pandora/pandora/item/timelines.py
2012-05-17 11:38:59 +02:00

257 lines
10 KiB
Python

# -*- coding: utf-8 -*-
# vi:si:et:sw=4:sts=4:ts=4
from __future__ import division, with_statement
import math
import os
import re
import Image
from ox.utils import json
def join_tiles(source_paths, target_path):
'''
This is an implementation of a join_tiles function for new-style timelines.
Timelines of files will be read from source_paths, the timeline of the item will
be written to target_path.
'''
def divide(num, by):
# divide(100, 3) -> [33, 33, 34]
arr = []
div = int(num / by)
mod = num % by
for i in range(int(by)):
arr.append(div + (i > by - 1 - mod))
return arr
def get_file_info(file_name):
for mode in modes:
if re.match('^timeline' + mode + '64p\d+\.jpg', file_name):
return {
'file': file_name,
'mode': mode,
'index': int(file_name[11 + len(mode):-4])
}
return None
def save_and_open(data):
# whenever a large tile is done or needed,
# this function saves the previous large tile
# (if any) and opens the next one (if any).
# in between, whenever required, small tiles
# are opened, rendered and saved, and the
# large full tile is being generated.
# 'keyframes' are only rendered in large size,
# 'keyframeswide' only resized to small size.
image_mode = 'L' if mode == 'audio' else 'RGB'
small_mode = 'keyframes' if mode == 'keyframeswide' else mode
large_tile_i = int(target_w / large_tile_w)
# save previous large tile
if large_tile_i > 0:
large_tile_i -= 1
if mode != 'keyframeswide':
image_file = '%stimeline%s%dp%d.jpg' % (
target_path, mode, large_tile_h, large_tile_i
)
data['target_images']['large'].save(image_file)
#print image_file
if mode != 'keyframes':
# open small tile
small_tile_i = int(large_tile_i / 60)
small_tile_x = (large_tile_i % 60) * 60
if small_tile_x == 0:
if small_tile_i < small_tile_n - 1:
w = small_tile_w
else:
w = small_tile_last_w
data['target_images']['small'] = Image.new(image_mode, (w, small_tile_h))
# paste large tile into small tile
w = 60 if large_tile_i < large_tile_n - 1 else small_tile_last_w % 60
data['target_images']['large'] = data['target_images']['large'].resize(
(w, small_tile_h), Image.ANTIALIAS
)
data['target_images']['small'].paste(
data['target_images']['large'], (small_tile_x, 0)
)
# save small tile
if small_tile_x == small_tile_w - 60 or large_tile_i == large_tile_n - 1:
image_file = '%stimeline%s%dp%d.jpg' % (
target_path, small_mode, small_tile_h, small_tile_i
)
data['target_images']['small'].save(image_file)
print image_file
if mode == 'antialias':
# render full tile
resized = data['target_images']['large'].resize((
data['full_tile_widths'][0], large_tile_h
), Image.ANTIALIAS)
data['target_images']['full'].paste(resized, (data['full_tile_offset'], 0))
data['full_tile_offset'] += data['full_tile_widths'][0]
data['full_tile_widths'] = data['full_tile_widths'][1:]
large_tile_i += 1
# open next large tile
if large_tile_i < large_tile_n:
w = large_tile_w if large_tile_i < large_tile_n - 1 else large_tile_last_w
data['target_images']['large'] = Image.new(image_mode, (w, large_tile_h))
data = {}
fps = 25
large_tile_w, large_tile_h = 1500, 64
small_tile_w, small_tile_h = 3600, 16
full_tile_w = 1920
modes = ['antialias', 'slitscan', 'keyframes', 'keyframeswide', 'audio']
source_files = {}
for mode in modes:
source_files[mode] = []
# read files
durations = [0] * len(source_paths)
frame_n = 0
for i, path in enumerate(source_paths):
file_info = map(get_file_info, os.listdir(path))
file_info = filter(lambda x: x != None, file_info)
for info in sorted(file_info, key=lambda x: x['index']):
mode = info['mode']
source_files[mode].append(path + info['file'])
if mode == modes[0]:
width = Image.open(source_files[mode][-1]).size[0]
durations[i] += width / fps
frame_n += width
large_tile_n = int(math.ceil(frame_n / large_tile_w))
large_tile_last_w = frame_n % large_tile_w
small_tile_n = int(math.ceil(frame_n / fps / small_tile_w))
small_tile_last_w = int(math.ceil(frame_n / fps)) % small_tile_w
