update versions

app.py

@@ -139,11 +139,12 @@ with gr.Blocks() as app:
     gr.Markdown('The models used in this app are based on the following papers:')
     gr.Markdown('Kusche, C., Reclik, T., Freund, M., Al-Samman, T., Kerzel, U., & Korte-Kerzel, S. (2019). Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning. PloS one, 14(5), e0216493. [Link](https://doi.org/10.1371/journal.pone.0216493)')
     #gr.Markdown('Medghalchi, S., Kusche, C. F., Karimi, E., Kerzel, U., & Korte-Kerzel, S. (2020). Damage analysis in dual-phase steel using deep learning: transfer from uniaxial to biaxial straining conditions by image data augmentation. Jom, 72, 4420-4430. [Link](https://link.springer.com/article/10.1007/s11837-020-04404-0)')
-    gr.Markdown('Setareh Medghalchi, Ehsan Karimi, Sang-Hyeok Lee, Benjamin Berkels, Ulrich Kerzel, Sandra Korte-Kerzel, Three-dimensional characterisation of deformation-induced damage in dual phase steel using deep learning, Materials & Design, Volume 232, 2023, 112108, ISSN 0264-1275, [link] (https://doi.org/10.1016/j.matdes.2023.112108)')
+    gr.Markdown('Setareh Medghalchi, Ehsan Karimi, Sang-Hyeok Lee, Benjamin Berkels, Ulrich Kerzel, Sandra Korte-Kerzel, Three-dimensional characterisation of deformation-induced damage in dual phase steel using deep learning, Materials & Design, Volume 232, 2023, 112108, ISSN 0264-1275, [link] (https://doi.org/10.1016/j.matdes.2023.112108')
     gr.Markdown('Original data and code, including the network weights,  can be found at Zenodo [link](https://zenodo.org/records/8065752)')
 
-    image_input = gr.Image()
-    with gr.Row():
+    #image_input = gr.Image(value='data/X4-Aligned_cropped_upperleft_small.png', label='Example SEM Image (DP800 steel)',)
+    image_input = gr.Image()    
+with gr.Row():
         cluster_threshold_input = gr.Number(label='Cluster Threshold', value = 20, 
                                             info='Grayscale value at which a pixel is attributed to a potential damage site')
         model1_threshold_input = gr.Number(label='Model 1 Threshold', value = 0.7, info='Threshold for the model identifying inclusions')   

