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As we find ourselves wanting a way to transfer frames between
HW devices (or more realistically, between APIs on the same device),
it's desirable to have a way to describe the relationship. While
we could imagine introducing a `hwtransfer` filter, there is
almost no difference from `hwupload`. The main new feature we need
is a way to specify the target device. Having a single device
for the filter chain is obviously insufficient if we're dealing
with two devices.
So let's add a way to specify the upload target device, and if none
is specified, continue with the existing behaviour.
We must also correctly preserve the sw_format on such a transfer.
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There was no consensus about separating AVExprState from AVExpr so here is a
minimal patch using the existing AVExpr to fix ticket #7528.
Signed-off-by: Marton Balint <cus@passwd.hu>
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Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
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Update spp command options docs
Reviewed-by: Gyan Doshi <ffmpeg@gyani.pro>
Suggested-by: Moritz Barsnick <barsnick@gmx.net>
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
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Width and height expressions in scale and scale2ref filters can now
reference frame index, timestamp and packet position.
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it's stranage to use option "level" in runtime change path but used
"quality" in option, add "quality" in runtime change path, it's more
intuitive and keep the "level" for compatibility.
Reviewe-by: Michael Niedermayer <michael@niedermayer.cc>
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
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libavformat/img2.h: New field export_path_metadata to
VideoDemuxData to only allow the use of the extra metadata
upon explicit user request, for security reasons.
libavformat/img2dec.c: Modify image2 demuxer to make available
two special metadata entries called lavf.image2dec.source_path
and lavf.image2dec.source_basename, which represents, respectively,
the complete path to the source image for the current frame and
the basename i.e. the file name related to the current frame.
These can then be used by filters like drawtext and others. The
metadata fields will only be available when explicitly enabled
with image2 option -export_path_metadata 1.
doc/demuxers.texi: Documented the new metadata fields available
for image2 and how to use them.
doc/filters.texi: Added an example on how to use the new metadata
fields with drawtext filter, in order to plot the input file path
to each output frame.
Usage example:
ffmpeg -f image2 -export_path_metadata 1 -pattern_type glob
-framerate 18 -i '/path/to/input/files/*.jpg'
-filter_complex drawtext="fontsize=40:fontcolor=white:
fontfile=FreeSans.ttf:borderw=2:bordercolor=black:
text='%{metadata\:lavf.image2dec.source_basename\:NA}':x=5:y=50"
output.avi
Fixes #2874.
Signed-off-by: Alexandre Heitor Schmidt <alexandre.schmidt@gmail.com>
Signed-off-by: Marton Balint <cus@passwd.hu>
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Useful for monitoring sparse data in realtime
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Currently, switching is not free of artifacts, to be resolved later.
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The following is a python script to halve the value of the gray
image. It demos how to setup and execute dnn model with python+tensorflow.
It also generates .pb file which will be used by ffmpeg.
import tensorflow as tf
import numpy as np
from skimage import color
from skimage import io
in_img = io.imread('input.jpg')
in_img = color.rgb2gray(in_img)
io.imsave('ori_gray.jpg', np.squeeze(in_img))
in_data = np.expand_dims(in_img, axis=0)
in_data = np.expand_dims(in_data, axis=3)
filter_data = np.array([0.5]).reshape(1,1,1,1).astype(np.float32)
filter = tf.Variable(filter_data)
x = tf.placeholder(tf.float32, shape=[1, None, None, 1], name='dnn_in')
y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID', name='dnn_out')
sess=tf.Session()
sess.run(tf.global_variables_initializer())
graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
tf.train.write_graph(graph_def, '.', 'halve_gray_float.pb', as_text=False)
print("halve_gray_float.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate halve_gray_float.model\n")
output = sess.run(y, feed_dict={x: in_data})
output = output * 255.0
output = output.astype(np.uint8)
io.imsave("out.jpg", np.squeeze(output))
To do the same thing with ffmpeg:
- generate halve_gray_float.pb with the above script
- generate halve_gray_float.model with tools/python/convert.py
- try with following commands
./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png
./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow out.tf.png
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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do not request AVFrame's format in vf_ddn_processing with 'fmt',
but to add another filter for the format.
command examples:
./ffmpeg -i input.jpg -vf format=bgr24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png
./ffmpeg -i input.jpg -vf format=rgb24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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