# open full timeline
if large_tile_n == 1:
data['full_tile_widths'] = [large_tile_last_w]
else:
w = full_tile_w
n = large_tile_n
if large_tile_last_w < large_tile_w:
factor = full_tile_w / frame_n
last_w = int(round(large_tile_last_w * factor))
w -= last_w
n -= 1
data['full_tile_widths'] = divide(w, n)
if large_tile_last_w < large_tile_w:
data['full_tile_widths'].append(last_w)
data['full_tile_offset'] = 0
full_tile_image = Image.new('RGB', (full_tile_w, large_tile_h))
# main loop
data['target_images'] = {'large': None, 'small': None, 'full': full_tile_image}
for mode in modes:
target_w = 0
for source_file in source_files[mode]:
source_image = Image.open(source_file)
source_w = source_image.size[0]
target_x = target_w % large_tile_w
if target_x == 0:
save_and_open(data)
data['target_images']['large'].paste(source_image, (target_x, 0))
target_w += source_w
if target_x + source_w > large_tile_w:
# target tile overflows into next source tile
save_and_open(data)
target_x -= large_tile_w
data['target_images']['large'].paste(source_image, (target_x, 0))
target_w += source_w
save_and_open(data)
# save full timelines
image_file = '%stimelineantialias%dp.jpg' % (target_path, large_tile_h)
data['target_images']['full'].save(image_file)
#print image_file
image_file = '%stimelineantialias%dp.jpg' % (target_path, small_tile_h)
data['target_images']['full'].resize(
(full_tile_w, small_tile_h), Image.ANTIALIAS
).save(image_file)
#print image_file
# join cuts
cuts = []
offset = 0
for i, path in enumerate(source_paths):
with open(os.path.join(path, 'cuts.json'), 'r') as f:
path_cuts = json.load(f)
if i > 0:
cuts.append(offset)
for cut in path_cuts:
cuts.append(offset + cut)
offset += durations[i]
with open(os.path.join(target_path, 'cuts.json'), 'w') as f:
# avoid float rounding artefacts
f.write('[' + ', '.join(map(lambda x: '%.2f' % x, cuts)) + ']')
def split_tiles(path, paths, durations):
def is_timeline_file(file_name):
return file_name.startswith('timeline') and file_name.endswith('.png')
file_names = filter(is_timeline_file, os.listdir(path))
tiles = {}
for file_name in file_names:
mode = re.split('\d+', file_name[8:])[0]
print file_name, mode
split = re.split('[a-z]+', file_name[8 + len(mode):-4])
height, index = map(lambda x: int(x) if len(x) else -1, split)
if not mode in tiles:
tiles[mode] = {}
if not height in tiles[mode]:
tiles[mode][height] = 0
if index + 1 > tiles[mode][height]:
tiles[mode][height] = index + 1
print tiles
# for each mode
for mode in tiles:
image_mode = 'L' if mode == 'audio' else 'RGB'
# and for each size of that mode
for i, height in enumerate(tiles[mode]):
tile_width = 1500 if i == 0 else 3600
px_per_sec = 25 if i == 0 else 1
target_images = []
target_data = []
# and for each split item
for item_index, duration in enumerate(durations):
tile_index = 0
px = int(math.ceil(duration * px_per_sec))
# create a flat list of all target images
# (and store the split item and tile index)
while px:
width = tile_width if px > tile_width else px
target_images.append(
Image.new(image_mode, (width, height))
)
target_data.append(
{'item': item_index, 'tile': tile_index}
)
tile_index += 1
px -= width
target_index = 0
offset = 0
# for each source tile
for source_index in range(tiles[mode][height]):
source_image = Image.open('%stimeline%s%dp%d.png' % (
path, mode, height, source_index
))
source_width = source_image.size[0]
target_width = target_images[target_index].size[0]
target_images[target_index].paste(source_image, (offset, 0))
# paste it into as many target tiles as needed
while source_width + offset > target_width:
offset -= target_width
target_index += 1
target_width = target_images[target_index].size[0]
target_images[target_index].paste(source_image, (offset, 0))
for i, target_image in enumerate(target_images):
file_name = '%stimeline%s%dp%d' % (
paths[target_data[i]['item']], mode, height, target_data[i]['tile']
)
# target_image.save(file_name)
print file_name, target_image.size