Merge pull request #266 from JdeRobot/265-update-docs

Update docs

Author: dpascualhe

README.md

@@ -7,11 +7,11 @@
 
 >⚠️ ***DetectionMetrics v1*** website referenced in our *Sensors* paper is still available [here](https://jderobot.github.io/DetectionMetrics/v1)
 
-*DetectionMetrics* is a family of toolkits designed to unify and streamline the evaluation of perception models across different frameworks and datasets. Looking for our published ***DetectionMetrics v1***? Check out all the [relevant links](#v1) below.
+*DetectionMetrics* is a toolkit designed to unify and streamline the evaluation of perception models across different frameworks and datasets. Looking for our published ***DetectionMetrics v1***? Check out all the [relevant links](#v1) below.
 
 Now, we're excited to introduce ***DetectionMetrics v2***! While retaining the flexibility of our previous release, *DetectionMetrics* has been redesigned with an expanded focus on image and LiDAR segmentation. As we move forward, *v2* will be the actively maintained version, featuring continued updates and enhancements to keep pace with evolving AI and computer vision technologies.
 
-<table style='font-size:100%'>
+<table style='font-size:100%; margin: auto;'>
   <tr>
     <th>&#128187; <a href="https://github.com/JdeRobot/DetectionMetrics">Code</a></th>
     <th>&#128295; <a href="https://jderobot.github.io/DetectionMetrics/v2/installation">Installation</a></th>

detectionmetrics/utils/metrics.py

@@ -24,8 +24,7 @@ def update(
         :type pred: np.ndarray
         :param gt: Array containing ground truth
         :type gt: np.ndarray
-        :param valid_mask: Binary mask where False elements will be igonred, defaults
-        to None
+        :param valid_mask: Binary mask where False elements will be igonred, defaults to None
         :type valid_mask: Optional[np.ndarray], optional
         """
         raise NotImplementedError
@@ -61,8 +60,7 @@ def update(
         :type pred: np.ndarray
         :param gt: one-hot encoded ground truth array (batch, class, width, height)
         :type gt: np.ndarray
-        :param valid_mask: Binary mask where False elements will be igonred, defaults
-        to None
+        :param valid_mask: Binary mask where False elements will be igonred, defaults to None
         :type valid_mask: Optional[np.ndarray], optional
         """
         assert pred.shape == gt.shape, "Pred. and GT shapes don't match"
@@ -117,8 +115,7 @@ def update(
         :type pred: np.ndarray
         :param gt: Array containing ground truth
         :type gt: np.ndarray
-        :param valid_mask: Binary mask where False elements will be ignored, defaults
-        to None
+        :param valid_mask: Binary mask where False elements will be ignored, defaults to None
         :type valid_mask: Optional[np.ndarray], optional
         :return: Updated confusion matrix
         :rtype: np.ndarray
@@ -162,7 +159,7 @@ def get_accuracy(self) -> Tuple[np.ndarray, float]:
         return acc_per_class, acc
 
     def get_precision(self) -> Tuple[np.ndarray, float]:
-        """Compute precision from confusion matrix. 
+        """Compute precision from confusion matrix.
         Precision = true positives/ predicted positives.
 
         :return: per class precision
@@ -208,5 +205,5 @@ def get_f1_score(self) -> Tuple[np.ndarray, float]:
         global_f1_score = (
             2 * global_precision * global_recall
         ) / (global_precision + global_recall) if (global_precision + global_recall) > 0 else 0.0
-        
+
         return f1_score_per_class, global_f1_score

docs/_pages/home.md

@@ -4,28 +4,30 @@ permalink: /
 header:
   overlay_color: "#5e616c"
   overlay_image: /assets/images/cover/test_header_shear_3.png
-  #actions:
-  #  - label: "<i class='fas fa-download'></i> Install now"
-  #    url: "/installation/"
+  actions:
+   - label: "<i class='fas fa-download'></i> Learn more and contribute"
+     url: "https://github.com/JdeRobot/DetectionMetrics"
 excerpt:
   Unified evaluation for perception models
 ---
 >&#9888;&#65039; ***DetectionMetrics v1*** website referenced in our *Sensors* paper is still available [here](https://jderobot.github.io/DetectionMetrics/v1)
 
