Hand gestures are analyzed by the HandGestureRecognition class, especially by its recognize method. This class starts off with a few parameter initializations, which will be explained and used later:
class HandGestureRecognition:
def __init__(self):
# maximum depth deviation for a pixel to be considered # within range
self.abs_depth_dev = 14
# cut-off angle (deg): everything below this is a convexity
# point that belongs to two extended fingers
self.thresh_deg = 80.0The recognize method is where the real magic takes place. This method handles the entire process flow, from the raw grayscale image all the way to a recognized hand gesture. It implements the following procedure:
- It extracts the user's hand region by analyzing the depth map (
img_gray) and returning a hand region mask (segment):def recognize(self, img_gray): segment = self._segment_arm(img_gray) - It performs contour analysis on the hand region mask (
segment). Then, it returns the largest contour area found in the image (contours) and any convexity defects (defects):[contours, defects] = self._find_hull_defects(segment)
- Based on the contours found and the convexity defects, it detects the number of extended fingers (
num_fingers) in the image. Then, it annotates the output image (img_draw) with contours, defect points, and the number of extended fingers:img_draw = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB) [num_fingers, img_draw] = self._detect_num_fingers(contours, defects, img_draw) - It returns the estimated number of extended fingers (
num_fingers), as well as the annotated output image (img_draw):return (num_fingers, img_draw)
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