def parse(self, file):
"""
:param file: path to a video file.
:return: self
"""
container = av.open(file)
step = int(self.delay) # every 100 frames of video
warn("Video delay: ", str(step))
currentframe = 0
for packet in container.demux():
for frame in packet.decode():
currentframe += 1
if currentframe >= step:
self.images.append(misc.fromimage(frame.to_image()))
currentframe = 0
return self
def parse(self, file):
"""
:param file: path to a video file.
:return: self
"""
container = av.open(file)
step = int(self.delay) # every 100 frames of video
warn("Video delay: ", str(step))
currentframe = 0
for packet in container.demux():
for frame in packet.decode():
currentframe += 1
if currentframe >= step:
self.images.append(misc.fromimage(frame.to_image()))
currentframe = 0
return self
def process(self, canvasimg, images, img_areas, allocs):
"""
:param canvasimg: packed canvas img
:param images: frames of animation
:param img_areas: changes
:param allocs: position of changes
:return:
"""
imgpath = os.path.join(self.path, "packed.png")
# save packed image
misc.imsave(imgpath, canvasimg)
# try pngquant
# Don't completely fail if we don't have pngcrush
if os.system("pngquant --ext=.png --force %s" % imgpath) != 0:
console.warn(
"pngquant not found",
'Try installing pngquant for better results (http://pngquant.org/)'
)
# Generate JSON to represent the data
timeline = []
for i in range(len(images)):
src_rects = img_areas[i]
dst_rects = allocs[i]
blitlist = []
for j in range(len(src_rects)):
assert isinstance(j, object)
metadata = computemeta(src_rects[j], dst_rects[j])
blitlist.append(metadata)
timeline.append(blitlist)
metadata = {}
metadata['size'] = {
"width": timeline[0][0][2],
"height": timeline[0][0][3]
}
metadata['frames'] = timeline
filemeta = open(os.path.join(self.path, "timeline.json"), 'wb')
timeline = json.dumps(metadata, filemeta)
filemeta.write(bytes(timeline, 'UTF-8'))
filemeta.close()
filemeta = open(os.path.join(self.path, 'example/example.timeline.js'), 'wb')
filemeta.write(bytes("_timeline = ", 'UTF-8'))
timeline = json.dumps(metadata, filemeta)
filemeta.write(bytes(timeline, 'UTF-8'))
filemeta.close()
shutil.copyfile(
os.path.join(
os.path.split(os.path.abspath(os.path.dirname(__file__)))[0],
'./player/example.html'
),
os.path.join(self.path, 'example/example.html')
)
shutil.copyfile(
os.path.join(
os.path.split(os.path.abspath(os.path.dirname(__file__)))[0],
'../../player/dist/jquery.html5anim.min.js'
),
os.path.join(self.path, 'example/jquery.html5anim.min.js')
)
console.success('Animation generated.', 'Check out output path: %s' % self.path)
def generate(self, export):
"""
Generate from self.images[] animation and send it to export
:param export: Export class
:return:
"""
zero = self.images[0] - self.images[0]
pairs = zip([zero] + self.images[:-1], self.images)
diffs = [sign((b - a).max(2)) for a, b in pairs]
if self.talk:
console.warn("Looking for diffs")
# Find different objects for each frame
img_areas = [me.find_objects(me.label(d)[0]) for d in diffs]
# Simplify areas
img_areas = [simplify(x, SIMPLIFICATION_TOLERANCE) for x in img_areas]
if self.talk:
console.warn("Areas found and simplified")
ih, iw, _ = shape(self.images[0])
# Generate a packed image
allocator = Allocator2D(MAX_PACKED_HEIGHT, iw)
packed = zeros((MAX_PACKED_HEIGHT, iw, 3), dtype=uint8)
# Sort the rects to be packed by largest size first, to improve the packing
rects_by_size = []
for i in range(len(self.images)):
src_rects = img_areas[i]
for j in range(len(src_rects)):
rects_by_size.append((slice_tuple_size(src_rects[j]), i, j))
rects_by_size.sort(reverse=True)
if self.talk:
console.warn("Areas sorted by size")
allocs = [[None] * len(src_rects) for src_rects in img_areas]
console.warn(
"Packing",
"num rects: {0} num frames: {1}".format(len(rects_by_size),
len(self.images)))
t0 = time()
counter = 0
for size, i, j in rects_by_size:
src = self.images[i]
src_rects = img_areas[i]
a, b = src_rects[j]