requirements.txt

@@ -1,102 +1,103 @@
-absl-py==2.1.0 ; python_version >= "3.10" and python_version < "3.12"
-aiofiles==23.2.1 ; python_version >= "3.10" and python_version < "3.12"
-altair==5.2.0 ; python_version >= "3.10" and python_version < "3.12"
-annotated-types==0.6.0 ; python_version >= "3.10" and python_version < "3.12"
-anyio==4.3.0 ; python_version >= "3.10" and python_version < "3.12"
-astunparse==1.6.3 ; python_version >= "3.10" and python_version < "3.12"
-attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.12"
-cachetools==5.3.3 ; python_version >= "3.10" and python_version < "3.12"
-certifi==2024.2.2 ; python_version >= "3.10" and python_version < "3.12"
-charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.12"
-click==8.1.7 ; python_version >= "3.10" and python_version < "3.12"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.12"
-contourpy==1.2.0 ; python_version >= "3.10" and python_version < "3.12"
-cycler==0.12.1 ; python_version >= "3.10" and python_version < "3.12"
-exceptiongroup==1.2.0 ; python_version >= "3.10" and python_version < "3.11"
-fastapi==0.110.0 ; python_version >= "3.10" and python_version < "3.12"
-ffmpy==0.3.2 ; python_version >= "3.10" and python_version < "3.12"
-filelock==3.13.1 ; python_version >= "3.10" and python_version < "3.12"
-flatbuffers==24.3.7 ; python_version >= "3.10" and python_version < "3.12"
-fonttools==4.50.0 ; python_version >= "3.10" and python_version < "3.12"
-fsspec==2024.3.1 ; python_version >= "3.10" and python_version < "3.12"
-gast==0.5.4 ; python_version >= "3.10" and python_version < "3.12"
-google-auth-oauthlib==1.2.0 ; python_version >= "3.10" and python_version < "3.12"
-google-auth==2.29.0 ; python_version >= "3.10" and python_version < "3.12"
-google-pasta==0.2.0 ; python_version >= "3.10" and python_version < "3.12"
-gradio-client==0.13.0 ; python_version >= "3.10" and python_version < "3.12"
-gradio==4.22.0 ; python_version >= "3.10" and python_version < "3.12"
-grpcio==1.62.1 ; python_version >= "3.10" and python_version < "3.12"
-h11==0.14.0 ; python_version >= "3.10" and python_version < "3.12"
-h5py==3.10.0 ; python_version >= "3.10" and python_version < "3.12"
-httpcore==1.0.4 ; python_version >= "3.10" and python_version < "3.12"
-httpx==0.27.0 ; python_version >= "3.10" and python_version < "3.12"
-huggingface-hub==0.21.4 ; python_version >= "3.10" and python_version < "3.12"
-idna==3.6 ; python_version >= "3.10" and python_version < "3.12"
-imageio==2.34.0 ; python_version >= "3.10" and python_version < "3.12"
-importlib-resources==6.4.0 ; python_version >= "3.10" and python_version < "3.12"
-jinja2==3.1.3 ; python_version >= "3.10" and python_version < "3.12"
-joblib==1.3.2 ; python_version >= "3.10" and python_version < "3.12"
-jsonschema-specifications==2023.12.1 ; python_version >= "3.10" and python_version < "3.12"
-jsonschema==4.21.1 ; python_version >= "3.10" and python_version < "3.12"
-keras==2.15.0 ; python_version >= "3.10" and python_version < "3.12"
-kiwisolver==1.4.5 ; python_version >= "3.10" and python_version < "3.12"
-libclang==18.1.1 ; python_version >= "3.10" and python_version < "3.12"
-markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "3.12"
-markdown==3.6 ; python_version >= "3.10" and python_version < "3.12"
-markupsafe==2.1.5 ; python_version >= "3.10" and python_version < "3.12"
-matplotlib==3.8.3 ; python_version >= "3.10" and python_version < "3.12"
-mdurl==0.1.2 ; python_version >= "3.10" and python_version < "3.12"
-ml-dtypes==0.2.0 ; python_version >= "3.10" and python_version < "3.12"
-numpy==1.26.4 ; python_version < "3.12" and python_version >= "3.10"
-oauthlib==3.2.2 ; python_version >= "3.10" and python_version < "3.12"
-opt-einsum==3.3.0 ; python_version >= "3.10" and python_version < "3.12"
-orjson==3.9.15 ; python_version >= "3.10" and python_version < "3.12"
-packaging==24.0 ; python_version >= "3.10" and python_version < "3.12"
-pandas==2.2.1 ; python_version >= "3.10" and python_version < "3.12"
-pillow==10.2.0 ; python_version >= "3.10" and python_version < "3.12"
-protobuf==4.25.3 ; python_version >= "3.10" and python_version < "3.12"
