update jenkins and files to run automatically

Jenkinsfile

@@ -1,10 +1,27 @@
 pipeline {
     agent any
+
     stages {
-        stage('Stage 1') {
+        stage('Checkout') {
             steps {
-                echo 'Hello world!'
+                // Clone the public repository
+                git url: 'https://git.wmi.amu.edu.pl/s495715/iumKC.git'
+            }
+        }
+
+        stage('Run Python Script') {
+            steps {
+                // Execute the main.py script
+                sh 'python3 main.py'
+            }
+        }
+
+        stage('Archive Artifacts') {
+            steps {
+                // Archive any artifacts generated by the script
+                // Adjust the path according to your script's output
+                archiveArtifacts artifacts: './football_dataset/*', fingerprint: true
             }
         }
     }
-}
+}

data.py

@@ -112,7 +112,8 @@ def get_data():
         api.dataset_download_files(dataset_slug, path=download_dir, unzip=True)
 
     all_images = glob(download_dir + "/images/*.jpg")
-    all_paths = [path.replace(".jpg", ".jpg___fuse.png") for path in all_images]
+    all_paths = [path.replace(".jpg", ".jpg___fuse.png")
+                 for path in all_images]
     return all_images, all_paths
 
 
@@ -134,7 +135,7 @@ def calculate_mean_std(image_paths):
     return mean, std
 
 
-def plot_random_images(indices, data_loader):
+def plot_random_images(indices, data_loader, suffix='train'):
     plt.figure(figsize=(8, 8))
     for i, index in enumerate(indices):
         image, label_map = data_loader.dataset[index]
@@ -148,4 +149,6 @@ def plot_random_images(indices, data_loader):
         plt.imshow(label_map)
         plt.axis("off")
 
-    plt.show()
+    # Save the figure to a file instead of displaying it
+    plt.savefig(f'random_images_{suffix}.png', dpi=250, bbox_inches='tight')
+    plt.close()  # Close the figure to free up memory

main.py

@@ -29,8 +29,8 @@ def main():
     train_indices = random.sample(range(len(train_loader.dataset)), 5)
     test_indices = random.sample(range(len(test_loader.dataset)), 5)
 
-    plot_random_images(train_indices, train_loader)
-    plot_random_images(test_indices, test_loader)
+    plot_random_images(train_indices, train_loader, 'train')
+    plot_random_images(test_indices, test_loader, 'test')
     statistics = SegmentationStatistics(all_paths, train_loader, test_loader, mean, std)
     statistics.count_colors()
     statistics.print_statistics()