 # What is DetectionMetrics?
-*DetectionMetrics* is a family of toolkits designed to unify and streamline the evaluation of perception models across different frameworks and datasets. Looking for our published ***DetectionMetrics v1***? Check out all the [relevant links](#v1) below.
+*DetectionMetrics* is a toolkit designed to unify and streamline the evaluation of perception models across different frameworks and datasets. Looking for our published ***DetectionMetrics v1***? Check out all the [relevant links](#v1) below.
 
 Now, we're excited to introduce ***DetectionMetrics v2***! While retaining the flexibility of our previous release, *DetectionMetrics* has been redesigned with an expanded focus expanded focus on image and LiDAR segmentation. As we move forward, *v2* will be the actively maintained version, featuring continued updates and enhancements to keep pace with evolving AI and computer vision technologies.
 
-<table style='font-size:100%'>
-  <tr>
-    <th>&#128187; <a href="https://github.com/JdeRobot/DetectionMetrics">Code</a></th>
-    <th>&#128295; <a href="https://jderobot.github.io/DetectionMetrics/v2/installation">Installation</a></th>
-    <th>&#129513; <a href="https://jderobot.github.io/DetectionMetrics/v2/compatibility">Compatibility</a></th>
-    <th>&#128214; <a href="https://jderobot.github.io/DetectionMetrics/py_docs/_build/html/index.html">Docs</a></th>
-  </tr>
+<table class='centered-table'>
+    <tr>
+        <th>&#128187; <a href="https://github.com/JdeRobot/DetectionMetrics">Code</a></th>
+        <th>&#128295; <a href="https://jderobot.github.io/DetectionMetrics/v2/installation">Installation</a></th>
+        <th>&#129513; <a href="https://jderobot.github.io/DetectionMetrics/v2/compatibility">Compatibility</a></th>
+        <th>&#128214; <a href="https://jderobot.github.io/DetectionMetrics/py_docs/_build/html/index.html">Docs</a></th>
+    </tr>
 </table>
 
+{% include video id="PYS57dNiDAg" provider="youtube" %}
+
 ![diagram](../assets/images/detectionmetricsv2_diagram.png)
 
 # What's supported in DetectionMetrics

docs/assets/css/main.scss

@@ -5,4 +5,15 @@
 @charset "utf-8";
 
 @import "minimal-mistakes/skins/{{ site.minimal_mistakes_skin | default: 'default' }}"; // skin
-@import "minimal-mistakes"; // main partials
+@import "minimal-mistakes"; // main partials
+
+.centered-table {
+  display: flex;
+  justify-content: center;
+  font-size: 100%;
+}
+
+.centered-table table {
+  width: auto;
+  border-collapse: collapse;
+}

docs/py_docs/_build/doctrees/detectionmetrics.cli.doctree

@@ -81,7 +81,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 <span id="detectionmetrics-cli-evaluate-module"></span><h2>detectionmetrics.cli.evaluate module<a class="headerlink" href="#module-detectionmetrics.cli.evaluate" title="Link to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="detectionmetrics.cli.evaluate.get_dataset">
-<span class="sig-prename descclassname"><span class="pre">detectionmetrics.cli.evaluate.</span></span><span class="sig-name descname"><span class="pre">get_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_format</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_fname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_suffix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_suffix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ontology</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#detectionmetrics.cli.evaluate.get_dataset" title="Link to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">detectionmetrics.cli.evaluate.</span></span><span class="sig-name descname"><span class="pre">get_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_format</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_fname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_dataset_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">images_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_suffix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_suffix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ontology</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#detectionmetrics.cli.evaluate.get_dataset" title="Link to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">