openmv/scripts/examples/02-Image-Processing/03-Frame-Differencing/in_memory_shadow_removal.py
2023-07-05 19:03:37 +02:00

55 lines
2.6 KiB
Python

# 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
import 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.