diff --git a/docs/src/did/config.md b/docs/src/did/config.md index 9a721dc3..8708ca6a 100644 --- a/docs/src/did/config.md +++ b/docs/src/did/config.md @@ -1,68 +1,51 @@ -The configuration can be managed directly through the **admin panel**, which provides a simple way to modify settings without changing the codebase. Navigate to: +### Default Configuration - Home › Constance › Config +The default configuration can be managed directly through the **admin panel**, providing a simple way to modify settings without changing the codebase. Navigate to: -Here, you will find all the configurable settings that affect the behavior of the system, allowing for quick adjustments and better control over application behavior. + Home › Constance › Config -## Deep neural networks (DNN) +This section contains the **default configuration**, which applies to the system globally. It defines the base settings for the application and ensures consistent behavior across all deduplication sets. -The deep learning component of the system is crucial for performing advanced inference tasks, including **face detection**, **face recognition**, and **finding duplicate images** using a pre-trained model. These tasks are fundamental to ensuring the accuracy and efficiency of the system in identifying and managing images. +#### Key Parameters: -This component relies on **Convolutional Neural Networks (CNNs)**, a type of deep learning model particularly well-suited for processing visual data. CNNs are used to automatically extract relevant features from images, such as facial landmarks and distinctive patterns, without the need for manual feature engineering. +##### **MODEL_NAME** +Specifies the face recognition model to be used for **encoding** face landmarks. **VGG-Face** (default) -### DNN_BACKEND +##### **DETECTOR_BACKEND** +Specifies the face detector backend used during the **Face Detection** and **Alignment** stages to locate faces and ensure consistent facial alignment in images. **RetinaFace** (default) -Specifies the computation backend to be used by [OpenCV](https://github.com/opencv/opencv) library for deep learning inference. +##### **FACE_DISTANCE_THRESHOLD** + Specifies the maximum allowable **distance** between two face embeddings for them to be considered a match. This **similarity threshold** is crucial for assessing whether two faces belong to the same individual. Lower values result in stricter matching, while higher values allow for more lenient matches. -### DNN_TARGET +--- -Specifies the target device on which [OpenCV](https://github.com/opencv/opencv) library will perform the deep learning computations. +### Custom Configuration +Custom configurations can be created for specific **deduplication sets** through the **admin panel** by navigating to: -## Face Detection + Home › API › Configs -This component is responsible for locating and identifying faces in images. It uses advanced deep learning algorithms to scan images and detect the regions that contain human faces. This section outlines the key configuration parameters that influence how the face detection model processes input images and optimizes detection results. +At this path, you can define a custom configuration in **JSON format**. Custom configurations include only the parameters you wish to override from the default configuration. -### BLOB_FROM_IMAGE_SCALE_FACTOR +#### Details +- Parameters not included in the custom configuration will default to the values from the global configuration and will be used by the system in subsequent calculations. This ensures that all required parameters are available, even if they are not explicitly defined in the custom configuration. +- A custom configuration can be associated with a specific deduplication set via: -Specifies the scaling factor applied to all pixel values when converting an image to a blob. Mostly it equals 1.0 for no scaling or 1.0/255.0 and normalizing to the [0, 1] range. + Home › API › Deduplication sets › -Remember that scaling factor is also applied to mean values. Both scaling factor and mean values must be the same for the training and inference to get the correct results. +- If no custom configuration is associated with a deduplication set, the system will entirely use the **default configuration** for that set. -### BLOB_FROM_IMAGE_MEAN_VALUES +- Parameter names in custom configurations are case-sensitive and must be in **lowercase**. -Specifies the mean BGR values used in image preprocessing to normalize pixel values by subtracting the mean values of the training dataset. This helps in reducing model bias and improving