Segmonitor build

config/config.json

@@ -0,0 +1,8 @@
+{
+    "model_type": "MTLSD",
+    "iterations": 100000,
+    "warmup": 100000,
+    "raw_file": "path/to/zarr/or/n5",
+    "voxel_size": 33,
+    "python_script_path": "path/to/python_script.py"
+}

segmonitor/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "segmonitor"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+mongodb = "2.0"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"

segmonitor/src/lib.rs

@@ -0,0 +1,55 @@
+use std::process::Command;
+use serde::{Deserialize, Serialize};
+use mongodb::{Client, options::ClientOptions};
+
+#[derive(Debug, Deserialize, Serialize)]
+struct Config {
+    model_type: String,
+    iterations: i32,
+    warmup: i32,
+    raw_file: String,
+    voxel_size: i32,
+    python_script_path: String,
+}
+
+pub mod segmonitor {
+    pub fn train_model_from_config(config_path: &str) {
+        let config = load_config(config_path);
+
+        println!("Training model: {}", config.model_type);
+        println!("Iterations: {}", config.iterations);
+        println!("Warmup: {}", config.warmup);
+        println!("Raw file: {}", config.raw_file);
+        println!("Voxel size: {}", config.voxel_size);
+
+        call_python_train(&config.python_script_path);
+
+        save_to_mongodb(&config);
+    }
+
+    fn load_config(config_path: &str) -> Config {
+        // Load configuration from JSON file
+        let config_str = std::fs::read_to_string(config_path).expect("Error reading config file");
+        serde_json::from_str(&config_str).expect("Error parsing JSON")
+    }
+
+    fn call_python_train(script_path: &str) {
+        let output = Command::new("python")
+            .arg(script_path)
+            .output()
+            .expect("Failed to execute training");
+
+        if output.status.success() {
+            println!("Traning executed successfully!");
+        } else {
+            println!("Error executing training:\n{}", String::from_utf8_lossy(&output.stderr));
+        }
+    }
+
+    fn save_to_mongodb(config: &Config) {
+        println!("Saving metrics to MongoDB...");
+        let client_options = ClientOptions::parse("mongodb://localhost:27017").unwrap();
+        let client = Client::with_options(client_options).unwrap();
+        // TODO: dd MongoDB insertion logic here
+    }
+}

segmonitor/src/main.rs

@@ -0,0 +1,6 @@
+use segmonitor;
+
+fn main() {
+    let config_path = "path/to/config.json";
+    segmonitor::train_model_from_config(config_path);
+}

src/autoseg/eval/eval_db.py

@@ -1,6 +1,7 @@
 import sqlite3
 import json
 
+
 class Database:
     """
     Simple SQLite Database Wrapper for Storing and Retrieving Scores.
@@ -9,17 +10,17 @@ class Database:
     Each score entry is associated with a network, checkpoint, threshold, and a dictionary of scores.
 
     Args:
-        db_name (str): 
+        db_name (str):
             The name of the SQLite database file.
-        table_name (str): 
+        table_name (str):
             The name of the table within the database (default is 'scores_table').
 
     Attributes:
-        conn (sqlite3.Connection): 
+        conn (sqlite3.Connection):
             The SQLite database connection.
-        cursor (sqlite3.Cursor): 
+        cursor (sqlite3.Cursor):
             The SQLite database cursor.
-        table_name (str): 
+        table_name (str):
             The name of the table within the database.
 
     Methods:
@@ -50,13 +51,13 @@ def add_score(self, network, checkpoint, threshold, scores_dict):
         Add a score entry to the database.
 
         Args:
-            network (str): 
+            network (str):
                 The name of the network.
-            checkpoint (int): 
+            checkpoint (int):
                 The checkpoint number.
-            threshold (float): 
+            threshold (float):
                 The threshold value.
-            scores_dict (dict): 
+            scores_dict (dict):
                 A dictionary containing scores.
         """
         assert type(network) is str
@@ -74,11 +75,11 @@ def get_scores(self, networks=None, checkpoints=None, thresholds=None):
         Retrieve scores from the database based on specified conditions.
 
         Args:
-            networks (str, list): 
+            networks (str, list):
                 The name or list of names of networks to filter on.
-            checkpoints (int, list): 
+            checkpoints (int, list):
                 The checkpoint number or list of checkpoint numbers to filter on.
-            thresholds (float, list): 
+            thresholds (float, list):
                 The threshold value or list of threshold values to filter on.
 
         Returns:

src/autoseg/eval/evaluate.py

@@ -23,15 +23,15 @@ def segment_and_validate(
     It logs information about the segmentation and validation process.
 
     Args:
-        model_checkpoint (str): 
+        model_checkpoint (str):
             The checkpoint of the segmentation model to use (default is "latest").
-        checkpoint_num (int): 
+        checkpoint_num (int):
             The checkpoint number for the affinity model (default is 250000).
-        setup_num (str): 
+        setup_num (str):
             The setup number for the affinity model (default is "1738").
 
     Returns:
-        dict: 
+        dict:
             A dictionary containing scores and evaluation metrics.
     """
     logger.info(
@@ -85,27 +85,27 @@ def validate(
     Validate segmentation results using specified parameters.
 
     Args:
-        checkpoint (str): 
+        checkpoint (str):
             The checkpoint identifier.
-        threshold (float): 
+        threshold (float):
             The threshold value.
-        offset (str): 
+        offset (str):
             The offset for ROI (default is "3960,3960,3960").
-        roi_shape (str): 
+        roi_shape (str):
             The shape of ROI (default is "31680,31680,31680").
-        skel (str): 
+        skel (str):
             The path to the skeleton data file (default is "../../data/XPRESS_validation_skels.npz").
-        zarr (str): 
+        zarr (str):
             The path to the Zarr file for storing segmentation data (default is "./validation.zarr").
-        h5 (str): 
+        h5 (str):
             The path to the HDF5 file for storing validation data (default is "validation.h5").
-        ds (str): 
+        ds (str):
             The dataset name (default is "pred_seg").
-        print_errors (bool): 
+        print_errors (bool):
             Print errors during validation (default is False).
-        print_in_xyz (bool): 
+        print_in_xyz (bool):
             Print coordinates in XYZ format (default is False).
-        downsample (int): 
+        downsample (int):
             Downsample factor for evaluation (default is None).
     """
     network = os.path.abspath(".").split(os.path.sep)[-1]