Add frame differencing example scripts.

Better ones that can work in memory now.
This commit is contained in:
Kwabena W. Agyeman 2017-12-27 14:43:36 -05:00
parent 27f5110ab7
commit a6b659131d
6 changed files with 177 additions and 4 deletions

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# Advanced Frame Differencing Example
#
# This example demonstrates using frame differencing with your OpenMV Cam. This
# example is advanced because it preforms a background update to deal with the
# backgound image changing overtime.
import sensor, image, pyb, os, 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.
# 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!")
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 backgound. 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(extra_fb, alpha=(256-BG_UPDATE_BLEND))
extra_fb.replace(img)
# Replace the image with the "abs(NEW-OLD)" frame difference.
img.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.

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# In Memory Basic Frame Differencing Example
#
# This example demonstrates using frame differencing with your OpenMV Cam. It's
# called basic frame differencing because there's no background image update.
# So, as time passes the background image may change resulting in issues.
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.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.
# 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.
# Replace the image with the "abs(NEW-OLD)" frame difference.
img.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.

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# Structural Similarity (SSIM) Example
#
# This example shows off how to use the SSIM algorithm on your OpenMV Cam
# to detect differences between two images. The SSIM algorithm compares
# 8x8 blocks of pixels between two images to determine a similarity
# score between two images.
import sensor, image, pyb, os, time
# The image has likely changed if the sim.min() is lower than this.
MIN_TRIGGER_THRESHOLD = -0.4
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
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.
# 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!")
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
sim = img.get_similarity(extra_fb)
change = "- Change -" if sim.min() < MIN_TRIGGER_THRESHOLD else "- No Change -"
print(clock.fps(), change, sim)

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import sensor, image, pyb, os, 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.GRAYSCALE) # or sensor.RGB565
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.
@ -25,13 +27,15 @@ 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:
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 backgound. Not the background into the
@ -45,5 +49,13 @@ while(True):
# Replace the image with the "abs(NEW-OLD)" frame difference.
img.difference("temp/bg.bmp")
print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
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.

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@ -8,6 +8,8 @@
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.QVGA) # or sensor.QQVGA (or others)
@ -29,5 +31,13 @@ while(True):
# Replace the image with the "abs(NEW-OLD)" frame difference.
img.difference("temp/bg.bmp")
print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
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.