small changes

pointcloud.py

@@ -68,7 +68,7 @@ def read_pointcloud(path, delimiter=' ', hasHeader=True):
         if hasHeader:
             # Get rid of the Header
             _ = f.readline()
-        # This itrates over all lines, splits them anc converts values to floats. Will fail on wrong values.
+        # This iterates over all lines, splits them and converts values to floats. Will fail on wrong values.
         pc = [[float(x) for x in line.rstrip().split(delimiter)] for line in f if line != '']
 
     return np.asarray(pc)
@@ -215,7 +215,8 @@ def write_clusters(path, clusters, type_column=6):
             return t
 
         types = np.array([type_mapping(t) for t in types])
-        print("Types: ", types)
+        print("Types: {}, Points: {}".format(types, cluster.shape[0]))
+        # draw_sample_data(cluster)
 
         np.savetxt(file, types.reshape(1, types.shape[0]),delimiter=';', header='', comments='', fmt='%i')
         np.savetxt(file, cluster[:, :6], header=str(len(cluster)) + ' ' + str(6), comments='')

predict/predict.py

@@ -63,7 +63,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../')  # add proj
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
-parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_131.pth', help='model path')
+parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_0.pth', help='model path')
 parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
 parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
 parser.add_argument('--with_normals', type=strtobool, default=True, help='if training will include normals')
@@ -84,7 +84,19 @@ if __name__ == '__main__':
     print('Create data set ..')
 
     dataset_folder = './data/raw/predict/'
-    pointcloud_file = './pointclouds/m3.xyz'
+
+    # pointcloud_file = './pointclouds/0_0.xyz'
+
+    # pointcloud_file = 'C:/Projekte/csg-fitter/csg-fitter/models/0/0.xyz'
+
+    param_set = {"test1": {"cells": 2, "eps": 0.15, "min_samples": 100}}
+
+    file = "test1"
+
+    used_params = param_set[file]
+
+    pointcloud_file = 'C:/Projekte/visigrapp2020/data/' + file + '/pc.txt'
+
 
     # Load and pre-process point cloud
     pcloud = pc.read_pointcloud(pointcloud_file)
@@ -93,7 +105,8 @@ if __name__ == '__main__':
     #pc_clusters = pc.hierarchical_clustering(pcloud, selected_indices_0=[0, 1, 2, 3, 4, 5],
     #                                         selected_indices_1=[0, 1, 2, 3, 4, 5], eps=0.7, min_samples=5)
 
-    pc_clusters = pc.cluster_cubes(pcloud, [4, 4, 4])
+    n_cells = used_params["cells"]
+    pc_clusters = pc.cluster_cubes(pcloud, [n_cells, n_cells, n_cells])
     print("Pre-Processing: Clustering")
     pc.draw_clusters(pc_clusters)
 
@@ -196,12 +209,12 @@ if __name__ == '__main__':
     # Clustering that results in per-primitive type clusters
     # ------------------------------------------------------------------------------------------------------------------
 
-    # labeled_dataset = np.loadtxt('labeled_dataset.txt')
+    #labeled_dataset = np.loadtxt('labeled_dataset.txt')
     pc.draw_sample_data(labeled_dataset)
 
     # Try to get rid of outliers.
-    labeled_dataset,outliers = pc.split_outliers(labeled_dataset, columns=[0,1,2,3,4,5])
-    pc.draw_sample_data(outliers, False)
+    # labeled_dataset,outliers = pc.split_outliers(labeled_dataset, columns=[0,1,2,3,4,5])
+    # pc.draw_sample_data(outliers, False)
 
     print("Final clustering..")
 
@@ -211,9 +224,9 @@ if __name__ == '__main__':
 
     total_clusters = []
 
-    clusters = pc.cluster_dbscan(labeled_dataset, [0,1,2,3,4,5], eps=0.1, min_samples=100)
+    clusters = pc.cluster_dbscan(labeled_dataset, [0,1,2,3,4,5], eps=used_params["eps"], min_samples=used_params["min_samples"])
     print("Pre-clustering done. Clusters: ", len(clusters))
-    pc.draw_clusters(clusters)
+    #pc.draw_clusters(clusters)
 
     for cluster in clusters:
         #cluster = pc.normalize_pointcloud(cluster)
@@ -225,11 +238,11 @@ if __name__ == '__main__':
             print("No need for 2nd level clustering since there is only a single primitive type in the cluster.")
             total_clusters.append(cluster)
         else:
-            sub_clusters = pc.cluster_dbscan(cluster, [0,1,2,7,8,9,10], eps=0.1, min_samples=100)
+            sub_clusters = pc.cluster_dbscan(cluster, [0,1,2,7,8,9,10], eps=used_params["eps"], min_samples=used_params["min_samples"])
             print("Sub clusters: ", len(sub_clusters))
             total_clusters.extend(sub_clusters)
     
-    result_clusters = list(filter(lambda c: c.shape[0] > 100, total_clusters))
+    result_clusters = list(filter(lambda c: c.shape[0] > used_params["min_samples"], total_clusters))
 
     for cluster in result_clusters:
         print("Cluster: ", cluster.shape[0])