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dataset_to_yolo.py
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dataset_to_yolo.py
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import argparse
import json
import re
import h5py
import numpy as np
import spacy
from PIL import Image
from scipy import spatial
from tqdm import tqdm, trange
names = []
SCENES = [0, 200, 300, 400]
TRAIN_SPLIT = (1, 22)
TEST_SPLIT = (21, 26)
KITCHEN_OBJECT_CLASS_LIST_TRAIN = [
"Toaster",
"Microwave",
"Fridge",
"CoffeeMachine",
"GarbageCan",
"Bowl",
]
KITCHEN_OBJECT_CLASS_LIST_EVAL = [
"StoveBurner",
"Mug",
"Pot",
"Cup"
]
LIVING_ROOM_OBJECT_CLASS_LIST_TRAIN = [
"Pillow",
"Laptop",
"Television",
"GarbageCan",
"Bowl",
]
LIVING_ROOM_OBJECT_CLASS_LIST_EVAL = [
"Sofa",
"RemoteControl",
"Box",
"TableTop"
]
BEDROOM_OBJECT_CLASS_LIST_TRAIN = ["HousePlant", "Lamp", "Book", "AlarmClock"]
BEDROOM_OBJECT_CLASS_LIST_EVAL = ["Mirror", "CD", "CellPhone"]
BATHROOM_OBJECT_CLASS_LIST_TRAIN = [
"Sink", "ToiletPaper", "SoapBottle", "LightSwitch"]
BATHROOM_OBJECT_CLASS_LIST_EVAL = [
"Toilet", "Towel", "SprayBottle"]
scene_id_name = ["Kitchen", "LivingRoom", "Bedroom", "Bathroom"]
OBJECT_IDS = {
"Undefined": 0,
"Apple": 1,
"AppleSliced": 2,
"Tomato": 3,
"TomatoSliced": 4,
"Bread": 5,
"BreadSliced": 6,
"Sink": 7,
"Pot": 8,
"Pan": 9,
"Knife": 10,
"Fork": 11,
"Spoon": 12,
"Bowl": 13,
"Toaster": 14,
"CoffeeMachine": 15,
"Microwave": 16,
"StoveBurner": 17,
"Fridge": 18,
"Cabinet": 19,
"Egg": 20,
"Chair": 21,
"Lettuce": 22,
"Potato": 23,
"Mug": 24,
"Plate": 25,
"TableTop": 26,
"CounterTop": 27,
"GarbageCan": 28,
"Omelette": 29,
"EggShell": 30,
"EggCracked": 31,
"StoveKnob": 32,
"Container": 33,
"Cup": 34,
"ButterKnife": 35,
"PotatoSliced": 36,
"MugFilled": 37,
"BowlFilled": 38,
"Statue": 39,
"LettuceSliced": 40,
"ContainerFull": 41,
"BowlDirty": 42,
"Sandwich": 43,
"Television": 44,
"HousePlant": 45,
"TissueBox": 46,
"VacuumCleaner": 47,
"Painting": 48,
"WateringCan": 49,
"Laptop": 50,
"RemoteControl": 51,
"Box": 52,
"Newspaper": 53,
"TissueBoxEmpty": 54,
"PaintingHanger": 55,
"KeyChain": 56,
"Dirt": 57,
"CellPhone": 58,
"CreditCard": 59,
"Cloth": 60,
"Candle": 61,
"Toilet": 62,
"Plunger": 63,
"Bathtub": 64,
"ToiletPaper": 65,
"ToiletPaperHanger": 66,
"SoapBottle": 67,
"SoapBottleFilled": 68,
"SoapBar": 69,
"ShowerDoor": 70,
"SprayBottle": 71,
"ScrubBrush": 72,
"ToiletPaperRoll": 73,
"Lamp": 74,
"LightSwitch": 75,
"Bed": 76,
"Book": 77,
"AlarmClock": 78,
"SportsEquipment": 79,
"Pen": 80,
"Pencil": 81,
"Blinds": 82,
"Mirror": 83,
"TowelHolder": 84,
"Towel": 85,
"Watch": 86,
"MiscTableObject": 87,
"ArmChair": 88,
"BaseballBat": 89,
"BasketBall": 90,
"Faucet": 91,
"Boots": 92,
"Glassbottle": 93,
"DishSponge": 94,
"Drawer": 95,
"FloorLamp": 96,
"Kettle": 97,
"LaundryHamper": 98,
"LaundryHamperLid": 99,
"Lighter": 100,
"Ottoman": 101,
"PaintingSmall": 102,
"PaintingMedium": 103,
"PaintingLarge": 104,
"PaintingHangerSmall": 105,
"PaintingHangerMedium": 106,
"PaintingHangerLarge": 107,
"PanLid": 108,
"PaperTowelRoll": 109,
"PepperShaker": 110,
"PotLid": 111,
"SaltShaker": 112,
"Safe": 113,
"SmallMirror": 114,
"Sofa": 115,
"SoapContainer": 116,
"Spatula": 117,
