Metadata-Version: 2.1
Name: tf_efficientdet_lite2
Version: 0.10
Summary: Object detection with tensorflow
Home-page: https://github.com/hansalemaos/tf_efficientdet_lite2
Author: Johannes Fischer
Author-email: <aulasparticularesdealemaosp@gmail.com>
License: MIT
Keywords: tensorflow,object detection
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Editors :: Text Processing
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Filters
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
License-File: LICENSE.rst


# Object detection with tensorflow



<img src="https://github.com/hansalemaos/screenshots/raw/main/tensorflowscreen.png"/>



```python

$pip install tf-efficientdet-lite2

from tf_efficientdet_lite2 import TfEfficientdetLite2

tflo= TfEfficientdetLite2(sevenzip_path = r"C:\Program Files\7-Zip\7z.exe",set_visible_devices_0 = True) #works without 7-zip as well

tflo.detect(r'https://images.pexels.com/photos/14139354/pexels-photo-14139354.jpeg?cs=srgb&dl=pexels-olena-bohovyk-14139354.jpg&fm=jpg&_gl=1*19indnx*_ga*ODM0MDU4MTIyLjE2NjgzMzEwODk.*_ga_8JE65Q40S6*MTY2ODMzMTA4OS4xLjEuMTY2ODMzMTA5My4wLjAuMA..',draw_results=True,draw_result_min_score=.3)

tflo.get_df()



        aa_class  aa_x_start  aa_y_start  aa_x_end  aa_y_end   aa_conf

0         person         449         124       870       611  0.915973

1            cup         916         521      1022       654  0.642683

2            cup         652         360       732       469  0.561634

3   dining table         829          21      1200       793  0.545763

4          chair         926           1      1198       176  0.235585

..           ...         ...         ...       ...       ...       ...

95         chair         988          13      1188       253  0.012212

96           cup         857         491       947       616  0.011954

97         chair        1149         728      1196       793  0.011811

98         spoon         651         371       666       409  0.011780

99         spoon        1013         290      1062       358  0.011758





tflo.get_drawn_results()



```
