solve merge

preprocess/pack_preprocessed_data.py

@@ -0,0 +1,48 @@
+import os
+import shutil
+
+def pack_scene_data(root, scene, output_dir):
+    scene_dir = os.path.join(output_dir, scene)
+    if not os.path.exists(scene_dir):
+        os.makedirs(scene_dir)
+
+    pts_dir = os.path.join(root, scene, "pts")
+    if os.path.exists(pts_dir):
+        shutil.move(pts_dir, os.path.join(scene_dir, "pts"))
+        
+    scan_points_indices_dir = os.path.join(root, scene, "scan_points_indices")
+    if os.path.exists(scan_points_indices_dir):
+        shutil.move(scan_points_indices_dir, os.path.join(scene_dir, "scan_points_indices")) 
+
+    scan_points_file = os.path.join(root, scene, "scan_points.txt")
+    if os.path.exists(scan_points_file):
+        shutil.move(scan_points_file, os.path.join(scene_dir, "scan_points.txt"))
+
+    model_pts_nrm_file = os.path.join(root, scene, "points_and_normals.txt")
+    if os.path.exists(model_pts_nrm_file):
+        shutil.move(model_pts_nrm_file, os.path.join(scene_dir, "points_and_normals.txt"))
+
+    camera_dir = os.path.join(root, scene, "camera_params")
+    if os.path.exists(camera_dir):
+        shutil.move(camera_dir, os.path.join(scene_dir, "camera_params"))
+
+    scene_info_file = os.path.join(root, scene, "scene_info.json")
+    if os.path.exists(scene_info_file):
+        shutil.move(scene_info_file, os.path.join(scene_dir, "scene_info.json"))
+
+def pack_all_scenes(root, scene_list, output_dir):
+    for idx, scene in enumerate(scene_list):
+        print(f"正在打包场景 {scene} ({idx+1}/{len(scene_list)})")
+        pack_scene_data(root, scene, output_dir)
+
+if __name__ == "__main__":
+    root = r"H:\AI\Datasets\nbv_rec_part2"
+    output_dir = r"H:\AI\Datasets\scene_info_part2"
+    scene_list = os.listdir(root)
+    from_idx = 0
+    to_idx = len(scene_list)
+    print(f"正在打包场景 {scene_list[from_idx:to_idx]}")
+
+    pack_all_scenes(root, scene_list[from_idx:to_idx], output_dir)
+    print("打包完成")
+

preprocess/pack_upload_data.py

@@ -0,0 +1,41 @@
+import os
+import shutil
+
+def pack_scene_data(root, scene, output_dir):
+    scene_dir = os.path.join(output_dir, scene)
+    if not os.path.exists(scene_dir):
+        os.makedirs(scene_dir)
+
+    pts_dir = os.path.join(root, scene, "pts")
+    if os.path.exists(pts_dir):
+        shutil.move(pts_dir, os.path.join(scene_dir, "pts"))
+
+    camera_dir = os.path.join(root, scene, "camera_params")
+    if os.path.exists(camera_dir):
+        shutil.move(camera_dir, os.path.join(scene_dir, "camera_params"))
+
+    scene_info_file = os.path.join(root, scene, "scene_info.json")
+    if os.path.exists(scene_info_file):
+        shutil.move(scene_info_file, os.path.join(scene_dir, "scene_info.json"))
+
+    label_dir = os.path.join(root, scene, "label")
+    if os.path.exists(label_dir):
+        shutil.move(label_dir, os.path.join(scene_dir, "label"))
+
+
+def pack_all_scenes(root, scene_list, output_dir):
+    for idx, scene in enumerate(scene_list):
+        print(f"packing {scene} ({idx+1}/{len(scene_list)})")
+        pack_scene_data(root, scene, output_dir)
+
+if __name__ == "__main__":
+    root = r"H:\AI\Datasets\nbv_rec_part2"
+    output_dir = r"H:\AI\Datasets\upload_part2"
+    scene_list = os.listdir(root)
+    from_idx = 0
+    to_idx = len(scene_list)
+    print(f"packing {scene_list[from_idx:to_idx]}")
+
+    pack_all_scenes(root, scene_list[from_idx:to_idx], output_dir)
+    print("packing done")
+

preprocess/preprocessor.py

@@ -164,10 +164,10 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
 
 if __name__ == "__main__":
     #root = "/media/hofee/repository/new_data_with_normal"
-    root = r"C:\Document\Datasets\nbv_rec_part2"
+    root = r"H:\AI\Datasets\nbv_rec_part2"
     scene_list = os.listdir(root)
-    from_idx = 600 # 1000
-    to_idx = len(scene_list) # 1500
+    from_idx = 0 # 1000
+    to_idx = 600 # 1500
 