"TeddyBear": 118,
"TennisRacket": 119,
"Tissue": 120,
"Vase": 121,
"WallMirror": 122,
"MassObjectSpawner": 123,
"MassScale": 124,
"Footstool": 125,
"Shelf": 126,
"Dresser": 127,
"Desk": 128,
"NightStand": 129,
"Pillow": 130,
"Bench": 131,
"Cart": 132,
"ShowerGlass": 133,
"DeskLamp": 134,
"Window": 135,
"BathtubBasin": 136,
"SinkBasin": 137,
"CD": 138,
"Curtains": 139,
"Poster": 140,
"HandTowel": 141,
"HandTowelHolder": 142,
"Ladle": 143,
"WineBottle": 144,
"ShowerCurtain": 145,
"ShowerHead": 146
}
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Create param.json experiment file')
parser.add_argument('--train_range', nargs=2, default=TRAIN_SPLIT,
help='train scene range Ex : 1 12')
parser.add_argument('--eval_range', nargs=2, default=TEST_SPLIT,
help='train scene range Ex : 22 27')
parser.add_argument('--method', type=str, default="word2vec",
help='Method to use Ex : word2vec')
parser.add_argument('--reward', type=str, default="soft_goal",
help='Method to use Ex : soft_goal')
parser.add_argument('--eval', action="store_true")
args = vars(parser.parse_args())
str_range = list(args["train_range"])
for i, s in enumerate(str_range):
str_range[i] = int(s)
args["train_range"] = str_range
str_range = list(args["eval_range"])
for i, s in enumerate(str_range):
str_range[i] = int(s)
args["eval_range"] = str_range
data = {}
scene_tasks = [KITCHEN_OBJECT_CLASS_LIST_TRAIN, LIVING_ROOM_OBJECT_CLASS_LIST_TRAIN,
BEDROOM_OBJECT_CLASS_LIST_TRAIN, BATHROOM_OBJECT_CLASS_LIST_TRAIN]
training = {}
set_obj = None
obs_id = 0
if args["eval"]:
range_scene = args['eval_range']
base_path = "yolo_dataset/obs_label_eval/"
else:
range_scene = args['train_range']
base_path = "yolo_dataset/obs_label/"
for idx_scene, scene in enumerate(tqdm(SCENES)):
for t in trange(*range_scene, position=1):
name = "FloorPlan" + str(scene + t)
f = h5py.File("data/"+name+".h5", 'r')
# Use h5py object available
obs = f['observation']
bbox = [json.loads(bb) for bb in f['bbox']]
for curr_obs, curr_bbox in tqdm(zip(obs, bbox), total=len(obs), position=2):
im = Image.fromarray(curr_obs)
img_name = "img_{:010d}".format(obs_id)
im.save(base_path + img_name + '.jpeg')
bboxs = []
for key, value in curr_bbox.items():
keys = key.split('|')
# Convert to x center
x = (value[0] + value[2]) / 2
# Convert to y center
y = (value[1] + value[3]) / 2
width = value[2] - value[0]
height = value[3] - value[1]
# Normalize
x = x / 400
width = width / 400
y = y / 300
height = height / 300
if width < 0.025 or height < 0.025:
continue
bbox_str = str(OBJECT_IDS[keys[0]]) + \
" {} {} {} {}".format(x, y, width, height)
bboxs.append(bbox_str)
with open(base_path + img_name + '.txt', 'w') as bbox_f:
for bb in bboxs:
bbox_f.write(bb + '\n')
obs_id = obs_id + 1
# if args['eval_objects']:
# scene_tasks = [KITCHEN_OBJECT_CLASS_LIST_EVAL, LIVING_ROOM_OBJECT_CLASS_LIST_EVAL,
# BEDROOM_OBJECT_CLASS_LIST_EVAL, BATHROOM_OBJECT_CLASS_LIST_EVAL]
# evaluation = {}
# evaluation_set = dict()
# for idx_scene, scene in enumerate(SCENES):
# evaluation_set[scene] = list()
# for t in range(*args['eval_range']):
# name = "FloorPlan" + str(scene + t)
# # Use h5py object available
# f = h5py.File("data/"+name+".h5", 'r')