out-of-the-box computer vision
Computer vision models OOB (out-of-the-bottle).
__ [__] ___ .+'. '+. )_( /:;/ _.+'\ + + +:._ .++ .+'+'+. _ |:._ | /+::_..+_[_]_+:._CV | )_ /_ _\:._ | +;: )_``'_(:._ + +;::+..+;:.._++.____.+' `+.._..`+...+'
Step into the cellar and select a bottle of computer visions.
- Face detection & localization
- Face classification
- Object tracking
- Object classification w/o localization
- v0.1.0 - improve asset loading
- annotations for face/object detection
- v0.2.0 - reduce and combine models to save space
- v0.3.0 - implement basic models to support classification
- face detection
- generic object detection & localization
- style transfer
- face recognition
Detection results have the following:
boxes: Boxes follow PIL format of (left, upper, right, lower)
- top-left corner is (0,0) and offsets go down/right from there (physics indexing)
scores: confidence score for each detected object
labels: label description of the object ('face')
portraits: the object cropped from its source image
base_image: the source image the objects were found in
(not implemented yet)
annotated: the source image with objects annotated
# Get a PIL image from somewhere: image = ... # Use PIL image as input: import huasca results = huasca.detect.faces(image) results.portraits.show() annotated.save('test.png')
# Get a PIL image from somewhere: image = ... import huasca gender,age = huasca.classify.demographics(image)
import huasca data = json.load(json_data) object_log = huasca.object_tracking.track_objects(data) output_json = [obj.to_json() for obj in object_log]
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