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import glob
import re
import tensorflow as tf
import json
import numpy as np
from PIL import Image
def make_json_parser(json_file, image_dir, image_size, greyscale_images=False):
def json_parser():
with open(json_file, 'r') as file:
data = json.load(file)
for timestep in data['timesteps']:
image_path = os.path.join(image_dir, timestep['image_name'])
image = Image.open(image_path)
if greyscale_images:
image = image.convert('L')
image = np.array(image.resize(image_size))
if greyscale_images:
image = np.expand_dims(image, axis=0)
image = image.reshape((*image_size, 1))
current_pose = np.array(timestep['current_pose']['data'])
desired_pose = np.array(timestep['desired_pose']['data'])
yield (image, current_pose, desired_pose)
return json_parser
def make_timestep_dataset(make_json_parser):
return tf.data.Dataset.from_generator(
make_json_parser,
output_signature=(tf.TensorSpec(shape=(None, None, None), dtype=tf.uint8),
tf.TensorSpec(shape=(4, 4), dtype=tf.float32),
tf.TensorSpec(shape=(4, 4), dtype=tf.float32))
)
def make_trajectory_dataset(timestep_datasets):
return tf.data.Dataset.from_tensor_slices(timestep_datasets)
def flatten_nested_dataset(dataset):
return dataset.flat_map(lambda x: x)
def get_files(directory):
if not os.path.isdir(directory):
raise ValueError(f"{directory} is not a valid directory.")
search_path = os.path.join(directory, "*.json")
files = glob.glob(search_path)
return files
def make_future_timesteps_dataset(timestep_dataset, future_steps, lookahead_fields):
def lookahead(*args):
return [field[0] if i not in lookahead_fields else field for (i, field) in enumerate(args)]
return timestep_dataset \
.window(future_steps, shift=1, stride=1, drop_remainder=True) \
.flat_map(lambda *args: tf.data.Dataset.zip(args)) \
.batch(future_steps) \
.map(lookahead)
import unittest
class TestDataset(unittest.TestCase):
@staticmethod
def create_synthetic_timestep_dataset(size, image_size):
values = []
for i in range(size):
image = np.random.rand(*image_size, 3)
current_pose = np.random.rand(4, 4)
desired_pose = np.random.rand(4, 4)
values.append((image, current_pose, desired_pose))
def generator():
for value in values:
yield value
return tf.data.Dataset.from_generator(
generator,
output_signature=(
tf.TensorSpec(shape=(None, None, 3), dtype=tf.uint8),
tf.TensorSpec(shape=(4, 4), dtype=tf.float32),
tf.TensorSpec(shape=(4, 4), dtype=tf.float32)
)
)
@classmethod
def get_test_dir(cls):
current_file_path = os.path.realpath(__file__)
base_dir = os.path.dirname(current_file_path)
return os.path.join(base_dir, "test_data")
@classmethod
def get_trajectory_files(cls, data_dir):
data_dir = os.path.join(data_dir, "trajectories")
json_files = get_files(data_dir)
return json_files
@classmethod
def get_test_trajectory_files(cls):
return cls.get_trajectory_files(cls.get_test_dir())
@classmethod
def get_image_dir(cls, data_dir):
return os.path.join(data_dir, "images")
@classmethod
def get_test_image_dir(cls):
return cls.get_image_dir(cls.get_test_dir())
def test_greyscale_image_generation(self):
json_files = self.get_test_trajectory_files()
self.assertTrue(len(json_files) > 0, "There should be at least one trajectory file")
trajectory_dataset = flatten_nested_dataset(
make_trajectory_dataset([
make_timestep_dataset(make_json_parser(json_file, self.get_test_image_dir(), (100, 100), greyscale_images=True))
for json_file in json_files
])
)
for image, _, _ in trajectory_dataset:
self.assertEqual(image.shape[-1], 1, f"Image should be greyscale. found shape {image.shape}")
