solve merge

Readme.md

@@ -0,0 +1,192 @@
+# Next Best View for Reconstruction
+
+## 1. Setup Environment
+### 1.1 Install Main Project
+```bash
+mkdir nbv_rec
+cd nbv_rec
+git clone https://git.hofee.top/hofee/nbv_reconstruction.git
+```
+### 1.2 Install PytorchBoot
+the environment is based on PytorchBoot, clone and install it from [PytorchBoot](https://git.hofee.top/hofee/PyTorchBoot.git)
+```bash
+git clone https://git.hofee.top/hofee/PyTorchBoot.git
+cd PyTorchBoot
+pip install .
+cd ..
+```
+### 1.3 Install Blender (Optional)
+If you want to render your own dataset as described in [section 2. Render Datasets](#2-render-datasets), you'll need to install Blender version 4.0 from [Blender Release](https://download.blender.org/release/Blender4.0/). Here is an example of installing Blender on Ubuntu:
+```bash
+wget https://download.blender.org/release/Blender4.0/blender-4.0.2-linux-x64.tar.xz
+tar -xvf blender-4.0.2-linux-x64.tar.xz
+```
+If blender is not in your PATH, you can add it by:
+```bash
+export PATH=$PATH:/path/to/blender/blender-4.0.2-linux-x64
+```
+To run the blender script, you need to install the `pyyaml` and `scipy` package into your blender python environment. Run the following command to print the python path of your blender:
+```bash
+./blender -b --python-expr "import sys; print(sys.executable)"
+```
+Then copy the python path `/path/to/blender_python` shown in the output and run the following command to install the packages:
+```bash
+/path/to/blender_python -m pip install pyyaml scipy
+```
+### 1.4 Install Blender Render Script (Optional)
+Clone the script from [nbv_rec_blender_render](https://git.hofee.top/hofee/nbv_rec_blender_render.git) and rename it to `blender`:
+```bash
+git clone https://git.hofee.top/hofee/nbv_rec_blender_render.git
+mv nbv_rec_blender_render blender
+```
+
+### 1.5 Check Dependencies
+Switch to the project root directory and run `pytorch-boot scan` or `ptb scan` to check if all dependencies are installed:
+```bash
+cd nbv_reconstruction
+pytorch-boot scan
+# or
+ptb scan
+```
+If you see project structure information in the output, it means all dependencies are correctly installed. Otherwise, you may need to run `pip install xxx` to install the missing packages.
+
+## 2. Render Datasets (Optional)
+### 2.1 Download Object Mesh Models
+Download the mesh models divided into three parts from: 
+ - [object_meshes_part1.zip](None)
+ - [object_meshes_part2.zip](https://pan.baidu.com/s/1pBPhrFtBwEGp1g4vwsLIxA?pwd=1234)
+ - [object_meshes_part3.zip](https://pan.baidu.com/s/1peE8HqFFL0qNFhM5OC69gA?pwd=1234)
+
+or download the whole dataset from [object_meshes.zip](https://pan.baidu.com/s/1ilWWgzg_l7_pPBv64eSgzA?pwd=1234)
+
+Download the table model from [table.obj](https://pan.baidu.com/s/1sjjiID25Es_kmcdUIjU_Dw?pwd=1234)
+
+### 2.2 Set Render Configurations
+Open file `configs/local/view_generate_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameters in `runner-generate`:
+ - `object_dir`: the directory of the downloaded object mesh models
+ - `output_dir`: the directory to save the rendered dataset
+ - `table_model_path`: the path of the downloaded table model
+
+### 2.3 Render Dataset
+
+There are two ways to render the dataset:
+
+#### 2.3.1 Render with Visual Monitoring
+
+If you want to visually monitor the rendering progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to `Project Dashboard - Project Structure - Applications - generate_view`
+4. Click the `Run` button to execute the rendering script
+
+#### 2.3.2 Render in Terminal
+
+If you don't need visual monitoring and prefer to run the rendering process directly in the terminal, simply run:
+
+```
+ptb run generate_view
+```
+
+This command will start the rendering process without launching the UI.
+
+## 3. Preprocess
+
+⚠️ The preprocessing code is currently not managed by `PytorchBoot`. To run the preprocessing:
+
+1. Open the `./preprocess/preprocessor.py` file.
+2. Locate the `if __name__ == "__main__":` block at the bottom of the file.
+3. Specify the dataset folder by setting `root = "path/to/your/dataset"`.
+4. Run the preprocessing script directly:
+
+   ```
+   python ./preprocess/preprocessor.py
+   ```
+
+This will preprocess the data in the specified dataset folder.
+
+## 4. Generate Strategy Label
+
+### 4.1 Set Configuration
+
+Open the file `configs/local/strategy_generate_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameter:
+
+- `datasets.OmniObject3d.root_dir`: the directory of your dataset
+
+### 4.2 Generate Strategy Label
+
+There are two ways to generate the strategy label:
+
+#### 4.2.1 Generate with Visual Monitoring
+
+If you want to visually monitor the generation progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to Project Dashboard - Project Structure - Applications - generate_strategy
+4. Click the `Run` button to execute the generation script
+
+#### 4.2.2 Generate in Terminal
+
+If you don't need visual monitoring and prefer to run the generation process directly in the terminal, simply run:
+
+```
+ptb run generate_strategy
+```
+
+This command will start the strategy label generation process without launching the UI.
+
+## 5. Train
+
+### 5.1 Set Configuration
+
+Open the file `configs/local/train_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameters in the `experiment` section:
+
+```yaml
+experiment:
+  name: your_experiment_name
+  root_dir: path/to/your/experiment_dir
+  use_checkpoint: False # if True, the checkpoint will be loaded
+  epoch: 600 # specific epoch to load, -1 stands for last epoch
+  max_epochs: 5000 # maximum epochs to train
+  save_checkpoint_interval: 1 # save checkpoint interval
+  test_first: True # if True, test process will be performed before training at each epoch
+```
+
+Adjust these parameters according to your training requirements.
+
+
+### 5.2 Start Training
+
+There are two ways to start the training process:
+
+#### 5.2.1 Train with Visual Monitoring
+
+If you want to visually monitor the training progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to Project Dashboard - Project Structure - Applications - train
+4. Click the `Run` button to start the training process
+
+#### 5.2.2 Train in Terminal
+
+If you don't need visual monitoring and prefer to run the training process directly in the terminal, simply run:
+
+```
+ptb run train
+```
+
+This command will start the training process without launching the UI.
+
+## 6. Evaluation
+...