accuracy. +#### Example Custom Configuration: +To override only the **FACE_DISTANCE_THRESHOLD** parameter, your custom configuration can look like this: -The specified mean values are subtracted from each channel (Blue, Green, Red) of the input image. +```json +{ + "face_distance_threshols": 0.7 +} +``` -Remember that mean values are also applied to scaling factor. Both scaling factor and mean values must be the same for the training and inference to get the correct results. - -### FACE_DETECTION_CONFIDENCE - -Specifies the minimum confidence score required for a detected face to be considered valid. Detections with confidence scores below this threshold are discarded as likely false positives. - -### NMS_THRESHOLD - -Specifies the Intersection over Union (IoU) threshold used in Non-Maximum Suppression (NMS) to filter out overlapping bounding boxes. If the IoU between two boxes exceeds this threshold, the box with the lower confidence score is suppressed. Lower values result in fewer, more distinct boxes; higher values allow more overlapping boxes to remain. - -## Face Recognition - -This component builds on face detection to identify and differentiate between individual faces. This involves generating face encodings, which are numerical representations of the unique facial features used for recognition. These encodings can then be compared to determine if two images contain the same person or to find matches in a database of known faces. - -### FACE_ENCODINGS_NUM_JITTERS - -Specifies the number of times to re-sample the face when calculating the encoding. Higher values increase accuracy but are computationally more expensive and slower. For example, setting 'num_jitters' to 100 makes the process 100 times slower. - -### FACE_ENCODINGS_MODEL - -Specifies the model type used for encoding face landmarks. It can be either 'small' which is faster and only 5 key facial landmarks, or 'large' which is more precise and identifies 68 key facial landmarks but requires more computational resources. - - -## Duplicate Finder - -This component is responsible for identifying duplicate images in the system by comparing face embeddings. These embeddings are numerical representations of facial features generated during the face recognition process. By calculating the distance between the embeddings of different images, the system can determine whether two images contain the same person, helping in the identification and removal of duplicates or grouping similar faces together. - -### FACE_DISTANCE_THRESHOLD - -Specifies the maximum allowable distance between two face embeddings for them to be considered a match. It helps determine if two faces belong to the same person by setting a threshold for similarity. Lower values result in stricter matching, while higher values allow for more lenient matches. +The configuration will undergo **validation** during saving to confirm the JSON structure and ensure all keys are valid and match the default configuration. Any invalid entries will prevent saving until corrected. +This flexibility allows you to define tailored configurations for specific deduplication sets, ensuring precise control over application behavior while preserving the stability of default settings. diff --git a/docs/src/did/workflow.md b/docs/src/did/workflow.md index c7f513e8..cc75da05 100644 --- a/docs/src/did/workflow.md +++ b/docs/src/did/workflow.md @@ -3,100 +3,152 @@ tags: - Deduplication --- -# Image Processing and Duplicate Detection +# **Image Processing and Duplicate Detection** -The workflow uses pre-trained models from [OpenCV](https://opencv.org/) for face detection and [dlib](http://dlib.net/) for face recognition and landmark detection. This setup provides a fast, reliable solution for real-time applications, without requiring the training of models from scratch. OpenCV handles face detection using a Caffe-based model, while **dlib**, accessed through the [face_recognition](https://pypi.org/project/face-recognition/) library, manages recognition and duplicate identification. +### How Face Recognition Works. -Future updates will involve custom-trained models to further improve performance. +The process of face recognition can be divided into a structured pipeline consisting of five key stages. Each step plays a vital role in transforming raw images into actionable insights, such as identifying individuals or detecting duplicates in a dataset. -## Inference Mode Operation +At the core of this process are Convolutional Neural Networks (CNNs), which excel at extracting hierarchical features from images, from edges to complex patterns. Their effectiveness makes them fundamental for tasks like face detection and recognition.