semvec/pypi-packages huggingface dataset:
error-catcher 0.4
A decorator to trace error and catch variables.
Requires Python: >=3.6

# Error Catcher

Convenient and comprehensive traceback decorator for Python scripts.

To use this decorator, simply attach it to your function:

```python
from error_catcher import silent

@silent(key_vars=[], log_file='', ascending=False)
def func():
    i, j = 1, 0
    return i / j

func()
```

## Parameters

- `key_vars`: list, default `[]`.
  - This is a list of variable names to be caught. Once an exception happens, the returned message will include the corresponding value of these variables in the most recent frame, both globally or locally.

- `log_file`: string, default `''`.
  - If this value is set non-empty, the returned message will be passed to a log file under the same folder as the Python scripts. You are suggested to use '.log' or '.txt' format.
  - By default, it is empty and the returned message will be printed directly.

- `ascending`: Bool, default `False`.
  - This parameter decides whether to append the returned message to the end of the log file (True) or to the top of the log file (False).
  - If log_file is set empty, this parameter will have no impact.

## Return

![](doc/example_image.png)
DCNN-LRS 1.0
leaf Rolling Scoring

# DCNN-Leaf Rolling Scoring   

## Requires：

tensorflow>=2.2.0
keras>=2.4.0
numpy=1.19.0

## Get Start:

    pip install DCNN-LRS
    import DCNNLRS
## Use it:

    
    '''
    FolderPath is a folder directory containing the image patches to be predicted.
    Run() will return a list of dicts with the image patch id as key and the leaf-rolling score as value.
    '''
    LRS = DCNNLRS.Run(floderPath)
    id1 = LRS[0]['id']
    score1 = LRS[0]['score']
DCNNLRS 1.0
leaf Rolling Scoring

# DCNN-Leaf Rolling Scoring   

## Requires：

tensorflow>=2.2.0

keras>=2.4.0

numpy=1.19.0

## Get Start:

    pip install DCNNLRS

    import DCNNLRS
## Use it:

    
    '''
    FolderPath is a folder directory containing images to predict.
    Run() wil retun a list containing  the same amount of your input images and every dict has a key- value:id and score.
    '''
    LRSs = DCNNLRS.Run(floderPath)
    id1 = LRS[0]['id']
    score1 = LRS[0]['score']