From 45b4022c4c3b68e63d6b1202a54c41d5b73857d0 Mon Sep 17 00:00:00 2001 From: dumball Date: Wed, 6 Mar 2024 23:26:08 +0530 Subject: [PATCH 1/3] basic working --- README.md | 2 +- alo2.py | 50 ++++++ filebrowser_demo.mp4 | Bin 0 -> 3412517 bytes interface.py | 48 +++--- interface_new.py | 50 ++++++ new.py | 49 ++++++ requirements-important.txt | 310 +++++++++++++++++++++++++++++++++++++ src/alo.py | 74 +++++++++ src/alo2.py | 75 +++++++++ src/captioning.py | 27 +++- src/indexing.py | 35 +++-- 11 files changed, 680 insertions(+), 40 deletions(-) create mode 100644 alo2.py create mode 100644 filebrowser_demo.mp4 create mode 100644 interface_new.py create mode 100644 new.py create mode 100644 requirements-important.txt create mode 100644 src/alo.py create mode 100644 src/alo2.py diff --git a/README.md b/README.md index d921ce8..1194740 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Deeplearning based filebrowser (WIP): ## image captioning demo -![demo for captioning](./demo.mp4) +![demo for captioning](./filebrowser_demo.mp4) - aim is to provide a better search in filebrowsers when the filebrowsere names are cryptic and it is hard to find an image or a document based on the standard search ## todo diff --git a/alo2.py b/alo2.py new file mode 100644 index 0000000..f6cad12 --- /dev/null +++ b/alo2.py @@ -0,0 +1,50 @@ +import streamlit as st +import os +from PIL import Image +from src.captioning import ImageCaptioning + +def main(): + """Main function of the Streamlit application.""" + st.title("Image Explorer") + + # Get directory path from user input or set default + default_dir = os.getcwd() # Use current working directory as default + user_dir = st.text_input("Enter directory path:", default_dir) + if user_dir: + dir_path = user_dir + else: + dir_path = default_dir + + # Check if directory exists + if not os.path.exists(dir_path): + st.error(f"Directory '{dir_path}' does not exist") + return + + # Create an instance of ImageCaptioning and generate the index + image_captioning = ImageCaptioning() + image_dict = image_captioning.make_index(dir_path) + + # Add search bar + search_term = st.text_input("Search images by description:", "") + + # Filter images based on search term + filtered_images = [ + (image_path, descriptions) + for image_path, descriptions in image_dict.items() + if any(search_term.lower() in description.lower() for description in descriptions) + ] + + # Define CSS class (assuming you have a style.css file linked) + st.markdown(f"", unsafe_allow_html=True) + + # Display images with descriptions + columns = st.columns(2) # Adjust the number of columns as desired + for i, (image_path, descriptions) in enumerate(filtered_images): + with columns[i % len(columns)]: + image = Image.open(image_path) + st.image(image) + for description in descriptions: + st.markdown(f"