-pyasn1-modules==0.3.0 ; python_version >= "3.10" and python_version < "3.12"
-pyasn1==0.5.1 ; python_version >= "3.10" and python_version < "3.12"
-pydantic-core==2.16.3 ; python_version >= "3.10" and python_version < "3.12"
-pydantic==2.6.4 ; python_version >= "3.10" and python_version < "3.12"
-pydub==0.25.1 ; python_version >= "3.10" and python_version < "3.12"
-pygments==2.17.2 ; python_version >= "3.10" and python_version < "3.12"
-pyparsing==3.1.2 ; python_version >= "3.10" and python_version < "3.12"
-python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "3.12"
-python-multipart==0.0.9 ; python_version >= "3.10" and python_version < "3.12"
-pytz==2024.1 ; python_version >= "3.10" and python_version < "3.12"
-pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.12"
-referencing==0.34.0 ; python_version >= "3.10" and python_version < "3.12"
-requests-oauthlib==1.4.0 ; python_version >= "3.10" and python_version < "3.12"
-requests==2.31.0 ; python_version >= "3.10" and python_version < "3.12"
-rich==13.7.1 ; python_version >= "3.10" and python_version < "3.12"
-rpds-py==0.18.0 ; python_version >= "3.10" and python_version < "3.12"
-rsa==4.9 ; python_version >= "3.10" and python_version < "3.12"
-ruff==0.3.4 ; python_version >= "3.10" and python_version < "3.12"
-scikit-learn==1.4.1.post1 ; python_version >= "3.10" and python_version < "3.12"
-scipy==1.12.0 ; python_version >= "3.10" and python_version < "3.12"
-semantic-version==2.10.0 ; python_version >= "3.10" and python_version < "3.12"
-setuptools==69.2.0 ; python_version >= "3.10" and python_version < "3.12"
-shellingham==1.5.4 ; python_version >= "3.10" and python_version < "3.12"
-six==1.16.0 ; python_version >= "3.10" and python_version < "3.12"
-sniffio==1.3.1 ; python_version >= "3.10" and python_version < "3.12"
-starlette==0.36.3 ; python_version >= "3.10" and python_version < "3.12"
-tensorboard-data-server==0.7.2 ; python_version >= "3.10" and python_version < "3.12"
-tensorboard==2.15.2 ; python_version >= "3.10" and python_version < "3.12"
-tensorflow-estimator==2.15.0 ; python_version >= "3.10" and python_version < "3.12"
-tensorflow-io-gcs-filesystem==0.36.0 ; python_version >= "3.10" and python_version < "3.12"
-tensorflow==2.15.0 ; python_version >= "3.10" and python_version < "3.12"
-termcolor==2.4.0 ; python_version >= "3.10" and python_version < "3.12"
-threadpoolctl==3.4.0 ; python_version >= "3.10" and python_version < "3.12"
-tomlkit==0.12.0 ; python_version >= "3.10" and python_version < "3.12"
-toolz==0.12.1 ; python_version >= "3.10" and python_version < "3.12"
-tqdm==4.66.2 ; python_version >= "3.10" and python_version < "3.12"
-typer[all]==0.9.0 ; python_version >= "3.10" and python_version < "3.12"
-typing-extensions==4.10.0 ; python_version >= "3.10" and python_version < "3.12"
-tzdata==2024.1 ; python_version >= "3.10" and python_version < "3.12"
-urllib3==2.2.1 ; python_version >= "3.10" and python_version < "3.12"
-uvicorn==0.29.0 ; python_version >= "3.10" and python_version < "3.12"
-websockets==11.0.3 ; python_version >= "3.10" and python_version < "3.12"
-werkzeug==3.0.1 ; python_version >= "3.10" and python_version < "3.12"
-wheel==0.43.0 ; python_version >= "3.10" and python_version < "3.12"
-wrapt==1.14.1 ; python_version >= "3.10" and python_version < "3.12"
+absl-py==2.3.0 ; python_version >= "3.11"
+aiofiles==24.1.0 ; python_version >= "3.11"
+annotated-types==0.7.0 ; python_version >= "3.11"
+anyio==4.9.0 ; python_version >= "3.11"
+astunparse==1.6.3 ; python_version >= "3.11"
+audioop-lts==0.2.1 ; python_version >= "3.13"
+certifi==2025.6.15 ; python_version >= "3.11"
+charset-normalizer==3.4.2 ; python_version >= "3.11"
+click==8.2.1 ; python_version >= "3.11" and sys_platform != "emscripten"
+colorama==0.4.6 ; python_version >= "3.11" and platform_system == "Windows"
+contourpy==1.3.2 ; python_version >= "3.11"
+cycler==0.12.1 ; python_version >= "3.11"
+fastapi==0.115.13 ; python_version >= "3.11"
+ffmpy==0.6.0 ; python_version >= "3.11"
+filelock==3.18.0 ; python_version >= "3.11"
+flatbuffers==25.2.10 ; python_version >= "3.11"
+fonttools==4.58.4 ; python_version >= "3.11"
+fsspec==2025.5.1 ; python_version >= "3.11"
