handDetection

Hand/gesture detection with OpenCV and Qt.

skin segmentation

Image segmentation starts with RGB to HSV color-space conversion of the input frame. Hue and Saturation channels are relevant for the segmentation process while Value is discarded as too dependent on lighting conditions.

In a Hue/Saturation representation, each pixel has a position on a 2D plane:

If a reference skin tone is set on the plane, each pixel in the input frame can be characterized by its euclidean distance from the reference point. A non-linear function (gaussian kernel) is applied to the euclidean distance for each pixel:

A median filter is used to reduce noise and binary thresholding is then used to obtain the segmented binary image:

distance transform

"A distance transform is a derived representation of a digital image which labels each pixel of the image with the distance to the nearest obstacle pixel ... A most common type of obstacle pixel is a boundary pixel in a binary image." (wiki)

Distance transform is applied to the binary image, resulting in a new image where each skin pixel is replaced by its distance from the nearest non-skin pixel (meaning that higher values are assigned to the center of the hand).

fingers detection

The pixel with the higher distance transform value is set as the center of the hand, and its value (distance to the closest non-skin pixel) is used to determine the radius of the hand's palm inscribed circle (i.e. the largest circle contained in it). Fingers start at the boundary of this circle. Fingers region is bounded to another circle with the same center and 3x radius.

The final step is to add-up the distance transform values for each direction looking from the hand's center (within the outer circle region). Sums from each direction (0 to 360 degrees) are stored in an histogram. Peaks in the histogram are detected as fingers (picture below, top left).

gesture detection

Basic gesture detection is achieved by counting the number of peaks in the histogram and the distance between them. A gesture is detected when these values match with an entry in a reference gestures library.

QT implementation and threads

Main QT thread takes care of the graphics and UI, while the computer vision workload is run in a separate processing thread. QT signals and slots are used for inter-thread communication:

1. the image processing thread takes care of capturing webcam frames and do the CV processing
2. when computation on a frame is completed, a signal is emitted to the GUI thread carrying a reference to the output from the processing performed. Next frame processing is started
3. the GUI thread takes care of updating the graphic output and handling the user interaction (buttons, sliders, etc..)

build

Builds with Qt 5.9.1 and OpenCV 3.3.0-2

Make sure openCV path in handDetection.pro is correct, then build with

    qmake handProject.pro
    make

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runtime slider settings

Sliders	Are
mirror	set to 1 to flip the image horizzontally
channel	output channel (HSV) selector
output	output modes selector - see list below
blend	blends the processed image with the original
sigma	sigma of the gaussian activation function
huePicker	sets hue level of the reference skin tone
satPicker	sets saturation level of the reference skin tone
median	median filter kerlen size
threshold	binary thresholding level

ouptut modes

0. Original image (single channel, selected through channel slider)
1. Hue distance from the reference skin tone
2. Saturaion distance from the reference skin ton
3. Post gaussian activation and median filtering
4. Binary image
5. Distance transform
6. Distance transform (fingers region only)
7. Split screen

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