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54 lines
2.6 KiB
Python
54 lines
2.6 KiB
Python
# In Memory Shadow Removal w/ Frame Differencing Example
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#
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# This example demonstrates using frame differencing with your OpenMV Cam using
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# shadow removal to help reduce the affects of cast shadows in your scene.
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import sensor, image, pyb, os, time
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TRIGGER_THRESHOLD = 5
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sensor.reset() # Initialize the camera sensor.
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sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
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sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)
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if sensor.get_id() == sensor.OV7725: # Reduce sensor PLL from 6x to 4x.
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sensor.__write_reg(0x0D, (sensor.__read_reg(0x0D) & 0x3F) | 0x40)
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sensor.skip_frames(time = 2000) # Let new settings take affect.
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sensor.set_auto_whitebal(False) # Turn off white balance.
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sensor.set_auto_gain(False) # Turn this off too.
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clock = time.clock() # Tracks FPS.
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# Take from the main frame buffer's RAM to allocate a second frame buffer.
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# There's a lot more RAM in the frame buffer than in the MicroPython heap.
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# However, after doing this you have a lot less RAM for some algorithms...
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# So, be aware that it's a lot easier to get out of RAM issues now. However,
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# frame differencing doesn't use a lot of the extra space in the frame buffer.
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# But, things like AprilTags do and won't work if you do this...
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extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.RGB565)
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print("About to save background image...")
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sensor.skip_frames(time = 2000) # Give the user time to get ready.
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extra_fb.replace(sensor.snapshot())
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print("Saved background image - Now frame differencing!")
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while(True):
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clock.tick() # Track elapsed milliseconds between snapshots().
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img = sensor.snapshot() # Take a picture and return the image.
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# Note that for shadow removal to work the background image must be
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# shadow free and have the same lighting as the latest image. Unlike max()
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# shadow removal won't remove all dark objects unless they were shadows...
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# Replace the image with the "abs(NEW-OLD)" frame difference.
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img.remove_shadows(extra_fb).difference(extra_fb)
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hist = img.get_histogram()
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# This code below works by comparing the 99th percentile value (e.g. the
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# non-outlier max value against the 90th percentile value (e.g. a non-max
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# value. The difference between the two values will grow as the difference
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# image seems more pixels change.
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diff = hist.get_percentile(0.99).l_value() - hist.get_percentile(0.90).l_value()
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triggered = diff > TRIGGER_THRESHOLD
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print(clock.fps(), triggered) # Note: Your OpenMV Cam runs about half as fast while
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# connected to your computer. The FPS should increase once disconnected.
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