# 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... ([0-255]==[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: frame_count = 0 # Blend in new frame. We're doing 255-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*(255-alpha)))/255. 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=(255 - 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.