{description}

", unsafe_allow_html=True) + +if __name__ == "__main__": + main() diff --git a/filebrowser_demo.mp4 b/filebrowser_demo.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..195a4db8eb226fabaebf5131f33b1c6dde9aa97c GIT binary patch literal 3412517 zcmV)CK*GNO001Cnba`-Tb8l?`00IDMb8l^Fb8j+Xc4IMZa5OOh000PPa%E)zGTC@- zWMOmw000XH00)5gF#sEPK!5-M00;&F1ONaC5P$#x@&jeK1OOBNeE6e>L%+cCto!`! zXm$fLR?r-Z85{}D50H3g*)DIhDj=r1ARjgj$`-$GQ0!75)>V1NbvJUWJ{0j~{ti>_ zyS5p)hj896#BQ)<;GIV(0t>6hRv7{I|s`Tdhim?>eq%44ayK2RY1 z$?$%M^nhYY-JHNX^4v7@vH$^fZ|y9mpbz}4RAf93x=ct{5ZIiIOMK+V*2}D`mo510 zEo5^e_;asXw^&Q%Zh@p3ASYf`5($?;kK=5w1X3^XFRL04t`HS-eAJEP)JObmGpJqUKNDY+ zIXF6mkv$CQ(X5#Bcn= zA@tgyG=I~LD5OS($buHMWW!gAC-Mjv<48MPvwm((Fig5<{RrKYFd7g%`WEK22}F*6 zl@xYsqZ>u#Mi;!+$2&dqA857Z>pPK&qc&;^-%jTsF&li=)AG$ymQq5h9TYz zHL&|>hVHA3(5WlsB#=Bc^VXCi)|9P2Z}cK5N!^hE_Y0-9F>e=v09w0Zd%5nl{Y;eLH;#hRt>i^I6RV0FP9O# z1Z8)&1=pP&pB9X56lYIfW&M39uu+h#Rz1gOQLkGOf9_H=d#tBq^)Z0~UkCjtN0)O^ z0BUYj+0vB6gvr>loPOR 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supported_extensions = [".jpg", ".jpeg", ".png"] image_list = [ - os.path.join(dir_path, f) + (os.path.join(dir_path, f), os.path.basename(f)) for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f)) and os.path.splitext(f)[1].lower() in supported_extensions ] # Add search bar - search_term = st.text_input("Search images:") + search_term = st.text_input("Search images:", "") - # Filter images based on search term - filtered_images = [ - image_path for image_path in image_list if search_term.lower() in os.path.basename(image_path).lower() - ] + # Filter images based on search term + # filtered_images = [ + # image_path_filename + # for image_path_filename in image_list + # if search_term.lower() in image_path_filename[1].lower() + # ] + + + image_features = [extract_image_features(path) for path in image_list] + index = create_faiss_index(image_features) + + filtered_images = search_images(search_term, index, image_features, top_k=5) + + # Define CSS class (assuming you have a style.css file linked) + st.markdown(f"", unsafe_allow_html=True) - # Sort images alphabetically - filtered_images.sort() - - # Display images in grid layout - num_cols = 4 # Adjust the number of columns as desired - for i, image_path in enumerate(filtered_images): - if i % num_cols == 0: - # Create a new row after every `num_cols` images - col1, col2, col3, col4 = st.columns(num_cols) - image = Image.open(image_path) - if i % num_cols == 0: - col1.image(image) - elif i % num_cols == 1: - col2.image(image) - elif i % num_cols == 2: - col3.image(image) - else: - col4.image(image) + # Display images with filename + columns = st.columns(2) # Adjust the number of columns as desired + for i, (image_path, filename) in enumerate(filtered_images): + with columns[i % len(columns)]: + image = Image.open(image_path) + st.image(image) + st.markdown(f"

{filename}

", unsafe_allow_html=True) if __name__ == "__main__": main() diff --git a/interface_new.py b/interface_new.py new file mode 100644 index 0000000..c5e1e25 --- /dev/null +++ b/interface_new.py @@ -0,0 +1,50 @@ +import streamlit as st +import os +from PIL import Image +from src.captioning import ImageCaptioning + +def main(): + """Main function of the Streamlit application.""" + st.title("Image Explorer") + + # Get directory path from user input or set default + default_dir = os.getcwd() # Use current working directory as default + user_dir = st.text_input("Enter directory path:", default_dir) + if user_dir: + dir_path = user_dir + else: + dir_path = default_dir + + # Check if directory exists + if not os.path.exists(dir_path): + st.error(f"Directory '{dir_path}' does not exist") + return + + # Create an instance of ImageCaptioning and generate the index + image_captioning = ImageCaptioning() + image_dict = image_captioning.make_index(dir_path) + + # Add search bar + search_term = st.text_input("Search images by description:", "") + + # Filter images based on search term + filtered_images = [ + (image_path, descriptions) + for image_path, descriptions in image_dict.items() + if any(search_term.lower() in description.lower() for description in descriptions) + ] + + # Define CSS class (assuming you have a style.css file linked) + st.markdown(f"", unsafe_allow_html=True) + + # Display images with descriptions + columns = st.columns(2) # Adjust the number of columns as desired + for i, (image_path, descriptions) in enumerate(filtered_images): + with columns[i % len(columns)]: + image = Image.open(image_path) + st.image(image) + for description in descriptions: + st.markdown(f"

{}{ description }

", unsafe_allow_html=True) + +if __name__ == "__main__": + main() diff --git a/new.py b/new.py new file mode 100644 index 0000000..d0e2001 --- /dev/null +++ b/new.py @@ -0,0 +1,49 @@ +import streamlit as st +import os +from PIL import Image +from src.captioning import ImageCaptioning + +def main(): + """Main function of the Streamlit application.""" + st.title("Image Explorer") + + # Get directory path from user input or set default + default_dir = os.getcwd() # Use current working directory as default + user_dir = st.text_input("Enter directory path:", default_dir) + if user_dir: + dir_path = user_dir + else: + dir_path = default_dir + + # Check if directory exists + if not os.path.exists(dir_path): + st.error(f"Directory '{dir_path}' does not exist") + return + + # Create an instance of ImageCaptioning and generate the index + image_captioning = ImageCaptioning() + image_dict = image_captioning.make_index(dir_path) + + # Add search bar + search_term = st.text_input("Search images by description:", "") + + # Filter images based on search term + filtered_images = [ + (image_path, descriptions) + for image_path, descriptions in image_dict.items() + if any(search_term.lower() in description.lower() for description in descriptions) + ] + + # Define CSS class (assuming you have a style.css file linked) + st.markdown(f"", unsafe_allow_html=True) + + # Display images with descriptions and file paths + for image_path, descriptions in filtered_images: + st.image(Image.open(image_path)) + st.markdown(f"

