--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/yolo_prediction.xml	Fri Jun 13 11:23:57 2025 +0000
@@ -0,0 +1,133 @@
+<tool id="yolo_predict" name="Perform YOLO image labeling" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="24.2">
+    <description>with ultralytics</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="creator" />
+    <expand macro="edam" />
+    <expand macro="requirements" />
+    <command detect_errors="aggressive">
+    <![CDATA[
+      export YOLO_CONFIG_DIR='\$HOME/.config/ultralytics' &&
+
+      mkdir -p ./input_images ./runs ./models ./results && 
+      
+      #for $filename in $input_images:
+          ln -s '$filename' './input_images/${filename.element_identifier}' &&
+      #end for
+
+      cp '$class_name' './models/class_name.txt' &&
+
+      cp '$model' './models/model.pt' &&
+		    
+      python '$__tool_directory__/yolov8.py' 
+		    --test_path='./input_images' 
+		    --model_path='./models' 
+		    --model_name='model' 
+		    --run_dir='./runs' 
+		    --save_dir='./results'
+		    --image_size='$image_size'
+		    --mode='$mode'
+		    --foldername='overlaid_images'
+		    --class_names_file='$class_name'
+		    --num_classes=`wc -l < ./models/class_name.txt`
+		    --confidence='$confidence'
+		    --iou='$iou'
+		    --max_det='$max_det'  
+
+    ]]>
+    </command>
+    <inputs>
+        <param name="input_images" type="data" format="jpg,png" multiple="true"  label="Input images"/>
+	<param name="class_name" type="data" format="txt" label="YOLO class name file" />
+	<param name="model" type="data" format="data" label="Model file" />
+	<param name="mode" type="select" label="Prediction mode">
+              <option value="segment">segment</option>
+              <option value="detect">detect</option>
+	</param>
+	<param name="image_size" type="integer" value="1000" min="16" label="Image size" help="All input images will be re-sized to squares with sides of this length (in pixels). This value governs the trade-offs of speed (smaller values) vs accuracy (larger values)." />
+ 	<param name="confidence" type="float" value="0.5" min="0.0" max="1.0" label="Confidence" help="Confidence value (0-1) for each detected bounding box." />
+	<param name="iou" type="float" value="0.7" min="0.1" max="1.0" label="IoU" help="Intersection over Union threshold for non-maximum suppression." />
+	<param name="max_det" type="integer" value="300" min="100" max="1000" label="Max. number of detections" help="Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes." />
+    </inputs>
+    <outputs>
+        <collection name="txt_results" format="txt" type="list" label="YOLO prediction results (text)">
+            <discover_datasets pattern="(?P&lt;name&gt;.*)\.txt$" directory="results" visible="true"/>
+	</collection>
+	<collection name="mask_results" format="tiff" type="list" label="YOLO prediction masks">
+            <discover_datasets pattern="(?P&lt;name&gt;.*)\.tiff$" directory="results" visible="true"/>
+        </collection>
+
+    </outputs>
+    <tests>
+        <test>
+            <param name="input_images" value="pred-test01.jpg" />
+	    <param name="model" location="https://zenodo.org/records/15611468/files/best.pt" />
+	    <param name="image_size" value="512" />
+	    <param name="confidence" value="0.003" />
+            <param name="iou" value="0.7" />
+            <param name="max_det" value="100" />
+	    <param name="mode" value="segment" />
+	    <param name="class_name" value="class_name.txt" />
+	    <output_collection name="txt_results" type="list" count="1">
+                <element name="pred-test01">
+                    <assert_contents>
+                        <has_n_lines n="100"/>
+                    </assert_contents>
+                </element>
+            </output_collection>
+
+            <output_collection name="mask_results" type="list" count="1">
+                <element name="pred-test01_mask">
+                    <assert_contents>
+			<has_image_width width="512"/>
+			<has_image_height height="1024"/>
+                        <has_image_channels channels="1"/>
+                   </assert_contents>
+                </element>
+            </output_collection>
+        </test>
+        <test>
+            <param name="input_images" value="pred-test01.png" />
+            <param name="model" location="https://zenodo.org/records/15611468/files/best.pt" />
+            <param name="image_size" value="512" />
+            <param name="confidence" value="0.003" />
+            <param name="iou" value="0.7" />
+            <param name="max_det" value="100" />
+            <param name="mode" value="segment" />
+            <param name="class_name" value="class_name.txt" />
+            <output_collection name="txt_results" type="list" count="1">
+                <element name="pred-test01">
+                    <assert_contents>
+                        <has_n_lines n="100"/>
+                    </assert_contents>
+                </element>
+            </output_collection>
+            <output_collection name="mask_results" type="list" count="1">
+                <element name="pred-test01_mask">
+                    <assert_contents>
+                        <has_image_width width="512"/>
+                        <has_image_height height="1024"/>
+                        <has_image_channels channels="1"/>
+                   </assert_contents>
+                </element>
+            </output_collection>
+        </test>
+    </tests>
+    <help><![CDATA[
+        **What it does**
+        This tool performs predictions on input images using yolo compatible model. It supports two modes of operation: segment, detect.
+        
+	**Outputs:**
+
+	 - **Coordinates TXT File** (`.txt`)
+            - A plain text file containing predictions for each detected object.
+         
+	 OR
+         - **Segmentation Mask** (`.tiff`)
+         - An image mask where each pixel value represents a class label.
+
+    ]]>
+    </help>
+    <expand macro="citations" />
+</tool>