Fix bug when pattern is only one.

exps/exp_xyz2density.py

@@ -55,7 +55,8 @@ def init_dataset(self):
 
         self.pat_dataset = MultiPatDataset(
             scene_folder=self.train_dir,
-            pat_idx_set=[0, 1, 2, 3, 4, 5, 6, 7],
+            # pat_idx_set=[0, 1, 2, 3, 4, 5, 6, 7],
+            pat_idx_set=[11],
             sample_num=self.sample_num,
             calib_para=config['Calibration'],
             device=self.device
@@ -277,7 +278,7 @@ def callback_img_visual(self, data, net_out, tag, step):
         #            encoding='utf-8')
 
         img_input = self.pat_dataset.img_set.unsqueeze(0).detach().cpu()
-        img_input_re = torch.nn.functional.interpolate(img_input, scale_factor=1 / resolution_level).squeeze()
+        img_input_re = torch.nn.functional.interpolate(img_input, scale_factor=1 / resolution_level).squeeze(dim=0)
         for c in range(channel):
             img_gt = img_input_re[c]
             img_viz = pvf.img_visual(img_gt)
@@ -291,7 +292,7 @@ def callback_img_visual(self, data, net_out, tag, step):
         depth_viz = pvf.disp_visual(depth_mat, range_val=self.bound[:, 2].to(depth_mat.device))
         self.loss_writer.add_image(f'{tag}/depth_map', depth_viz, step)
 
-        # plb.imviz(depth_mat, 'depth', 10, normalize=[300, 900])
+        # plb.imviz(depth_mat, 'depth', 0, normalize=[300, 900])
 
         # Mesh
         # mesh_bound = np.stack([np.min(pts_set, axis=0), np.max(pts_set, axis=0)])

networks/layers.py

@@ -138,7 +138,7 @@ def forward(self, xyz_set, mask_flag=False):
             mask[mask < 1.0] = 0.0
             sample_mat *= mask
 
-        return sample_mat.squeeze().permute(1, 0)  # [N, C]
+        return sample_mat.squeeze(axis=3)[0].permute(1, 0)  # [N, C]
 
         # hei_s, wid_s = src_mat.shape[-2:]
         #

networks/neus.py

@@ -766,8 +766,7 @@ def render_density(self, rays_d):
 
         # Section midpoints
         pts = rays_d[:, None, :] * mid_z_vals[..., :, None]  # n_rays, n_samples, 3
-        pts = pts.reshape(-1, 3)
-        pts_n = self.pts_normalize(pts)
+        pts_n = self.pts_normalize(pts.reshape(-1, 3))
 
         density = self.sdf_network(pts_n).reshape(z_vals.shape)
         sampled_color = self.color_network(pts_n).reshape(batch_size, n_samples, -1)

params/xyz2density_eval.ini

@@ -0,0 +1,21 @@
+config = ./params/xyz2density_eval.ini
+
+argset = xyz2density
+train_dir = C:/SLDataSet/20220617s
+out_dir = C:/SLDataSet/20220617s-out
+;test_dir =
+model_dir = C:/SLDataSet/20220617s-out/xyz2density-success/model
+exp_type = eval
+run_tag =
+debug_mode = True
+
+num_workers = 0
+epoch_start = 32001
+epoch_end = 32002
+; remove_history = True
+lr = 1e-3
+lr_step = 0
+report_stone = 32
+img_stone = 32
+model_stone = 32
+save_stone = 8

params/xyz2density_train.ini

@@ -7,7 +7,7 @@ out_dir = C:/SLDataSet/20220617s-out
 model_dir =
 exp_type = train
 run_tag =
-;debug_mode = True
+debug_mode = True
 
 num_workers = 0
 epoch_start = 0

tools/generate_pattern.py

@@ -40,19 +40,59 @@ def draw_gray(gray_num, hei, wid):
     return pat
 
 
+def generate_phase(interval, hei, wid):
+    intensity_base = 127.50
+    intensity_max = 127.50
+
+    shift = np.array([0.0, (2 / 3) * np.pi, (4 / 3) * np.pi], dtype=np.float32).reshape(1, 3)
+    theta = (np.arange(0, interval) / interval * (2 * np.pi)).reshape(-1, 1)
+    phase_set = intensity_base + intensity_max * np.cos(theta + shift)
+
+    step = wid // interval
+    phase_set_part = phase_set.reshape(1, interval, 3)
+    pats = np.tile(phase_set_part, [hei, step, 1])
+    return pats.astype(np.uint8)
+
+
+def decode_phase(pats, interval):
+    pats = pats.astype(np.float32)
+    ntr = np.sqrt(3) * (pats[:, :, 1] - pats[:, :, 2])
+    dtr = 2 * pats[:, :, 0] - (pats[:, :, 1] + pats[:, :, 2])
+    # tan_val = ntr / dtr
+    theta = - np.arctan2(ntr, dtr)
+    theta[theta < 0.0] += 2 * np.pi
+    pixel_val = theta / (2 * np.pi) * interval
+    return pixel_val
+
+
 def main():
-    target_folder = Path('C:/SLDataSet/20220617s/scene000/pat')
+    target_folder = Path('C:/SLDataSet/20220617s/pat')
 
