openmv/usr/examples/20-Frame-Differencing/on_disk_shadow_removal.py
Kwabena W. Agyeman fc9c416a21 Frame Differencing with Shadow Removal
Add in support for shadow removal from the current image using a shadow
free background image. Test results show the algorithm works similar to
max() while still keeping dark objects around. The preformance impact of
the algorithm is not too high. An in memory example can achieve 30 FPS.
2018-01-21 16:41:04 -05:00

50 lines
2.2 KiB
Python

# In Memory Shadow Removal w/ Frame Differencing Example
#
# Note: You will need an SD card to run this 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.
if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory
print("About to save background image...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
sensor.snapshot().save("temp/bg.bmp")
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("temp/bg.bmp").difference("temp/bg.bmp")
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.