Self hosted

Dockerfile

@@ -1,3 +1,9 @@
+# Build:
+# docker build -t ghcr.io/decodingraphael/unraphael .
+# Push to GitHub Container Registry:
+# docker push ghcr.io/decodingraphael/unraphael
+# Run:
+# docker run -p 8501:8501 ghcr.io/decodingraphael/unraphael
 FROM python:3.12-slim
 
 RUN pip install torch==2.3.1+cpu torchvision torchaudio 'numpy<2.0' --extra-index-url https://download.pytorch.org/whl/cpu

README.md

@@ -75,3 +75,70 @@ unraphael-dash
 Check out our [Contributing Guidelines](CONTRIBUTING.md#Getting-started-with-development) to get started with development.
 
 Suggestions, improvements, and edits are most welcome.
+
+## Self hosted deployment
+
+To run on dashboard with:
+
+```shell
+sudo apt-get update && apt-get install libgl1 libglib2.0-0 -y
+# As www-data user
+python3 -m venv venv
+pip install 'unraphael[dash]@git+https://github.com/DecodingRaphael/unraphael.git@0.3'
+```
+
+<details>
+  <summary>Systemd service</summary>
+
+To run unraphael as a service, you can create a systemd service file. This will allow you to start, stop, and restart unraphael using systemd.
+
+1.  Create a service file for unraphael, for example `/etc/systemd/system/unraphael.service`:
+
+```
+[Unit]
+Description=Unraphael dashboard
+After=network.target
+
+[Service]
+Environment="XDG_CACHE_HOME=/cache/dir" HOME="/writable/dir"
+User=youruser
+WorkingDirectory=/home/youruser
+ExecStart=/home/youruser/.local/bin/unraphael-dash
+Restart=on-failure
+User=youruser
+WorkingDirectory=/home/youruser
+ExecStart=/home/youruser/.local/bin/unraphael-dash
+Restart=on-failure
+
+[Install]
+WantedBy=multi-user.target
+```
+
+Replace `/cache/dir` and `/writable/dir` with the actual paths to your cache and writable directories.
+Replace `youruser` with your actual username.  Also, make sure that the path to `unraphael-dash` is correct. You can find the correct path using `which unraphael-dash`.
+
+2.  Enable the service:
+
+```console
+sudo systemctl enable unraphael.service
+```
+
+3.  Start the service:
+
+```console
+sudo systemctl start unraphael.service
+```
+
+4.  Check the status of the service:
+
+```console
+sudo systemctl status unraphael.service
+```
+
+5.  To stop the service:
+
+```console
+sudo systemctl stop unraphael.service
+```
+
+</details>

docs/credits.md

@@ -1,6 +1,6 @@
 # Credits and Contact Information
 
-Unraphael is maintained by the [Netherlands eScience Center](https://www.esciencecenter.nl/) in collaboration with the [Department of History and Art History](https://www.uu.nl/en/organisation/department-of-history-and-art-history) at the [University of Utrecht](https://www.uu.nl/). The work was supported through a *Small-Scale Initiatives Digital Approaches to the Humanities* grant 
+Unraphael is maintained by the [Netherlands eScience Center](https://www.esciencecenter.nl/) in collaboration with the [Department of History and Art History](https://www.uu.nl/en/organisation/department-of-history-and-art-history) at the [University of Utrecht](https://www.uu.nl/). The work was supported through a *Small-Scale Initiatives Digital Approaches to the Humanities* grant
 
 If you have any questions, feedback, or need support, please feel free to reach out to us. Below are the primary contacts and useful links for your reference:
 

docs/steps/analysis.md

@@ -32,7 +32,7 @@ This then allows us to answer how similar the areas of the main outlines in two
 ![Dimensions of paintings](painting_dimensions.png)
 
    - **Upload Photos:** Upload the digital photos of the paintings from a folder on your computer. For now, use the *unaligned photos*, preferably with the background removed.
-   
+
 1. **Overview of Image Sizes and DPI:**
    - **Check Image Metrics:** Review the sizes and DPI of the uploaded images. This information helps in converting pixel measurements to physical dimensions.
 
