lvt-eval 0.0.2
lvt evaluation of image object detrction and image classification.
License: MIT
Requires Python: >=3.8


# lvt-eval

#### Installation
1. python>=3.8, (windows: need c++ env, https://airesources.oss-cn-hangzhou.aliyuncs.com/jkl/%E8%BE%B9%E7%BC%98%E5%8D%A1/VisualStudioSetup.exe)

2. pip install pybind11  -i https://pypi.tuna.tsinghua.edu.cn/simple

3. pip install -e faster_coco_eval/ -i https://pypi.tuna.tsinghua.edu.cn/simple

4. pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple




#### Lvt-eval guideline

* studio aiport 
1. write your config files in "config/{your config files}.json". 

```
For example:
1) multi-object（studio json） vs. multi-object（interface return label） detection:
{
    "label":[{"objectLabel":["烟"], "attrLabel":[], "id":0, "prediction":"smoke"},{"objectLabel":["火"], "attrLabel":[], "id":1, "prediction":"fire"}],
    "aiport": "http://192.1.2.238:8893/vql/v1/serving/process",
    "rawdata": "data/yh.json",
    "draw": false,
    "download": false,
    "raw_prediction_path": "prediction_dirs/raw_predictions_8893.json",
    "save_gt_coco": "gt_dirs/coco_groundtruth.json",
    "save_pred_path": "prediction_dirs/prediction_results.json",
    "faster_coco_api": true
}

2) multi-object（studio json） vs. single-object（interface return label） detection:
{
    "label":[{"objectLabel":["人"], "attrLabel":["躺", "趴"], "id":0, "prediction":"睡岗"}],
    "aiport": "http://192.1.2.238:8324/vql/v1/serving/process",
    "rawdata": "data/sleep_test_json_0621.json",
    "draw": false,
    "download": false,
    "raw_prediction_path": "prediction_dirs/raw_predictions_8324.json",
    "save_gt_coco": "gt_dirs/coco_groundtruth.json",
    "save_pred_path": "prediction_dirs/prediction_results.json",
    "faster_coco_api": true
}

3) single-object（studio json） vs. single-object（interface return label） detection:
{
    "label":[{"objectLabel":["person"], "attrLabel":[], "id":0, "prediction":"person"}],
    "aiport": "http://192.1.2.238:8312/vql/v1/serving/process",
    "rawdata": "data/xingren.json",
    "draw": false,
    "download": false,
    "raw_prediction_path": "prediction_dirs/raw_predictions_8312.json",
    "save_gt_coco": "gt_dirs/coco_groundtruth.json",
    "save_pred_path": "prediction_dirs/prediction_results.json",
    "faster_coco_api": false
}
```
```
"label":
    "objectLabel": enter the obj tags of studio json 
    "attrLabel": enter the attr tags of studio json (same the obj tags)
    "id": default is 0. 
    "prediction": label of original outputs of interface 
"aiport": model interface
"rawdata": studio json format data
"draw": "true" means draw GT and Pred bbox
"download": download images, if draw=True, need download=True
"raw_prediction_path": save original outputs of interface, file name "raw_predictions_{model interface id}.json"
"save_gt_coco": save studio json format to coco json format
"save_pred_path": save original outputs of interface to coco json format
"faster_coco_api": "true" means use faster coco evaluation
```

2. then run 
```python
python od_evaluator.py --mode studio_json --config {your config files} 
```


* coco json

1. prepare your data (coco json format)
prepare groundtruth and prediction json.

2. then run 
```python
python od_evaluator.py --mode coco_json --gt_json {your gt coco format json}  --pred_json {your pred coco format json} 
or
python od_evaluator.py --mode faster_coco_json --gt_json {your gt coco format json} --pred_json {your pred coco format json} 
```


#### How to start (example)

1.  use mode = studio aiport:
python od_evaluator.py --mode studio_json --config config/config_xingren.json
2.  use mode = coco json:
python od_evaluator.py --mode coco_json --gt_json example_data/only_no_glove.json --pred_json example_data/only_no_glove_pred.json
3.  use mode = faster coco json:
python od_evaluator.py --mode faster_coco_json --gt_json example_data/only_no_glove.json --pred_json example_data/only_no_glove_pred.json

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