Apple-2-SW/ii-vision
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Compute median frame similarity. This turns out not to be a great

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metric though, because it doesn't penalize artifacts like colour
fringing, or diffs that persist across many frames.
This commit is contained in:
kris 2019-02-27 14:10:39 +00:00
parent 4840efc41e
commit 9d4edc6c4a
1 changed files with 9 additions and 3 deletions
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12
main.py
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@ -6,9 +6,9 @@ import opcodes
import screen
import video
MAX_OUT = 1 * 1024 * 1024
MAX_OUT = 10 * 1024 * 1024
VIDEO_FPS = 30
APPLE_FPS = 10
APPLE_FPS = 15
def main():
@ -16,6 +16,8 @@ def main():
"Computer Chronicles - 06x05 - The Apple II.mp4")
bytes_out = 0
sims = []
out_frames = 0
s = video.Video(APPLE_FPS)
screen_cls = screen.HGR140Bitmap
@ -57,14 +59,18 @@ def main():
bytes_out += len(stream)
bytes_left = MAX_OUT - bytes_out
sim = screen.bitmap_similarity(im, bm)
sims.append(sim)
out_frames += 1
print("Frame %d, %d bytes, similarity = %f" % (
idx, len(stream), screen.bitmap_similarity(im, bm)))
idx, len(stream), sim))
out.write(stream[:bytes_left])
if bytes_left <= 0:
out.write(bytes(s.done()))
break
print("Median similarity: %f" % sorted(sims)[out_frames//2])
if __name__ == "__main__":