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