openmv/scripts/examples/05-Feature-Detection/linear_regression_fast.py
2023-07-05 19:03:37 +02:00

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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...
import sensor
import time
THRESHOLD = (0, 100) # Grayscale threshold for dark things.
BINARY_VISIBLE = True # Binary pass first to see what linear regression is running on.
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].