def generate_test(output_dir, image_count, min_shapes=7, max_shapes=15):
data = []
for i in range(image_count):
bg = Background(ColorPool.default().random(count=2), (500, 500),
"random")
image = Image(bg, i)
for _ in range(np.random.randint(min_shapes, max_shapes + 1)):
image.add_shape(ShapePool.default().random().new(
color=ColorPool.default().random(bg.colors)))
image.save(os.path.join(output_dir, "images_test"))
questions = QuestionsGroup().generate_all(image)
show_progress(i, image_count, prefix="Test Generation")
data.extend(questions)
print()
with open(os.path.join(output_dir, "questions_test.json"), "w") as file:
json.dump(data, file)
print("Test data file:", file.name)
def generate_train_validation(output_dir,
image_repeats=1,
balance_approach="group",
validation_split_ratio=0.01):
# generate data
print()
all_data = []
all_space = pool_iteration("colors", "shapes", "colors", image_repeats)
total_iterations = ColorPool.default().colors_len() * \
ShapePool.default().shapes_len() * \
ColorPool.default().colors_len() * \
image_repeats
for i, (bg_color, shape, shape_color, image_index) in enumerate(all_space):
if bg_color == shape_color:
continue
# generate invalid image and question
bg = Background(bg_color, (50, 50))
image = Image(bg)
image.save(os.path.join(output_dir, "images"))
questions = QuestionsGroup().generate_all(image)
all_data.extend(questions)
bg = Background(bg_color, (50, 50))
# generate valid image and question
image = Image(bg, image_index + 1) if image_repeats > 1 else Image(bg)
image.add_shape(shape.new(shape_color))
image.save(os.path.join(output_dir, "images"))
questions = QuestionsGroup().generate_all(image)
all_data.extend(questions)
show_progress(i, total_iterations, prefix="Train Generation")
print()
print("Total generated questions:", len(all_data))
# balance data
random.shuffle(all_data)
data_table = {}
for d in all_data:
key = d["answer"][0]
if key not in data_table:
data_table[key] = []
data_table[key].append(d)
answer_count = {k: len(v) for k, v in data_table.items()}
print("Number of each answer:")
for answer, count in answer_count.items():
print(answer, count)
balanced_data = []
if balance_approach == "categorical":
data_groups = {
"colors":
[c.name for c in pool_iteration("colors")] + ["unknown_color"],
"shapes":
[s.name for s in pool_iteration("shapes")] + ["unknown_shape"]
}
for gname, glist in data_groups.items():
print(gname, glist)
print("Group minimums:")
for gname, glist in data_groups.items():
group_min = min(
[answer_count[a] if a in answer_count else 0 for a in glist])
print(gname, group_min)
if group_min == 0:
continue
for a in glist:
balanced_data.extend(data_table[a][:group_min])
elif balance_approach == "blindfold":
answers_min_count = min([v for _, v in answer_count.items()])
for answer, qlist in data_table.items():
balanced_data.extend(qlist[:answers_min_count])
elif balance_approach == "none":
balanced_data = all_data
else:
raise Exception(
f"{balance_approach} is not a valid data balance approach")
print("Total number of questions of balancing", len(balanced_data))
data_table = {}
for d in balanced_data:
key = d["answer"][0]
if key not in data_table:
data_table[key] = []
data_table[key].append(d)
answer_count = [(k, len(v)) for k, v in data_table.items()]
print("Number of each answer after balancing:")
for answer, count in answer_count:
print(answer, count)
# spliting to validation and test
from sklearn.model_selection import train_test_split
data_train, data_validation = train_test_split(
balanced_data, test_size=validation_split_ratio)
print("Total number of train data", len(data_train))
print("Total number of validation data", len(data_validation))
with open(os.path.join(output_dir, "questions_train.json"), "w") as file:
json.dump(data_train, file)
print("Train data file:", file.name)
with open(os.path.join(output_dir, "questions_validation.json"),
"w") as file:
json.dump(data_validation, file)
print("Validation data file:", file.name)
