matcher.cpu_extractor — Extracting Features from Images¶
The matcher.cpu_extractor module
New in version 0.1.0.
- autocnet.matcher.cpu_extractor.extract_features(array, extractor_method='sift', extractor_parameters={})[source]¶
This method finds and extracts features from an image using the given dictionary of keyword arguments. The input image is represented as NumPy array and the output features are represented as keypoint IDs with corresponding descriptors.
- Parameters
array (ndarray) – a NumPy array that represents an image
extractor_method ({'orb', 'sift', 'fast', 'surf', 'vl_sift'}) – The detector method to be used. Note that vl_sift requires that vlfeat and cyvlfeat dependencies be installed.
extractor_parameters (dict) – A dictionary containing OpenCV SIFT parameters names and values.
- Returns
keypoints (DataFrame) – data frame of coordinates (‘x’, ‘y’, ‘size’, ‘angle’, and other available information)
descriptors (ndarray) – Of descriptors
- autocnet.matcher.cpu_extractor.extract_most_interesting(image, extractor_method='orb', extractor_parameters={'nfeatures': 10})[source]¶
Given an image, extract the most interesting feature. Interesting is defined as the feature descriptor that has the maximum variance. By default, this func finds 10 features in the image and then selects the best.
- Parameters
image (ndarray) – of DN values
extractor_method (str) – Any valid, autocnet extractor. Default (orb)
exctractor_parameters (dict) – of extractor parameters passed through to the feature extractor
- Returns
The keypoints row with the higest variance. The row has ‘x’ and ‘y’ columns to get the location.
- Return type
pd.series