RoiCluster

sdcat/__main__.py

@@ -7,7 +7,7 @@
 import click
 from sdcat.logger import err, info, create_logger_file
 from sdcat import __version__
-from sdcat.cluster.commands import run_cluster
+from sdcat.cluster.commands import run_cluster_det, run_cluster_roi
 from sdcat.detect.commands import run_detect
 
 
@@ -28,7 +28,19 @@ def cli():
     pass
 
 cli.add_command(run_detect)
-cli.add_command(run_cluster)
+
+
+@cli.group(name="cluster")
+def cli_cluster():
+    """
+    Commands related to converting data
+    """
+    pass
+
+
+cli.add_command(cli_cluster)
+cli_cluster.add_command(run_cluster_det)
+cli_cluster.add_command(run_cluster_roi)
 
 
 if __name__ == '__main__':

sdcat/cluster/cluster.py

@@ -15,7 +15,7 @@
 from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.preprocessing import MinMaxScaler
 from sdcat.logger import info, warn, debug, err
-from sdcat.cluster.utils import cluster_grid, crop_square_image
+from sdcat.cluster.utils import cluster_grid, crop_square_image, square_image
 from sdcat.cluster.embedding import fetch_embedding, has_cached_embedding, compute_norm_embedding
 
 if find_spec("multicore_tsne"):
@@ -227,12 +227,14 @@ def cluster_vits(
         cluster_selection_epsilon: float,
         min_similarity: float,
         min_cluster_size: int,
-        min_samples: int):
+        min_samples: int,
+        roi: bool = False) -> pd.DataFrame:
     """  Cluster the crops using the VITS embeddings.
     :param prefix:  A unique prefix to save artifacts from clustering
     :param model: The model to use for clustering
     :param df_dets: The dataframe with the detections
     :param output_path: The output path to save the clustering artifacts to
+    :param roi:  Whether the detections are already cropped to the ROI
     :param cluster_selection_epsilon: The epsilon parameter for HDBSCAN
     :param alpha: The alpha parameter for HDBSCAN
     :param min_similarity: The minimum similarity score to use for -1 cluster reassignment
@@ -250,12 +252,18 @@ def cluster_vits(
 
     # Skip cropping if all the crops are already done
     if num_crop != len(df_dets):
-        # Crop and squaring the images in parallel using multiprocessing to speed up the processing
-        info(f'Cropping {len(df_dets)} detections in parallel using {multiprocessing.cpu_count()} processes...')
         num_processes = min(multiprocessing.cpu_count(), len(df_dets))
-        with multiprocessing.Pool(num_processes) as pool:
-            args = [(row, 224) for index, row in df_dets.iterrows()]
-            pool.starmap(crop_square_image, args)
+        if roi == True:
+            info('ROI crops already exist. Creating square crops in parallel using {multiprocessing.cpu_count()} processes...')
+            with multiprocessing.Pool(num_processes) as pool:
+                args = [(row, 224) for index, row in df_dets.iterrows()]
+                pool.starmap(square_image, args)
+        else:
+            # Crop and squaring the images in parallel using multiprocessing to speed up the processing
+            info(f'Cropping {len(df_dets)} detections in parallel using {multiprocessing.cpu_count()} processes...')
+            with multiprocessing.Pool(num_processes) as pool:
+                args = [(row, 224) for index, row in df_dets.iterrows()]
+                pool.starmap(crop_square_image, args)
 
     # Drop any rows with crop_path that have files that don't exist - sometimes the crops fail
     df_dets = df_dets[df_dets['crop_path'].apply(lambda x: os.path.exists(x))]
@@ -279,9 +287,15 @@ def cluster_vits(
         (output_path / prefix).mkdir(parents=True)
 
     # Remove everything except ancillary data to include in clustering
-    ancillary_df = df_dets.drop(
-        columns=['x', 'y', 'xx', 'xy', 'w', 'h', 'image_width', 'image_height', 'cluster_id', 'cluster', 'score',
-                 'class', 'image_path', 'crop_path'])
+    columns = ['x', 'y', 'xx', 'xy', 'w', 'h', 'image_width', 'image_height', 'cluster_id', 'cluster', 'score',
+               'class', 'image_path', 'crop_path']
+    # Check if the columns exist in the dataframe
+    if all(col in df_dets.columns for col in columns):
+        ancillary_df = df_dets.drop(
+            columns=['x', 'y', 'xx', 'xy', 'w', 'h', 'image_width', 'image_height', 'cluster_id', 'cluster', 'score',
+                     'class', 'image_path', 'crop_path'])
+    else:
+        ancillary_df = df_dets
 
