# Robust 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. # # We're using the robust=True argument for get_regression() in this script which # computes the linear regression using a much more robust algorithm... but potentially # much slower. The robust algorithm runs in O(N^2) time on the image. So, YOU NEED # TO LIMIT THE NUMBER OF PIXELS the robust algorithm works on or it can actually # take seconds for the algorithm to give you a result... THRESHOLD VERY CAREFULLY! 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.QQQVGA) # 80x60 (4,800 pixels) - O(N^2) max = 2,3040,000. sensor.skip_frames(time = 2000) # WARNING: If you use QQVGA it may take seconds clock = time.clock() # to process a frame sometimes. 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 means something # different for the robust linear regression. In general, the larger the value the # better... line = img.get_regression([(255,255) if BINARY_VISIBLE else THRESHOLD], robust = True) 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].