Image Path: {image_path}

", unsafe_allow_html=True) + for description in descriptions: + st.markdown(f"

Description: {description}

", unsafe_allow_html=True) + +if __name__ == "__main__": + main() + diff --git a/requirements-important.txt b/requirements-important.txt new file mode 100644 index 0000000..a931470 --- /dev/null +++ b/requirements-important.txt @@ -0,0 +1,310 @@ + - absl-py==2.0.0 + - accelerate==0.20.3 + - affine==2.4.0 + - aiofiles==23.2.1 + - aiohttp==3.8.4 + - aiosignal==1.3.1 + - alembic==1.13.1 + - altair==5.1.2 + - annotated-types==0.6.0 + - anyio==3.7.1 + - appdirs==1.4.4 + - argparse==1.4.0 + - asttokens==2.4.0 + - astunparse==1.6.3 + - async-timeout==4.0.2 + - attrs==23.1.0 + - auto-gptq==0.2.2 + - automat==22.10.0 + - av==10.0.0 + - backcall==0.2.0 + - beautifulsoup4==4.12.2 + - blinker==1.6.3 + - blis==0.7.11 + - boto3==1.28.61 + - botocore==1.31.61 + - buildtools==1.0.6 + - cachetools==5.3.1 + - camelot==12.6.29 + - catalogue==2.0.10 + - causal-conv1d==1.1.1 + - certifi==2023.5.7 + - chardet==5.2.0 + - charset-normalizer==3.1.0 + - click==8.1.7 + - click-plugins==1.1.1 + - cligj==0.7.2 + - cloudpathlib==0.16.0 + - cloudpickle==3.0.0 + - cmake==3.26.4 + - colorama==0.4.6 + - comm==0.2.0 + - confection==0.1.4 + - constantly==23.10.4 + - contourpy==1.1.1 + - cycler==0.12.1 + - cymem==2.0.8 + - dask==2024.1.0 + - datasets==2.13.0 + - debugpy==1.8.0 + - decorator==5.1.1 + - decord==0.6.0 + - defusedxml==0.7.1 + - dill==0.3.6 + - discord==2.3.2 + - discord-py==2.3.2 + - diskcache==5.6.3 + - dnspython==2.6.1 + - docker==7.0.0 + - docker-pycreds==0.4.0 + - docopt==0.6.2 + - einops==0.7.0 + - elixir==0.7.1 + - en-core-web-sm==3.7.1 (from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl) + - entrypoints==0.4 + - executing==2.0.0 + - faiss-cpu==1.7.4 + - fastapi==0.103.2 + - ffmpeg==1.4 + - ffmpeg-python==0.2.0 + - ffmpy==0.3.1 + - filelock==3.12.2 + - flask-sqlalchemy==3.1.1 + - flaskapp==1.0.5 + - flatbuffers==23.5.26 + - fonttools==4.43.1 + - frozendict==2.3.10 + - frozenlist==1.3.3 + - fsspec==2023.6.0 + - furl==2.1.3 + - future==0.18.3 + - fvcore==0.1.5.post20221221 + - gast==0.5.4 + - gitdb==4.0.10 + - gitpython==3.1.37 + - google-auth==2.23.2 + - google-auth-oauthlib==1.0.0 + - google-pasta==0.2.0 + - gradio==3.47.1 + - gradio-client==0.6.0 + - greenlet==3.0.0 + - grpcio==1.59.0 + - gunicorn==21.2.0 + - h11==0.14.0 + - h5py==3.10.0 + - html5lib==1.1 + - httpcore==0.18.0 + - httpx==0.25.0 + - huggingface-hub==0.15.1 + - hyperlink==21.0.0 + - idna==3.4 + - importlib-metadata==7.0.1 + - importlib-resources==6.1.0 + - imutils==0.5.4 + - incremental==22.10.0 + - inquirerpy==0.3.4 + - iopath==0.1.10 + - ipdb==0.13.13 + - ipykernel==6.27.1 + - ipython==8.16.1 + - itsdangerous==2.1.2 + - jax==0.4.25 + - jedi==0.19.1 + - jinja2==3.1.3 + - jmespath==1.0.1 + - joblib==1.3.2 + - jsonpickle==3.0.2 + - jsonschema==4.19.1 + - jsonschema-specifications==2023.7.1 + - jupyter-client==8.6.0 + - jupyter-core==5.5.1 + - keras==2.15.0 + - kiwisolver==1.4.5 + - langcodes==3.3.0 + - libclang==16.0.6 + - lightning-utilities==0.9.0 + - lit==16.0.6 + - llama-cpp-python==0.2.50 + - locket==1.0.0 + - lxml==4.9.4 + - mako==1.3.2 + - mamba-ssm==1.1.1 + - markdown==3.5 + - markdown-it-py==3.0.0 + - markupsafe==2.1.5 + - matplotlib==3.8.0 + - matplotlib-inline==0.1.6 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - mlflow==2.10.2 + - mpmath==1.3.0 + - multidict==6.0.4 + - multiprocess==0.70.14 + - multitasking==0.0.11 + - munch==2.5.0 + - murmurhash==1.0.10 + - nest-asyncio==1.5.8 + - networkx==3.1 + - ninja==1.11.1.1 + - noise==1.2.2 + - numpy==1.26.4 + - nvidia-cublas-cu11==11.10.3.66 + - nvidia-cuda-cupti-cu11==11.7.101 + - nvidia-cuda-nvrtc-cu11==11.7.99 + - nvidia-cuda-runtime-cu11==11.7.99 + - nvidia-cudnn-cu11==8.5.0.96 + - nvidia-cufft-cu11==10.9.0.58 + - nvidia-curand-cu11==10.2.10.91 + - nvidia-cusolver-cu11==11.4.0.1 + - nvidia-cusparse-cu11==11.7.4.91 + - nvidia-nccl-cu11==2.14.3 + - nvidia-nvtx-cu11==11.7.91 + - oauthlib==3.2.2 + - odc-geo==0.4.1 + - odc-stac==0.3.8 + - onnx==1.15.0 + - opencv-contrib-python==4.8.0.74 + - opencv-python==4.9.0.80 + - opencv-python-headless==4.9.0.80 + - opt-einsum==3.3.0 + - orderedmultidict==1.0.1 + - orjson==3.9.7 + - packaging==23.1 + - pandas==2.0.2 + - pandas-datareader==0.10.0 + - parameterized==0.9.0 + - parso==0.8.3 + - partd==1.4.1 + - pathlib==1.0.1 + - pbr==6.0.0 + - pdfminer==20191125 + - pdfminer3==2018.12.3.0 + - peewee==3.17.0 + - pexpect==4.8.0 + - pfzy==0.3.4 + - pickleshare==0.7.5 + - platformdirs==3.7.0 + - portalocker==2.8.2 + - preshed==3.0.9 + - prompt-toolkit==3.0.39 + - protobuf==4.24.4 + - psutil==5.9.5 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - py-cpuinfo==9.0.0 + - pyarrow==12.0.1 + - pyasn1==0.5.0 + - pyasn1-modules==0.3.0 + - pyaudio==0.2.14 + - pycryptodome==3.20.0 + - pydantic==2.4.2 + - pydantic-core==2.10.1 + - pydeck==0.8.1b0 + - pydub==0.25.1 + - pygame==2.5.2 + - pygments==2.16.1 + - pymongo==4.6.2 + - pyopencl==2023.1 + - pyparsing==3.1.1 + - pypdf2==3.0.1 + - pyproj==3.6.1 + - pystac==1.9.0 + - pystac-client==0.7.5 + - python-dateutil==2.8.2 + - python-multipart==0.0.6 + - pytools==2023.1 + - pytorch-lightning==1.2.2 + - pytorchvideo==0.1.5 + - pytz==2023.3 + - pyyaml==6.0 + - pyzmq==25.1.2 + - querystring-parser==1.2.4 + - rasterio==1.3.9 + - redo==2.0.4 + - referencing==0.30.2 + - regex==2023.6.3 + - requests-oauthlib==1.3.1 + - rich==13.7.0 + - rouge==1.0.1 + - rpds-py==0.10.4 + - rsa==4.9 + - s3transfer==0.7.0 + - sacred==0.8.4 + - safetensors==0.3.1 + - scikit-learn==1.4.1.post1 + - scipy==1.12.0 + - seaborn==0.13.2 + - semantic-version==2.10.0 + - sentry-sdk==1.40.2 + - setproctitle==1.3.3 + - shapely==2.0.2 + - simplejson==3.19.2 + - six==1.16.0 + - smart-open==6.4.0 + - smmap==5.0.1 + - sniffio==1.3.0 + - snuggs==1.4.7 + - sortedcontainers==2.4.0 + - soupsieve==2.5 + - spacy==3.7.4 + - spacy-legacy==3.0.12 + - spacy-loggers==1.0.5 + - sqlalchemy==2.0.27 + - sqlalchemy-migrate==0.11.0 + - sqlparse==0.4.4 + - srsly==2.4.8 + - stack-data==0.6.3 + - starlette==0.27.0 + - streamlit==1.31.1 + - supervision==0.18.0 + - sympy==1.12 + - tabulate==0.9.0 + - tempita==0.5.2 + - tenacity==8.2.3 + - tensorboard==2.15.2 + - tensorboard-data-server==0.7.1 + - tensorflow==2.15.0.post1 + - tensorflow-estimator==2.15.0 + - tensorflow-io-gcs-filesystem==0.36.0 + - termcolor==2.4.0 + - thinc==8.2.3 + - thop==0.1.1.post2209072238 + - threadpoolctl==3.3.0 + - timm==0.9.7 + - tinygrad==0.7.0 (from git+https://github.com/tinygrad/tinygrad.git@6e4a12ab68810ba7150a719ae4c2c274df587db9) + - tokenizers==0.13.3 + - toml==0.10.2 + - toolz==0.12.0 + - torchaudio==2.0.2 + - torchmetrics==1.2.0 + - torchvision==0.15.2 + - tornado==6.4 + - tqdm==4.65.0 + - traitlets==5.11.2 + - triton==2.0.0 + - twisted==23.10.0 + - typer==0.9.0 + - typing-extensions==4.9.0 + - tzdata==2023.3 + - tzlocal==5.2 + - ultralytics==8.1.8 + - uvicorn==0.23.2 + - validators==0.22.0 + - wandb==0.16.3 + - wasabi==1.1.2 + - watchdog==4.0.0 + - wcwidth==0.2.8 + - weasel==0.3.4 + - webencodings==0.5.1 + - websockets==11.0.3 + - werkzeug==3.0.0 + - wrapt==1.14.1 + - xarray==2023.12.0 + - xlrd==0.7.1 + - xlwt==0.7.2 + - xxhash==3.2.0 + - yacs==0.1.8 + - yarl==1.9.2 + - yfinance==0.2.33 + - zipp==3.17.0 + - zope-interface==6.1 diff --git a/src/alo.py b/src/alo.py new file mode 100644 index 0000000..fc29bba --- /dev/null +++ b/src/alo.py @@ -0,0 +1,74 @@ +import faiss +import torch +import numpy as np +from transformers import AutoTokenizer, AutoModel +from captioning import ImageCaptioning +from transformers import CLIPProcessor, CLIPModel +from PIL import Image +import joblib +from concurrent.futures import ThreadPoolExecutor + + +# Load pre-trained text embedding model (e.g., Sentence Transformer) +text_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") +text_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") +feature_extractor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") +image_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + +# Function to embed text queries (customize based on your model) +#@joblib.cache +def embed_text(text_query): + encoded_input = text_tokenizer(text_query, return_tensors="pt") + with torch.no_grad(): + output = text_model(**encoded_input) + return output.last_hidden_state.mean(dim=1).cpu().numpy() + +# Function to extract image features (customize based on your model) +#@joblib.cache +def extract_image_features(image_path): + image = Image.open(image_path) + inputs = feature_extractor(images=image, return_tensors="pt") + with torch.no_grad(): + output = image_model.get_image_features(**inputs) + return output.cpu().numpy() + +# Load your dataset of images and their corresponding textual descriptions +def load_dataset(image_paths, descriptions): + return image_paths, descriptions + +# Create FAISS index for efficient similarity search +#def create_faiss_index(image_features): +# d = image_features.shape[1] # Dimensionality of image features +# index = faiss.IndexFlatL2(d) +# index.add(np.ascontiguousarray(image_features)) +# return index +def create_faiss_index(image_features): + image_features = np.concatenate(image_features, axis=0) # Concatenate the individual NumPy arrays + d = image_features.shape[1] # Dimensionality of image features + index = faiss.IndexFlatL2(d) + index.add(np.ascontiguousarray(image_features)) + return index + +# Text-to-image search function +def search_images(text_query, index, image_paths, top_k=5): + text_embedding = embed_text(text_query) + distances, indices = index.search(text_embedding.reshape(1, -1), top_k) + return [image_paths[i] for i in indices.ravel()] + +# Example usage +alo = ImageCaptioning() +my_dict = alo.make_index() +image_paths = list(my_dict.keys()) +descriptions = [item for sublist in list(my_dict.values()) for item in sublist] + +print("Image paths:", image_paths, "\n", "Descriptions:", descriptions) + +image_features = [extract_image_features(path) for path in image_paths] +index = create_faiss_index(image_features) + +text_query = input("Enter your query: ") +results = search_images(text_query, index, image_paths) + +print(f"Top {len(results)} similar images for '{text_query}':") +for image_path in results: + print(image_path) diff --git a/src/alo2.py b/src/alo2.py new file mode 100644 index 0000000..be9508c --- /dev/null +++ b/src/alo2.py @@ -0,0 +1,75 @@ +import faiss +import torch +import numpy as np +from transformers import AutoTokenizer, AutoModel +from captioning import ImageCaptioning +from transformers import CLIPProcessor, CLIPModel +from PIL import Image +import joblib +from concurrent.futures import ThreadPoolExecutor + +# Load pre-trained text embedding model (e.g., Sentence Transformer) +text_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") +text_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") + +# Load pre-trained image feature extractor (e.g., CLIP) +feature_extractor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") +image_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + +# Function to embed text queries (customize based on your model) +#@joblib.cache +def embed_text(text_query): + encoded_input = text_tokenizer(text_query, return_tensors="pt") + with torch.no_grad(): + output = text_model(**encoded_input) + return output.last_hidden_state.mean(dim=1).cpu().numpy() + +# Function to extract image features (customize based on your model) +#@joblib.cache +def extract_image_features(image_path): + image = Image.open(image_path) + inputs = feature_extractor(images=image, return_tensors="pt") + with torch.no_grad(): + output = image_model.get_image_features(**inputs) + return output.cpu().numpy() + +# Create FAISS index for efficient similarity search +def create_faiss_index(image_features, text_embedding_dim): + image_features = np.concatenate(image_features, axis=0) # Concatenate the individual NumPy arrays + d = text_embedding_dim # Dimensionality of text embeddings + index = faiss.IndexFlatL2(d) + index.add(np.ascontiguousarray(image_features)) + return index + +# Text-to-image search function +def search_images(text_query, index, image_paths, top_k=5): + text_embedding = embed_text(text_query) + distances, indices = index.search(text_embedding.reshape(1, -1), top_k) + return [image_paths[i] for i in indices.ravel()] + +# Example usage +def main(): + alo = ImageCaptioning() + my_dict = alo.make_index() + image_paths = list(my_dict.keys()) + descriptions = [item for sublist in list(my_dict.values()) for item in sublist] + + print("Image paths:", image_paths, "\n", "Descriptions:", descriptions) + + with ThreadPoolExecutor() as executor: + image_features = list(executor.map(extract_image_features, image_paths)) + + # Get the