deploy: ae28b01

index.html

@@ -532,7 +532,16 @@ <h2 id="key-features">Key features ✨</h2>
 </ul>
 <h2 id="installation">Installation ⚙️</h2>
 <p>Install the latest version from PyPI:</p>
-<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>satalign
+<p><div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>satalign
+</code></pre></div>
+To use the <code>PCC</code> module, you need to install additional dependencies:</p>
+<p><div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>satalign<span class="o">[</span>pcc<span class="o">]</span>
+</code></pre></div>
+Alternatively, if you already have satalign installed:</p>
+<p><div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>scikit-image
+</code></pre></div>
+To use the LGM module, you need to install additional dependencies:</p>
+<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>satalign<span class="o">[</span>deep<span class="o">]</span>
 </code></pre></div>
 <h2 id="how-to-use">How to use 🛠️</h2>
 <h3 id="align-an-eeimagecollection-with-satalignpccpcc">Align an ee.ImageCollection with <code>satalign.pcc.PCC</code> 🌍</h3>
@@ -541,13 +550,12 @@ <h4 id="load-libraries">Load libraries</h4>
 <span class="kn">import</span> <span class="nn">fastcubo</span>
 <span class="kn">import</span> <span class="nn">satalign</span>
 <span class="kn">import</span> <span class="nn">satalign.pcc</span>
-<span class="kn">import</span> <span class="nn">satalign.ecc</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span><span class="p">,</span> <span class="n">display</span>
 </code></pre></div>
 <h4 id="auth-and-init-gee">Auth and Init GEE</h4>
 <div class="highlight"><pre><span></span><code><span class="c1"># Initialize depending on the environment</span>
-<span class="n">ee</span><span class="o">.</span><span class="n">Autenticate</span><span class="p">()</span>
+<span class="n">ee</span><span class="o">.</span><span class="n">Authenticate</span><span class="p">()</span>
 <span class="n">ee</span><span class="o">.</span><span class="n">Initialize</span><span class="p">(</span><span class="n">opt_url</span><span class="o">=</span><span class="s2">&quot;https://earthengine-highvolume.googleapis.com&quot;</span><span class="p">)</span> <span class="c1"># project = &quot;name&quot;</span>
 </code></pre></div>
 <h4 id="dataset">Dataset</h4>
@@ -623,8 +631,11 @@ <h4 id="graphics">Graphics</h4>
   <img src="https://huggingface.co/datasets/JulioContrerasH/DataMLSTAC/resolve/main/s2animation1.gif" width="100%">
 </p>
 
+<p>Here's an addition to clarify that <code>datacube</code> and <code>reference_image</code> have already been defined:</p>
 <h3 id="align-an-image-collection-with-sataligneececc">Align an Image Collection with <code>satalign.eec.ECC</code> 📚</h3>
-<div class="highlight"><pre><span></span><code><span class="c1"># Initialize the ECC model</span>
+<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">satalign.ecc</span>
+
+<span class="c1"># Initialize the ECC model</span>
 <span class="n">ecc_model</span> <span class="o">=</span> <span class="n">satalign</span><span class="o">.</span><span class="n">ecc</span><span class="o">.</span><span class="n">ECC</span><span class="p">(</span>
     <span class="n">datacube</span><span class="o">=</span><span class="n">s2_datacube</span><span class="p">,</span> 
     <span class="n">reference</span><span class="o">=</span><span class="n">reference_image</span><span class="p">,</span>
@@ -634,10 +645,13 @@ <h3 id="align-an-image-collection-with-sataligneececc">Align an Image Collection
 <span class="n">aligned_cube</span><span class="p">,</span> <span class="n">warp_matrices</span> <span class="o">=</span> <span class="n">ecc_model</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 </code></pre></div>
 <h3 id="align-using-local-features-with-satalignlgmlgm">Align using Local Features with <code>satalign.lgm.LGM</code> 🧮</h3>
-<div class="highlight"><pre><span></span><code><span class="c1"># Initialize the LGM model</span>
+<p>Here's the updated version with a note about using floating-point values or scaling:</p>
+<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">satalign.lgm</span>
+
+<span class="c1"># Initialize the LGM model</span>
 <span class="n">lgm_model</span> <span class="o">=</span> <span class="n">satalign</span><span class="o">.</span><span class="n">lgm</span><span class="o">.</span><span class="n">LGM</span><span class="p">(</span>
-    <span class="n">datacube</span><span class="o">=</span><span class="n">datacube</span><span class="p">,</span> 
-    <span class="n">reference</span><span class="o">=</span><span class="n">reference_image</span><span class="p">,</span> 
+    <span class="n">datacube</span><span class="o">=</span><span class="n">datacube</span> <span class="o">/</span> <span class="mi">10_000</span><span class="p">,</span> 
+    <span class="n">reference</span><span class="o">=</span><span class="n">reference_image</span> <span class="o">/</span> <span class="mi">10_000</span><span class="p">,</span> 
     <span class="n">feature_model</span><span class="o">=</span><span class="s2">&quot;superpoint&quot;</span><span class="p">,</span>
     <span class="n">matcher_model</span><span class="o">=</span><span class="s2">&quot;lightglue&quot;</span><span class="p">,</span>
 <span class="p">)</span>

