gridsearch parameters

handcrafted_solution.py

@@ -98,13 +98,11 @@ def convert_entry_to_human_readable(entry):
     return out
 
 
-def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
+def get_vertices_and_edges_from_segmentation(gest_seg_np, *, color_range=4., point_radius=30, max_angle=5., extend = 35, **kwargs):
     '''Get the vertices and edges from the gestalt segmentation mask of the house'''
     # Apex
-    color_range = 4.
     connections = []
-    edge_th = edge_th ** 2
-    deviation_threshold = np.cos(np.deg2rad(5))
+    deviation_threshold = np.cos(np.deg2rad(max_angle))
 
     apex_centroids, eave_end_point_centroids, apex_mask, eave_end_point_mask = get_vertices(gest_seg_np)
 
@@ -114,7 +112,7 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
     vertex_size = np.zeros(vertices.shape[0])
     for i, coords in enumerate(vertices):
         # coords = np.round(coords).astype(np.uint32)
-        radius = 25  # np.clip(int(max_depth//2 + depth_np[coords[1], coords[0]]), 10, 30)#int(np.clip(max_depth - depth_np[coords[1], coords[0]], 10, 20))
+        radius = point_radius  # np.clip(int(max_depth//2 + depth_np[coords[1], coords[0]]), 10, 30)#int(np.clip(max_depth - depth_np[coords[1], coords[0]], 10, 20))
         vertex_size[i] = (scale * radius) ** 2  # because we are using squared distances
 
     for edge_class in ['eave', 'ridge', 'rake', 'valley', 'flashing', 'step_flashing']:
@@ -150,7 +148,6 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
             line_directions = np.zeros((len(lines), 2))
             for line_idx, line in enumerate(lines):
                 for x1, y1, x2, y2 in line:
-                    extend = 35
                     if x1 < x2:
                         x1, y1, x2, y2 = x2, y2, x1, y1
                     direction = (np.array([x2 - x1, y2 - y1]))
@@ -338,7 +335,7 @@ def prune_not_connected(all_3d_vertices, connections_3d):
     return np.array(new_verts), connected_out
 
 
-def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
+def predict(entry, visualize=False, **kwargs) -> Tuple[np.ndarray, List[int]]:
     good_entry = convert_entry_to_human_readable(entry)
     if 'gestalt' not in good_entry or 'depthcm' not in good_entry or 'K' not in good_entry or 'R' not in good_entry or 't' not in good_entry:
         print('Missing required fields in the entry')
@@ -354,7 +351,7 @@ def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
         gest_seg_np = np.array(gest_seg).astype(np.uint8)
         # Metric3D
         depth_np = np.array(depth) / 2.5  # 2.5 is the scale estimation coefficient
-        vertices, connections = get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=60.)
+        vertices, connections = get_vertices_and_edges_from_segmentation(gest_seg_np, **kwargs)
         if (len(vertices) < 2) or (len(connections) < 1):
             print(f'Not enough vertices or connections in image {i}')
             vert_edge_per_image[i] = np.empty((0, 2)), [], np.empty((0, 3))

script.py

@@ -127,7 +127,7 @@ if __name__ == "__main__":
     with ProcessPoolExecutor(max_workers=8) as pool:
         results = []
         for i, sample in enumerate(tqdm(dataset)):
-            results.append(pool.submit(predict, sample, visualize=False))
+            results.append(pool.submit(predict, sample, visualize=False, point_radius=25, max_angle=9, extend=30))
         
         for i, result in enumerate(tqdm(results)):
             key, pred_vertices, pred_edges = result.result()