diagram graph build bug fixed
This commit is contained in:
Xinyang Jiang
Родитель
4183041aa7
Коммит
feafbf5098
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@ -20,14 +20,16 @@ a) Download and unzip the [Floorplans dataset](http://mathieu.delalandre.free.fr
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b) Run the following scripts to prepare the dataset for training/inference.
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```sh
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python utils/svg_utils/build_graph_bbox.py
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cd utils
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python svg_utils/build_graph_bbox.py
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```
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#### Diagrams
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a) Download and unzip the [Diagrams dataset](http://mathieu.delalandre.free.fr/projects/sesyd/symbols/diagrams.html) to the dataset folder: `data/diagrams`
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b) Run the following scripts to prepare the dataset for training/inference.
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```sh
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python utils/svg_utils/build_graph_bbox_diagram.py
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cd utils
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python svg_utils/build_graph_bbox_diagram.py
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```
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### 2. Training & Inference
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@ -1,6 +1,3 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import os, sys
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../'))
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@ -49,19 +46,17 @@ def shape2Path(type_dict):
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return paths
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def getConnnectedComponent(node_dict):
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#edges = node_dict['edge']['shape']
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edges = np.concatenate([node_dict['edge']['shape'], node_dict['edge']['control']], axis = 0)
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edges = node_dict['edge']['shape']
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pos = node_dict['pos']['spatial']
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is_control = node_dict['attr']['is_control']
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#print(edges)
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adj = np.eye(pos.shape[0], pos.shape[0]).astype(bool)
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adj = np.eye(pos.shape[0], pos.shape[0]).astype(np.bool)
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for e in edges:
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adj[e[0], e[1]] = True
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adj[e[1], e[0]] = True
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n_node = pos.shape[0]
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#visited = [False if not is_control[i] else True for i in range(n_node) ]
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visited = [False for i in range(n_node) ]
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visited = [False if not is_control[i] else True for i in range(n_node) ]
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clusters = []
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for start_node in range(0, n_node):
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@ -110,7 +105,6 @@ def draw_cluster_graph(svg_path, save_path, width, height, bboxs, pos, is_contro
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facecolor="none",
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)
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ax.add_patch(bbox)
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os.makedirs(save_path, exist_ok=True)
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plt.savefig(save_path, bbox_inches="tight", pad_inches=0.0, dpi=600)
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def mergeCluster(cc, bboxs, ratio=None, expand_length=None):
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@ -201,19 +195,15 @@ def mergeCC(node_dict, svg_path, width, height):
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#cc, bboxs = mergeCluster(cc, bboxs, ratio=0.5, expand_length=None)
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# hardcode 70 for expand_length
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cc, bboxs = mergeCluster(cc, bboxs, ratio=None, expand_length=(70 / width, 70 / height))
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#cc, bboxs = mergeCluster(cc, bboxs, ratio=None, expand_length=(40 / width, 40 / height))
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#cc, bboxs = mergeCluster(cc, bboxs, ratio=1)
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#cc, bboxs = mergeCluster(cc, bboxs, ratio=None, expand_length=(70 / width, 70 / height))
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cc, bboxs = mergeCluster(cc, bboxs, ratio=None, expand_length=(40 / width, 40 / height))
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paths = []
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shape_shape_edges = []
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for i, cluster in enumerate(cc):
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for idx in cluster:
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if is_control[idx]: continue
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for idx_j in cluster:
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if idx == idx_j: continue
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if is_control[idx_j]: continue
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shape_shape_edges.append((idx, idx_j))
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if True:
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@ -233,11 +223,12 @@ def mergeCC(node_dict, svg_path, width, height):
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))
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svg_path_list = svg_path.split('/')
