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