YOLOV5半自动标注工具（基于labelImg）.zip

# Additional tools ## Convert the label files to CSV ### Introduction To train the images on [Google Cloud AutoML](https://cloud.google.com/automl), we should prepare the specific csv files follow [this format](https://cloud.google.com/vision/automl/object-detection/docs/csv-format). `label_to_csv.py` can convert the `txt` or `xml` label files to csv file. The labels files should strictly follow to below structure. ### Structures * Images To train the object detection tasks, all the images should upload to the cloud storage and access it by its name. All the images should stay in the **same buckets** in cloud storage. Also, different classes should have their own folder as below. ``` <bucket_name> (on the cloud storage) | -- class1 | | -- class1_01.jpg | | -- class1_02.jpg | | ... | -- class2 | | -- class2_01.jpg | | -- class2_02.jpg | | ... | ... ``` Note, URI of the `class1_01.jpg` is `gs://<bucket_name>/class1/class1_01.jpg` * Labels There are four types of training data - `TRAINING`, `VALIDATION`, `TEST` and `UNASSIGNED`. To assign different categories, we should create four directories. Inside each folder, users should create the class folders with the same name in cloud storage (see below structure). ``` labels (on PC) | -- TRAINING | | -- class1 | | | -- class1_01.txt (or .xml) | | | ... | | -- class2 | | | -- class2_01.txt (or .xml) | | | ... | | ... | -- VALIDATION | | -- class1 | | | -- class1_02.txt (or .xml) | | | ... | | -- class2 | | | -- class2_02.txt (or .xml) | | | ... | | ... | -- TEST | | (same as TRAINING and VALIDATION) | -- UNASSIGNED | | (same as TRAINING and VALIDATION) ``` ### Usage To see the argument of `label_to_csv.py`, ```commandline python label_to_csv.py -h ``` ```commandline usage: label_to_csv.py [-h] -p PREFIX -l LOCATION -m MODE [-o OUTPUT] [-c CLASSES] optional arguments: -h, --help show this help message and exit -p PREFIX, --prefix PREFIX Bucket of the cloud storage path -l LOCATION, --location LOCATION Parent directory of the label files -m MODE, --mode MODE 'xml' for converting from xml and 'txt' for converting from txt -o OUTPUT, --output OUTPUT Output name of csv file -c CLASSES, --classes CLASSES Label classes path ``` For example, if mine bucket name is **test**, the location of the label directory is **/User/test/labels**, the mode I choose from is **txt**, the output name and the class path is same as default. ```commandline python label_to_csv.py \ -p test\ -l /User/test/labels \ -m txt ``` The output file is `res.csv` by default. Afterwards, upload the csv file to the cloud storage and you can start training!