完成实验四

Lab4/dataset/Haze/split.csv

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Lab4/dataset/Haze/split_dataset.py

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+import os
+import pandas as pd
+import random
+
+train_list = set()
+
+img_list = [i for i in os.listdir("raw/haze") if i.endswith(".jpg")]
+random.shuffle(img_list)
+for img in img_list[ : int(len(img_list) * 0.8)]:
+    train_list.add(img)
+img_list.sort()
+data = list()
+for img in img_list:
+    data.append([img, 1 if img in train_list else 0])
+
+pd.DataFrame(data=data, columns=["Image", "Train"]).to_csv("./split.csv", index=False)

Lab4/dataset/Vehicles/split_dataset.py

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+import os
+import random
+import pandas as pd
+
+train_list = list()
+test_list = list()
+
+root_dir = "raw"
+class_index = 0
+for vehicle in os.listdir(root_dir):
+    img_list = [i for i in os.listdir(os.path.join(root_dir, vehicle)) if i.endswith(".jpg")]
+    random.shuffle(img_list)
+    split_num = int(len(img_list) * 0.8)
+    for img in img_list[0 : split_num]:
+        train_list.append([os.path.join(root_dir, vehicle, img), class_index])
+    for img in img_list[split_num : ]:
+        test_list.append([os.path.join(root_dir, vehicle, img), class_index])
+    class_index += 1
+
+train_list.sort()
+test_list.sort()
+
+pd.DataFrame(data=train_list, columns=["Vehicle", "Label"]).to_csv("./train.csv", index=False)
+pd.DataFrame(data=test_list, columns=["Vehicle", "Label"]).to_csv("./test.csv", index=False)
+
+

Lab4/dataset/Vehicles/test.csv

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