 
     cnt = 0

runners/inferece_server.py

@@ -0,0 +1,109 @@
+import os
+import json
+import torch
+import numpy as np
+from flask import Flask, request, jsonify
+
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory import ComponentFactory
+
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.utils import Log
+
+from utils.pts import PtsUtil
+
+@stereotype.runner("inferencer")
+class InferencerServer(Runner):
+    def __init__(self, config_path):
+        super().__init__(config_path)
+    
+        ''' Web Server '''
+        self.app = Flask(__name__)
+        ''' Pipeline '''
+        self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
+        self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
+        self.pipeline = self.pipeline.to(self.device)
+            
+        ''' Experiment '''
+        self.load_experiment("nbv_evaluator")
+
+    def get_input_data(self, data):
+        input_data = {}
+        scanned_pts = data["scanned_pts"]
+        scanned_n_to_world_pose_9d = data["scanned_n_to_world_pose_9d"]
+        combined_scanned_views_pts = np.concatenate(scanned_pts, axis=0)
+        fps_downsampled_combined_scanned_pts, fps_idx = PtsUtil.fps_downsample_point_cloud(
+            combined_scanned_views_pts, self.pts_num, require_idx=True
+        )
+        combined_scanned_views_pts_mask = np.zeros(len(scanned_pts), dtype=np.uint8)
+        start_idx = 0
+        for i in range(len(scanned_pts)):
+            end_idx = start_idx + len(scanned_pts[i])
+            combined_scanned_views_pts_mask[start_idx:end_idx] = i
+            start_idx = end_idx
+
+        fps_downsampled_combined_scanned_pts_mask = combined_scanned_views_pts_mask[fps_idx]
+
+        input_data["scanned_pts_mask"] = np.asarray(fps_downsampled_combined_scanned_pts_mask, dtype=np.uint8)
+        input_data["scanned_n_to_world_pose_9d"] = np.asarray(scanned_n_to_world_pose_9d, dtype=np.float32)
+        input_data["combined_scanned_pts"] = np.asarray(fps_downsampled_combined_scanned_pts, dtype=np.float32)
+        return input_data
+    
+    def get_result(self, output_data):
+
+        estimated_delta_rot_9d = output_data["pred_pose_9d"]
+        result = {
+            "estimated_delta_rot_9d": estimated_delta_rot_9d.tolist()
+        }
+        return result
+    
+    def run(self):
+        Log.info("Loading from epoch {}.".format(self.current_epoch))
+    
+        @self.app.route("/inference", methods=["POST"])
+        def inference():
+            data = request.json
+            input_data = self.get_input_data(data)
+            output_data = self.pipeline.forward_test(input_data)
+            result = self.get_result(output_data)
+            return jsonify(result)
+        
+
+        self.app.run(host="0.0.0.0", port=5000)
+
+    def get_checkpoint_path(self, is_last=False):
+        return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
+                            "Epoch_{}.pth".format(
+                                self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
+
+    def load_checkpoint(self, is_last=False):
+        self.load(self.get_checkpoint_path(is_last))
+        Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}")
+        if is_last:
+            checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME)
+            meta_path = os.path.join(checkpoint_root, "meta.json")
+            if not os.path.exists(meta_path):
+                raise FileNotFoundError(
+                    "No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"]))
+            file_path = os.path.join(checkpoint_root, "meta.json")
+            with open(file_path, "r") as f:
+                meta = json.load(f)
+            self.current_epoch = meta["last_epoch"]
+            self.current_iter = meta["last_iter"]
+
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+        self.current_epoch = self.experiments_config["epoch"]
+        self.load_checkpoint(is_last=(self.current_epoch == -1))
+
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+       
+        
+    def load(self, path):
+        state_dict = torch.load(path)
+        self.pipeline.load_state_dict(state_dict)
+
+
+

utils/data_load.py

@@ -211,6 +211,17 @@ class DataLoadUtil:
             pts = np.load(npy_path)
         return pts
     
+    @staticmethod
+    def load_from_preprocessed_nrm(path, file_type="npy"):
+        npy_path = os.path.join(
+            os.path.dirname(path), "nrm", os.path.basename(path) + "." + file_type
+        )
+        if file_type == "txt":
+            nrm = np.loadtxt(npy_path)
+        else:
+            nrm = np.load(npy_path)
+        return nrm
+
     @staticmethod
     def cam_pose_transformation(cam_pose_before):
         offset = np.asarray([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])

utils/vis.py

@@ -158,17 +158,22 @@ class visualizeUtil:
         np.savetxt(os.path.join(output_dir, "target_normal.txt"), sampled_visualized_normal)
 
     @staticmethod
-    def save_pts_nrm(pts_nrm, output_dir):
-        pts = pts_nrm[:, :3]
-        nrm = pts_nrm[:, 3:]
+    def save_pts_nrm(root, scene, frame_idx, output_dir, binocular=False):
+        path = DataLoadUtil.get_path(root, scene, frame_idx)
+        pts_world = DataLoadUtil.load_from_preprocessed_pts(path, "npy")
+        nrm_camera = DataLoadUtil.load_from_preprocessed_nrm(path, "npy")
+        cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular)
+        cam_to_world = cam_info["cam_to_world"]
+        nrm_world = nrm_camera @ cam_to_world[:3, :3].T
         visualized_nrm = []
         num_samples = 10
-        for i in range(len(pts)):
-            visualized_nrm.append(pts[i] + 0.02*t * nrm[i] for t in range(num_samples))
-        visualized_nrm = np.array(visualized_nrm).reshape(-1, 3)
-        np.savetxt(os.path.join(output_dir, "nrm.txt"), visualized_nrm)
-        np.savetxt(os.path.join(output_dir, "pts.txt"), pts)
+        for i in range(len(pts_world)):
+            for t in range(num_samples):
+                visualized_nrm.append(pts_world[i] - 0.02 * t * nrm_world[i])
         
+        visualized_nrm = np.array(visualized_nrm)
+        np.savetxt(os.path.join(output_dir, "nrm.txt"), visualized_nrm)
+        np.savetxt(os.path.join(output_dir, "pts.txt"), pts_world)
         
 # ------ Debug ------
 
@@ -184,6 +189,4 @@ if __name__ == "__main__":
     # visualizeUtil.save_seq_cam_pos_and_cam_axis(root, scene, [0, 121, 286, 175, 111,366,45,230,232,225,255,17,199,78,60], 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)
-    pts_nrm = np.loadtxt(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\pts_nrm_target.txt")
-    visualizeUtil.save_pts_nrm(pts_nrm, output_dir)
-
+    visualizeUtil.save_pts_nrm(root, scene, "116", output_dir, binocular=True)