openmv/scripts/examples/02-Image-Processing/03-Frame-Differencing/on_disk_advanced_frame_differencing.py

69 lines
2.7 KiB
Python

# This work is licensed under the MIT license.
# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# Advanced Frame Differencing Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using frame differencing with your OpenMV Cam. This
# example is advanced because it performs a background update to deal with the
# background image changing overtime.
import sensor
import os
import time
TRIGGER_THRESHOLD = 5
BG_UPDATE_FRAMES = 50 # How many frames before blending.
BG_UPDATE_BLEND = 128 # How much to blend by... ([0-256]==[0.0-1.0]).
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.RGB565
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time=2000) # Let new settings take affect.
sensor.set_auto_whitebal(False) # Turn off white balance.
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!")
triggered = False
frame_count = 0
while True:
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
frame_count += 1
if frame_count > BG_UPDATE_FRAMES and not triggered:
frame_count = 0
# Blend in new frame. We're doing 256-alpha here because we want to
# blend the new frame into the background. Not the background into the
# new frame which would be just alpha. Blend replaces each pixel by
# ((NEW*(alpha))+(OLD*(256-alpha)))/256. So, a low alpha results in
# low blending of the new image while a high alpha results in high
# blending of the new image. We need to reverse that for this update.
img.blend("temp/bg.bmp", alpha=(256 - BG_UPDATE_BLEND))
img.save("temp/bg.bmp")
# Replace the image with the "abs(NEW-OLD)" frame difference.
img.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.