplash-dataset/photo_ids.csv



if not Path('unsplash-dataset/features.npy').exists():

  !wget https://github.com/haltakov/natural-language-image-search/releases/download/1.0.0/features.npy -O unsplash-dataset/features.npy

```



Example of joint query search.



```python

import torch

import numpy as np

import pandas as pd

from PIL import Image



from CLIP.clip import clip



def encode_search_query(search_query):

    with torch.no_grad():

        text_encoded, weight = model.encode_text(clip.tokenize(search_query).to(device))

        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)

        return text_encoded.cpu().numpy()



def find_best_matches(text_features, photo_features, photo_ids, results_count):

  similarities = (photo_features @ text_features.T).squeeze(1)

  best_photo_idx = (-similarities).argsort()

  return [photo_ids[i] for i in best_photo_idx[:results_count]]



device = "cuda" if torch.cuda.is_available() else "cpu"

model, preprocess = clip.load("ViT-B/32", device=device)



photo_ids = pd.read_csv("unsplash-dataset/photo_ids.csv")

photo_ids = list(photo_ids['photo_id'])

photo_features = np.load("unsplash-dataset/features.npy")



# text to image

search_query = "Tokyo Tower at night."

text_features = model.encode_search_query(search_query)

best_photo_ids = find_best_matches(text_features, photo_features, photo_ids, 5)

for photo_id in best_photo_ids:

  print("https://unsplash.com/photos/{}/download".format(photo_id))



# image to image

source_image = "images/borna-hrzina-8IPrifbjo-0-unsplash.jpg"

with torch.no_grad():

  image_feature = model.encode_image(preprocess(Image.open(source_image)).unsqueeze(0).to(device))

  image_feature = (image_feature / image_feature.norm(dim=-1, keepdim=True)).cpu().numpy()

best_photo_ids = find_best_matches(image_feature, photo_features, photo_ids, 5)

for photo_id in best_photo_ids:

  print("https://unsplash.com/photos/{}/download".format(photo_id))



# text+text to image

search_query = "red flower"

search_query_extra = "blue sky"

text_features = encode_search_query(search_query)

text_features_extra = encode_search_query(search_query_extra)

mixed_features = text_features + text_features_extra

best_photo_ids = find_best_matches(mixed_features, photo_features, photo_ids, 5)

for photo_id in best_photo_ids:

  print("https://unsplash.com/photos/{}/download".format(photo_id))



# image+text to image

search_image = "images/borna-hrzina-8IPrifbjo-0-unsplash.jpg"

search_text = "cars"

with torch.no_grad():

  image_feature = model.encode_image(preprocess(Image.open(search_image)).unsqueeze(0).to(device))

  image_feature = (image_feature / image_feature.norm(dim=-1, keepdim=True)).cpu().numpy()

text_feature = encode_search_query(search_text)

modified_feature = image_feature + text_feature

best_photo_ids = find_best_matches(modified_feature, photo_features, photo_ids, 5)

for photo_id in best_photo_ids:

  print("https://unsplash.com/photos/{}/download".format(photo_id))

```



Example of CLIP attention visualization. You can know which keywords does CLIP use to retrieve the results. To be convenient, all punctuations are removed.



```python

import torch

import numpy as np

import pandas as pd

from PIL import Image



from CLIP.clip import clip

from CLIP.clip import model



from Attention_visualizer.attention_visualizer import *



def find_best_matches(text_features, photo_features, photo_ids, results_count):

  similarities = (photo_features @ text_features.T).squeeze(1)

  best_photo_idx = (-similarities).argsort()

  return [photo_ids[i] for i in best_photo_idx[:results_count]]



device = "cuda" if torch.cuda.is_available() else "cpu"

model, preprocess = clip.load("ViT-B/32", device=device, jit=False)



photo_ids = pd.read_csv("unsplash-dataset/photo_ids.csv")

photo_ids = list(photo_ids['photo_id'])

photo_features = np.load("unsplash-dataset/features.npy")



search_query = "A red flower is under the blue sky and there is a bee on the flower"



with torch.no_grad():

    text_token = clip.tokenize(search_query).to(device)

    text_encoded, weight = model.encode_text(text_token)

    text_encoded /= text_encoded.norm(dim=-1, keepdim=True)



text_features = text_encoded.cpu().numpy()

best_photo_ids = find_best_matches(text_features, photo_features, photo_ids, 5)



for photo_id in best_photo_ids:

  print("https://unsplash.com/photos/{}/download".format(photo_id))



sentence = search_query.split(" ")

attention_weights = list(weight[-1][0][1+len(sentence)].cpu().numpy())[:2+len(sentence)][1:][:-1]

attention_weights = [float(item) for item in attention_weights]

display_attention(sentence,attention_weights)

```



You can also run these example on Colab via [joint-query-search](https://colab.research.google.com/github/haofanwang/natural-language-joint-query-search/blob/main/colab/unsplash_image_search.ipynb) and [clip-attention](https://colab.research.google.com/github/haofanwang/natural-language-joint-query-search/blob/main/colab/clip_attention.ipynb).



## Example



### Text-to-Image



##### "Tokyo tower at night."

![Search results for "Tokyo tower at night."](images/example-text-image-1.png)



##### "People come and go on the street."

![Search results for "People come and go on the street."](images/example-text-image-2.png)



### Image-to-Image



##### A normal street view. (The left side is the source image)

![Search results for a street view image](images/example-image-image-1.png)



### Text+Text-to-Image



##### "Flower" + "Blue sky"

![Search results for "flower" and "blue sky"](images/example-text2-image-1.png)



##### "Flower" + "Bee"

![Search results for "flower" and "bee"](images/example-text2-image-2.png)



### Image+Text-to-Image



##### A normal street view + "cars"

![Search results for an empty street with query "cars"](images/example-image+text-image-1.png)



## Visualization



##### "A woman holding an umbrella standing next to a man in a rainy day"



![Search results for "A woman holding an umbrella standing next to a man in a rainy day"](images/attention-2.png)



"umbrella", "standing" and "rainy" receive the most of attention.



##### "A red flower is under the blue sky and there is a bee on the flower"



![Search results for "A red flower is under the blue sky and there is a bee on the flower"](images/attention-1.png)



"flower", "sky" and "bee" receive the most of attention.



## Acknowledgements



Search photos on Unsplash using natural language descriptions. The search is powered by OpenAI's [CLIP model](https://github.com/openai/CLIP) and the [Unsplash Dataset](https://unsplash.com/data). This project is mostly based on [natural-language-image-search](https://github.com/haltakov/natural-language-image-search).



This project was inspired by these projects:



- [OpenAI's CLIP](https://github.com/openai/CLIP)

- [natural-language-image-search](https://github.com/haltakov/natural-language-image-search)