([Read More](https://www.researchgate.net/publication/379189553_A_review_of_convolutional_neural_networks_in_computer_vision)) -This application operates entirely in inference mode, relying on pre-trained models for both face detection and recognition tasks. **OpenCV** handles face detection, and **face_recognition**, a Python wrapper for **dlib**, performs face recognition and duplicate identification. This approach ensures efficient, real-time processing without the need for additional training, allowing the application to quickly deploy its capabilities. +Central to this pipeline is [DeepFace](https://github.com/serengil/deepface), a versatile Python library for facial recognition. Supporting multiple pre-trained models and detection backends, it simplifies face verification and duplicate detection with static images. -- **OpenCV**: Optimized for fast face detection, ideal for real-time image and video applications. -- **dlib's face_recognition**: Focuses on generating face embeddings for comparison, providing high accuracy in identification. +--- + +### Steps in the Workflow. -By combining OpenCV for detection and dlib for recognition, the system offers a balance of speed and precision. +??? abstract "Face Detection" + #### _**Face Detection**_ -### Pre-Trained Models Storage + The first step involves identifying and locating faces within an image. [DeepFace](https://github.com/serengil/deepface) leverages detectors such as *OpenCV*, *RetinaFace*, *MTCNN*, and others. By default, the service [uses](config.md#detector_backend) **RetinaFace** that is a state-of-the-art single-stage face detector that performs pixel-wise face localization by jointly predicting face scores, bounding boxes, and five facial landmarks. It achieves high accuracy even under challenging conditions, such as varying poses and occlusions.([Read More](https://arxiv.org/abs/1905.00641)) -- **OpenCV** uses a pre-trained [Caffe model](https://caffe.berkeleyvision.org/) stored in Azure Blob Storage, automatically downloaded at application startup. -- **face_recognition** utilizes a pre-trained [dlib model](https://pypi.org/project/face_recognition_models/) stored locally within the container’s library directory. + This process includes: -Administrators can manually update the **Caffe model** via the admin panel, allowing flexible updates or new model versions without altering the application code. + - Determining the location of faces (bounding boxes). + - Extracting facial regions for further processing. + - Anti-Spoofing (optional). [DeepFace](https://github.com/serengil/deepface) supports anti-spoofing to detect fraudulent attempts, such as using photos or masks instead of real faces. It uses models like **MiniFASNet** to assess the authenticity of detected faces. However, anti-spoofing is less relevant for static photos, as dynamic cues like blinking or texture variations are unavailable. This step is optional and disabled by default, requiring explicit configuration. + + Accurate face detection is crucial, as errors at this stage can propagate through the pipeline, affecting overall performance. --- -## Face Detection and Recognition Models +??? abstract "Alignment" -### OpenCV Model Details + #### _**Alignment**_ + Alignment eliminates facial tilts and rotations, standardizing the orientation of the detected face. This process relies on facial landmarks (eyes, nose, mouth) to ensure consistent positioning. By default, the service [uses](config.md#detector_backend) **RetinaFace** that provides accurate localization of these landmarks, facilitating effective alignment.([Read More](https://arxiv.org/abs/1905.00641)) -OpenCV powers the face detection component using a pre-trained model designed for real-time performance. + Benefits of alignment: -#### Model Components + - Improved accuracy in face recognition. + - Reduced sensitivity to variations in camera angles. -- **deploy.prototxt**: Defines the network architecture and parameters for model execution. -- **res10_300x300_ssd_iter_140000.caffemodel**: Contains trained weights, generated after 140,000 iterations using the **Caffe** framework. +--- -#### Model Architecture +??? abstract "Normalization" -- **Res10 Architecture**: A lightweight model that balances speed and accuracy, perfect for real-time detection. -- **300x300 Input Resolution**: Optimized for face detection at this resolution, ensuring a balance between detail and efficiency. -- **SSD (Single Shot MultiBox Detector)**: A method that predicts bounding boxes and confidence scores in a