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# Convert the data to dataloader formater.
import os
# Check root directory exists,
# If not, create it.
if not os.path.exists("dataset/root"):
os.makedirs("dataset/root")
# Check if the labels.csv file exists, if it does, delete it.
if os.path.exists("dataset/root/labels.csv"):
os.remove("dataset/root/labels.csv")
# Create a labels csv file.
print("Creating labels.csv file.")
classes_to_model_output = {"left": 0, "right": 1}
with open("dataset/root/labels.csv", "w") as file:
# file.write("image,class\n")
classes = ["left", "right"]
for class_name in classes:
image_files = os.listdir(os.path.join("dataset", class_name))
for image in image_files:
file.write(f"{image},{classes_to_model_output[class_name]}\n")
print("Creating uniform image dataset.")
# Create a uniform image dataset, named train
if not os.path.exists("dataset/root/train"):
os.makedirs("dataset/root/train")
# Copy the images to the root directory.
for class_name in classes:
image_files = os.listdir(os.path.join("dataset", class_name))
for image in image_files:
os.system(f"cp dataset/{class_name}/{image} dataset/root/train/{image}")
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