@@ -50,4 +50,4 @@ This then allows us to answer how similar the areas of the main outlines in two
 
 5. More detailed information is provided in the table below the heatmap indicating whether the areas of the main figures in the two paintings are similar, given the set tolerance.
 
-![Example of matching ares of main figure in real paintings](img_identical.png)
+![Example of matching ares of main figure in real paintings](img_identical.png)

docs/steps/clustering.md

@@ -13,7 +13,7 @@ By following the steps described below, you can effectively group your images ba
 
 ### Clustering Methods
 
-- We make use of functionality provided by the [clusteval package](https://erdogant.github.io/clusteval/pages/html/index.html) to derive the optimal number of clusters using silhouette, dbindex, and derivatives in combination with clustering methods, such as agglomerative, kmeans, dbscan and hdbscan. 
+- We make use of functionality provided by the [clusteval package](https://erdogant.github.io/clusteval/pages/html/index.html) to derive the optimal number of clusters using silhouette, dbindex, and derivatives in combination with clustering methods, such as agglomerative, kmeans, dbscan and hdbscan.
 - For more information on the methods or interpreting the results, we highly recommend looking into the [clusteval documentation](https://erdogant.github.io/clusteval/pages/html/index.html).
 - Multiple clustering algorithms
 
@@ -62,10 +62,10 @@ These aligned images are now prepared for clustering, having been standardized i
 
 ### 4 Clustering Images
 
-Two primary clustering approaches are available: 
+Two primary clustering approaches are available:
 
-- *Outer Contours Clustering* 
-- *Complete Figures Clustering*. 
+- *Outer Contours Clustering*
+- *Complete Figures Clustering*.
 
 Both of these clustering processes group images based on structural similarities. Unlike semantic clustering, which might group images based on their color and content (e.g., animals, landscapes), structural clustering focuses on patterns, textures, and shapes.
 

pyproject.toml

@@ -118,7 +118,7 @@ module = ["yaml.*", "toml.*"]
 ignore_missing_imports = true
 
 [tool.ruff]
-line-length = 96
+line-length = 270
 target-version = "py310"
 extend-include = ["*.ipynb"]
 

src/unraphael/dash/align.py

@@ -16,9 +16,7 @@
 from unraphael.types import ImageType
 
 
-def detect_and_compute_features(
-    image_gray: np.ndarray, method: str, maxFeatures: int
-) -> Tuple[list, np.ndarray]:
+def detect_and_compute_features(image_gray: np.ndarray, method: str, maxFeatures: int) -> Tuple[list, np.ndarray]:
     """Detects and computes features in the image."""
     if method == 'SIFT':
         feature_detector = cv2.SIFT_create()
@@ -58,14 +56,10 @@ def compute_homography(matches: list, kpsA: list, kpsB: list, keepPercent: float
     return cv2.findHomography(ptsA, ptsB, method=cv2.RANSAC)[0]
 
 
-def apply_homography(
-    target: np.ndarray, H: np.ndarray, template_shape: Tuple[int, int, int]
-) -> np.ndarray:
+def apply_homography(target: np.ndarray, H: np.ndarray, template_shape: Tuple[int, int, int]) -> np.ndarray:
     """Applies the homography matrix to the target image."""
     h, w, c = template_shape
-    return cv2.warpPerspective(
-        target, H, (w, h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0)
-    )
+    return cv2.warpPerspective(target, H, (w, h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
 
 
 def homography_matrix(
@@ -205,9 +199,7 @@ def feature_align(
 
     # apply the homography matrix to align the images, including the rotation
     h, w, c = template.shape
-    aligned = cv2.warpPerspective(
-        target, H, (w, h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0)
-    )
+    aligned = cv2.warpPerspective(target, H, (w, h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
 
     out = image.replace(data=aligned)
     out.metrics.update(angle=angle)

src/unraphael/dash/equalize.py

@@ -54,9 +54,7 @@ def normalize_brightness(
 
     # Adjust the L channel (brightness) of the target image based
     # on the mean brightness of the template
-    l_target = (
-        (l_target * (np.mean(l_template) / np.mean(l_target))).clip(0, 255).astype(np.uint8)
-    )
+    l_target = (l_target * (np.mean(l_template) / np.mean(l_target))).clip(0, 255).astype(np.uint8)
 
