Add CLI argument parsing, requirements file, and minor fixes for BranchPoseNet usability

README.md

@@ -19,7 +19,6 @@ conda activate BranchPoseNet
 
 Then install the required libraries
 ```
-cd whorl_pose_detector
 pip install -r requirements.txt
 ```
 
@@ -31,7 +30,7 @@ For predicting on your own data just point the --dir_root to the path to the fol
 
 ### 🖥️ To run using command line (CLI): 
 ```
-python whorl_pose_detect_CLI.py --dir_root data --my_model whorl_pose_nano_1000px/weights/best.pt --alpha 0.5 --min_internodal_d 0.3 --tree_id_label treeID --semantic_label semantic  
+python whorl_pose_detect.py --dir_root data --my_model models/whorl_pose_nano_1000px/weights/best.pt --alpha 0.5 --min_internodal_d 0.3 --tree_id_label treeID --semantic_label semantic  
 ```
 
 ### 🎮 Demo version

demo_predict_whorl_pose.ipynb

@@ -149,7 +149,7 @@
     "min_internodal_d= 0.3 # in m (it can be set to something like 0.01 to allow the full output to post-process later)\n",
     "\n",
     "# pose model\n",
-    "my_model=\"whorl_pose_nano_1000px/weights/best.pt\"\n",
+    "my_model=\"models/whorl_pose_nano_1000px/weights/best.pt\"\n",
     "\n",
     "# label for the column with tree instance unique identifiers\n",
     "tree_id_label='treeID' \n",

requirements.txt

@@ -0,0 +1,7 @@
+pandas
+numpy
+laspy[lazrs]
+Pillow
+scipy
+matplotlib
+ultralytics

whorl_pose_detect.py

@@ -12,7 +12,7 @@
 import matplotlib
 matplotlib.use('Agg')  # Use Agg backend for non-GUI rendering
 import matplotlib.pyplot as plt
-
+import argparse
 
 import ultralytics
 from ultralytics import YOLO
@@ -24,23 +24,37 @@
 
 
 ####################################################################################################################################
+# Parse CLI arguments first
+parser = argparse.ArgumentParser()
+parser.add_argument("--dir_root", default="data", help="Path to root folder where .las/.laz files are stored")
+parser.add_argument("--my_model", default="models/whorl_pose_nano_1000px/weights/best.pt", help="Path to YOLO model weights")
+parser.add_argument("--alpha", type=float, default=0.5, help="Alpha value for plotting")
+parser.add_argument("--min_internodal_d", type=float, default=0.3, help="Minimum internodal distance in meters")
+parser.add_argument("--tree_id_label", default="treeID", help="Column name for tree IDs")
+parser.add_argument("--semantic_label", default="semantic", help="Column name for semantic labels")
+args = parser.parse_args()
+
+# ---------------------------------------------------------
+# Default parameters (can be overridden by CLI arguments)
+# ---------------------------------------------------------
+
 # directory to where YOLO temp predictions are stored
-dir_root="data" # path to root folder where .las/.laz files are stored
+dir_root = args.dir_root  # path to root folder where .las/.laz files are stored
 
 # alpha for plotting
-alpha= 0.5
+alpha = args.alpha
 
 # minimum distance between whorls (used to remove duplicates)
-min_internodal_d= 0.3 # in m (it can be set to something like 0.01 to allow the full output to post-process later)
+min_internodal_d = args.min_internodal_d  # in m (can be set to 0.01 to allow full output post-processing later)
 
 # pose model
-my_model="whorl_pose_nano_1000px/weights/best.pt"
+my_model = args.my_model
 
 # label for the column with tree instance unique identifiers
-tree_id_label='treeID' 
+tree_id_label = args.tree_id_label
 
-# label for the column with semantic labels (non required)
-semantic_label='semantic' 
+# label for the column with semantic labels (non-required)
+semantic_label = args.semantic_label
 
 ####################################################################################################################################
 # files_to_process= list_las_laz_files(dir_root)
@@ -145,3 +159,6 @@
     # Cleanup temp files
     import shutil
     shutil.rmtree(dir_pred)
+
+
+