+gast==0.6.0 ; python_version >= "3.11"
+google-pasta==0.2.0 ; python_version >= "3.11"
+gradio-client==1.10.3 ; python_version >= "3.11"
+gradio==5.34.2 ; python_version >= "3.11"
+groovy==0.1.2 ; python_version >= "3.11"
+grpcio==1.73.0 ; python_version >= "3.11"
+h11==0.16.0 ; python_version >= "3.11"
+h5py==3.14.0 ; python_version >= "3.11"
+hf-xet==1.1.5 ; python_version >= "3.11" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
+httpcore==1.0.9 ; python_version >= "3.11"
+httpx==0.28.1 ; python_version >= "3.11"
+huggingface-hub==0.33.0 ; python_version >= "3.11"
+idna==3.10 ; python_version >= "3.11"
+imageio==2.37.0 ; python_version >= "3.11"
+jinja2==3.1.6 ; python_version >= "3.11"
+joblib==1.5.1 ; python_version >= "3.11"
+keras==3.10.0 ; python_version >= "3.11"
+kiwisolver==1.4.8 ; python_version >= "3.11"
+libclang==18.1.1 ; python_version >= "3.11"
+markdown-it-py==3.0.0 ; python_version >= "3.11"
+markdown==3.8.2 ; python_version >= "3.11"
+markupsafe==3.0.2 ; python_version >= "3.11"
+matplotlib==3.10.3 ; python_version >= "3.11"
+mdurl==0.1.2 ; python_version >= "3.11"
+ml-dtypes==0.5.1 ; python_version >= "3.11"
+namex==0.1.0 ; python_version >= "3.11"
+numpy==2.1.3 ; python_version >= "3.11"
+nvidia-cublas-cu12==12.5.3.2 ; python_version >= "3.11"
+nvidia-cuda-cupti-cu12==12.5.82 ; python_version >= "3.11"
+nvidia-cuda-nvcc-cu12==12.5.82 ; python_version >= "3.11"
+nvidia-cuda-nvrtc-cu12==12.5.82 ; python_version >= "3.11"
+nvidia-cuda-runtime-cu12==12.5.82 ; python_version >= "3.11"
+nvidia-cudnn-cu12==9.3.0.75 ; python_version >= "3.11"
+nvidia-cufft-cu12==11.2.3.61 ; python_version >= "3.11"
+nvidia-curand-cu12==10.3.6.82 ; python_version >= "3.11"
+nvidia-cusolver-cu12==11.6.3.83 ; python_version >= "3.11"
+nvidia-cusparse-cu12==12.5.1.3 ; python_version >= "3.11"
+nvidia-nccl-cu12==2.23.4 ; python_version >= "3.11"
+nvidia-nvjitlink-cu12==12.5.82 ; python_version >= "3.11"
+opt-einsum==3.4.0 ; python_version >= "3.11"
+optree==0.16.0 ; python_version >= "3.11"
+orjson==3.10.18 ; python_version >= "3.11"
+packaging==25.0 ; python_version >= "3.11"
+pandas==2.3.0 ; python_version >= "3.11"
+pillow==11.2.1 ; python_version >= "3.11"
+protobuf==5.29.5 ; python_version >= "3.11"
+pydantic-core==2.33.2 ; python_version >= "3.11"
+pydantic==2.11.7 ; python_version >= "3.11"
+pydub==0.25.1 ; python_version >= "3.11"
+pygments==2.19.2 ; python_version >= "3.11"
+pyparsing==3.2.3 ; python_version >= "3.11"
+python-dateutil==2.9.0.post0 ; python_version >= "3.11"
+python-multipart==0.0.20 ; python_version >= "3.11"
+pytz==2025.2 ; python_version >= "3.11"
+pyyaml==6.0.2 ; python_version >= "3.11"
+requests==2.32.4 ; python_version >= "3.11"
+rich==14.0.0 ; python_version >= "3.11"
+ruff==0.12.0 ; python_version >= "3.11" and sys_platform != "emscripten"
+safehttpx==0.1.6 ; python_version >= "3.11"
+scikit-learn==1.7.0 ; python_version >= "3.11"
+scipy==1.16.0 ; python_version >= "3.11"
+semantic-version==2.10.0 ; python_version >= "3.11"
+setuptools==80.9.0 ; python_version >= "3.11"
+shellingham==1.5.4 ; python_version >= "3.11" and sys_platform != "emscripten"
+six==1.17.0 ; python_version >= "3.11"
+sniffio==1.3.1 ; python_version >= "3.11"
+starlette==0.46.2 ; python_version >= "3.11"
+tensorboard-data-server==0.7.2 ; python_version >= "3.11"
+tensorboard==2.19.0 ; python_version >= "3.11"
+tensorflow-io-gcs-filesystem==0.37.1 ; python_version == "3.11"
+tensorflow==2.19.0 ; python_version >= "3.11"
+termcolor==3.1.0 ; python_version >= "3.11"
+threadpoolctl==3.6.0 ; python_version >= "3.11"
+tomlkit==0.13.3 ; python_version >= "3.11"
+tqdm==4.67.1 ; python_version >= "3.11"
+typer==0.16.0 ; python_version >= "3.11" and sys_platform != "emscripten"
+typing-extensions==4.14.0 ; python_version >= "3.11"
+typing-inspection==0.4.1 ; python_version >= "3.11"
+tzdata==2025.2 ; python_version >= "3.11"
+urllib3==2.5.0 ; python_version >= "3.11"
+uvicorn==0.34.3 ; python_version >= "3.11" and sys_platform != "emscripten"
+websockets==15.0.1 ; python_version >= "3.11"
+werkzeug==3.1.3 ; python_version >= "3.11"
+wheel==0.45.1 ; python_version >= "3.11"
+wrapt==1.17.2 ; python_version >= "3.11"

