out-of-the-box computer vision
Computer vision models OOB (out-of-the-bottle).
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Huasca enables prototyping by prioritizing generalization and rapid development over accuracy.
Step into the cellar and select a bottle of computer visions.
- Face detection & localization
- Face classification
- Object detection & localization
- Object tracking
- Object classification w/o localization
Face and object localization include convenient cropping and annotation methods to feed classifiers.
- v0.3.0 - reduce and combine models to save space
- v0.4.x - add style transfer
- v0.4.x - face recognition
Detection results include:
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 e.g. ['dog','person']
portraits: cropped objects from base image (PIL.Image format)
base_image: the source image (PIL.Image format)
annotated: the source image with objects annotated (PIL.Image format)
Face & Object Detection
# Get a PIL image from somewhere: image = ... # Use PIL image as input: import huasca faces = huasca.detect.faces(image) objects = huasca.detect.objects(image) # Display the first face faces.portraits.show() # Check classes print(objects.labels) # Retrieve annotated & labeled version of either faces.annotated.show() objects.annotated.show()
# Get a PIL image of a face from face detector: face = faces.portraits gender,age = huasca.classify.demographics(face)
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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