Merge pull request #22 from hi-paris/add_sentinel_tops_model

Add sentinel tops model

README.md

@@ -34,6 +34,8 @@ In order to get the right model, the `model_name` has to be specified when build
 This `model_name` can either be :
 - `"spotlight"` for SAR images retrieved with spotlight mode 
 - `"stripmap"` for SAR images retrieved with stripmap mode
+- `"Sentinel-TOPS"` for SAR images retrieved with TOPS mode
+
 
 During the preprocessing steps of the noisy image for MERLIN, the real and the imaginary parts are <strong>"symetrised"</strong> (to match the theoretical assumptions of MERLIN). To skip this step, you can set the `symetrise` parameter to `False`
 
@@ -47,7 +49,7 @@ from deepdespeckling.merlin.merlin_denoiser import MerlinDenoiser
 
 # Path to one image (cos or npy file)
 image_path="path/to/cosar/image"
-# Model name, can be "spotlight" or "stripmap"
+# Model name, can be "spotlight", "stripmap" or "Sentinel-TOPS"
 model_name = "spotlight"
 symetrise = True
 

deepdespeckling/despeckling.py

@@ -13,7 +13,7 @@
 logging.basicConfig(level=logging.INFO)
 
 
-def get_denoiser(model_name: str, symetrise: bool = True) -> Denoiser:
+def _get_denoiser(model_name: str, symetrise: bool = True) -> Denoiser:
     """Get the right denoiser object from the model name
 
     Args:
@@ -24,7 +24,7 @@ def get_denoiser(model_name: str, symetrise: bool = True) -> Denoiser:
     Returns:
         denoiser (Denoiser): the right denoiser, Sar2SarDenoiser or MerlinDenoiser
     """
-    if model_name in ["spotlight", "stripmap"]:
+    if model_name in ["spotlight", "stripmap", "Sentinel_TOPS"]:
         denoiser = MerlinDenoiser(model_name=model_name, symetrise=symetrise)
     elif model_name == "sar2sar":
         denoiser = Sar2SarDenoiser()
@@ -41,7 +41,7 @@ def despeckle(sar_images_path: str, destination_directory_path: str, model_name:
     Args:
         sar_images_path (str): path of sar images
         destination_directory_path (str): path of folder in which results will be stored
-        model_name (str): model name, either "spotlight" or "stripmap" to select MERLIN model on the 
+        model_name (str): model name, either "spotlight", "stripmap" or "Sentinel_TOPS" to select MERLIN model on the 
             right cosar image format or "sar2sar" for SAR2SAR model. Default to "spotlight"
         patch_size (int): patch size. Defaults to constant PATCH_SIZE.
         stride_size (int): stride size. Defaults to constant STRIDE_SIZE.
@@ -60,7 +60,7 @@ def despeckle(sar_images_path: str, destination_directory_path: str, model_name:
     logging.info(
         f"Starting inference.. Collecting data from {sar_images_path} and storing test results in {destination_directory_path}")
 
-    denoiser = get_denoiser(model_name=model_name, symetrise=symetrise)
+    denoiser = _get_denoiser(model_name=model_name, symetrise=symetrise)
     denoiser.denoise_images(images_to_denoise_path=processed_images_path, save_dir=destination_directory_path,
                             patch_size=patch_size, stride_size=stride_size)
 
@@ -73,7 +73,7 @@ def despeckle_from_coordinates(sar_images_path: str, coordinates_dict: dict, des
         sar_images_path (str): path of sar images
         coordinates_dict (dict): dictionary containing pixel boundaries of the area to despeckle (x_start, x_end, y_start, y_end)
         destination_directory_path (str): path of folder in which results will be stored
-        model_name (str): model name, either "spotlight" or "stripmap" to select MERLIN model on the 
+        model_name (str): model name, either "spotlight", "stripmap" or "Sentinel_TOPS" to select MERLIN model on the 
             right cosar image format or "sar2sar" for SAR2SAR model. Default to "spotlight"
         patch_size (int): patch size. Defaults to constant PATCH_SIZE.
         stride_size (int): stride size. Defaults to constant STRIDE_SIZE.
@@ -92,7 +92,7 @@ def despeckle_from_coordinates(sar_images_path: str, coordinates_dict: dict, des
     logging.info(
         f"Starting inference.. Collecting data from {sar_images_path} and storing test results in {destination_directory_path}")
 
-    denoiser = get_denoiser(model_name=model_name, symetrise=symetrise)
+    denoiser = _get_denoiser(model_name=model_name, symetrise=symetrise)
     denoiser.denoise_images(images_to_denoise_path=processed_images_path, save_dir=destination_directory_path,
                             patch_size=patch_size, stride_size=stride_size)
 
@@ -106,7 +106,7 @@ def despeckle_from_crop(sar_images_path: str, destination_directory_path: str, m
         destination_directory_path (str): path of folder in which results will be stored
         patch_size (int): patch size. Defaults to constant PATCH_SIZE.
         stride_size (int): stride size. Defaults to constant STRIDE_SIZE.
-        model_name (str): model name, either "spotlight" or "stripmap" to select MERLIN model on the 
+        model_name (str): model name, either "spotlight", "stripmap" or "Sentinel_TOPS" to select MERLIN model on the 
             right cosar image format or "sar2sar" for SAR2SAR model. Default to "spotlight"
         fixed (bool) : If True, crop size is limited to 256*256. Defaults to True
         symetrise (bool) : if using spotlight or stripmap model, if True, will symetrise the real and 
@@ -119,7 +119,8 @@ def despeckle_from_crop(sar_images_path: str, destination_directory_path: str, m
     processed_images_path = create_empty_folder_in_directory(destination_directory_path=destination_directory_path,
                                                              folder_name="processed_images")
 
-    ext = "cos" if model_name in ["spotlight", "stripmap"] else "tiff"
+    ext = "cos" if model_name in ["spotlight",
+                                  "stripmap", "Sentinel_TOPS"] else "tiff"
     images_paths = glob(os.path.join(sar_images_path, f"*.{ext}")) + \
         glob(os.path.join(sar_images_path, "*.npy"))
 
@@ -139,6 +140,6 @@ def despeckle_from_crop(sar_images_path: str, destination_directory_path: str, m
     logging.info(
         f"Starting inference.. Collecting data from {sar_images_path} and storing results in {destination_directory_path}")
 
-    denoiser = get_denoiser(model_name=model_name, symetrise=symetrise)
+    denoiser = _get_denoiser(model_name=model_name, symetrise=symetrise)
     denoiser.denoise_images(images_to_denoise_path=processed_images_path, save_dir=destination_directory_path,
                             patch_size=patch_size, stride_size=stride_size)

deepdespeckling/merlin/merlin_denoiser.py

@@ -23,7 +23,7 @@ def __init__(self, model_name, symetrise, **params):
         """Initialize MerlinDenoiser class
 
         Args:
-            model_name (str): name to be used, can be "spotlight" or "stripmap"
+            model_name (str): name to be used, can be "spotlight", "stripmap" or "Sentinel_TOPS"
         """
         super().__init__(**params)
         self.model_name = model_name
@@ -42,6 +42,9 @@ def init_model_weights_path(self) -> str:
         elif self.model_name == "stripmap":
             model_weights_path = os.path.join(
                 current_dir, "saved_models/stripmap.pth")
+        elif self.model_name == "Sentinel_TOPS":
+            model_weights_path = os.path.join(
+                current_dir, "saved_models/sentinel_tops.pth")
         else:
             raise ValueError(
                 "The model name doesn't refer to an existing model ")