lx1

.gitattributes

@@ -32,10 +32,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-results_mapple.png filter=lfs diff=lfs merge=lfs -text
-results.png filter=lfs diff=lfs merge=lfs -text
-studysites.png filter=lfs diff=lfs merge=lfs -text
-testset.png filter=lfs diff=lfs merge=lfs -text
-test_sar_images/COL_2011-06-18_TDX_7_1_024_small.png filter=lfs diff=lfs merge=lfs -text
-test_sar_images/COL_2016-05-20_TDX_7_1_092_small.png filter=lfs diff=lfs merge=lfs -text
-test_sar_images/DBE_2006-02-23_RSAT_12_3_282_small.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,13 @@
 ---
 title: NnUNet Calvingfront Detection
-emoji: 🧊
-colorFrom: blue
-colorTo: blue
+emoji: 💻
+colorFrom: green
+colorTo: green
 sdk: gradio
 sdk_version: 3.27.0
 app_file: app.py
-pinned: true
+pinned: false
 license: cc-by-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -15,7 +15,6 @@ model_path = 'Task500_Glacier_zonefronts'
 def run_front_detection(input_img):
     image_gray = input_img.convert("L")
     image_gray.save('data_raw/test.png')
-
     subprocess.run(
         ['python3', 'nnunet/dataset_conversion/Task500_Glacier_inference.py', '-data_percentage', '100', '-base',
          os.environ['data_raw']])
@@ -29,49 +28,15 @@ def run_front_detection(input_img):
     subprocess.run(cmd)
     subprocess.run(['python3', 'nnunet/dataset_conversion/Task500_Glacier_reverse.py', '-i',
                     os.path.join(os.environ['RESULTS_FOLDER'], 'fold_0')])
-    
-    front = Image.open(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png'))
-    zone = Image.open(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_zone.png'))
-    front = np.array(front)
-    zone = np.array(zone)
-
-    image_rgb = image_gray.convert('RGB')
-    image_rgb = np.asarray(image_rgb).copy()
-
-    image_rgb = np.array(image_rgb * 0.5, dtype=np.uint8)
-    image_rgb[zone[:,:,0] == 0] += np.array(np.array([0, 0, 0]) / 2, dtype=np.uint8)
-    image_rgb[zone[:,:,0] == 64] += np.array(np.array([52, 46, 55]) / 2, dtype=np.uint8)
-    image_rgb[zone[:,:,0] == 127] += np.array(np.array([254, 254, 254]) / 2, dtype=np.uint8)
-    image_rgb[zone[:,:,0] == 254] += np.array(np.array([60, 145, 230]) / 2, dtype=np.uint8)
-    image_rgb[front[:, :, 0] == 255] = [255, 0, 0]
-    # Convert NumPy array back to PIL image
-    pil_image_modified = Image.fromarray(image_rgb)
-    os.remove(os.path.join(os.environ['RESULTS_FOLDER'],'fold_0/test.nii.gz'))
-    os.remove(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png'))
-    os.remove(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_zone.png'))
     os.remove('data_raw/test.png')
     os.remove('nnUNet_raw_data_base/nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/test_0000.nii.gz')
 
 
-    return pil_image_modified
-
-demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image", title='Calving front detection with nnU-Net',
-                    description="This app can delineate the glacier calving front in radar images from satellites (Sentinal-1, Tan-DEMX, ...). The method is described in \
-                                'Out-of-the-box calving front detection method using deep-learning' by Herrmann et al.\
-                                 https://tc.copernicus.org/preprints/tc-2023-34/. The project was build up on the nnU-Net project \
-                                 by Isensee, F., Jaeger, P. F. (2020) https://github.com/MIC-DKFZ/nnUNet and  \
-                                and was trained and tested with the dataset of Gourmelon et al. \
-                                https://doi.pangaea.de/10.1594/PANGAEA.940950. <p> <p>\
-                                Drag & Drop a PNG image and click 'Submit' to apply the calving front detection or clock on an example image below. <p> \
-                                For images larger than 200KB we suggest to run on your own PC, this platform providese limited computational resources. <p>\
-                                The output shows an overlay of the segmentation mask generated by the network over the input image. Area of ocean is colored in blue, glacier in white, rock in gray and radar shadow in black.\
-                                The claving front is a thin red line.",
-                    min_height=5000,
-                    examples=[os.path.join(os.path.dirname(__file__), "test_sar_images/Mapple_2018-06-11_S1_20_2_009.png"),
-                              os.path.join(os.path.dirname(__file__), "test_sar_images/COL_2011-06-18_TDX_7_1_024_small.png"),])
-
-
-
+    front = Image.open(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png'))
+    os.remove(os.path.join(os.environ['RESULTS_FOLDER'],'fold_0/test.nii.gz'))
+    os.remove(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png'))
 
+    return front
 
+demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image",interface_size=(1000, 1000))
 demo.launch()

nnunet/dataset_conversion/Task500_Glacier_reverse.py

@@ -39,19 +39,19 @@ def main(input_folder):
             color_zone[glacier_dil == 1] = 127
 
             color_front = extract_front_from_zones(color_zone, 10)
-            color_front[color_front==255] = 255
+            color_front[color_front==255] =1
 
             output_path_zone = join(output_folder, file[:-len('.nii.gz')] + '_zone.png')
             output_path_front = join(output_folder, file[:-len('.nii.gz')] + '_front.png')
 
-            #imageio.imwrite(output_path_zone, color_zone)
-            #imageio.imwrite(output_path_front, color_front)
-
-            img_zone = Image.fromarray(color_zone).convert('RGB')
-            img_front = Image.fromarray(color_front).convert('RGB')
+            imageio.imwrite(output_path_zone, color_zone)
+            imageio.imwrite(output_path_front, color_front)
+            """
+            img_zone = Image.fromarray(color_zone)
+            img_front = Image.fromarray(color_front)
             img_zone.save(output_path_zone)
             img_front.save(output_path_front)
-
+            """
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()

readme.md

@@ -1,4 +1,3 @@
-test
 # Multi-Task Learning for Glacier Segmentation and Calving Front Prediction with the nnU-Net.
 
 This project contains the script for the experiments that are described in the paper "Out-of-the-box calving front detection method using deep-learning" by Herrmann et al. https://tc.copernicus.org/preprints/tc-2023-34/