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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import random
import ast
SEED=42
random.seed(SEED)
def generate_shape_color_question_and_answer(info):
"""
Ask about a specific (shape, color) pair.
Returns:
question (str): e.g.
"In the scene 'skywalk' under 'middle' lighting, how many black cones are there?"
answer (int)
"""
# parse the stringified keys into real tuples
parsed = {}
for k, count in info.get("shape_color_counts", {}).items():
shape, color = ast.literal_eval(k)
parsed[(shape, color)] = count
(shape, color), cnt = random.choice(list(parsed.items()))
question = (
f"In the scene, "
f"how many {color} {shape}'s are there?"
)
return question, cnt
def generate_shape_question_and_answer(info):
"""
Ask about the total number of a given shape, agnostic of color.
Returns:
question (str): e.g.
"In the scene 'skywalk' under 'middle' lighting, how many cones are there in total?"
answer (int)
"""
# first try the provided shape_counts
shape_counts = info.get("shape_counts", {})
# fallback: aggregate from shape_color_counts if needed
if not shape_counts:
agg = {}
for k, count in info.get("shape_color_counts", {}).items():
shape, _ = ast.literal_eval(k)
agg[shape] = agg.get(shape, 0) + count
shape_counts = agg
shape, total = random.choice(list(shape_counts.items()))
question = (
f"In the scene, "
f"how many {shape}'s are there in total?"
)
return question, total
import os
import json
base_dir = "3D_DoYouSeeMe/shape_discrimination"
os.listdir(base_dir)
data_list = []
for filename in os.listdir(base_dir):
if filename.endswith(".json"):
# print(filename)
with open(os.path.join(base_dir, filename), "r") as f:
data = f.read()
data = json.loads(data)
q, a = generate_shape_question_and_answer(data)
num_shapes = data["num_shapes"]
max_instances_per_shape = data["max_instances_per_shape"]
min_visibility = data["min_visibility"]
data_list.append({"filename": os.path.splitext(filename)[0] + ".png",
"question": q,
"answer": a,
"sweep": [num_shapes, max_instances_per_shape, min_visibility]})
import pandas as pd
df = pd.DataFrame(data_list)
df.to_csv(os.path.join(base_dir, "dataset_info.csv"), index=False) |