semvec/pypi-packages huggingface dataset:
store-validations-oracle 0.1.4
Used with Great_Expectations to store validation results in an Oracle Database.
Requires Python: >=3.8,<4.0


# ge-store-validations-oracle-plugin

## Steps to use the OracleStoreValidationResultsAction from the plugin

Install this plugin by pasting store_validations_oracle.py from great_expectations/plugins in your corresponding great_expectations/plugins folder.

1. Install the plugin by `pip install store-validations-oracle`. You can put `store-validations-oracle` in the requirements.txt file for CI/CD operations.

2. In your required `checkpoint`, add the following action to your checkpoint `.yml` file.

```yml
  - name: store_validations_oracle
    action:
      class_name: OracleStoreValidationResultsAction
      module_name: store_validations_oracle.store_validations_oracle
      username: ${USERNAME}
      password: ${PASSWORD}
      hostname: ${HOSTNAME}
      port: ${PORT}
      service_name: ${SERVICE_NAME}
      table_name: ${TABLE_NAME}
```

3. In your uncommited/config_variables.yml file or if you are using environment variables, add the following variables related to the Oracle Database account:

    * USERNAME
    * PASSWORD
    * TABLE_NAME

    Either:

    * HOSTNAME
    * PORT (defaults to 1521)
    * SERVICE_NAME (defaults to ORCL)

    Or:

    * CONNECTION_STRING


4. If you already have a given table in oracledb, make sure table has below schema.( If you don't have table script will create one , with required schema. )

  ```sql
  CREATE TABLE TABLE1 
(
  BATCH_KEY VARCHAR2(100) NOT NULL 
, META CLOB 
, EVALUATION_PARAMETERS CLOB 
, STATISTICS CLOB 
, SUCCESS VARCHAR2(10) 
, RESULT CLOB 
);
  ```
cerberuspackage 0.1.3
first version of package scrfd

# borrow many code from: https://github.com/deepinsight/insightface/tree/master/detection/scrfd


## How to install: 

- pip install cerberuspackage

## code python:

``
from cerberuspackage import SCRFD
detector = SCRFD("your onnx model")

#bboxes: [top_left_x, top_left_y, bottom_right_x, bottom_right_y], 
#kpss: corrdinate 5 point on the face (left_eye, right_eye, nose, left_lip, right_lip)

bboxes, kpss = detector(img) 
#your postprocess with data predict by dcrfd 
``