TODO.md

@@ -1,22 +0,0 @@
-# TODO
-## 预处理数据
-### 1. 生成view阶段
-**input**: 物体mesh
-
-### 2. 生成label阶段
-**input**: 目标物体点云、目标物体点云法线、桌面扫描点、被拍到的桌面扫描点
-
-**可以删掉的数据**: mask、normal
-
-### 3. 训练阶段
-**input**: 完整点云、pose、label
-
-**可以删掉的数据**：depth
-
-### view生成后
-预处理目标物体点云、目标物体点云法线、桌面扫描点、被拍到的桌面扫描点、完整点云
-
-删除depth、mask、normal
-
-### label生成后
-只上传：完整点云、pose、label

configs/local/strategy_generate_config.yaml

@@ -14,12 +14,6 @@ runner:
     voxel_threshold: 0.003
     soft_overlap_threshold: 0.3
     hard_overlap_threshold: 0.6
-    filter_degree: 75
-    to_specified_dir: True # if True, output_dir is used, otherwise, root_dir is used
-    save_points: True
-    load_points: True
-    save_best_combined_points: False
-    save_mesh: True
     overwrite: False
     seq_num: 15
     dataset_list:
@@ -27,11 +21,8 @@ runner:
   
 datasets:
     OmniObject3d:
-      #"/media/hofee/data/data/temp_output"
       root_dir: /media/hofee/repository/full_data_output 
-      model_dir: /media/hofee/data/data/scaled_object_meshes
       from: 0
       to: -1 # -1 means end
-      #output_dir: "/media/hofee/data/data/label_output"
 