satalign/ecc.py

@@ -14,27 +14,28 @@
 
 import cv2
 import numpy as np
+import xarray as xr
 
 from satalign.main import SatAlign
 
 
 class ECC(SatAlign):
     def __init__(
         self,
-        datacube: np.ndarray,
-        reference: np.ndarray,
+        datacube: Union[np.ndarray, xr.DataArray],
+        reference: Union[np.ndarray, xr.DataArray],
         criteria: Tuple[int, int, float] = (cv2.TERM_CRITERIA_COUNT, 100, 0),
         gauss_kernel_size: int = 3,
         **kwargs,
     ):
         """
-
         Args:
-            datacube (xr.DataArray): The data cube to be aligned. The data cube
-                needs to have the following dimensions: (time, bands, height, width).
-            reference (Optional[xr.DataArray], optional): The reference image.
-                The reference image needs to have the following dimensions:
-                (bands, height, width).
+            datacube (Union[np.ndarray, xr.DataArray]): Data cube to align with 
+                dimensions (time, bands, height, width). Ensure values are 
+                floats; if not, divide by 10,000.
+            reference (Union[np.ndarray, xr.DataArray]): Reference image with 
+                dimensions (bands, height, width). Ensure values are floats; 
+                if not, divide by 10,000.
             criteria (Tuple[int, int, float], optional): The strategy for the termination
                 of the iterative search algorithm. The cv2.TERM_CRITERIA_COUNT indicates
                 that the algorithm should terminate after a certain number of iterations.

satalign/lgm.py

@@ -1,6 +1,7 @@
 import warnings
 from typing import List, Optional, Tuple, Union
 
+import xarray as xr
 import numpy as np
 import torch
 
@@ -13,22 +14,22 @@
 class LGM(SatAlign):
     def __init__(
         self,
-        datacube: np.ndarray,
-        reference: np.ndarray,
+        datacube: Union[np.ndarray, xr.DataArray],
+        reference: Union[np.ndarray, xr.DataArray],
         feature_model: str = "superpoint",
         matcher_model: str = "lightglue",
         max_num_keypoints: int = 2048,
         device: str = "cpu",
         **kwargs,
     ):
         """
-
         Args:
-            datacube (xr.DataArray): The data cube to be aligned. The data cube
-                needs to have the following dimensions: (time, bands, height, width).
-            reference (Optional[xr.DataArray], optional): The reference image.
-                The reference image needs to have the following dimensions:
-                (bands, height, width).
+            datacube (Union[np.ndarray, xr.DataArray]): Data cube to align with 
+                dimensions (time, bands, height, width). Ensure values are 
+                floats; if not, divide by 10,000.
+            reference (Union[np.ndarray, xr.DataArray]): Reference image with 
+                dimensions (bands, height, width). Ensure values are floats; 
+                if not, divide by 10,000.
             feature_model (str, optional): The feature extractor model. Defaults to
                 'superpoint'. Options are: 'superpoint'. Options are: 'superpoint',
                 'disk', 'sift', 'aliked', 'doghardnet'.
@@ -190,7 +191,11 @@ def get_reference_points(self) -> dict:
         """
 
         # Create the reference layer (H x W) to torch
-        reference_layer = self.create_layer(img=self.reference[self.rgb_bands])
+        if isinstance(self.reference, xr.DataArray):
+            reference_layer = self.create_layer(img=self.reference.values)
+        else:
+            reference_layer = self.create_layer(img=self.reference)
+
         reference_layer_torch = (
             torch.from_numpy(reference_layer).float()[None].to(self.device)
         )

satalign/pcc.py

@@ -1,6 +1,7 @@
 import warnings
 from typing import List, Optional, Tuple, Union
 
+import xarray as xr
 import numpy as np
 from skimage.registration import phase_cross_correlation
 
@@ -10,22 +11,22 @@
 class PCC(SatAlign):
     def __init__(
         self,
-        datacube: np.ndarray,
-        reference: np.ndarray,
+        datacube: Union[np.ndarray, xr.DataArray],
+        reference: Union[np.ndarray, xr.DataArray],
         upsample_factor: Optional[int] = 50,
         space: Optional[str] = "real",
         disambiguate: Optional[bool] = False,
         overlap_ratio: Optional[float] = 0.3,
         **kwargs,
     ):
         """
-
         Args:
-            datacube (xr.DataArray): The data cube to be aligned. The data cube
-                needs to have the following dimensions: (time, bands, height, width).
-            reference (Optional[xr.DataArray], optional): The reference image.
-                The reference image needs to have the following dimensions:
-                (bands, height, width).
+            datacube (Union[np.ndarray, xr.DataArray]): Data cube to align with 
+                dimensions (time, bands, height, width). Ensure values are 
+                floats; if not, divide by 10,000.
+            reference (Union[np.ndarray, xr.DataArray]): Reference image with 
+                dimensions (bands, height, width). Ensure values are floats; 
+                if not, divide by 10,000.
             upsample_factor (Optional[int], optional): Upsampling factor. Images will
                 be registered to within ``1 / upsample_factor`` of a pixel. For example
                 ``upsample_factor == 20`` means the images will be registered within 1/20th