# 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].