     

configs/local/view_generate_config.yaml

@@ -7,12 +7,21 @@ runner:
     name: debug
     root_dir: experiments
   generate:
+<<<<<<< HEAD
     port: 5002
     from: 600
     to: -1 # -1 means all
     object_dir: /media/hofee/data/data/object_meshes_part1
     table_model_path: "/media/hofee/data/data/others/table.obj"
     output_dir: /media/hofee/repository/data_part_1
+=======
+    port: 5000
+    from: 0
+    to: -1 # -1 means all
+    object_dir: H:\\AI\\Datasets\\object_meshes_part2
+    table_model_path: "H:\\AI\\Datasets\\table.obj"
+    output_dir: C:\\Document\\Datasets\\nbv_rec_part2
+>>>>>>> c55a398b6d5c347497b528bdd460e26ffdd184e8
     binocular_vision: true
     plane_size: 10
     max_views: 512

configs/server/server_split_dataset_config.yaml

@@ -1,22 +0,0 @@
-
-runner:
-  general:
-    seed: 0
-    device: cpu
-    cuda_visible_devices: "0,1,2,3,4,5,6,7"
-    
-  experiment:
-    name: debug
-    root_dir: "experiments"
-  
-  split: # 
-    root_dir: "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy"
-    type: "unseen_instance" # "unseen_category"
-    datasets:
-      OmniObject3d_train: 
-        path: "../data/sample_for_training_preprocessed/OmniObject3d_train.txt"
-        ratio: 0.9
-
-      OmniObject3d_test: 
-        path: "../data/sample_for_training_preprocessed/OmniObject3d_test.txt"
-        ratio: 0.1

configs/server/server_strategy_generate_config.yaml

@@ -1,37 +0,0 @@
-
-runner:
-  general:
-    seed: 0
-    device: cpu
-    cuda_visible_devices: "0,1,2,3,4,5,6,7"
-
-    
-  experiment:
-    name: debug
-    root_dir: "experiments"
-      
-  generate:
-    voxel_threshold: 0.003
-    soft_overlap_threshold: 0.3
-    hard_overlap_threshold: 0.6
-    filter_degree: 75
-    to_specified_dir: True # if True, output_dir is used, otherwise, root_dir is used
-    save_points: True
-    load_points: True
-    save_best_combined_points: False
-    save_mesh: True
-    overwrite: False
-    seq_num: 15
-    dataset_list:
-      - OmniObject3d
-  
-datasets:
-    OmniObject3d:
-      #"/media/hofee/data/data/temp_output"
-      root_dir: /data/hofee/data/packed_preprocessed_data
-      model_dir: /media/hofee/data/data/scaled_object_meshes
-      from: 0
-      to: -1 # -1 means end
-      #output_dir: "/media/hofee/data/data/label_output"
-
-    

preprocess/preprocessor.py

@@ -9,8 +9,6 @@ from utils.reconstruction import ReconstructionUtil
 from utils.data_load import DataLoadUtil
 from utils.pts import PtsUtil
 
-# scan shoe 536
-
 def save_np_pts(path, pts: np.ndarray, file_type="txt"):
     if file_type == "txt":
         np.savetxt(path, pts)
@@ -24,6 +22,12 @@ def save_target_points(root, scene, frame_idx, target_points: np.ndarray, file_t
         os.makedirs(os.path.join(root,scene, "pts"))
     save_np_pts(pts_path, target_points, file_type)
 
+def save_target_normals(root, scene, frame_idx, target_normals: np.ndarray, file_type="txt"):
+    pts_path = os.path.join(root,scene, "nrm", f"{frame_idx}.{file_type}")
+    if not os.path.exists(os.path.join(root,scene, "nrm")):
+        os.makedirs(os.path.join(root,scene, "nrm"))
+    save_np_pts(pts_path, target_normals, file_type)
+    
 def save_scan_points_indices(root, scene, frame_idx, scan_points_indices: np.ndarray, file_type="txt"):
     indices_path = os.path.join(root,scene, "scan_points_indices", f"{frame_idx}.{file_type}")
     if not os.path.exists(os.path.join(root,scene, "scan_points_indices")):
@@ -137,7 +141,7 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
             has_points = target_points.shape[0] > 0
 
         if has_points:
-            target_points = PtsUtil.filter_points(
+            target_points, target_normals = PtsUtil.filter_points(
                 target_points, sampled_target_normal_L, cam_info["cam_to_world"], theta_limit = filter_degree, z_range=(min_z, max_z)
                 )
 