     # Merge LAB channels back for the adjusted target image
     equalized_img_lab = cv2.merge([l_target, a_target, b_target])
@@ -133,9 +131,7 @@ def normalize_contrast(
         std_target = np.std(target_lab[:, :, 0])
 
         # Adjust contrast of target image to match template image
-        l_target = (
-            (target_lab[:, :, 0] * (std_template / std_target)).clip(0, 255).astype(np.uint8)
-        )
+        l_target = (target_lab[:, :, 0] * (std_template / std_target)).clip(0, 255).astype(np.uint8)
         normalized_img_lab = cv2.merge([l_target, target_lab[:, :, 1], target_lab[:, :, 2]])
 
         # Convert the adjusted LAB image back to RGB
@@ -207,17 +203,11 @@ def normalize_sharpness(
     mean_grad_target = np.mean(grad_target)
 
     # Adjust sharpness of target image to match template image
-    normalized_img = (
-        (target * (mean_grad_template / mean_grad_target)).clip(0, 255).astype(np.uint8)
-    )
+    normalized_img = (target * (mean_grad_template / mean_grad_target)).clip(0, 255).astype(np.uint8)
 
     # Calculate sharpness value for the normalized image
-    grad_x_normalized = cv2.Sobel(
-        cv2.cvtColor(normalized_img, cv2.COLOR_RGB2GRAY), cv2.CV_64F, 1, 0, ksize=3
-    )
-    grad_y_normalized = cv2.Sobel(
-        cv2.cvtColor(normalized_img, cv2.COLOR_RGB2GRAY), cv2.CV_64F, 0, 1, ksize=3
-    )
+    grad_x_normalized = cv2.Sobel(cv2.cvtColor(normalized_img, cv2.COLOR_RGB2GRAY), cv2.CV_64F, 1, 0, ksize=3)
+    grad_y_normalized = cv2.Sobel(cv2.cvtColor(normalized_img, cv2.COLOR_RGB2GRAY), cv2.CV_64F, 0, 1, ksize=3)
     grad_normalized = cv2.magnitude(grad_x_normalized, grad_y_normalized)
     mean_grad_normalized = np.mean(grad_normalized)
 

src/unraphael/dash/home.py

@@ -13,12 +13,7 @@
     menu_items={
         'Get Help': 'https://unraphael.readthedocs.io',
         'Report a bug': 'https://github.com/DedodingRaphael/unraphael/issues',
-        'About': (
-            f'**unraphael**: a dashboard for unraphael ({__version__}). '
-            '\n\nPython toolkit for *unraveling* image similarity with a focus '
-            'on artistic practice. '
-            '\n\nFor more information, see: https://github.com/DedodingRaphael/unraphael'
-        ),
+        'About': (f'**unraphael**: a dashboard for unraphael ({__version__}). ' '\n\nPython toolkit for *unraveling* image similarity with a focus ' 'on artistic practice. ' '\n\nFor more information, see: https://github.com/DedodingRaphael/unraphael'),
     },
 )
 
@@ -36,7 +31,8 @@
 
 This tool aims to provide new insights into Raphael's working methods through new digital
 approaches for the study of artistic practice in art history.
-""")
+"""
+)
 
 # Center-align using Streamlit's layout
 col1, col2, col3 = st.columns([1, 2, 1])  # Middle column is wider
@@ -81,7 +77,7 @@
 
 This project is maintained by the [Netherlands eScience Center](https://www.esciencecenter.nl/) in collaboration with the [Department of History and Art History](https://www.uu.nl/en/organisation/department-of-history-and-art-history) at the University of Utrecht.
 
-**Principal Investigator:** Dr. L. Costiner ([l.costiner@uu.nl](mailto:l.costiner@uu.nl))  
+**Principal Investigator:** Dr. L. Costiner ([l.costiner@uu.nl](mailto:l.costiner@uu.nl))
 **Technical Support:** Thijs Vroegh, Stef Smeets ([t.vroegh@esciencecenter.nl](mailto:t.vroegh@esciencecenter.nl), [s.smeets@esciencecenter.nl](mailto:s.smeets@esciencecenter.nl))
 
 Supported through a *Small-Scale Initiatives Digital Approaches to the Humanities* grant.