utils.py

@@ -51,133 +51,107 @@ def show_boxes(image : np.ndarray, damage_sites : dict, box_size = [250,250],
     """
 
     _, ax = plt.subplots(1)
-    fig = plt.imshow(image,cmap='gray')
-    # do not show axis ticks (indicating pixels)
-    plt.xticks([])
-    plt.yticks([]) 
+    ax.imshow(image, cmap='gray')  # show image on correct axis
+    ax.set_xticks([])
+    ax.set_yticks([])
 
     for key, label in damage_sites.items():
-        
-        position = list([key[0],key[1]])
-
-        # define colours of the rectangles overlaid on the image per damage type
-        match label:
-            case 'Inclusion':
-                edgecolor = 'b'
-            case 'Interface' :
-                edgecolor = 'g'
-            case 'Martensite' :
-                edgecolor = 'r'
-            case 'Notch':
-                edgecolor = 'y'
-            case 'Shadowing' :
-                edgecolor = 'm'
-            case _:
-                edgecolor = 'k'
-        
-            
-        rectangle = patches.Rectangle((position[1]-box_size[1]/2., position[0]-box_size[0]/2),
-                                       box_size[0],box_size[1],
-                                       linewidth=1,edgecolor=edgecolor,facecolor='none')
-        ax.add_patch(rectangle)
-
-
-    legend_elements = [Line2D([0], [0], color='b', lw=4, label='Inclusion'),
-                       Line2D([0], [0], color='g', lw=4, label='Interface'),
-                       Line2D([0], [0], color='r', lw=4, label='Martensite'),
-                       Line2D([0], [0], color='y', lw=4, label='Notch'),
-                       Line2D([0], [0], color='m', lw=4, label='Shadow'),
-                       Line2D([0], [0], color='k', lw=4, label='Not Classified')
-        ]
-
-    ax.legend(handles=legend_elements,bbox_to_anchor=(1.04, 1), loc="upper left")
-
-    if save_image:
-        plt.savefig(image_path,dpi=1200,bbox_inches='tight' )
-
-    canvas = plt.gca().figure.canvas
+        position = [key[0], key[1]]
+        edgecolor = {
+            'Inclusion': 'b',
+            'Interface': 'g',
+            'Martensite': 'r',
+            'Notch': 'y',
+            'Shadowing': 'm'
+        }.get(label, 'k')  # default: black
+
+        rect = patches.Rectangle((position[1] - box_size[1] / 2., position[0] - box_size[0] / 2),
+                                 box_size[1], box_size[0],
+                                 linewidth=1, edgecolor=edgecolor, facecolor='none')
+        ax.add_patch(rect)
+
+    legend_elements = [
+        Line2D([0], [0], color='b', lw=4, label='Inclusion'),
+        Line2D([0], [0], color='g', lw=4, label='Interface'),
+        Line2D([0], [0], color='r', lw=4, label='Martensite'),
+        Line2D([0], [0], color='y', lw=4, label='Notch'),
+        Line2D([0], [0], color='m', lw=4, label='Shadow'),
+        Line2D([0], [0], color='k', lw=4, label='Not Classified')
+    ]
+    ax.legend(handles=legend_elements, bbox_to_anchor=(1.04, 1), loc="upper left")
+
+    fig = ax.figure
+    fig.tight_layout(pad=0)
+
+    if save_image and image_path:
+        fig.savefig(image_path, dpi=1200, bbox_inches='tight')
+
+    canvas = fig.canvas
     canvas.draw()
-    data = np.frombuffer(canvas.tostring_rgb(), dtype=np.uint8)
-    image = data.reshape(canvas.get_width_height()[::-1] + (3,))
-
-    plt.show()
 
+    data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8).reshape(
+        canvas.get_width_height()[::-1] + (4,))
+    data = data[:, :, :3]  # RGB only
 
+    plt.close(fig)
 