@@ -151,6 +155,7 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
             target_points = np.zeros((0, 3))
             
         save_target_points(root, scene, frame_id, target_points, file_type=file_type)
+        save_target_normals(root, scene, frame_id, target_normals, file_type=file_type)
         save_scan_points_indices(root, scene, frame_id, scan_points_indices, file_type=file_type)
     
     save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess

runners/strategy_generator.py

@@ -22,13 +22,7 @@ class StrategyGenerator(Runner):
             "app_name": "generate_strategy",
             "runner_name": "strategy_generator"
         }
-        self.to_specified_dir = ConfigManager.get("runner", "generate", "to_specified_dir")
-        self.save_best_combined_pts = ConfigManager.get("runner", "generate", "save_best_combined_points")
-        self.save_mesh = ConfigManager.get("runner", "generate", "save_mesh")
-        self.load_pts = ConfigManager.get("runner", "generate", "load_points")
-        self.filter_degree = ConfigManager.get("runner", "generate", "filter_degree")
         self.overwrite = ConfigManager.get("runner", "generate", "overwrite")
-        self.save_pts = ConfigManager.get("runner","generate","save_points")
         self.seq_num = ConfigManager.get("runner","generate","seq_num")
         
 

utils/data_load.py

@@ -14,19 +14,12 @@ class DataLoadUtil:
     
     @staticmethod
     def load_exr_image(file_path):
-        # 打开 EXR 文件
         exr_file = OpenEXR.InputFile(file_path)
-        
-        # 获取 EXR 文件的头部信息，包括尺寸
         header = exr_file.header()
         dw = header['dataWindow']
         width = dw.max.x - dw.min.x + 1
         height = dw.max.y - dw.min.y + 1
-
-        # 定义通道，通常法线图像是 RGB
         float_channels = ['R', 'G', 'B']
-
-        # 读取 EXR 文件中的每个通道并转化为浮点数数组
         img_data = []
         for channel in float_channels:
             channel_data = exr_file.channel(channel)

utils/pts.py

@@ -84,14 +84,14 @@ class PtsUtil:
         theta = np.arccos(cos_theta) * 180 / np.pi
         idx = theta < theta_limit
         filtered_sampled_points = points[idx]
-        
+        filtered_normals = normals[idx]
         
         """ filter with z range """
         points_cam = PtsUtil.transform_point_cloud(filtered_sampled_points, np.linalg.inv(cam_pose))
         idx = (points_cam[:, 2] > z_range[0]) & (points_cam[:, 2] < z_range[1])
         z_filtered_points = filtered_sampled_points[idx]
-        
+        z_filtered_normals = filtered_normals[idx]
-        return z_filtered_points[:, :3]
+        return z_filtered_points[:, :3], z_filtered_normals
     
     @staticmethod
     def point_to_hash(point, voxel_size):

utils/vis.py

@@ -128,10 +128,10 @@ class visualizeUtil:
 if __name__ == "__main__":
     root = r"/home/yan20/nbv_rec/project/franka_control/temp"
     model_dir = r"H:\\AI\\Datasets\\scaled_object_box_meshes"
-    scene = "cad_model_world"
+    scene = "box"
-    output_dir = r"/home/yan20/nbv_rec/project/franka_control/temp/output"
+    output_dir = r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\test"
     
-    visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir)
+    #visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir)
     visualizeUtil.save_all_combined_pts(root, scene, output_dir)
     visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene)
     #visualizeUtil.save_points_and_normals(root, scene,"10", output_dir, binocular=True)