Merge pull request from 'safijari'

.gitignore

@@ -0,0 +1,17 @@
+#ignore all kind of files
+*.*
+#except php files
+!*.py
+!*.yml
+!*.txt
+!*.md
+!*LICENSE
+!image/moon.jpg
+!depth/moon.npy
+!video/moon_swing.mp4
+!video/moon_zoom-in.mp4
+!video/moon_dolly-zoom-in.mp4
+!video/moon_straight-line.mp4
+!video/moon.mp4
+!video/moon_circle.mp4
+!misc/moon_40.gif

bilateral_filtering.py

@@ -213,4 +213,3 @@ def acc_shape(i):
     _strides = [acc_shape(i)*s*a.itemsize for i,s in enumerate(strides)] + list(a.strides)
 
     return np.lib.stride_tricks.as_strided(a, shape=shape, strides=_strides)
-

main.py

@@ -47,7 +47,9 @@
     print("Current Source ==> ", sample['src_pair_name'])
     mesh_fi = os.path.join(config['mesh_folder'], sample['src_pair_name'] +'.ply')
     image = imageio.imread(sample['ref_img_fi'])
+
     print(f"Running depth extraction at {time.time()}")
+
     run_depth([sample['ref_img_fi']], config['src_folder'], config['depth_folder'],
               config['MiDaS_model_ckpt'], MonoDepthNet, MiDaS_utils, target_w=640)
     config['output_h'], config['output_w'] = np.load(sample['depth_fi']).shape[:2]
@@ -76,6 +78,7 @@
         depth_edge_model.load_state_dict(depth_edge_weight)
         depth_edge_model = depth_edge_model.to(device)
         depth_edge_model.eval()
+
         print(f"Loading depth model at {time.time()}")
         depth_feat_model = Inpaint_Depth_Net()
         depth_feat_weight = torch.load(config['depth_feat_model_ckpt'],
@@ -92,16 +95,19 @@
         rgb_model.eval()
         rgb_model = rgb_model.to(device)
         graph = None
+
+
         print(f"Writing depth ply (and basically doing everything) at {time.time()}")
         rt_info = write_ply(image,
-                            depth,
-                            sample['int_mtx'],
-                            mesh_fi,
-                            config,
-                            rgb_model,
-                            depth_edge_model,
-                            depth_edge_model,
-                            depth_feat_model)
+                              depth,
+                              sample['int_mtx'],
+                              mesh_fi,
+                              config,
+                              rgb_model,
+                              depth_edge_model,
+                              depth_edge_model,
+                              depth_feat_model)
+
         if rt_info is False:
             continue
         rgb_model = None
@@ -113,6 +119,8 @@
         verts, colors, faces, Height, Width, hFov, vFov = read_ply(mesh_fi)
     else:
         verts, colors, faces, Height, Width, hFov, vFov = rt_info
+
+
     print(f"Making video at {time.time()}")
     videos_poses, video_basename = copy.deepcopy(sample['tgts_poses']), sample['tgt_name']
     top = (config.get('original_h') // 2 - sample['int_mtx'][1, 2] * config['output_h'])

pyproject.toml

@@ -0,0 +1,3 @@
+[tool.black]
+line-length = 120
+target-version = ['py37']

requirements.txt

@@ -2,4 +2,6 @@ opencv-python==4.2.0.32
 vispy==0.6.4
 moviepy==1.0.2
 transforms3d==0.3.1
-networkx==2.3
+networkx==2.3
+cynetworkx
+scikit-image