seeded semantic segm.

bash_helper_scripts/get_austrian_sample_instance_p2.sh

+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp /home/nibio/mutable-outside-world/data/austrian_example_stefano/p2_instance.las $TARGET_FOLDER
+

bash_helper_scripts/get_simple_data_for_playground.sh

@@ -2,5 +2,4 @@
 SOURCE_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/sample_playground
 rm -rf $SOURCE_FOLDER/*
 
-cp /home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/playground_data/* \
-/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/sample_playground
\ No newline at end of file
+cp /home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/playground_data/* $SOURCE_FOLDER
\ No newline at end of file

bash_helper_scripts/get_terrestial_sem_seg_test_plot73_tile_-25_-25.sh

+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp /home/nibio/mutable-outside-world/data/terestial_data_for_training_sem_seg_model/test/plot73_tile_-25_-25.las $TARGET_FOLDER
+

bash_helper_scripts/get_terrestial_sem_seg_validation_after_sem_seg.sh

+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp -r /home/nibio/mutable-outside-world/data/terestial_data_for_training_sem_seg_model/validation_after_seg/* $TARGET_FOLDER
+

fsct/fit_cylinders.py

@@ -3,6 +3,8 @@ from scipy import optimize
 from scipy.spatial.transform import Rotation 
 from scipy.stats import variation
 import numpy as np
+np.random.seed(0)
+
 
 from matplotlib.patches import Circle
 import matplotlib.pyplot as plt

fsct/inference.py

@@ -3,6 +3,7 @@ import time
 import shutil
 import sys
 import glob
+import pickle
 
 import pandas as pd
 import numpy as np
@@ -71,23 +72,36 @@ def SemanticSegmentation(params):
     with torch.no_grad():
         output_point_cloud = np.zeros((0, 3 + 4))
         output_list = []
+        # out_list = []
+        counter = 0
         for data in tqdm(test_loader, disable=False if params.verbose else True):
             data = data.to(params.device)
             out = model(data)
+            # out_list.append(out.cpu().numpy())
             out = out.permute(2, 1, 0).squeeze()
             batches = np.unique(data.batch.cpu())
             out = torch.softmax(out.cpu().detach(), axis=1)
             pos = data.pos.cpu()
             output = np.hstack((pos, out))
+            counter += 1
 
             for batch in batches:
                 outputb = np.asarray(output[data.batch.cpu() == batch])
                 outputb[:, :3] = outputb[:, :3] + np.asarray(data.local_shift.cpu())[3 * batch:3 + (3 * batch)]
                 output_list.append(outputb)
 #             break
+            # save intermediate out_list to npy with a filename what contains the counter
+            # pickle.dump(out_list, open(os.path.join(params.odir, 'out_list_{}.pkl'.format(counter)), 'wb'))
+        
 
         classified_pc = np.vstack(output_list)
 
+    # sage classified as a pickle
+    pickle.dump(classified_pc, open(os.path.join(params.odir, 'classified_pc.pkl'), 'wb'))
+
+    # save classified point cloud to npy
+    # np.save(os.path.join(params.working_dir, 'classified_pc.npy'), classified_pc)
+
     # clean up anything no longer needed to free RAM.
     del outputb, out, batches, pos, output  
 

fsct/model.py

@@ -17,6 +17,10 @@ from matplotlib.lines import Line2D
 from matplotlib.animation import FuncAnimation
 from torch.optim.lr_scheduler import ExponentialLR
 
+from helpers.seed_everything import seed_everything
+
+seed_everything(42)
+
 
 class SAModule(torch.nn.Module):
     def __init__(self, ratio, r, NN):

fsct/other_parameters.py

 import os
 import numpy as np
+np.random.seed(0)
+
 import fsct
 
 # Don't change these unless you really understand what you are doing with them/are learning the code base.

fsct/preprocessing.py

@@ -9,6 +9,8 @@ from tqdm import tqdm
 
 from fsct.tools import *
 
+from helpers.seed_everything import seed_everything
+
 def save_pts(params, I, bx, by, bz):
 
     pc = params.pc.loc[(params.pc.x.between(bx, bx + params.box_dims[0])) &
@@ -18,6 +20,7 @@ def save_pts(params, I, bx, by, bz):
     if len(pc) > params.min_points_per_box:
 
         if len(pc) > params.max_points_per_box:
+            print('Warning: box contains more points than max_points_per_box. Downsampling...')
             pc = pc.sample(n=params.max_points_per_box)
 
         np.save(os.path.join(params.working_dir, f'{I:07}'), pc[['x', 'y', 'z']].values)
@@ -113,6 +116,9 @@ def Preprocessing(params):
     for x in threads:
         x.join()
 
+    # for i, (bx, by, bz) in tqdm(enumerate(itertools.product(x_cnr, y_cnr, z_cnr))):
+    #     save_pts(params, i, bx, by, bz)
+
     if params.verbose: print("Preprocessing done in {} seconds\n".format(time.time() - start_time))
-    
+
     return params

fsct/run.py

@@ -3,6 +3,8 @@ import os
 import argparse
 import pickle
 
+from helpers.seed_everything import seed_everything
+
 # from fsct.run_tools import FSCT
 from fsct.other_parameters import other_parameters
 from fsct.tools import dict2class
@@ -10,6 +12,8 @@ from fsct.preprocessing import Preprocessing
 from fsct.inference import SemanticSegmentation
 from fsct.segmentation import Segmentation
 
+seed_everything(42)
+
 if __name__ == '__main__':
     
     parser = argparse.ArgumentParser()

helpers/compare_files_with_hash.py

@@ -58,6 +58,6 @@ if __name__ == "__main__":
 
     # Comparing the hashes
     if file1_hash == file2_hash:
-        print("Files are identical")
+        print("identical")
     else:
-        print("Files are different")
+        print("different")