charlie880624
ac3c30a1d2
Some checks failed
build / build_cpu (3.7, 1.5.1, torch1.5, 0.6.1) (push) Has been cancelled
build / build_cpu (3.7, 1.6.0, torch1.6, 0.7.0) (push) Has been cancelled
build / build_cpu (3.7, 1.7.0, torch1.7, 0.8.1) (push) Has been cancelled
build / build_cpu (3.7, 1.8.0, torch1.8, 0.9.0) (push) Has been cancelled
build / build_cpu (3.7, 1.9.0, torch1.9, 0.10.0) (push) Has been cancelled
build / build_cuda101 (3.7, 1.5.1+cu101, torch1.5, 0.6.1+cu101) (push) Has been cancelled
build / build_cuda101 (3.7, 1.6.0+cu101, torch1.6, 0.7.0+cu101) (push) Has been cancelled
build / build_cuda101 (3.7, 1.7.0+cu101, torch1.7, 0.8.1+cu101) (push) Has been cancelled
build / build_cuda101 (3.7, 1.8.0+cu101, torch1.8, 0.9.0+cu101) (push) Has been cancelled
build / build_cuda102 (3.6, 1.9.0+cu102, torch1.9, 0.10.0+cu102) (push) Has been cancelled
build / build_cuda102 (3.7, 1.9.0+cu102, torch1.9, 0.10.0+cu102) (push) Has been cancelled
build / build_cuda102 (3.8, 1.9.0+cu102, torch1.9, 0.10.0+cu102) (push) Has been cancelled
build / build_cuda102 (3.9, 1.9.0+cu102, torch1.9, 0.10.0+cu102) (push) Has been cancelled
build / test_windows (windows-2022, cpu, 3.8) (push) Has been cancelled
build / test_windows (windows-2022, cu111, 3.8) (push) Has been cancelled
deploy / build-n-publish (push) Has been cancelled
lint / lint (push) Has been cancelled
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import os
|
||
import cv2
|
||
import numpy as np
|
||
import pandas as pd
|
||
from pathlib import Path
|
||
from tqdm import tqdm
|
||
|
||
# ✅ 設定各資料夾的來源與輸出
|
||
datasets = [
|
||
{
|
||
"name": "train",
|
||
"input_dir": r"C:\Users\rd_de\kneronstdc\data\06-03_danger_object_segmentation\train",
|
||
"output_img_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\leftImg8bit\train",
|
||
"output_mask_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\gtFine\train",
|
||
},
|
||
{
|
||
"name": "val",
|
||
"input_dir": r"C:\Users\rd_de\kneronstdc\data\06-03_danger_object_segmentation\valid",
|
||
"output_img_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\leftImg8bit\val",
|
||
"output_mask_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\gtFine\val",
|
||
},
|
||
{
|
||
"name": "test",
|
||
"input_dir": r"C:\Users\rd_de\kneronstdc\data\06-03_danger_object_segmentation\test",
|
||
"output_img_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\leftImg8bit\test",
|
||
"output_mask_dir": r"C:\Users\rd_de\kneronstdc\data\cityscapes\gtFine\test",
|
||
}
|
||
]
|
||
|
||
# ✅ 建立 label ID 對應表（原始 ➝ 訓練用 label）
|
||
label_mapping = {
|
||
1: 0, # bunker
|
||
2: 1, # car
|
||
3: 2, # grass
|
||
4: 3, # greenery
|
||
5: 4, # person
|
||
6: 5, # road
|
||
7: 6 # tree
|
||
}
|
||
|
||
for dataset in datasets:
|
||
name = dataset["name"]
|
||
input_dir = dataset["input_dir"]
|
||
output_img_dir = dataset["output_img_dir"]
|
||
output_mask_dir = dataset["output_mask_dir"]
|
||
|
||
os.makedirs(output_img_dir, exist_ok=True)
|
||
os.makedirs(output_mask_dir, exist_ok=True)
|
||
|
||
# ✅ 嘗試讀取 _classes.csv
|
||
csv_path = os.path.join(input_dir, "_classes.csv")
|
||
if not os.path.exists(csv_path):
|
||
print(f"❌ 缺少 _classes.csv: {csv_path}")
|
||
continue
|
||
|
||
df = pd.read_csv(csv_path)
|
||
valid_labels = set(df.iloc[:, 0].values)
|
||
print(f"\n📂 資料集: {name}")
|
||
print(f"✅ 合法類別 ID: {valid_labels}")
|
||
|
||
for file in tqdm(os.listdir(input_dir), desc=f"轉換 {name}"):
|
||
if not file.endswith("_mask.png"):
|
||
continue
|
||
|
||
mask_path = os.path.join(input_dir, file)
|
||
image_name = file.replace("_mask.png", ".jpg")
|
||
image_path = os.path.join(input_dir, image_name)
|
||
|
||
if not os.path.exists(image_path):
|
||
print(f"⚠️ 找不到對應圖片: {image_name}")
|
||
continue
|
||
|
||
img = cv2.imread(image_path)
|
||
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
|
||
|
||
if img is None or mask is None:
|
||
print(f"❌ 無法讀取圖像或 mask: {file}")
|
||
continue
|
||
|
||
labels = set(np.unique(mask))
|
||
valid_ids = set(label_mapping.keys()) | {0}
|
||
if not labels.issubset(valid_ids):
|
||
print(f"❌ 非法類別出現在: {file}, labels={labels}")
|
||
continue
|
||
|
||
# ✅ 映射 label，0 ➝ 255（ignore），其餘照 mapping
|
||
remapped_mask = np.full_like(mask, 255, dtype=np.uint8) # 預設全部是 ignore
|
||
for old_id, new_id in label_mapping.items():
|
||
remapped_mask[mask == old_id] = new_id
|
||
|
||
# ✅ 命名符合 Cityscapes 格式
|
||
stem = Path(file).stem.replace("_mask", "")
|
||
out_img_path = os.path.join(output_img_dir, f"{stem}_leftImg8bit.png")
|
||
out_mask_path = os.path.join(output_mask_dir, f"{stem}_gtFine_labelIds.png")
|
||
|
||
cv2.imwrite(out_img_path, img)
|
||
cv2.imwrite(out_mask_path, remapped_mask)
|
||
|
||
print("\n🎉 全部資料集轉換完成！Cityscapes 格式 OK")
|