dimensionality of the text embeddings + text_embedding_dim = embed_text("").shape[1] + + index = create_faiss_index(image_features, text_embedding_dim) + + text_query = input("Enter your query: ") + results = search_images(text_query, index, image_paths) + + print(f"Top {len(results)} similar images for '{text_query}':") + for image_path in results: + print(image_path) + +if __name__ == "__main__": + main() diff --git a/src/captioning.py b/src/captioning.py index 918e6b8..d9924bb 100644 --- a/src/captioning.py +++ b/src/captioning.py @@ -2,8 +2,8 @@ import torch from PIL import Image import os - - +import faiss +import joblib class ImageCaptioning: def __init__(self): @@ -19,6 +19,11 @@ def __init__(self): self.gen_kwargs = {"max_length": self.max_length, "num_beams": self.num_beams} # self.embedding = model(**inputs) + + def get_embedding(self, inputs): + return self.model(**inputs) + + def predict_caption(self, image_paths): images = [] for image_path in image_paths: @@ -36,6 +41,8 @@ def predict_caption(self, image_paths): preds = [pred.strip() for pred in preds] return preds + + @staticmethod def is_image(filename): try: @@ -56,6 +63,8 @@ def get_images_in_directory(directory): return image_paths + + def rename_file(self, image_path, new_name): if os.path.isfile(image_path): dirname = os.path.dirname(image_path) @@ -73,14 +82,24 @@ def rename_directory(self, directory_path, new_name): self.rename_file(image_path, new_filename) - def make_index(self): - directory_path = "/home/dumball/pngs" + def make_index(self, directory_path): +# directory_path = "/home/dumball/pngs/" image_paths = self.get_images_in_directory(directory_path) dictionary = {image_path: self.predict_caption([image_path]) for image_path in image_paths} return dictionary + + + + + + +#alo = ImageCaptioning() +#a = alo.make_index() + +#print(a) diff --git a/src/indexing.py b/src/indexing.py index 398d153..9551b57 100644 --- a/src/indexing.py +++ b/src/indexing.py @@ -3,7 +3,9 @@ import numpy as np from transformers import AutoTokenizer, AutoModel from captioning import ImageCaptioning - +import timm +import PIL +from transformers import CLIPProcessor, CLIPModel # Load pre-trained text embedding model (e.g., Sentence Transformer) tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") @@ -13,19 +15,28 @@ def embed_text(text_query): encoded_input = tokenizer(text_query, return_tensors="pt") with torch.no_grad(): features = model(**encoded_input) - return features[0].mean(dim=0).cpu().numpy() # Average pooled hidden state + return features.cpu().numpy() # Average pooled hidden state + # Load pre-trained image feature extractor (e.g., EfficientNet) -from transformers import CLIPProcessor, CLIPModel feature_extractor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") # Function to extract image features (customize based on your model) def extract_image_features(image_path): - image = feature_extractor(image_path, return_tensors="pt") - with torch.no_grad(): - features = model.encode_image(**image) - return features[0].cpu().numpy() # Image features + model = timm.create_model('vit_base_patch16_224', pretrained=True) + model = model.eval() + + with torch.no_grad(): + image = preprocess_image(image_path) # Preprocess image based on ViT requirements + images = image.unsqueeze(0).to('cuda') # Add batch dimension and move to GPU if available + features = model(images) + +#def extract_image_features(image_path): +# image = feature_extractor(image_path, return_tensors="pt") +# with torch.no_grad(): +# features = model.encode_image(**image) +# return features[0].cpu().numpy() # Image features # Load your dataset of images and their corresponding textual descriptions def load_dataset(a, b): @@ -48,16 +59,18 @@ def search_images(text_query, index, image_features, top_k=5): # Example usage alo = ImageCaptioning() -dic = alo.make_index() +my_dict = alo.make_index() +a = list(my_dict.keys()) +b = [item for sublist in list(my_dict.values()) for item in sublist] -a = list(dic.keys()) -b = list(dic.values()) +#b = list(my_dict.values()) +print("a:", a, "\n", "b:",b) image_paths, descriptions = load_dataset(a,b) image_features = [extract_image_features(path) for path in image_paths] index = create_faiss_index(image_features) -text_query = "A cat sitting on a chair" +text_query = input("query: ") results = search_images(text_query, index, image_paths) print(f"Top {len(results)} similar images for '{text_query}':") for image_path in results: From 4009b508c3de3349d6630f2feb47eb95c0a01ffe Mon Sep 17 00:00:00 2001 From: dumball Date: Wed, 6 Mar 2024 23:42:01 +0530 Subject: [PATCH 2/3] docker update --- Dockerfile | 20 ++++++++++++---- README.md | 11 +++++---- interface.py | 60 ----------------------------------------------- interface_new.py | 50 --------------------------------------- new.py => main.py | 0 5 files changed, 22 insertions(+), 119 deletions(-) delete mode 100644 interface.py delete mode 100644 interface_new.py rename new.py => main.py (100%) diff --git a/Dockerfile b/Dockerfile index cb44ed7..868666c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,20 @@ -FROM python:3.11 +FROM python:3.8.10-slim + WORKDIR /app -COPY requirements.txt requirements.txt -RUN pip install -r requirements.txt + +COPY poetry.lock pyproject.toml ./ + +RUN pip install poetry && poetry config virtualenvs.create false + +RUN /root/.cargo/bin/uv pip install --no-cache -r requirements.txt + COPY . . + EXPOSE 5001 -CMD ["python", "app.py"] + +# Command to run the application (assuming it's a Flask app) +CMD ["streamlit", "run","main.py"] # Replace with your application's entry point if different + +# Alternative command for uvicorn (if your application uses it) +# CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "5001"] diff --git a/README.md b/README.md index 1194740..152162f 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,14 @@ # Deeplearning based filebrowser (WIP): -## image captioning demo -![demo for captioning](./filebrowser_demo.mp4) +## filebrowser demo +![demo](https://youtu.be/CIXq6xz7tHc) - aim is to provide a better search in filebrowsers when the filebrowsere names are cryptic and it is hard to find an image or a document based on the standard search ## todo -- make ui -- make easily deoployable with docker and add onnx runtime or optimuim -- add support for pdfs, and videos using sentence transformer embeddings and video captioning models +- [x] Make UI +- [ ] Make easily deployable with Docker and add ONNX Runtime or Optimuim +- [ ] Add support for PDFs and videos using Sentence Transformer embeddings and video captioning models +- [ ] Optimize using `joblib` cache ## to run. do: diff --git a/interface.py b/interface.py deleted file mode 100644 index cbc32ff..0000000 --- a/interface.py +++ /dev/null @@ -1,60 +0,0 @@ -import streamlit as st -import os -from PIL import Image - -def main(): - """Main function of the Streamlit application.""" - st.title("Image Explorer") - - # Get directory path from user input or set default - default_dir = os.getcwd() # Use current working directory as default - user_dir = st.text_input("Enter directory path:", default_dir) - if user_dir: - dir_path = user_dir - else: - dir_path = default_dir - - # Check if directory exists - if not os.path.exists(dir_path): - st.error(f"Directory '{dir_path}' does not exist") - return - - # Get filtered list of images - supported_extensions = [".jpg", ".jpeg", ".png"] - image_list = [ - (os.path.join(dir_path, f), os.path.basename(f)) - for f in os.listdir(dir_path) - if os.path.isfile(os.path.join(dir_path, f)) - and os.path.splitext(f)[1].lower() in supported_extensions - ] - - # Add search bar - search_term = st.text_input("Search images:", "") - - # Filter images based on search term - # filtered_images = [ - # image_path_filename - # for image_path_filename in image_list - # if search_term.lower() in image_path_filename[1].lower() - # ] - - - image_features = [extract_image_features(path) for path in image_list] - index = create_faiss_index(image_features) - - filtered_images = search_images(search_term, index, image_features, top_k=5) - - # Define CSS class (assuming you have a style.css file linked) - st.markdown(f"", unsafe_allow_html=True) - - # Display images with filename - columns = st.columns(2) # Adjust the number of columns as desired - for i, (image_path, filename) in enumerate(filtered_images): - with columns[i % len(columns)]: - image = Image.open(image_path) - st.image(image) - st.markdown(f"

