mirror of
https://github.com/wassname/IndicoIo-python.git
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jagreene
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
import requests
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from indicoio.utils.image import image_preprocess
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from indicoio.utils.api import api_handler
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def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
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"""
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Given an grayscale input image of a face, returns a 48 dimensional feature vector explaining that face.
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Useful as a form of feature engineering for face oriented tasks.
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Input should be in a list of list format, resizing will be attempted internally but for best
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performance, images should be already sized at 48x48 pixels.
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Example usage:
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.. code-block:: python
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>>> from indicoio import facial_features
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>>> import numpy as np
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>>> face = np.zeros((48,48)).tolist()
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>>> features = facial_features(face)
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>>> len(features)
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48
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:param image: The image to be analyzed.
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:type image: list of lists
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:rtype: List containing feature responses
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"""
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image = image_preprocess(image, batch=batch)
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url_params = {"batch": batch, "api_key": api_key}
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return api_handler(image, cloud=cloud, api="facialfeatures", url_params=url_params, **kwargs)
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def image_features(image, cloud=None, batch=False, api_key=None, **kwargs):
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"""
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Given an input image, returns a 2048 dimensional sparse feature vector explaining that image.
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Useful as a form of feature engineering for image oriented tasks.
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* Input can be either grayscale or rgb color and should either be a numpy array or nested list format.
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* Input data should be either uint8 0-255 range values or floating point between 0 and 1.
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* Large images (i.e. 1024x768+) are much bigger than needed, resizing will be done internally to 64x64 if needed.
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* For ideal performance, images should be square aspect ratio but non-square aspect ratios are supported as well.
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Example usage:
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.. code-block:: python
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>>> from indicoio import image_features
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>>> import numpy as np
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>>> image = np.zeros((64,64,3))
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>>> features = image_features(image)
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>>> len(features),np.min(features),np.max(features),np.sum(np.asarray(f)!=0)
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(2048, 0.0, 6.97088623046875, 571)
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Since the image features returned are a semantic description of the contents of an image they can be used
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to implement many other common image related tasks such as object recognition or image similarity and retrieval.
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For image similarity, simple distance metrics applied to collections of image feature vectors can work very well.
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:param image: The image to be analyzed.
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:type image: numpy.ndarray
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:rtype: List containing features
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"""
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image = image_preprocess(image, batch=batch, size=(64,64))
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url_params = {"batch": batch, "api_key": api_key}
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return api_handler(image, cloud=cloud, api="imagefeatures", url_params=url_params, **kwargs)
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