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out-of-the-box computer vision

Project description

Huasca

Computer vision models OOB (out-of-the-bottle).
                 __
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   ___        .+'. '+.
   )_(       /:;/ _.+'\
   + +       +:._   .++
 .+'+'+.  _  |:._     |
/+::_..+_[_]_+:._CV   |
)_     /_   _\:._     |
+;:    )_``'_(:._     +
+;::+..+;:.._++.____.+'
`+.._..`+...+'
Step into the cellar and select a bottle of computer visions.
  • Face detection & localization
  • Face classification
    • age
    • gender
  • Object tracking
  • Object classification w/o localization

Roadmap

  • 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

Examples

Detection

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
  • annotated: the source image with objects annotated (not implemented yet)

Face Detection

# Get a PIL image from somewhere:
image = ...

# Use PIL image as input:
import huasca

results = huasca.detect.faces(image)

results.portraits[0].show()
annotated.save('test.png')

Face Demographics

# Get a PIL image from somewhere:
image = ...

import huasca
gender,age = huasca.classify.demographics(image)

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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