single pass, allowing rapid detection of multiple faces in a single image. + #### _**Normalization**_ + Normalization prepares the face for processing by: + - Resizing the image to a standard size. + - Adjusting brightness and contrast. + - Converting the color space (e.g., grayscale conversion). -### Dlib Model Details + These steps ensure the data is more suitable for deep learning models. -The **dlib** models used for recognition and facial landmark detection include: + [DeepFace](https://github.com/serengil/deepface) handles normalization internally using standard image preprocessing techniques provided by popular image-processing libraries such as [OpenCV](https://opencv.org/) and [Pillow](https://pillow.readthedocs.io/en/stable/). These steps ensure consistency in the input data, making it suitable for processing by deep learning models like **VGG-Face** or others. The resizing operation ensures compatibility with the input dimensions required by the selected model (e.g., 224x224 pixels for VGG-Face). -1. **dlib_face_recognition_resnet_model_v1.dat** +--- + +??? abstract "Representation" - A modified **ResNet-34** model generating **128-dimensional face embeddings** for face recognition, achieving **99.38% accuracy** on the LFW benchmark. + #### _**Representation**_ + At this stage, unique facial features are extracted and encoded into numerical vector representations, commonly referred to as **embeddings**. These embeddings are high-dimensional mathematical representations that capture the distinctive characteristics of a face, such as the relative positions of facial landmarks, texture, and shape. By transforming faces into embeddings, systems can efficiently compare, verify, and search for faces within datasets, enabling accurate recognition and matching. -2. **mmod_human_face_detector.dat** - A **CNN-based Max-Margin Object Detector (MMOD)** for accurate face detection, especially under difficult conditions like varied orientations or lighting. + [DeepFace](https://github.com/serengil/deepface) supports several pre-trained models for this purpose, including vgg-face, facenet, facenet512, openface, deepid, arcface, dlib, sface, and ghostfacenet. -3. **shape_predictor_5_face_landmarks.dat** - Detects **5 key facial landmarks** (eye corners and nose base), optimized for fast face alignment. + By default, the service [uses](config.md#model_name) **VGG-Face**, a model developed by the Visual Geometry Group at the University of Oxford. ([Read More](https://www.robots.ox.ac.uk/~vgg/software/vgg_face/)) -4. **shape_predictor_68_face_landmarks.dat** - Detects **68 facial landmarks** (eyes, nose, mouth, jawline), used for more detailed facial alignment and analysis. + The vgg-face model is based on the VGG-Very-Deep-16 CNN architecture and was trained on a dataset of 2.6 million images of 2,622 identities. ([Read More](https://arxiv.org/abs/1503.03832)) --- -## Workflow Diagram +??? abstract "Verification and Duplicate Detection" + + #### _**Verification and Duplicate Detection**_ + In the final stage of the face recognition pipeline, the system performs two critical tasks: -The workflow diagram illustrates the overall process of image processing and duplicate detection. **OpenCV** is used for face detection, while **face_recognition** (built on **dlib**) handles face recognition and duplicate identification. + - **Verification:** This process involves comparing two facial embeddings to ascertain whether they represent the same individual. [DeepFace](https://github.com/serengil/deepface) utilizes similarity metrics such as cosine similarity, Euclidean distance, and L2-normalized Euclidean distance for this purpose. By default, the library employs **cosine similarity** as the distance metric due to its efficiency and effectiveness in comparing high-dimensional vectors. Cosine similarity measures the angle between vectors, focusing on their relative orientation rather than magnitude, which makes it particularly suitable for facial recognition tasks where the relative differences between features are more critical than their absolute values.([Read More](https://en.wikipedia.org/wiki/Cosine_similarity)) + + - **Duplicate Detection:** This task entails scanning a database of facial embeddings to identify multiple entries corresponding to the same person. By measuring the similarity between embeddings and applying a predefined **similarity threshold**, the system determines whether two embeddings represent the same individual. This threshold ensures a balance between detecting duplicates and minimizing false positives, allowing the system to accurately consolidate duplicate records while maintaining the integrity and accuracy of the dataset. The similarity threshold is [adjustable](config.md#face_distance_threshold), enabling fine-tuning for specific use cases, such as stricter matching criteria or broader detection in diverse datasets. + +--- + +## **General Process Diagram.