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out-of-the-box computer vision

Project description


Computer vision models OOB (out-of-the-bottle).
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Step into the cellar and select a bottle of computer visions.
  • Face detection & localization
  • Face classification
    • age
    • gender
  • 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

# Check classes

# Retrieve annotated & labeled version of either

Face Demographics

# Get a PIL image of a face from face detector:
face = faces.portraits[0]

gender,age = huasca.classify.demographics(face)

Object Tracking

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]

Project details

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Files for huasca, version 0.2.0
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