-    digit_num = 8
+    # digit_num = 8
     hei = 800
     wid = 1280
+    #
+    # gray_code = generate_gray(digit_num)
+    # for i in range(digit_num):
+    #     pat = draw_gray(gray_code[:, i], hei, wid)
+    #     cv2.imshow('pat', pat)
+    #     cv2.waitKey(10)
+    #     cv2.imwrite(str(target_folder / f'pat_{i}.png'), pat)
+
+    interval = 40
+    start_idx = 8
+    pats = generate_phase(interval, hei, wid)
+
+    pixel_val = decode_phase(pats, interval)
+    pix_show = (pixel_val * 6).astype(np.uint8)
+    cv2.imshow('pixel', pix_show)
+    cv2.waitKey(0)
 
-    gray_code = generate_gray(digit_num)
-    for i in range(digit_num):
-        pat = draw_gray(gray_code[:, i], hei, wid)
-        cv2.imshow('pat', pat)
-        cv2.waitKey(10)
-        cv2.imwrite(str(target_folder / f'pat_{i}.png'), pat)
+    # for i in range(3):
+    #     pat = pats[:, :, i]
+    #     cv2.imshow('pat', pat)
+    #     cv2.waitKey(10)
+    #     cv2.imwrite(str(target_folder / f'pat_{i + start_idx}.png'), pat)
 
     # pass
 

tools/render_imgs.py

@@ -92,33 +92,33 @@ def load_render(folder):  # TODO: 标定参数还是有很多小问题。先跳
     config = ConfigParser()
     config.read(str(folder / 'config.ini'), encoding='utf-8')
 
-    ext_tran = plb.str2array(config['Calibration']['ext_tran'], np.float32)
-    target_pt = np.array([0.0, 100.0, 800.0], dtype=np.float32)
-    look = target_pt - ext_tran
-    up = np.array([0.0, -1.0, 0.0], dtype=np.float32)
-    z_vec = look / np.linalg.norm(look)
-    right = np.cross(look, up)
-    x_vec = right / np.linalg.norm(right)
-    y_vec = np.cross(z_vec, x_vec)
-    rot_mat = np.stack([x_vec, y_vec, z_vec], axis=1)
-    rot_str = ','.join([str(x) for x in rot_mat.reshape(-1)])
-    config['Calibration']['ext_rot'] = rot_str
+    # ext_tran = plb.str2array(config['Calibration']['ext_tran'], np.float32)
+    # target_pt = np.array([0.0, 100.0, 800.0], dtype=np.float32)
+    # look = target_pt - ext_tran
+    # up = np.array([0.0, -1.0, 0.0], dtype=np.float32)
+    # z_vec = look / np.linalg.norm(look)
+    # right = np.cross(look, up)
+    # x_vec = right / np.linalg.norm(right)
+    # y_vec = np.cross(z_vec, x_vec)
+    # rot_mat = np.stack([x_vec, y_vec, z_vec], axis=1)
+    # rot_str = ','.join([str(x) for x in rot_mat.reshape(-1)])
+    # config['Calibration']['ext_rot'] = rot_str
 
     depth2uv = CoordCompute(config['Calibration'])
 
     # load img, depth
-    scene_list = plb.subfolders(folder)
+    # scene_list = plb.subfolders(folder)
 
-    scene_folder = scene_list[0]
+    scene_folder = folder
     depth = plb.imload(scene_folder / 'depth' / f'depth_0.png', scale=10.0).squeeze(0)
     uu, vv = depth2uv(depth)
     warp_layer = WarpLayer2D()
     pat_num = len(list((scene_folder / 'pat').glob('*.png')))
     for pat_idx in range(pat_num):
         pat = plb.imload(scene_folder / 'pat' / f'pat_{pat_idx}.png')
         img = warp_layer(uu, vv, pat, mask_flag=True)
-        plb.imviz(img, 'img', 0)
-        plb.imsave(scene_folder / 'img' / f'img_{pat_idx}.png', img)
+        plb.imviz(img[0], 'img', 0)
+        plb.imsave(scene_folder / 'img' / f'img_{pat_idx}.png', img[0])
 
 
 def main():