** ```mermaid -flowchart LR - subgraph ImageProcessing[Image Processing] - direction LR - - subgraph FaceDetection[Face Detection] - - subgraph DNNManager[DNN Manager] - direction TB - load_model[Load Caffe Model] -- computation backend\ntarget device --> set_preferences[Set Preferences] - end - - DNNManager --> run_model - - direction TB - load_image[Load Image] -- decoded image as 3D numpy array\n(height, width, channels of BlueGreeRed color space) --> prepare_image[Prepare Image] -- blob 4D tensor\n(normalized size, use scale factor and means) --> run_model[Run Model] -- shape (1, 1, N, 7),\n1 image\nN is the number of detected faces\neach face is described by the 7 detection values--> filter_results[Filter Results] -- confidence is above the minimum threshold,\nNMS to suppress overlapping bounding boxes --> return_detections[Return Detections] - end - - subgraph FaceRecognition[Face Recognition] - direction TB - load_image_[Load Image] --> detect_faces[Detect Faces] -- detected face regions\nnumber of times to re-sample the face\nkey facial landmarks --> generate_encodings[Generate Encodings] -- numerical representations of the facial features\n(face's geometry and appearance) --> save_encodings[Save Encodings] - end +flowchart TB + + subgraph FaceDetection["1\. Face Detection"] + direction LR + extract_faces["Extract Faces Regions"] + determine_faces["Determine Faces"] + detect_landmarks["Detect Landmarks"] end - subgraph DuplicateFinder[Duplicate Finder] - direction TB - load_encodings[Load Encodings] --> compare_encodings[Compare Encodings] -- face distance less then threshold --> return_duplicates[Return Duplicates] + subgraph Alignment["2\. Alignment"] + direction TB + align_faces["Align Faces"] end - ImageProcessing --> DuplicateFinder - FaceDetection --> FaceRecognition + subgraph Normalization["3\. Normalization"] + direction TB + normalize_image["Adjust Brightness and Contrast,
Convert Color Space,
etc."] + resize_image["Resize Image"] + end + + subgraph Preprocessing["Preprocessing"] + direction TB + FaceDetection + Alignment + Normalization + end + + subgraph Representation["4\. Representation"] + direction TB + extract_features["Encode Face Features"] + end + + subgraph Verification["5\. Verification"] + direction TB + duplicate_detection["Duplicate Detection"] + compare_embeddings["Compare Embeddings"] + end + + determine_faces -- bounding boxes (coordinates for detected face regions)--> extract_faces + determine_faces -- bounding boxes (coordinates for detected face regions) --> detect_landmarks + extract_faces -- cropped face regions --> detect_landmarks + extract_faces -- cropped face regions --> Alignment + detect_landmarks -- facial landmarks (coordinates for eyes, nose, mouth, ...) --> Alignment + resize_image -- resized facial regions (scaled to the model's required dimensions) --> normalize_image + compare_embeddings -- similarity scores --> duplicate_detection + + Alignment -- aligned facial regions (images with standardized orientation based on facial landmarks) --> Normalization + Normalization -- Normalized facial regions (resized, brightness/contrast adjusted, color space standardized, formatted as 4D tensors) --> Representation + Representation -- embeddings (high-dimensional vectors representing facial features) --> Verification + + backend(["backend (default: RetinaFace)"]) .-o FaceDetection & Alignment + backend@{ shape: doc} + click backend "../config/#detector_backend" + + image_libraries(["image-processing libraries
OpenCV, Pillow"]) .-o Normalization + image_libraries@{ shape: doc} + + model(["model (default: VGG-Face)"]) .