-    return image
+    return data 
 
 
 ###
 ### cut out small images from panorama, append colour information
 ###
-def prepare_classifier_input(panorama : np.ndarray, centroids : list, window_size = [250,250]) -> list :
-    """Create a list of smaller images from the SEM panoramic image. 
-       The neural networks expect images of a given size that are centered around a single damage site candiates.
-       For each centroid (from the clustering step before), we cut out a smaller image from the panorama of the size
-       expected by the classfier network.
-       Since the networks expect colour images, we repeat the gray-scale image 3 times for a given candiate site.
-
-    Args:
-        panorama (np.ndarray): SEM input image
-        centroids (list): list of centroids for the damage site candidates
-        window_size (list, optional): Size of the image expected by the neural network later. Defaults to [250,250].
-
-    Returns:
-        list: List of "colour" images cut out from the SEM panorama, one per damage site candidate
+def prepare_classifier_input(panorama: np.ndarray, centroids: list, window_size=[250, 250]) -> list:
     """
+    Extracts square image patches centered at each given centroid from a grayscale panoramic SEM image.
+    
+    Each extracted patch is resized to the specified window size and converted into a 3-channel (RGB-like)
+    normalized image suitable for use with classification neural networks that expect color input.
 
-    panorama_shape = panorama.shape
+    Parameters
+    ----------
+    panorama : np.ndarray
+        Input SEM image. Should be a 2D array (H, W) or a 3D array (H, W, 1) representing grayscale data.
+    
+    centroids : list of [int, int]
+        List of (y, x) coordinates marking the centers of regions of interest. These are typically damage sites
+        identified in preprocessing (e.g., clustering).
+
+    window_size : list of int, optional
+        Size [height, width] of each extracted image patch. Defaults to [250, 250].
+
+    Returns
+    -------
+    list of np.ndarray
+        List of extracted and normalized 3-channel image patches, each with shape (height, width, 3). Only
+        centroids that allow full window extraction within image bounds are used.
+    """
+    if panorama.ndim == 2:
+        panorama = np.expand_dims(panorama, axis=-1)  # (H, W, 1)
 
-    # list of the small images cut out from the panorama,
-    # each of these is then fed into the classfier model
+    H, W, _ = panorama.shape
+    win_h, win_w = window_size
     images = []
 
-    for i in range(len(centroids)):
-        x1 = int(math.floor(centroids[i][0] - window_size[0]/2))
-        y1 = int(math.floor(centroids[i][1] - window_size[1]/2))
-        x2 = int(math.floor(centroids[i][0] + window_size[0]/2))
-        y2 = int(math.floor(centroids[i][1] + window_size[1]/2))
-    
+    for (cy, cx) in centroids:
+        x1 = int(cx - win_w / 2)
+        y1 = int(cy - win_h / 2)
+        x2 = x1 + win_w
+        y2 = y1 + win_h
 
-        ##
-        ## Catch the cases in which the extract would go
-        ## over the boundaries of the original image
-        ##
-        if x1<0:
-            x1 = 0
-            x2 = window_size[0]
-        if x2>= panorama_shape[0]:
-            x1 = panorama_shape[0] - window_size[0]
-            x2 = panorama_shape[0]
-        if y1<0:
-            y1 = 0
-            y2 = window_size[1]
-        if y2>= panorama_shape[1]:
-            y1 = panorama_shape[1] - window_size[1]
-            y2 = panorama_shape[1]
-
-        # we now need to create the image path from the panoramic image that corresponds to the 
-        # centroid, with the size determined by the window_size. 
-        # First, we create an empty container with np.zeros()
-        tmp_img = np.zeros((window_size[1],  window_size[0],1), dtype=float)
-
-        # Then we copy over the patch of the panomaric image.
-        # The later classfier expects colour images, i.e. 3 colour channels for RGB
-        # Since we use gray-scale images, we only have one colour information, so we add the image to the first colour channel
-        tmp_img[:,:,0] = panorama[x1:x2,y1:y2,0]
-
-        # rescale the colour values
-        tmp_img = tmp_img*2./255. - 1.
-
-        # The classifier expects colour images, i.e. 3 colour channels.
-        # We "fake" this by repeating the same gray-scale information 3 times, once per colour channel
-        tmp_img_colour = np.repeat(tmp_img,3, axis=2) #3
-
-        images.append(tmp_img_colour)
-    
+        # Skip if patch would go out of bounds
+        if x1 < 0 or y1 < 0 or x2 > W or y2 > H:
+            continue
 
-    return images
+        # Extract and normalize patch
+        patch = panorama[y1:y2, x1:x2, 0].astype(np.float32)
+        patch = patch * 2. / 255. - 1.
 
+        # Replicate grayscale channel to simulate RGB
+        patch_color = np.repeat(patch[:, :, np.newaxis], 3, axis=2)
+        images.append(patch_color)
+
+    return images