def check_timesteps_shape(self, timestep_dataset):
count = 0
for image, current_pose, desired_pose in timestep_dataset:
count += 1
self.assertEqual(image.shape, (100, 100, 3), "Image should have shape (100, 100, 3)")
self.assertEqual(current_pose.shape, (4, 4), "Current pose should have shape (4, 4)")
self.assertEqual(desired_pose.shape, (4, 4), "Desired pose should have shape (4, 4)")
self.assertGreater(count, 0)
def check_lookahead_timestep_shape(self, lookahead_count, timestep_dataset):
count = 0
for image, current_pose, desired_pose in timestep_dataset:
count += 1
self.assertEqual(image.shape, (100, 100, 3), "Image should have shape (100, 100, 3)")
self.assertEqual(current_pose.shape, (4, 4), "Current pose should have shape (4, 4)")
self.assertEqual(desired_pose.shape, (lookahead_count, 4, 4), "Desired pose should have shape (4, 4)")
self.assertGreater(count, 0)
def test_json_dataset(self):
json_files = self.get_test_trajectory_files()
self.assertTrue(len(json_files) > 0, "There should be at least one trajectory file")
trajectory_dataset = flatten_nested_dataset(make_trajectory_dataset([make_timestep_dataset(make_json_parser(json_file, self.get_test_image_dir(), (100, 100))) for json_file in json_files]))
self.check_timesteps_shape(trajectory_dataset)
def test_synthetic_timestep_dataset(self):
synthetic_dataset = self.create_synthetic_timestep_dataset(5, (100, 100))
self.check_timesteps_shape(synthetic_dataset)
def test_make_future_trajectory_dataset(self):
single_timestep_dataset = self.create_synthetic_timestep_dataset(5, (100, 100))
single_timestep_data = []
for data in single_timestep_dataset:
single_timestep_data.append(data)
future_steps = 2
future_dataset = make_future_timesteps_dataset(single_timestep_dataset, future_steps=future_steps, lookahead_fields=[2])
loop_count = 0
self.check_lookahead_timestep_shape(future_steps, future_dataset)
for i, (future_image, future_current_pose, future_desired_poses) in enumerate(future_dataset):
image = single_timestep_data[i][0]
current_pose = single_timestep_data[i][1]
desired_poses = np.array([single_timestep_data[i + j][2] for j in range(future_steps)])
self.assertTrue(np.array_equal(future_image, image))
self.assertTrue(np.array_equal(future_current_pose, current_pose))
self.assertTrue(np.array_equal(future_desired_poses, desired_poses))
loop_count += 1
self.assertEqual(loop_count, len(single_timestep_data) - future_steps + 1)
def test_generate_trajectory_unflattened_dataset(self):
single_timestep_datasets = [self.create_synthetic_timestep_dataset(5, (100, 100)) for _ in range(5)]
future_steps = 2
future_datasets = [make_future_timesteps_dataset(single_timestep_dataset, future_steps=future_steps, lookahead_fields=[2]) for single_timestep_dataset in single_timestep_datasets]
trajectory_dataset = make_trajectory_dataset(future_datasets)
for timesteps in trajectory_dataset:
self.check_lookahead_timestep_shape(future_steps, timesteps)
def test_generate_trajectory_flattened_dataset(self):
single_timestep_datasets = [self.create_synthetic_timestep_dataset(5, (100, 100)) for _ in range(5)]
future_steps = 2
future_datasets = [make_future_timesteps_dataset(single_timestep_dataset, future_steps=future_steps, lookahead_fields=[2]) for single_timestep_dataset in single_timestep_datasets]
trajectory_dataset = make_trajectory_dataset(future_datasets)
flattened_dataset = flatten_nested_dataset(trajectory_dataset)
self.check_lookahead_timestep_shape(future_steps, flattened_dataset)
if __name__ == '__main__':
unittest.main() |