-o Representation + model@{ shape: doc} + click model "../config/#model_name" + + load_image["Load Image"] -- preprocessed image (3D numpy array: height, width, BGR channels) --> determine_faces + load_image@{ shape: in-out} + duplicate_detection -- findings, filtered by threshold (list of detected duplicates with status_code) --> Findings["Findings"] + Findings@{ shape: out-in} ``` diff --git a/docs/src/setup.md b/docs/src/setup.md index d90f7fb1..03ed634f 100644 --- a/docs/src/setup.md +++ b/docs/src/setup.md @@ -5,7 +5,7 @@ tags: ## Prerequisites -This project utilizes [PDM](https://pdm-project.org/) as the package manager for managing Python dependencies and environments. +This project utilizes [UV](https://docs.astral.sh/uv/) as the package manager for managing Python dependencies and environments. To successfully set up and run this project, ensure that you have the following components in place: @@ -23,26 +23,29 @@ Essential steps for verifying and configuring the environment settings required ### Display the Current Configuration - $ docker run -it -t unicef/hope-dedupe-engine:release-0.1 django-admin env + $ docker run -it -t unicef/hope-dedupe-engine:release-0.x django-admin env ### Mandatory Environment Variables Check Environment Variables - $ docker run -it -t unicef/hope-dedupe-engine:release-0.1 django-admin env --check + $ docker run -it -t unicef/hope-dedupe-engine:release-0.x django-admin env --check Ensure the following environment variables are properly configured: - - DATABASE_URL - SECRET_KEY + + ADMIN_EMAIL + ADMIN_PASSWORD + ALLOWED_HOSTS CACHE_URL CELERY_BROKER_URL - MEDIA_ROOT - STATIC_ROOT - DEFAULT_ROOT - FILE_STORAGE_DNN + CSRF_COOKIE_SECURE + DATABASE_URL + DEEPFACE_HOME FILE_STORAGE_HOPE FILE_STORAGE_STATIC FILE_STORAGE_MEDIA + MEDIA_ROOT + SECRET_KEY + STATIC_ROOT ### Variables Breakdown @@ -50,11 +53,14 @@ Detailed information about the required environment variables is provided for cl #### Operational -##### DATABASE_URL -The URL for the database connection. *Example:* `postgres://hde:password@db:5432/hope_dedupe_engine` +##### ADMIN_EMAIL +The email address for the admin user that can access the Django admin panel with full privileges. *Example:* `adm@myhost.org` -##### SECRET_KEY -A secret key for the Django installation. *Example:* `django-insecure-pretty-strong` +##### ALLOWED_HOSTS +A list of host/domain names that the application can serve. *Example:* `['*']` + +##### ADMIN_PASSWORD +The password for the admin user. *Example:* `123` ##### CACHE_URL The URL for the cache server. *Example:* `redis://redis:6379/1` @@ -62,37 +68,41 @@ The URL for the cache server. *Example:* `redis://redis:6379/1` ##### CELERY_BROKER_URL The URL for the Celery broker. *Example:* `redis://redis:6379/9` +##### CSRF_COOKIE_SECURE +A boolean value that determines whether the CSRF cookie should be secure. *Example:* `False` + +##### DATABASE_URL +The URL for the database connection. *Example:* `postgres://hde:password@db:5432/hope_dedupe_engine` + +##### SECRET_KEY +A secret key for the Django installation. *Example:* `django-insecure-pretty-strong` + #### Root directories -##### DEFAULT_ROOT -The root directory for locally stored files. *Example:* `/var/hope_dedupe_engine/default` +#### DEEPFACE_HOME +The root directory for storing model weights for the DeepFace library. *Example:* `/var/hope_dedupe_engine/deepface` -##### MEDIA_ROOT -The root directory for media files. *Example:* `/var/hope_dedupe_engine/media` +!!! warning "Model Weights Needed" + In this directory, the model weights files for the chosen model(s) will be stored. These files are essential for the application to function correctly. Without them, the application will not work. To download the required weights, run the following command: -##### STATIC_ROOT -The root directory for static files. *Example:* `/var/hope_dedupe_engine/static` + docker run -it unicef/hope-dedupe-engine:release-0.x django-admin syncmodels -#### Storages + By default, the `syncmodels` command checks if the files already exist and skips downloading them if they do. To forcefully re-download the files, you can run the command with the `--force` flag: -##### FILE_STORAGE_DEFAULT -This backend is used for storing locally downloaded DNN model files and encoded data. - ``` - FILE_STORAGE_DEFAULT=django.core.files.storage.FileSystemStorage - ``` -##### FILE_STORAGE_DNN -This backend is dedicated to storing DNN model files. Ensure that the following two files are present in this storage: + docker run -it unicef/hope-dedupe-engine:release-0.x django-admin syncmodels --force + + This directory must be mounted as a shared volume with: + + - **Write** permissions for the container running the backend service. + - **Read-only** permissions for the containers running the Celery workers. -1. *deploy.prototxt.txt*: Defines the model architecture. -2. *res10_300x300_ssd_iter_140000.caffemodel*: Contains the pre-trained model weights. +##### MEDIA_ROOT +The root directory for media files. *Example:* `/var/hope_dedupe_engine/media` -The current process involves downloading files from a [GitHub repository](https://github.com/sr6033/face-detection-with-OpenCV-and-DNN) and saving them to this specific Azure Blob Storage using command `django-admin upgrade --with-dnn-setup`, or the specialized`django-admin dnnsetup` command . -In the future, an automated pipeline related to model training could handle file updates. +##### STATIC_ROOT +The root directory for static files. *Example:* `/var/hope_dedupe_engine/static` -The storage configuration is as follows: -``` -FILE_STORAGE_DNN="storages.backends.azure_storage.AzureStorage?account_name=&account_key=&overwrite_files=true&azure_container=dnn" -``` +#### Storage backends ##### FILE_STORAGE_HOPE This backend is used for storing HOPE dataset images. It should be configured as read-only for the service. @@ -113,14 +123,14 @@ To get the application up and running, follow the steps outlined below. The firs Before starting the application, perform the initial setup using the following command. This will configure the necessary environment settings and prepare the application for runtime: - docker run -d -t unicef/hope-dedupe-engine:release-0.1 setup + docker run -d -t unicef/hope-dedupe-engine:release-0.x setup ### Starting the Server and Services Once the initial setup is complete, run the commands below to start the server and the required background services: - docker run -d -t unicef/hope-dedupe-engine:release-0.1 run - docker run -d -t unicef/hope-dedupe-engine:release-0.1 worker - docker run -d -t unicef/hope-dedupe-engine:release-0.1 beat + docker run -d -t unicef/hope-dedupe-engine:release-0.x run + docker run -d -t unicef/hope-dedupe-engine:release-0.x worker + docker run -d -t unicef/hope-dedupe-engine:release-0.x beat These commands will ensure that the application server, worker processes, and task scheduler are all running in the background, allowing the full functionality of the application to be available. diff --git a/docs/src/troubleshooting.md b/docs/src/troubleshooting.md index d4be2abe..e306d1e7 100644 --- a/docs/src/troubleshooting.md +++ b/docs/src/troubleshooting.md @@ -1,5 +1,39 @@ +# **Troubleshooting** + If you encounter issues while running the service, the **admin panel** can be a useful tool for diagnosing and resolving problems. The admin panel provides access to various configurations, logs, and status indicators that can help identify potential causes of issues. + +### Reprocessing Deduplication Sets + +If you need to rerun the entire cycle of image processing and duplicate detection for a specific deduplication set, you can easily requeue the corresponding job from the admin panel. + +To do this: + +1. Navigate to: + + Home › Api › Dedup jobs + +2. Select the relevant deduplication set. + +3. Click the **Queue** button to restart the job. This action will delete all previous findings and process the images again from scratch. + +You can monitor the **status** of the Celery tasks related to this process by navigating to: + + Home › Celery Results › Task results + +This feature is particularly **useful** if: + +- You have updated the configuration of the deuplications set. + +- Images have been added or removed from the deduplication set. + +- The previous processing attempt was unsuccessful. + +By requeuing, you ensure the deduplication set reflects the most accurate and up-to-date results. + + +### Sentry + To efficiently track and monitor errors within the application, **Sentry** is integrated as the primary tool for error logging and alerting. !!! warning "Sentry environment" diff --git a/mkdocs.yml b/mkdocs.yml index 73402fb5..f86b3356 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -18,6 +18,7 @@ exclude_docs: | markdown_extensions: - admonition + - pymdownx.details - pymdownx.magiclink - pymdownx.superfences: custom_fences: @@ -76,6 +77,9 @@ plugins: # - gen-files: # scripts: # - docs/_scripts/get_settings.py + - panzoom: + full_screen: true + watch: - src/ diff --git a/pyproject.toml b/pyproject.toml index fe1f090a..47aaeae5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ authors = [ {name = "ddinicola"}, {name = "Domenico DiNicola", email = "dom.dinicola@gmail.com"}, ] -requires-python = ">=3.12" +requires-python = "==3.12.*" license = {text = "MIT"} dependencies = [ @@ -55,6 +55,7 @@ docs = [ "mkdocs-awesome-pages-plugin>=2.9.3", "mkdocstrings-python", "mkdocs-gen-files>=0.5.0", + "mkdocs-panzoom-plugin>=0.1.3", ] distribution = [ diff --git a/uv.lock b/uv.lock index a7941a16..4d41fd25 100644 --- a/uv.lock +++ b/uv.lock @@ -1,12 +1,9 @@ version = 1 -requires-python = ">=3.12" +requires-python = "==3.12.*" resolution-markers = [ - "python_full_version < '3.13' and platform_system == 'Darwin'", - "python_full_version >= '3.13' and platform_system == 'Darwin'", - 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