Metadata-Version: 2.4
Name: fluid-vision
Version: 1.0.3
Summary: Contains utilities for computer vision tasks
Author: Ameya Kirtane
Author-email: ameya.kirtane@fluidanalytics.ai
Requires-Python: >=3.10, <3.14
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Provides-Extra: embeddings
Requires-Dist: boto3 (>=1.42.36,<2.0.0)
Requires-Dist: google-genai (>=1.60.0,<2.0.0)
Requires-Dist: imagehash (>=4.3.2,<5.0.0)
Requires-Dist: opencv-python (>=4.13.0.90,<5.0.0.0)
Requires-Dist: pillow (>=12.1.0,<13.0.0)
Requires-Dist: pydantic (>=2.12.5,<3.0.0)
Requires-Dist: python-dotenv (>=1.2.1,<2.0.0)
Requires-Dist: torch (>=2.10.0,<3.0.0) ; extra == "embeddings"
Requires-Dist: torchvision (>=0.25.0,<0.26.0) ; extra == "embeddings"
Requires-Dist: tqdm (>=4.67.1,<5.0.0)
Description-Content-Type: text/markdown

# Fluid Vision

## Directory Structure

```
fluid_vision/
├── .coverage
├── poetry.lock
├── pyproject.toml
├── usage.txt
├── notebooks/
├── src/
│   └── fluid_vision/
│       ├── __init__.py
│       ├── data/
│       │   ├── __init__.py
│       │   └── frame_extractor/
│       │       ├── __init__.py
│       │       └── frame_extractor.py
│       ├── embeddings/
│       │   └── frame_embedder.py
│       ├── encoder/
│       │   └── encoder.py
│       ├── models/
│       │   ├── __init__.py
│       │   ├── embedding_models_enum.py
│       │   └── model_loader/
│       │       └── model_loader.py
│       └── utils/
│           ├── cropping.py
│           └── load_files.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_cropping.py
│   ├── test_dino_embedding.py
│   ├── test_embedding_large.py
│   ├── test_embeddings_base.py
│   ├── test_encoding.py
│   ├── test_frame_extraction_phash.py
│   └── test_frame_fps.py
└── .pytest_cache/
    ├── .gitignore
    ├── CACHEDIR.TAG
    ├── README.md
    └── v/
        └── cache/
            └── nodeids
```

## Module Usage

### 1. Frame Extraction

Extract frames at a target FPS:
```python
from fluid_vision.data.frame_extractor.frame_extractor import FrameExtractor

extractor = FrameExtractor(target_fps=5)
result = extractor.extract_frames_fps("path/to/video.mp4")
frames_dict = result["frames_dict"]  # {frame_number: PIL.Image}
```

Extract frames by perceptual hash (skip duplicates):
```python
extractor = FrameExtractor(frame_skip=10)
result = extractor.extract_frames_by_hash("path/to/video.mp4")
frames_dict = result["frames_dict"]
```

Extract frames with cropping:
```python
extractor = FrameExtractor(target_fps=5, cropping={"quadrant": 2}, skip_similar=0.5)
result = extractor.extract_frames_fps("path/to/video.mp4")
cropped_frames = result["frames_dict"]

# Optionally filter frames based on average hash function
# By default all duplicate frames are skipped
filtered_frames_dict = extractor.filter_frames_by_hash(frames_dict)["filtered_frames_dict"]
```

---

### 2. Loading Images and Pickle Files

Load a dictionary from a pickle file:
```python
from fluid_vision.utils.load_files import load_dict_from_pickle

data = load_dict_from_pickle("path/to/file.pkl")
```

Load all images from a folder:
```python
from fluid_vision.utils.load_files import load_image_list

images = load_image_list("path/to/folder")
```

---

### 3. Embedding Models

Load a model and processor:
```python
from fluid_vision.models.model_loader.model_loader import ModelLoader

model_loader = ModelLoader(model_name="openai/clip-vit-base-patch16")
model, processor = model_loader.load_model()
```

---

### 4. Frame Embedding

Get embedding for a single frame:
```python
from fluid_vision.embeddings.frame_embedder import FrameEmbedder

embedder = FrameEmbedder(model_name="openai/clip-vit-base-patch16", model=model, processor=processor, device="cpu")
embedding = embedder.get_frame_embedding(frame)  # frame is a PIL.Image
```

Get embeddings for a batch of frames:
```python
result = embedder.get_batch_embeddings([frame1, frame2, ...])
embeddings = result["embeddings"]  # numpy.ndarray
```

---

### 5. Encoding Embeddings as Images

Create an encoding matrix image from embeddings:
```python
from fluid_vision.encoder.encoder import EmbeddingEncoder

encoder = EmbeddingEncoder()
result = encoder.create_encoding_matrix(embeddings)  # embeddings: np.ndarray
encoding_image = result["encoding_image"]  # numpy.ndarray (image)
```


---

## Build and Install with Poetry

1. **Install Poetry** (if not already installed):

   ```
   pip install poetry
   ```

2. **Install dependencies**:

   ```
   poetry install
   ```

3. **Build the package**:

   ```
   poetry build
   ```

4. **Run tests**:

   ```
   poetry run pytest
   ```

5. **Activate the virtual environment**:

   ```
   poetry shell
   ```

---
## Remote Installation via devpi server:

**Add this to your pip config to enable installation from devpi server** 

   ```
   global.extra-index-url='https://pypi.org/simple'
   global.index-url='https://devpi.fluidanalytics.ai/fluid_vision/dev/+simple/'
   global.trusted-host='devpi.fluidanalytics.ai'
   ```
---



