ii-vision/video.py

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import functools
import heapq
import random
import os
import threading
import queue
import subprocess
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from typing import List, Iterator, Tuple, Iterable
from PIL import Image
import numpy as np
import skvideo.io
import weighted_levenshtein
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import opcodes
import screen
def hamming_weight(n):
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"""Compute hamming weight of 8-bit int"""
n = (n & 0x55) + ((n & 0xAA) >> 1)
n = (n & 0x33) + ((n & 0xCC) >> 2)
n = (n & 0x0F) + ((n & 0xF0) >> 4)
return n
# TODO: what about increasing transposition cost? Might be better to have
# any pixel at the right place even if the wrong colour?
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substitute_costs = np.ones((128, 128), dtype=np.float64)
# Penalty for turning on/off a black bit
for c in "01GVWOB":
substitute_costs[(ord('K'), ord(c))] = 5
substitute_costs[(ord(c), ord('K'))] = 5
# Penalty for changing colour
for c in "01GVWOB":
for d in "01GVWOB":
substitute_costs[(ord(c), ord(d))] = 1
substitute_costs[(ord(d), ord(c))] = 1
insert_costs = np.ones(128, dtype=np.float64) * 1000
delete_costs = np.ones(128, dtype=np.float64) * 1000
@functools.lru_cache(None)
def edit_weight(a: int, b: int, is_odd_offset: bool):
a_pixels = byte_to_colour_string(a, is_odd_offset)
b_pixels = byte_to_colour_string(b, is_odd_offset)
dist = weighted_levenshtein.dam_lev(
a_pixels, b_pixels,
insert_costs=insert_costs,
delete_costs=delete_costs,
substitute_costs=substitute_costs,
)
return np.int64(dist)
@functools.lru_cache(None)
def byte_to_colour_string(b: int, is_odd_offset: bool) -> str:
pixels = []
idx = 0
if is_odd_offset:
pixels.append("01"[b & 0x01])
idx += 1
# K = black
# G = green
# V = violet
# W = white
palettes = (
(
"K", # 0x00
"V", # 0x01
"G", # 0x10
"W" # 0x11
), (
"K", # 0x00
"B", # 0x01
"O", # 0x10
"W" # 0x11
)
)
palette = palettes[(b & 0x80) != 0]
for _ in range(3):
pixel = palette[(b >> idx) & 0b11]
pixels.append(pixel)
idx += 2
if not is_odd_offset:
pixels.append("01"[b & 0x40 != 0])
idx += 1
return "".join(pixels)
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class Video:
"""Apple II screen memory map encoding a bitmapped frame."""
CLOCK_SPEED = 1024 * 1024
def __init__(
self,
filename: str):
self.filename = filename # type: str
self._reader = skvideo.io.FFmpegReader(filename)
# Compute frame rate from input video
data = skvideo.io.ffprobe(self.filename)['video']
rate_data = data['@r_frame_rate'].split("/") # e.g. 12000/1001
self._input_frame_rate = float(rate_data[0]) / float(rate_data[1])
self.cycles_per_frame = 1024. * 1024 / self._input_frame_rate
self.frame_number = 0
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# Initialize empty
self.memory_map = screen.MemoryMap(
screen_page=1) # type: screen.MemoryMap
# Accumulates pending edit weights across frames
self.update_priority = np.zeros((32, 256), dtype=np.int64)
def tick(self, cycles) -> bool:
# print(cycles, self.cycles_per_frame, self.cycles_per_frame *
# self.frame_number)
if cycles > (self.cycles_per_frame * self.frame_number):
self.frame_number += 1
return True
return False
def _frame_grabber(self):
for frame_array in self._reader.nextFrame():
yield Image.fromarray(frame_array)
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def frames(self) -> Iterator[screen.MemoryMap]:
"""Encode frame to HGR using bmp2dhr.
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We do the encoding in a background thread to parallelize.
