openmv/usr/examples/09-Feature-Detection/linear_regression_fast.py
Kwabena W. Agyeman cd4ad0dff3 Add linear regression
For easy line following mainly. In non-robust mode the line is computed
using least squares. In robust mode the line is computed using the
Theil-Sen median of slopes method. We do not use the Siegel Median of
Medians operation because it costs more CPU time... but, more
importantly there's no way to improve the centroid estimate so even if
the slope is more robust the line will be drawn in the wrong place.
2017-06-28 01:16:15 -04:00

44 lines
1.8 KiB
Python

# Fast Linear Regression Example
#
# This example shows off how to use the get_regression() method on your OpenMV Cam
# to get the linear regression of a ROI. Using this method you can easily build
# a robot which can track lines which all point in the same general direction
# but are not actually connected. Use find_blobs() on lines that are nicely
# connected for better filtering options and control.
#
# This is called the fast linear regression because we use the least-squares
# method to fit the line. However, this method is NOT GOOD FOR ANY images that
# have a lot (or really any) outlier points which corrupt the line fit...
THRESHOLD = (0, 100) # Grayscale threshold for dark things...
BINARY_VISIBLE = True # Does binary first so you can see what the linear regression
# is being run on... might lower FPS though.
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot().binary([THRESHOLD]) if BINARY_VISIBLE else sensor.snapshot()
# Returns a line object similar to line objects returned by find_lines() and
# find_line_segments(). You have x1(), y1(), x2(), y2(), length(),
# theta() (rotation in degrees), rho(), and magnitude().
#
# magnitude() represents how well the linear regression worked. It goes from
# (0, INF] where 0 is returned for a circle. The more linear the
# scene is the higher the magnitude.
line = img.get_regression([(255,255) if BINARY_VISIBLE else THRESHOLD])
if (line): img.draw_line(line.line(), color = 127)
print("FPS %f, mag = %s" % (clock.fps(), str(line.magnitude()) if (line) else "N/A"))
# About negative rho values:
#
# A [theta+0:-rho] tuple is the same as [theta+180:+rho].