{filename}

", unsafe_allow_html=True) - -if __name__ == "__main__": - main() - diff --git a/interface_new.py b/interface_new.py deleted file mode 100644 index c5e1e25..0000000 --- a/interface_new.py +++ /dev/null @@ -1,50 +0,0 @@ -import streamlit as st -import os -from PIL import Image -from src.captioning import ImageCaptioning - -def main(): - """Main function of the Streamlit application.""" - st.title("Image Explorer") - - # Get directory path from user input or set default - default_dir = os.getcwd() # Use current working directory as default - user_dir = st.text_input("Enter directory path:", default_dir) - if user_dir: - dir_path = user_dir - else: - dir_path = default_dir - - # Check if directory exists - if not os.path.exists(dir_path): - st.error(f"Directory '{dir_path}' does not exist") - return - - # Create an instance of ImageCaptioning and generate the index - image_captioning = ImageCaptioning() - image_dict = image_captioning.make_index(dir_path) - - # Add search bar - search_term = st.text_input("Search images by description:", "") - - # Filter images based on search term - filtered_images = [ - (image_path, descriptions) - for image_path, descriptions in image_dict.items() - if any(search_term.lower() in description.lower() for description in descriptions) - ] - - # Define CSS class (assuming you have a style.css file linked) - st.markdown(f"", unsafe_allow_html=True) - - # Display images with descriptions - columns = st.columns(2) # Adjust the number of columns as desired - for i, (image_path, descriptions) in enumerate(filtered_images): - with columns[i % len(columns)]: - image = Image.open(image_path) - st.image(image) - for description in descriptions: - st.markdown(f"

{}{ description }

", unsafe_allow_html=True) - -if __name__ == "__main__": - main() diff --git a/new.py b/main.py similarity index 100% rename from new.py rename to main.py From c91e169a761f39c83f98fbd1cec1b688dc597827 Mon Sep 17 00:00:00 2001 From: dumball Date: Wed, 6 Mar 2024 23:42:37 +0530 Subject: [PATCH 3/3] docker update --- alo2.py | 50 ------------------------------------------- demo.mp4 | Bin 3110096 -> 0 bytes filebrowser.png | Bin 157462 -> 0 bytes filebrowser_demo.mp4 | Bin 3412517 -> 0 bytes 4 files changed, 50 deletions(-) delete mode 100644 alo2.py delete mode 100644 demo.mp4 delete mode 100644 filebrowser.png delete mode 100644 filebrowser_demo.mp4 diff --git a/alo2.py b/alo2.py deleted file mode 100644 index f6cad12..0000000 --- a/alo2.py +++ /dev/null @@ -1,50 +0,0 @@ -import streamlit as st -import os -from PIL import Image -from src.captioning import ImageCaptioning - -def main(): - """Main function of the Streamlit application.""" - st.title("Image Explorer") - - # Get directory path from user input or set default - default_dir = os.getcwd() # Use current working directory as default - user_dir = st.text_input("Enter directory path:", default_dir) - if user_dir: - dir_path = user_dir - else: - dir_path = default_dir - - # Check if directory exists - if not os.path.exists(dir_path): - st.error(f"Directory '{dir_path}' does not exist") - return - - # Create an instance of ImageCaptioning and generate the index - image_captioning = ImageCaptioning() - image_dict = image_captioning.make_index(dir_path) - - # Add search bar - search_term = st.text_input("Search images by description:", "") - - # Filter images based on search term - filtered_images = [ - (image_path, descriptions) - for image_path, descriptions in image_dict.items() - if any(search_term.lower() in description.lower() for description in descriptions) - ] - - # Define CSS class (assuming you have a style.css file linked) - st.markdown(f"", unsafe_allow_html=True) - - # Display images with descriptions - columns = st.columns(2) # Adjust the number of columns as desired - for i, (image_path, descriptions) in enumerate(filtered_images): - with columns[i % len(columns)]: - image = Image.open(image_path) - st.image(image) - for description in descriptions: - st.markdown(f"

{description}

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