Update README.md

README.md

@@ -1,3 +1,151 @@
----
-license: mit
----
+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 1M<n<10M
+task_categories:
+- object-detection
+- feature-extraction
+tags:
+- movies
+- tv-series
+- facial-recognition
+- computer-vision
+- embeddings
+- face-detection
+- imdb
+pretty_name: CineFace Database
+dataset_info:
+  features:
+    - name: "Unnamed: 0"
+      dtype: int32
+      description: "Row index from export"
+    - name: frame_num
+      dtype: int32
+      description: "The frame number in the video file"
+    - name: face_num
+      dtype: int32
+      description: "The index of the face within that specific frame"
+    - name: x1
+      dtype: float32
+      description: "Top-left X coordinate of the bounding box"
+    - name: y1
+      dtype: float32
+      description: "Top-left Y coordinate of the bounding box"
+    - name: x2
+      dtype: float32
+      description: "Bottom-right X coordinate of the bounding box"
+    - name: y2
+      dtype: float32
+      description: "Bottom-right Y coordinate of the bounding box"
+    - name: confidence
+      dtype: float32
+      description: "Face detection confidence score"
+    - name: predicted_name
+      dtype: string
+      description: "The real-world name of the actor/person"
+    - name: predicted_character
+      dtype: string
+      description: "The name of the character played in the film/series"
+    - name: predicted_tmdb_id
+      dtype: string
+      description: "The TMDb identifier for the actor"
+    - name: predicted_confidence
+      dtype: float32
+      description: "Recognition/Matching confidence score"
+---
+
+# CineFace
+
+**CineFace** is a comprehensive ecosystem for facial analysis in entertainment media. It consists of:
+1.  **The CineFace Dataset:** A massive collection of detections and embeddings from over 6,000 movies and TV series.
+2.  **The CineFace Toolkit:** Pipeline for large-scale facial detection, encoding, and identification in TV and Film.
+
+[**📊 View Dashboard**](https://app.powerbi.com/view?r=eyJrIjoiMWE4YzViOWMtY2RiYy00ZTk1LWExNTgtMTg5YjZjNTE2NjIzIiwidCI6ImI3Yzk1YTkyLTBlYWQtNDRlOS04YjgzLTdjMGY5NmNiMDUyMSIsImMiOjF9) | [**🤗 Hugging Face Dataset**](https://huggingface.co/datasets/astaileyyoung/CineFaceDB)
+
+
+## Dataset
+The CineFace database contains metadata and facial detections for over 6,000 titles. You can download the components directly from Hugging Face:
+
+* **Film List:** [`film_list.csv`](https://huggingface.co/datasets/astaileyyoung/CineFaceDB/blob/main/film_list.csv) — Comprehensive list of all movies and series in the DB.
+* **Detections:** [`faces.tar.gz`](https://huggingface.co/datasets/astaileyyoung/CineFaceDB/blob/main/faces.tar.gz) — Bounding boxes and identifications.
+* **Encodings:** [`embeddings.tar.gz`](https://huggingface.co/datasets/astaileyyoung/CineFaceDB/blob/main/embeddings.tar.gz) — Pre-computed face embeddings.
+* **Relational DB:** [`CineFaceDW.db`](https://huggingface.co/datasets/astaileyyoung/CineFaceDB/blob/main/CineFaceDW.db) — SQLite version of the dataset.
+
+### Using the Encodings
+The encodings are saved as `.npz` files. Since the encoded faces are stored in sequence, you can join them to the detection metadata by loading the corresponding CSV and adding the array as a column:
+
+```python
+import numpy as np
+import pandas as pd
+
+# Load metadata and embeddings
+df = pd.read_csv("movie_12345.csv")
+embeddings = np.load("movie_12345.npz")['embeddings']
+
+# Join (sequence based)
+df['encoding'] = list(embeddings)
+```
+
+## Toolkit (Installation and Usage)
+### Requirements
+CineFace relies on [Docker](https://docs.docker.com/get-started/get-docker/) and [Qdrant](https://qdrant.tech/). To install Qdrant, just run with Docker. It will download the image automatically
+```
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+### Install
+Simply download the source code
+```
+git clone https://github.com/astaileyyoung/CineFace.git
+```
+
+Then install the required dependencies
+```
+pip install -r requirements.txt
+```
+
+Finally, install CineFace
+```
+pip install -e .
+```
+
+CineFace uses Visage as a backend for accurate, high-performance facial detection and encoding. [Visage](https://github.com/astaileyyoung/Visage) can also be used independently.
+
+**Be advised that the associated docker image is quite large (~17GB) since it relies on heavy ML libraries built from source, so it will take a while to download (~10-15 minutes). 
+
+
+### Usage
+Running CineFace is straightforward.
+
+#### **Basic Command**
+```
+cineface <src> <dst> [options]
+```
+- `<src>`: Path to the input video file
+- `<dst>`: Path to the output file
+#### **Command-Line Arguments**
+
+| Argument                | Type     | Default                    | Description                                         |
+|-------------------------|----------|----------------------------|-----------------------------------------------------|
+| `src`                   | str      | (required)                 | Path to input video file or directory.              |
+| `dst`                   | str      | (required)                 | Path to output directory or results file.           |
+| `imdb_id`               | int      | (required)                 | IMDb ID (just the numbers).                         |
+| `--faces_dir`           | str      | `None`                     | Directory to save face images to                    |
+| `--encoding_col`        | str      | `'embedding'`              | Column name for face embeddings.                    |
+| `--image`               | str      | `'astaileyyoung/visage'`   | Container/image name (for debugging/development).   |
+| `--frameskip`           | int      | `24`                       | Number of frames to skip between detections.        |
+| `--threshold`, `-t`     | float    | `0.5`                      | Recognition confidence threshold.                   |
+| `--timeout`             | int      | `60`                       | Timeout (in seconds) for matching.                  |
+| `--batch_size`          | int      | `256`                      | Batch size for matching.                            |
+| `--season`              | int      | `None`                     | Season number (required for matching tv show).      |
+| `--episode`             | int      | `None`                     | Episode number (requird for matching tv show).      |
+| `--qdrant_client`       | str      | `'localhost'`              | Qdrant client address (vector DB).                  |
+| `--qdrant_port`         | int      | `6333`                     | Qdrant port.                                        |
+
+**Automatic tv/movie identification by filename is no longer working due to change in the IMDb API that has broken Cinemagoer search, which automatic identification depends on. If analyzing a movie, you must enter the imdb_id. If analyzing a TV show, you must enter the imdb_id, season, and episode.
+
+
+## Research and Analysis
+Notebooks analyzing the dataset can be found in CineFace/notebooks/research. Feel free to submit a ticket if you encounter bugs or have feature requests for the dashboard.