     # Cluster the images
     cluster_sim, unique_clusters, cluster_means, coverage = _run_hdbscan_assign(prefix,

sdcat/cluster/commands.py

@@ -14,14 +14,15 @@
 import pandas as pd
 import pytz
 import torch
+from PIL import Image
 
 from sdcat import common_args
 from sdcat.config import config as cfg
 from sdcat.logger import info, err, warn
 from sdcat.cluster.cluster import cluster_vits
 
 
-@click.command('cluster', help='Cluster detections. See cluster --config-ini to override cluster defaults.')
+@click.command('detections', help='Cluster detections. See cluster --config-ini to override cluster defaults.')
 @common_args.config_ini
 @common_args.start_image
 @common_args.end_image
@@ -31,7 +32,7 @@
 @click.option('--alpha', help='Alpha is a parameter that controls the linkage. See https://hdbscan.readthedocs.io/en/latest/parameter_selection.html. Default is 0.92. Increase for less conservative clustering, e.g. 1.0', type=float)
 @click.option('--cluster-selection-epsilon', help='Epsilon is a parameter that controls the linkage. Default is 0. Increase for less conservative clustering', type=float)
 @click.option('--min-cluster-size', help='The minimum number of samples in a group for that group to be considered a cluster. Default is 2. Increase for less conservative clustering, e.g. 5, 15', type=int)
-def run_cluster(det_dir, save_dir, device, config_ini, alpha, cluster_selection_epsilon, min_cluster_size, start_image, end_image):
+def run_cluster_det(det_dir, save_dir, device, config_ini, alpha, cluster_selection_epsilon, min_cluster_size, start_image, end_image):
     config = cfg.Config(config_ini)
     max_area = int(config('cluster', 'max_area'))
     min_area = int(config('cluster', 'min_area'))
@@ -259,7 +260,7 @@ def is_day(utc_dt):
         shutil.copy(Path(config_ini), save_dir / f'{prefix}_config.ini')
     else:
         warn(f'No detections found to cluster')
-
+ 
 @click.command('roi', help='Cluster roi. See cluster --config-ini to override cluster defaults.')
 @common_args.config_ini
 @click.option('--roi-dir', help='Input folder(s) with raw ROI images', multiple=True)

sdcat/cluster/utils.py

@@ -92,9 +92,45 @@ def gen_grid(with_attention: bool):
     # gen_grid(with_attention=True)
 
 
+def square_image(row, square_dim: int):
+    """
+    Squares an image to the model dimension, filling it with black bars if necessary
+    :param row:
+    :param square_dim: dimension of the square image
+    :return:
+    """
+    try:
+        if not Path(row.image_path).exists():
+            warn(f'Skipping {row.crop_path} because the image {row.image_path} does not exist')
+            return
+
+        if Path(row.crop_path).exists():  # If the crop already exists, skip it
+            return
+
+        # Determine the size of the new square
+        max_side = max(row.image_width, row.image_height)
+
+        # Create a new square image with a black background
+        new_image = Image.new('RGB', (max_side, max_side), (0, 0, 0))
+
+        img = Image.open(row.image_path)
+
+        # Paste the original image onto the center of the new image
+        new_image.paste(img, ((max_side - row.image_width) // 2, (max_side - row.image_height) // 2))
+
+        # Resize the image to square_dim x square_dim
+        img = img.resize((square_dim, square_dim), Image.LANCZOS)
+
+        # Save the image
+        img.save(row.crop_path)
+        img.close()
+    except Exception as e:
+        exception(f'Error cropping {row.image_path} {e}')
+        raise e
+
 def crop_square_image(row, square_dim: int):
     """
-    Crop the image to a square padding the shorted dimension, then resize it to square_dim x square_dim
+    Crop the image to a square padding the shortest dimension, then resize it to square_dim x square_dim
     This also adjusts the crop to make sure the crop is fully in the frame, otherwise the crop that
     exceeds the frame is filled with black bars - these produce clusters of "edge" objects instead
     of the detection