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draw_cluster_graph(svg_path, "data/diagram_expand_len_graph/{}_{}".format(svg_path_list[-2], svg_path_list[-1].replace('.svg', '.pdf')), width, height, bboxs, pos, is_control, edges)
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print("draw bbox and node of {}".format(svg_path))
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#draw_cluster_graph(svg_path, "/home/v-luliu1/datasets/diagram_expand_len_graph/{}_{}".format(svg_path_list[-2], svg_path_list[-1].replace('.svg', '.pdf')), width, height, bboxs, pos, is_control, edges)
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#print("draw bbox and node of {}".format(svg_path))
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cross_shape_edges = []
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same_cc = np.zeros((len(bboxs), len(bboxs))).astype(bool)
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same_cc = np.zeros((len(bboxs), len(bboxs))).astype(np.bool)
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for i, parent_bb in enumerate(bboxs):
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for j, child_bb in enumerate(bboxs):
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if i == j: continue
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@ -263,9 +254,7 @@ def mergeCC(node_dict, svg_path, width, height):
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if is_parent_child:
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for parent_idx in cc[i]:
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if is_control[parent_idx]: continue
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for child_idx in cc[j]:
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if is_control[child_idx]: continue
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cross_shape_edges.append((parent_idx, child_idx))
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same_cc[i, j] = True
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same_cc[j, i] = True
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@ -282,7 +271,7 @@ def mergeCC(node_dict, svg_path, width, height):
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visited[i] = True
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get_all_neighboors(i, ret)
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visited = np.zeros(same_cc.shape[0]).astype(bool)
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visited = np.zeros(same_cc.shape[0]).astype(np.bool)
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merged_cc = []
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for i, all_neighbors in enumerate(same_cc):
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if visited[i]: continue
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@ -318,9 +307,6 @@ def mergeCC(node_dict, svg_path, width, height):
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shape_shape_edge_attr = get_attr(shape_shape_edges)
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cross_shape_edge_attr = get_attr(cross_shape_edges)
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print(new_cc)
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return np.array(shape_shape_edges), np.array(cross_shape_edges), np.array(shape_shape_edge_attr), np.array(cross_shape_edge_attr), paths, new_cc
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@ -328,9 +314,10 @@ def mergeCC(node_dict, svg_path, width, height):
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if __name__ == '__main__':
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graph_builder = SVGGraphBuilderBezier()
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input_dir = 'data/diagrams/'
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output_dir = 'data/diagrams/'
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#input_dir = '/home/v-luliu1/datasets/floorplans_test'
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#output_dir = '/home/v-luliu1/datasets/floorplans_test'
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input_dir = '/data/xinyangjiang/Datasets/SESYD/diagram2'
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output_dir = '/data/xinyangjiang/Datasets/SESYD/diagram2'
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dir_list = os.listdir(input_dir)
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angles = []
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@ -346,7 +333,6 @@ if __name__ == '__main__':
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p = SVGParser(filepath)
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# split_cross splits the segments into multiple small segments if there is a cross-point
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type_dict = split_cross(p.get_all_shape())
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width, height = p.get_image_size()
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paths = shape2Path(type_dict)
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# {'pos': {'spatial': the positions of nodes}, {'attr': {'color': color of every node, 'stroke_width': stroke width, 'is_control': if the node is control node}}, {'edge': {'control': control edge, 'spatial': spatial edge}}, 'edge_attr': N * 6}
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@ -393,7 +379,7 @@ if __name__ == '__main__':
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node_dict['edge']['super'] = np.concatenate([shape_shape_edges, cross_shape_edges], axis = 0)
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#node_dict['attr']['is_control'] = np.concatenate([node_dict['attr']['is_control'], np.zeros((super_pos.shape[0], 1)).astype(np.bool)], axis = 0)
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#node_dict['attr']['is_super'] = np.concatenate([np.zeros((start_end_size, 1)).astype(np.bool), np.ones((super_pos.shape[0], 1)).astype(np.bool)], axis = 0)
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node_dict['attr']['is_super'] = np.zeros((start_end_size, 1)).astype(bool)
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node_dict['attr']['is_super'] = np.zeros((start_end_size, 1)).astype(np.bool)
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if len(cross_shape_edge_attr) == 0:
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node_dict['edge_attr']['super'] = shape_shape_edge_attr
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else:
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