fix demo script

docs/examples/rl_examples.md

@@ -51,7 +51,8 @@ Config files used for this task are:
 
 A variation of the Cartpole task showcases the usage of RGB image data as observations. This example
 can be launched with command line argument `task=CartpoleCamera`. Note that to use camera data as
-observations, `enable_cameras` must be set to `True` in the task config file. In addition, the example must be run with the `omni.isaac.sim.python.gym.camera.kit` app file provided under `apps`, which applies necessary settings to enable camera training. By default, this app file will be used automatically when `enable_cameras` is set to `True`.
+observations, `enable_cameras` must be set to `True` in the task config file. In addition, the example must be run with the `omni.isaac.sim.python.gym.camera.kit` app file provided under `apps`, which applies necessary settings to enable camera training. By default, this app file will be used automatically when `enable_cameras` is set to `True`. Due to this limitation, this 
+example is currently not available in the extension workflow.
 
 Config files used for this task are:
 

omniisaacgymenvs/scripts/rlgames_demo.py

@@ -29,10 +29,11 @@
 
 import datetime
 import os
-
+import gym
 import hydra
 import torch
 from omegaconf import DictConfig
+import omniisaacgymenvs
 from omniisaacgymenvs.envs.vec_env_rlgames import VecEnvRLGames
 from omniisaacgymenvs.scripts.rlgames_train import RLGTrainer
 from omniisaacgymenvs.utils.config_utils.path_utils import retrieve_checkpoint_path
@@ -55,6 +56,28 @@ def parse_hydra_configs(cfg: DictConfig):
     headless = cfg.headless
     env = VecEnvRLGames(headless=headless, sim_device=cfg.device_id, enable_livestream=cfg.enable_livestream)
 
+    # parse experiment directory
+    module_path = os.path.abspath(os.path.join(os.path.dirname(omniisaacgymenvs.__file__)))
+    experiment_dir = os.path.join(module_path, "runs", cfg.train.params.config.name)
+
+    # use gym RecordVideo wrapper for viewport recording
+    if cfg.enable_recording:
+        if cfg.recording_dir == '':
+            videos_dir = os.path.join(experiment_dir, "videos")
+        else:
+            videos_dir = cfg.recording_dir
+        video_interval = lambda step: step % cfg.recording_interval == 0
+        video_length = cfg.recording_length
+        env.is_vector_env = True
+        if env.metadata is None:
+            env.metadata = {"render_modes": ["rgb_array"], "render_fps": cfg.recording_fps}
+        else:
+            env.metadata["render_modes"] = ["rgb_array"]
+            env.metadata["render_fps"] = cfg.recording_fps
+        env = gym.wrappers.RecordVideo(
+            env, video_folder=videos_dir, step_trigger=video_interval, video_length=video_length
+        )
+
     # ensure checkpoints can be specified as relative paths
     if cfg.checkpoint:
         cfg.checkpoint = retrieve_checkpoint_path(cfg.checkpoint)
@@ -90,7 +113,7 @@ def parse_hydra_configs(cfg: DictConfig):
 
     rlg_trainer = RLGDemo(cfg, cfg_dict)
     rlg_trainer.launch_rlg_hydra(env)
-    rlg_trainer.run()
+    rlg_trainer.run(module_path, experiment_dir)
     env.close()
 
     if cfg.wandb_activate: