ffmpeg-python/examples/tensorflow_stream.py at master · allansp84/ffmpeg-python · GitHub

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'''Example streaming ffmpeg numpy processing.

Demonstrates using ffmpeg to decode video input, process the frames in

python, and then encode video output using ffmpeg.

This example uses two ffmpeg processes - one to decode the input video

and one to encode an output video - while the raw frame processing is

done in python with numpy.

At a high level, the signal graph looks like this:

(input video) -> [ffmpeg process 1] -> [python] -> [ffmpeg process 2] -> (output video)

This example reads/writes video files on the local filesystem, but the

same pattern can be used for other kinds of input/output (e.g. webcam,

rtmp, etc.).

The simplest processing example simply darkens each frame by

multiplying the frame's numpy array by a constant value; see

``process_frame_simple``.

A more sophisticated example processes each frame with tensorflow using

the "deep dream" tensorflow tutorial; activate this mode by calling

the script with the optional `--dream` argument. (Make sure tensorflow

is installed before running)

'''

from __future__ import print_function

import argparse

import ffmpeg

import logging

import numpy as np

import os

import subprocess

import zipfile

parser = argparse.ArgumentParser(description='Example streaming ffmpeg numpy processing')

parser.add_argument('in_filename', help='Input filename')

parser.add_argument('out_filename', help='Output filename')

parser.add_argument(

'--dream', action='store_true', help='Use DeepDream frame processing (requires tensorflow)')

logger = logging.getLogger(__name__)

logging.basicConfig(level=logging.INFO)

def get_video_size(filename):

logger.info('Getting video size for {!r}'.format(filename))

probe = ffmpeg.probe(filename)

video_info = next(s for s in probe['streams'] if s['codec_type'] == 'video')

width = int(video_info['width'])

height = int(video_info['height'])

return width, height

def start_ffmpeg_process1(in_filename):

logger.info('Starting ffmpeg process1')

args = (

ffmpeg

.input(in_filename)

.output('pipe:', format='rawvideo', pix_fmt='rgb24')

.compile()

)

return subprocess.Popen(args, stdout=subprocess.PIPE)

def start_ffmpeg_process2(out_filename, width, height):

logger.info('Starting ffmpeg process2')

args = (

ffmpeg

.input('pipe:', format='rawvideo', pix_fmt='rgb24', s='{}x{}'.format(width, height))

.output(out_filename, pix_fmt='yuv420p')

.overwrite_output()

.compile()

)

return subprocess.Popen(args, stdin=subprocess.PIPE)

def read_frame(process1, width, height):

logger.debug('Reading frame')

# Note: RGB24 == 3 bytes per pixel.

frame_size = width * height * 3

in_bytes = process1.stdout.read(frame_size)

if len(in_bytes) == 0:

frame = None

else:

assert len(in_bytes) == frame_size

frame = (

.frombuffer(in_bytes, np.uint8)

.reshape([height, width, 3])

)

return frame

def process_frame_simple(frame):

'''Simple processing example: darken frame.'''

return frame * 0.3

def write_frame(process2, frame):

logger.debug('Writing frame')

process2.stdin.write(

frame

.astype(np.uint8)

.tobytes()

)

def run(in_filename, out_filename, process_frame):

width, height = get_video_size(in_filename)

process1 = start_ffmpeg_process1(in_filename)

process2 = start_ffmpeg_process2(out_filename, width, height)

while True:

in_frame = read_frame(process1, width, height)

if in_frame is None:

logger.info('End of input stream')

break

logger.debug('Processing frame')

out_frame = process_frame(in_frame)

write_frame(process2, out_frame)

logger.info('Waiting for ffmpeg process1')

process1.wait()

logger.info('Waiting for ffmpeg process2')

process2.stdin.close()

process2.wait()

logger.info('Done')

class DeepDream(object):

'''DeepDream implementation, adapted from official tensorflow deepdream tutorial:

https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/deepdream

Credit: Alexander Mordvintsev

'''

_DOWNLOAD_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'

_ZIP_FILENAME = 'deepdream_model.zip'

_MODEL_FILENAME = 'tensorflow_inception_graph.pb'

@staticmethod

def _download_model():

logger.info('Downloading deepdream model...')

try:

from urllib.request import urlretrieve # python 3

except ImportError:

from urllib import urlretrieve # python 2

urlretrieve(DeepDream._DOWNLOAD_URL, DeepDream._ZIP_FILENAME)

logger.info('Extracting deepdream model...')

zipfile.ZipFile(DeepDream._ZIP_FILENAME, 'r').extractall('.')

@staticmethod

def _tffunc(*argtypes):

'''Helper that transforms TF-graph generating function into a regular one.

See `_resize` function below.

'''

placeholders = list(map(tf.placeholder, argtypes))

def wrap(f):

out = f(*placeholders)

def wrapper(*args, **kw):

return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))

return wrapper

return wrap

@staticmethod

def _base_resize(img, size):

'''Helper function that uses TF to resize an image'''

img = tf.expand_dims(img, 0)

return tf.image.resize_bilinear(img, size)[0,:,:,:]

def __init__(self):

if not os.path.exists(DeepDream._MODEL_FILENAME):

self._download_model()

self._graph = tf.Graph()

self._session = tf.InteractiveSession(graph=self._graph)

self._resize = self._tffunc(np.float32, np.int32)(self._base_resize)

with tf.gfile.FastGFile(DeepDream._MODEL_FILENAME, 'rb') as f:

graph_def = tf.GraphDef()

graph_def.ParseFromString(f.read())

self._t_input = tf.placeholder(np.float32, name='input') # define the input tensor

imagenet_mean = 117.0

t_preprocessed = tf.expand_dims(self._t_input-imagenet_mean, 0)

tf.import_graph_def(graph_def, {'input':t_preprocessed})

self.t_obj = self.T('mixed4d_3x3_bottleneck_pre_relu')[:,:,:,139]

#self.t_obj = tf.square(self.T('mixed4c'))

def T(self, layer_name):

'''Helper for getting layer output tensor'''

return self._graph.get_tensor_by_name('import/%s:0'%layer_name)

def _calc_grad_tiled(self, img, t_grad, tile_size=512):

'''Compute the value of tensor t_grad over the image in a tiled way.

Random shifts are applied to the image to blur tile boundaries over

multiple iterations.'''

sz = tile_size

h, w = img.shape[:2]

sx, sy = np.random.randint(sz, size=2)

img_shift = np.roll(np.roll(img, sx, 1), sy, 0)

grad = np.zeros_like(img)

for y in range(0, max(h-sz//2, sz),sz):

for x in range(0, max(w-sz//2, sz),sz):

sub = img_shift[y:y+sz,x:x+sz]

g = self._session.run(t_grad, {self._t_input:sub})

grad[y:y+sz,x:x+sz] = g

return np.roll(np.roll(grad, -sx, 1), -sy, 0)

def process_frame(self, frame, iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):

t_score = tf.reduce_mean(self.t_obj) # defining the optimization objective

t_grad = tf.gradients(t_score, self._t_input)[0] # behold the power of automatic differentiation!

# split the image into a number of octaves

img = frame

octaves = []

for i in range(octave_n-1):

hw = img.shape[:2]

lo = self._resize(img, np.int32(np.float32(hw)/octave_scale))

hi = img-self._resize(lo, hw)

img = lo

octaves.append(hi)

# generate details octave by octave

for octave in range(octave_n):

if octave>0:

hi = octaves[-octave]

img = self._resize(img, hi.shape[:2])+hi

for i in range(iter_n):

g = self._calc_grad_tiled(img, t_grad)

img += g*(step / (np.abs(g).mean()+1e-7))

#print('.',end = ' ')

return img

if __name__ == '__main__':

args = parser.parse_args()

if args.dream:

import tensorflow as tf

process_frame = DeepDream().process_frame

else:

process_frame = process_frame_simple

run(args.in_filename, args.out_filename, process_frame)

ffmpeg-python/examples/tensorflow_stream.py at master · allansp84/ffmpeg-python · GitHub

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