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Adding additional interface for Object Detection #7

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Adding additional interface for Object Detection #7

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4 changes: 2 additions & 2 deletions hugging_py_face/__about__.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
__title__ = 'hugging_py_face'
__package_name__ = 'hugging_py_face'
__version__ = '0.2.0'
__version__ = '0.2.1'
__description__ = "Hugging-Py-Face, the Python client for the Hugging Face Inference API."
__email__ = "[email protected]"
__author__ = 'Minura Punchihewa'
__github__ = 'https://github.com/MinuraPunchihewa/hugging_py_face'
__pypi__ = 'https://pypi.org/project/hugging-py-face/'
__license__ = 'GPL-3.0'
__copyright__ = 'Copyright (C) 2023 Minura Punchihewa'
__copyright__ = 'Copyright (C) 2023 Minura Punchihewa'
24 changes: 22 additions & 2 deletions hugging_py_face/computer_vision.py
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Expand Up @@ -31,7 +31,10 @@ def image_classification_in_df(self, df: DataFrame, column: Text, model: Optiona
:return: a pandas DataFrame with the label for the images. Each label added will be the one with the highest confidence score for that particular image. The label will be added as a new column called 'predictions' to the original DataFrame.
"""
predictions = self._query_in_df(df, column, model=model, task="image-classification")
df["predictions"] = [prediction[0]['label'] for prediction in predictions]
try:
df["predictions"] = [prediction[0]['label'] for prediction in predictions]
except Exception:
df["predictions"] = predictions
return df

def object_detection(self, inputs: Union[Text, List], model: Optional[Text] = None) -> List:
Expand All @@ -45,4 +48,21 @@ def object_detection(self, inputs: Union[Text, List], model: Optional[Text] = No
if type(inputs) == list:
return self._query(inputs, model=model, task="object-detection")
elif type(inputs) == str:
return self._query(inputs, model=model, task="object-detection")
return self._query(inputs, model=model, task="object-detection")

def object_detection_in_df(self, df: DataFrame, column: Text, model: Optional[Text] = None) -> DataFrame:
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This is really awesome! I was just thinking that since this is object detection, we might need to include the coordinates as well? What do you think? We can maybe include these in separate columns in the resulting DataFrame?

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All thanks goes to you good sir! I'm just a fan who hopes I'm not making things worse. Coordinates will definitely be helpful especially if we want to augment images to pinpoint objects. I was messing around in my development area but need a little more time to try that without adding too much bloat to the codebase.

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You are making it much better! I was wondering how to tackle the object detection task and your help is much appreciated. Please take your time and reach out to me anytime if you want to talk it out.

"""
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Something else that I was thinking about is, given the situation that multiple objects are detected in a single image, we will only be returning the first one. Maybe we can include all of the objects detected for a particular image in separate rows? What do you feel about this?

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Yeah I noticed this behavior. I think having all objects might be a good option and allows for extensibility. In my case I was happy with a single result for the first iteration. Having any accurate result was a huge win to start as far as I was concerned.

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Awesome! Let's see what we can do to support this.

Detect objects from a dataframe.

:param df: a pandas DataFrame containing the images to perform object detection on.
:param column: the name of the column containing the file paths or urls of the images perform object detection on.
:param model: the model to use for the object detection task. If not provided, the recommended model from Hugging Face will be used.
:return: a pandas DataFrame with the label for the images. Each label added will be the one with the highest confidence score for that particular image. The label will be added as a new column called 'predictions' to the original DataFrame.
"""
predictions = self._query_in_df(df, column, model=model, task="object-detection")

try:
df["predictions"] = [prediction[0]['label'] for prediction in predictions]
except Exception:
df["predictions"] = predictions
return df