add playground.py for testing functions

playground.py

@@ -0,0 +1,183 @@
+# # from dm_control import suite
+# # import numpy as np
+# #
+# # # Load one task:
+# # env = suite.load(domain_name="cartpole", task_name="swingup")
+# #
+# # # Iterate over a task set:
+# # for domain_name, task_name in suite.BENCHMARKING:
+# #     env = suite.load(domain_name, task_name)
+# #
+# # # Step through an episode and print out reward, discount and observation.
+# # action_spec = env.action_spec()
+# # time_step = env.reset()
+# # while not time_step.last():
+# #     action = np.random.uniform(action_spec.minimum,
+# #                                action_spec.maximum,
+# #                                size=action_spec.shape)
+# #     time_step = env.step(action)
+# #     print(time_step.reward, time_step.discount, time_step.observation)
+# #
+#
+#
+#
+# # from dm_control import suite
+# # from dm_control import viewer
+# # import numpy as np
+# #
+# # env = suite.load(domain_name="humanoid", task_name="stand")
+# # action_spec = env.action_spec()
+# #
+# # # Define a uniform random policy.
+# # def random_policy(time_step):
+# #   del time_step  # Unused.
+# #   return np.random.uniform(low=action_spec.minimum,
+# #                            high=action_spec.maximum,
+# #                            size=action_spec.shape)
+# #
+# # # Launch the viewer application.
+# # viewer.launch(env, policy=random_policy)
+#
+#
+# from dm_control import suite
+# from dm_control.suite.wrappers import pixels
+#
+# env = suite.load('hopper', 'hop')
+#
+# wrapped_env = pixels.Wrapper(env, render_kwargs={'camera_id': 'cam0'})
+
+import cv2
+from dm_control import suite
+from dm_control.suite.wrappers import pixels
+
+import numpy as np
+
+# Load one task:
+env = suite.load(domain_name="cartpole", task_name="swingup")
+# wrapped_env = pixels.Wrapper(env, render_kwargs={'camera_id': 0})
+
+# Iterate over a task set:
+# for domain_name, task_name in suite.BENCHMARKING:
+#     env = suite.load(domain_name, task_name)
+
+# Step through an episode and print out reward, discount and observation.
+
+height, width = 480, 480
+
+action_spec = env.action_spec()
+time_step = env.reset()
+image = env.physics.render(height, width, camera_id=0)
+images = []
+for i in range(100):
+# while not time_step.last():
+    action = np.random.uniform(action_spec.minimum,
+                             action_spec.maximum,
+                             size=action_spec.shape)
+    time_step = env.step(action)
+    image1 = env.physics.render(height, width, camera_id=0)
+    image2 = env.physics.render(height, width, camera_id=1)
+    image = np.zeros((image1.shape[0], image1.shape[1] * 2, image1.shape[2]), dtype=image1.dtype)
+    image[:, :image1.shape[1], :] = image1.copy()
+    image[:, image1.shape[1]:, :] = image2.copy()
+    images.append(image)
+    # image3 = env.physics.render(height, width, camera_id=2)
+
+    # time_step = wrapped_env.step(action)
+    # cv2.imshow('image', image[..., ::-1])
+    # cv2.imshow('image2', image2)
+    # cv2.imshow('image3', image3)
+    # cv2.waitKey()
+    # print(time_step.reward, time_step.discount, time_step.observation['pixels'].shape)
+
+
+import imageio
+from pathlib import Path
+video_path = Path.home() / '.curl/sample.mp4'
+imageio.mimsave(str(video_path), images, fps=24)
+
+
+
+# # Copyright 2017 The dm_control Authors.
+# #
+# # Licensed under the Apache License, Version 2.0 (the "License");
+# # you may not use this file except in compliance with the License.
+# # You may obtain a copy of the License at
+# #
+# #    http://www.apache.org/licenses/LICENSE-2.0
+# #
+# # Unless required by applicable law or agreed to in writing, software
+# # distributed under the License is distributed on an "AS IS" BASIS,
+# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# # See the License for the specific language governing permissions and
+# # limitations under the License.
+# # ============================================================================
+#
+# """Demonstration of amc parsing for CMU mocap database.
+#
+# To run the demo, supply a path to a `.amc` file:
+#
+#     python mocap_demo --filename='path/to/mocap.amc'
+#
+# CMU motion capture clips are available at mocap.cs.cmu.edu
+# """
+#
+# from __future__ import absolute_import
+# from __future__ import division
+# from __future__ import print_function
+#
+# import time
+# # Internal dependencies.
+#
+# from absl import app
+# from absl import flags
+#
+# from dm_control.suite import humanoid_CMU
+# from dm_control.suite.utils import parse_amc
+#
+# import matplotlib.pyplot as plt
+# import numpy as np
+#
+# FLAGS = flags.FLAGS
+# flags.DEFINE_string('filename', None, 'amc file to be converted.')
+# flags.DEFINE_integer('max_num_frames', 90,
+#                      'Maximum number of frames for plotting/playback')
+#
+#
+# def main(unused_argv):
+#   env = humanoid_CMU.stand()
+#
+#   # Parse and convert specified clip.
+#   converted = parse_amc.convert(FLAGS.filename,
+#                                 env.physics, env.control_timestep())
+#
+#   max_frame = min(FLAGS.max_num_frames, converted.qpos.shape[1] - 1)
+#
+#   width = 480
+#   height = 480
+#   video = np.zeros((max_frame, height, 2 * width, 3), dtype=np.uint8)
+#
+#   for i in range(max_frame):
+#     p_i = converted.qpos[:, i]
+#     with env.physics.reset_context():
+#       env.physics.data.qpos[:] = p_i
+#     video[i] = np.hstack([env.physics.render(height, width, camera_id=0),
+#                           env.physics.render(height, width, camera_id=1)])
+#
+#   tic = time.time()
+#   for i in range(max_frame):
+#     if i == 0:
+#       img = plt.imshow(video[i])
+#     else:
+#       img.set_data(video[i])
+#     toc = time.time()
+#     clock_dt = toc - tic
+#     tic = time.time()
+#     # Real-time playback not always possible as clock_dt > .03
+#     plt.pause(max(0.01, 0.03 - clock_dt))  # Need min display time > 0.0.
+#     plt.draw()
+#   plt.waitforbuttonpress()
+#
+#
+# if __name__ == '__main__':
+#   flags.mark_flag_as_required('filename')
+#   app.run(main)