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Some useful extensions to thumbor - extra filters and detectors.

Project description


Some useful extensions to thumbor - extra filters and detectors.

pip install git+


Add these detectors to your thumbor.conf:



An improved face detector that uses a deep neural network. More specifically, it uses the SSD object detection model, trained specifically on faces. According to the OpenCV repo, it was trained with "some huge and available online dataset." :/

For more information on how the normal thumbor face detector compares to this one in both accuracy and efficiency, see this comparison script.


The full SSD object detection model, trained on the COCO dataset. It was built as a MobileNet, a class of efficient, light weight deep neural network models meant to work well in mobile and embedded applications.


Add these filters to your available filters in thumbor.conf:

    ...other filters,


Draws a circle at the calculated center of mass, according to the focal points.

Usage: draw_center_of_mass([radius, r, g, b])


  • draw_center_of_mass(): draws a circle with defaults (10 pixel radius and red color).
  • draw_center_of_mass(50,0,0,255): draws a blue circle with a 50 pixel radius.


Draws a box around the focal points, for displaying the results of the detectors on a given image. Takes optional arguments for box color and line width.

Usage: draw_focal_points([line_width, show_heatmap, show_labels, r, g, b])


  • line_width: the width of the box lines
  • show_heatmap: show a darker shade of green for higher-confidence detections; pass false to provide a single color RGB value for box color
  • show_labels: print the class label at the top of the box
  • r: R component of RGB color of box, default 0
  • g: G component of RGB color of box, default 255
  • b: B component of RGB color of box, default 0


  • draw_focal_points(): draws boxes with defaults (green color and 3 pixel line width).
  • draw_focal_points(5,false,false,255,0,0): draws solid red boxes with 5 pixel line width, and no class labels.


  • Choose thresholds empirically

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