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create_vision_dataset.py
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create_vision_dataset.py
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import os
import numpy as np
import cv2
from PIL import Image
from ai2thor.controller import Controller
from ai2thor.platform import CloudRendering
from tqdm import tqdm
import json
# Train
kitchens_train = [f"FloorPlan{i}" for i in range(1, 21)]
living_rooms_train = [f"FloorPlan{200 + i}" for i in range(1, 21)]
bedrooms_train = [f"FloorPlan{300 + i}" for i in range(1, 21)]
bathrooms_train = [f"FloorPlan{400 + i}" for i in range(1, 21)]
# Val
kitchens_val = [f"FloorPlan{i}" for i in range(21, 26)]
living_rooms_val = [f"FloorPlan{200 + i}" for i in range(21, 26)]
bedrooms_val = [f"FloorPlan{300 + i}" for i in range(21, 26)]
bathrooms_val = [f"FloorPlan{400 + i}" for i in range(21, 26)]
# Test
kitchens_test = [f"FloorPlan{i}" for i in range(26, 31)]
living_rooms_test = [f"FloorPlan{200 + i}" for i in range(26, 31)]
bedrooms_test = [f"FloorPlan{300 + i}" for i in range(26, 31)]
bathrooms_test = [f"FloorPlan{400 + i}" for i in range(26, 31)]
scenes_train = kitchens_train + living_rooms_train + bedrooms_train + bathrooms_train
scenes_val = kitchens_val + living_rooms_val + bedrooms_val + bathrooms_val
scenes_test = kitchens_test + living_rooms_test + bedrooms_test + bathrooms_test
# scenes_val = scenes_val + scenes_test
print("Number of training scenes: ", len(scenes_train))
print("Number of validation scenes: ", len(scenes_val))
controller = Controller(
agentMode="default",
visibilityDistance=1.5,
scene="FloorPlan319",
platform=CloudRendering,
makeAgentsVisible=False,
# step sizes
gridSize=0.75,
snapToGrid=True,
rotateStepDegrees=90,
# image modalities
renderDepthImage=True,
renderInstanceSegmentation=True,
# camera properties
width=1024,
height=1024,
fieldOfView=90
)
def save_rgb_frame(rgb_image, base_path, save_name):
save_path = os.path.join(base_path, save_name + ".png")
cv2.imwrite(save_path, rgb_image)
def save_depth_frame(depth_img, base_path, save_name):
save_path = os.path.join(base_path, save_name + ".npy")
depth_img = np.asarray(depth_img)
np.save(save_path, depth_img)
def save_json_dict(json_file, base_path, save_name):
save_path = os.path.join(base_path, save_name + ".json")
with open(save_path, 'w') as f:
json_str = json.dumps(json_file, indent=2)
f.write(json_str)
f.write('\n')
def mkdir_dataset_file(scene_path):
if not os.path.exists(scene_path):
os.makedirs(scene_path)
def create_dataset(scene_list, mode=None):
base_path = os.getcwd()
bad_scene = []
rgb_base_path = os.path.join(base_path, "ai2thor", "dataset", mode, "rgb_img")
position_dict_base_path = os.path.join(base_path, "ai2thor", "dataset", mode, "position_dict")
mkdir_dataset_file(rgb_base_path)
mkdir_dataset_file(position_dict_base_path)
grid_size = float(mode.split("_")[1])
if "random" in mode.lower():
grid_size = 0.75
for scene_one in tqdm(scene_list):
# The simulator resets the scene
try:
controller.reset(scene=scene_one, gridSize=grid_size)
except:
bad_scene.append(scene_one)
continue
# Start BFS navigation module
positions = controller.step(
action="GetReachablePositions"
).metadata["actionReturn"]
degree_step = 60
position_dict = {}
for position_one in positions:
pos_id = positions.index(position_one)
position_dict[str(pos_id)] = position_one
event = controller.step(action='Teleport', position=position_one)
for i in range(int(360 / degree_step)):
event = controller.step(action="RotateRight", degrees=degree_step)
last_event = controller.last_event
rgb_image = last_event.cv2img
# File naming rules: scene_name + position_id + view_id.png
# position_id: scene_name + .json
image_file_save_name = scene_one + "_" + str(pos_id) + "_" + str(i)
position_dict_save_name = scene_one
save_rgb_frame(rgb_image, rgb_base_path, image_file_save_name)
save_json_dict(position_dict, position_dict_base_path, position_dict_save_name)
# Some bugs in the AI2THOR simulator?
print(bad_scene)
print(len(bad_scene))
def main():
# mode_list = ["train", "val"]
mode_list = ["Random_1_60", "Random_1_120", "Random_75_60",
"Random_75_120", "Traversal_0.25_60", "Traversal_0.25_120",
"Traversal_0.75_60", "Traversal_0.75_120"]
for mode_one in mode_list:
if mode_one == "train":
create_dataset(scenes_train, mode_one)
else:
create_dataset(scenes_val, mode_one)
if __name__ == '__main__':
main()