#263 add ability to save thresholds used for classification and segme…

…ntation models

5_zoo_workflow_local_model.py

@@ -29,6 +29,7 @@
             "tta": False,  # Test Time Augmentation
             "use_local_model": True,  # Use local model (not one from zeneodo)
             "local_model_path": r"C:\development\doodleverse\coastseg\CoastSeg\src\coastseg\downloaded_models\non_validation_model",  # path to the local model
+            "apply_segmentation_filter": True, # apply segmentation filter to the model outputs to sort them into good or bad
         }
 
 # Available models can run input "RGB" # or "MNDWI" or "NDWI"

6_zoo_workflow_with_coregistration.py

@@ -27,6 +27,7 @@
             "model_type": "global_segformer_RGB_4class_14036903", # model name from the zoo
             "otsu": False, # Otsu Thresholding
             "tta": False,  # Test Time Augmentation
+            "apply_segmentation_filter": True, # apply segmentation filter to the model outputs to sort them into good or bad
         }
 # Available models can run input "RGB" # or "MNDWI" or "NDWI"
 img_type = "RGB"  # make sure the model name is compatible with the image type

7_automatic_image_sorter.py

@@ -8,7 +8,7 @@
 # It is meant be used on CoastSeg/data/<ROI NAME>/jpg_files/preprocessed/RGB
 
 # select the RGB directory for an ROI from /data
-input_directory =r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_azb1_datetime11-01-24__02_43_46\jpg_files\preprocessed\RGB"
+input_directory =r"C:\development\doodleverse\coastseg\CoastSeg\data\ID_wra5_datetime03-04-24__03_43_01\jpg_files\preprocessed\RGB"
 
 # run the classifier to automatically sort the images
 classifier.sort_images_with_model(input_directory,threshold=0.40)

src/coastseg/classifier.py

@@ -1,6 +1,7 @@
 import os
 import glob
 import pandas as pd
+import numpy as np
 import shutil
 import pooch
 import tensorflow as tf
@@ -12,6 +13,7 @@
 # Some of these functions were originally written by Mark Lundine and have been modified for this project.
 
 
+
 def filter_segmentations(
     session_path: str,
     threshold: float = 0.40,
@@ -173,13 +175,13 @@ def run_inference_rgb_image_classifier(path_to_model_ckpt,
     path_to_model_ckpt (str): path to the saved keras model
     path_to_inference_imgs (str): path to the folder containing images to run the model on
     output_folder (str): path to save outputs to
-    csv_path (str): csv path to save results to. If not provided, the results will be saved to output_folder/classification_results.csv
+    csv_path (str): csv path to save results to. If not provided, the results will be saved to output_folder/image_classification_results.csv
     threshold (float): threshold on sigmoid of model output (ex: 0.6 means mark images as good if model output is >= 0.6, or 60% sure it's a good image)
     returns:
     csv_path (str): csv path of saved results
     """
     if not csv_path:
-        csv_path = os.path.join(output_folder, 'classification_results.csv')
+        csv_path = os.path.join(output_folder, 'image_classification_results.csv')
 
     os.makedirs(output_folder,exist_ok=True)
 
@@ -194,7 +196,6 @@ def run_inference_rgb_image_classifier(path_to_model_ckpt,
     im_classes = [None]*len(im_paths)
     i=0
     for im_path in im_paths:
-        print(im_path)
         img = keras.utils.load_img(im_path, color_mode='rgb',target_size=image_size)
         img_array = keras.utils.img_to_array(img)
         img_array = tf.expand_dims(img_array, 0)
@@ -204,7 +205,8 @@ def run_inference_rgb_image_classifier(path_to_model_ckpt,
         i=i+1
     ##save results to a csv
     df = pd.DataFrame({'im_paths':im_paths,
-                       'model_scores':model_scores
+                       'model_scores':model_scores,
+                       'threshold':np.full(len(im_paths), threshold)
                        }
                       )
 
@@ -229,13 +231,13 @@ def run_inference_gray_image_classifier(path_to_model_ckpt,
     path_to_model_ckpt (str): path to the saved keras model
     path_to_inference_imgs (str): path to the folder containing images to run the model on
     output_folder (str): path to save outputs to
-    csv_path (str): csv path to save results to. If not provided, the results will be saved to output_folder/classification_results.csv
+    csv_path (str): csv path to save results to. If not provided, the results will be saved to output_folder/image_classification_results.csv
     threshold (float): threshold on sigmoid of model output (ex: 0.6 means mark images as good if model output is >= 0.6, or 60% sure it's a good image)
     returns:
     csv_path (str): csv path of saved results
     """
     if not csv_path:
-        csv_path = os.path.join(output_folder, 'classification_results.csv')
+        csv_path = os.path.join(output_folder, 'image_classification_results.csv')
 
     os.makedirs(output_folder,exist_ok=True)
     image_size = (128, 128)
@@ -258,7 +260,8 @@ def run_inference_gray_image_classifier(path_to_model_ckpt,
         i=i+1
     ##save results to a csv
     df = pd.DataFrame({'im_paths':im_paths,
-                       'model_scores':model_scores
+                       'model_scores':model_scores,
+                       'threshold':np.full(len(im_paths), threshold)
                        }
                       )
     df.to_csv(csv_path)
@@ -349,7 +352,6 @@ def get_image_classifier(type:str='rgb') -> str:
             )
     else: # get the grayscale model
         model_name ='ImageGrayClassifier'
-        print(model_name)
         model_directory = file_utilities.create_directory(
             downloaded_models_path, model_name
         )
@@ -429,7 +431,7 @@ def run_inference_segmentation_classifier(path_to_model_ckpt:str,
     path_to_inference_imgs (str): path to the folder containing images to run the model on
     output_folder (str): path to save outputs to
     csv_path (str): csv path to save results to
-        If not provided, the results will be saved to output_folder/classification_results.csv
+        If not provided, the results will be saved to output_folder/image_classification_results.csv
     threshold (float): threshold on sigmoid of model output (ex: 0.6 means mark images as good if model output is >= 0.6, or 60% sure it's a good image)
     
     returns:
@@ -478,12 +480,13 @@ def run_inference_segmentation_classifier(path_to_model_ckpt:str,
         i=i+1
     ##save results to a csv
     df = pd.DataFrame({'im_paths':im_paths,
-                       'model_scores':model_scores
+                       'model_scores':model_scores,
+                       'threshold':np.full(len(im_paths), threshold)
                        }
                       )
 
     if not csv_path:
-        csv_path = os.path.join(output_folder, 'classification_results.csv')
+        csv_path = os.path.join(output_folder, 'segmentation_classification_results.csv')
 
     df.to_csv(csv_path)
     good_path,bad_path=sort_images(csv_path,