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"""
frame_dir = self.filename.split(".")[0]
try:
os.mkdir(frame_dir)
except FileExistsError:
pass
q = queue.Queue(maxsize=10)
def worker():
for _idx, _frame in enumerate(self._frame_grabber()):
outfile = "%s/%08dC.BIN" % (frame_dir, _idx)
bmpfile = "%s/%08d.bmp" % (frame_dir, _idx)
try:
os.stat(outfile)
except FileNotFoundError:
_frame = _frame.resize((280, 192))
_frame.save(bmpfile)
subprocess.call(
["/usr/local/bin/bmp2dhr", bmpfile, "hgr", "D9"])
os.remove(bmpfile)
_frame = np.fromfile(outfile, dtype=np.uint8)
q.put(_frame)
q.put(None)
t = threading.Thread(target=worker)
t.start()
while True:
frame = q.get()
if frame is None:
break
yield screen.FlatMemoryMap(
screen_page=1, data=frame).to_memory_map()
q.task_done()
t.join()
def encode_frame(
self, target: screen.MemoryMap
) -> Iterator[opcodes.Opcode]:
"""Update to match content of frame within provided budget."""
print("Similarity %f" % (self.update_priority.mean()))
yield from self._index_changes(self.memory_map, target)
def _diff_weights(
self,
source: screen.MemoryMap,
target: screen.MemoryMap
):
diff_weights = np.zeros((32, 256), dtype=np.int64)
it = np.nditer(
source.page_offset ^ target.page_offset, flags=['multi_index'])
while not it.finished:
# If no diff, don't need to bother
if not it[0]:
it.iternext()
continue
diff_weights[it.multi_index] = edit_weight(
source.page_offset[it.multi_index],
target.page_offset[it.multi_index],
it.multi_index[1] % 2 == 1
)
it.iternext()
return diff_weights
def _heapify_priorities(self) -> List:
priorities = []
it = np.nditer(self.update_priority, flags=['multi_index'])
while not it.finished:
priority = it[0]
if not priority:
it.iternext()
continue
page, offset = it.multi_index
# Don't use deterministic order for page, offset
nonce = random.random()
heapq.heappush(priorities, (-priority, nonce, page, offset))
it.iternext()
return priorities
@functools.lru_cache(None)
def _compute_delta(self, content, target, old, is_odd):
return edit_weight(content, target, is_odd, error=True) - old
def _compute_error(self, page, content, target, old_error):
offsets = []
old_error_page = old_error[page]
tpo = target.page_offset[page]
page_priorities = [(-p, random.random(), o) for o, p in enumerate(
self.update_priority[page]) if p]
heapq.heapify(page_priorities)
# Iterate in descending priority order and take first 3 offsets with
# negative delta
while page_priorities:
_, _, o = heapq.heappop(page_priorities)
# If we store content at this offset, what is the difference
# between this edit distance and the ideal target edit distance?
delta = self._compute_delta(
content, tpo[o], o % 2 == 1, old_error_page[o])
# Getting further away from goal, no thanks!
if delta >= 0:
continue
#
# # print("Offset %d prio %d: %d -> %d = %d" % (
# # o, p, content,
# # target.page_offset[page, o],
# # delta
# # ))
offsets.append(o)
if len(offsets) == 3:
break
return offsets
def _index_changes(
self,
source: screen.MemoryMap,
target: screen.MemoryMap
) -> Iterator[Tuple[int, int, int, int, int]]:
"""Transform encoded screen to sequence of change tuples.
Change tuple is (update_priority, page, offset, content, run_length)
"""
diff_weights = self._diff_weights(source, target)
# Clear any update priority entries that have resolved themselves
# with new frame
self.update_priority[diff_weights == 0] = 0
self.update_priority += diff_weights
priorities = self._heapify_priorities()
while priorities:
_, _, page, offset = heapq.heappop(priorities)
# Check whether we've already cleared this diff while processing
# an earlier opcode
if self.update_priority[page, offset] == 0:
continue
offsets = [offset]
content = target.page_offset[page, offset]
# print("Priority %d: page %d offset %d content %d" % (
# priority, page, offset, content))
# Clear priority for the offset we're emitting
self.update_priority[page, offset] = 0
self.memory_map.page_offset[page, offset] = content
# Need to find 3 more offsets to fill this opcode
for o in self._compute_error(
page,
content,
target,
diff_weights
):
offsets.append(o)
# Clear priority for the offset we're emitting
self.update_priority[page, o] = 0
self.memory_map.page_offset[page, o] = content
# Pad to 4 if we didn't find enough
for _ in range(len(offsets), 4):
offsets.append(offsets[0])
# print("Page %d, content %d: offsets %s" % (page+32, content,
# offsets))
yield (page + 32, content, offsets)
# If we run out of things to do, pad forever
content = target.page_offset[(0, 0)]
while True:
yield (32, content, [0, 0, 0, 0])