sx, sy = b.start, a.start
w, h = b.stop - b.start, a.stop - a.start
# finding matching rectangle is very expensive and its disabled for now.
# existing = find_matching_rect(
# allocator.bitmap,
# allocator.num_used_rows,
# packed,
# src,
# sx,
# sy,
# w,
# h
# )
# if existing:
# dy, dx = existing
# allocs[i][j] = (dy, dx)
# else:
counter += 1
if self.talk:
console.warn(
"Allocation area ({0}/{1}): ".format(
counter, len(rects_by_size)),
"{0}x{1} ({2})".format(i, j, size))
dy, dx = allocator.allocate(w, h)
allocs[i][j] = (dy, dx)
packed[dy:dy + h, dx:dx + w] = src[sy:sy + h, sx:sx + w]
console.success("Packing finished", "took: %s" % (time() - t0))
packed = packed[0:allocator.num_used_rows]
export.process(packed, self.images, img_areas, allocs)
def generate(self, export):
"""
Generate from self.images[] animation and send it to export
:param export: Export class
:return:
"""
zero = self.images[0] - self.images[0]
pairs = zip([zero] + self.images[:-1], self.images)
diffs = [sign((b - a).max(2)) for a, b in pairs]
if self.talk:
console.warn("Looking for diffs")
# Find different objects for each frame
img_areas = [me.find_objects(me.label(d)[0]) for d in diffs]
# Simplify areas
img_areas = [simplify(x, SIMPLIFICATION_TOLERANCE) for x in img_areas]
if self.talk:
console.warn("Areas found and simplified")
ih, iw, _ = shape(self.images[0])
# Generate a packed image
allocator = Allocator2D(MAX_PACKED_HEIGHT, iw)
packed = zeros((MAX_PACKED_HEIGHT, iw, 3), dtype=uint8)
# Sort the rects to be packed by largest size first, to improve the packing
rects_by_size = []
for i in range(len(self.images)):
src_rects = img_areas[i]
for j in range(len(src_rects)):
rects_by_size.append((slice_tuple_size(src_rects[j]), i, j))
rects_by_size.sort(reverse=True)
if self.talk:
console.warn("Areas sorted by size")
allocs = [[None] * len(src_rects) for src_rects in img_areas]
console.warn(
"Packing",
"num rects: {0} num frames: {1}".format(
len(rects_by_size),
len(self.images)
)
)
t0 = time()
counter = 0
for size, i, j in rects_by_size:
src = self.images[i]
src_rects = img_areas[i]
a, b = src_rects[j]
sx, sy = b.start, a.start
w, h = b.stop - b.start, a.stop - a.start
# finding matching rectangle is very expensive and its disabled for now.
# existing = find_matching_rect(
# allocator.bitmap,
# allocator.num_used_rows,
# packed,
# src,
# sx,
# sy,
# w,
# h
# )
# if existing:
# dy, dx = existing
# allocs[i][j] = (dy, dx)
# else:
counter += 1
if self.talk:
console.warn("Allocation area ({0}/{1}): ".format(
counter,
len(rects_by_size)
), "{0}x{1} ({2})".format(i, j, size))
dy, dx = allocator.allocate(w, h)
allocs[i][j] = (dy, dx)
packed[dy:dy+h, dx:dx+w] = src[sy:sy+h, sx:sx+w]
console.success("Packing finished", "took: %s" % (time() - t0))
packed = packed[0:allocator.num_used_rows]
export.process(packed, self.images, img_areas, allocs)