Commit Graph

8477 Commits

Author SHA1 Message Date
Guo, Yejun
3c05c8a15f dnn_backend_tf.c: fix build issue for tensorflow backend
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
2020-08-14 08:59:39 +08:00
Andreas Rheinhardt
cfc6552032 avfilter/formats: Avoid allocations when merging channel layouts
When one merges two AVFilterChannelLayouts structs, there is no need to
allocate a new one. Instead one can reuse one of the two given ones.
If one does this, one also doesn't need to update the references of the
AVFilterChannelLayouts that is reused. Therefore this commit reuses the
structure with the higher refcount.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-13 16:28:49 +02:00
Andreas Rheinhardt
4147f63d63 avfilter/formats: Fix heap-buffer overflow when merging channel layouts
The channel layouts accepted by ff_merge_channel_layouts() are of two
types: Ordinary channel layouts and generic channel layouts. These are
layouts that match all layouts with a certain number of channels.
Therefore parsing these channel layouts is not done in one go; instead
first the intersection of the ordinary layouts of the first input
list of channel layouts with the ordinary layouts of the second list is
determined, then the intersection of the ordinary layouts of the first
one and the generic layouts of the second one etc. In order to mark the
ordinary channel layouts that have already been matched as used they are
zeroed. The inner loop that does this is as follows:

for (j = 0; j < b->nb_channel_layouts; j++) {
    if (a->channel_layouts[i] == b->channel_layouts[j]) {
        ret->channel_layouts[ret_nb++] = a->channel_layouts[i];
        a->channel_layouts[i] = b->channel_layouts[j] = 0;
    }
}

(Here ret->channel_layouts is the array containing the intersection of
the two input arrays.)

Yet the problem with this code is that after a match has been found, the
loop continues the search with the new value a->channel_layouts[i].
The intention of zeroing these elements was to make sure that elements
already paired at this stage are ignored later. And while they are indeed
ignored when pairing ordinary and generic channel layouts later, it has
the exact opposite effect when pairing ordinary channel layouts.

To see this consider the channel layouts A B C D E and E D C B A. In the
first round, A and A will be paired and added to ret->channel_layouts.
In the second round, the input arrays are 0 B C D E and E D C B 0.
At first B and B will be matched and zeroed, but after doing so matching
continues, but this time it will search for 0, which will match with the
last entry of the second array. ret->channel_layouts now contains A B 0.
In the third round, C 0 0 will be added to ret->channel_layouts etc.
This gives a quadratic amount of elements, yet the amount of elements
allocated for said array is only the sum of the sizes of a and b.

This issue can e.g. be reproduced by
ffmpeg -f lavfi -i anullsrc=cl=7.1 \
-af 'aformat=cl=mono|stereo|2.1|3.0|4.0,aformat=cl=4.0|3.0|2.1|stereo|mono' \
-f null -

The fix is easy: break out of the inner loop after having found a match.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-13 16:28:26 +02:00
Andreas Rheinhardt
c4c10feaa8 Revert "lavfi/avfiltergraph: add check before free the format"
This reverts commit f156f4ab23.

The checks added by said commit are nonsense because they did not help
in case ff_merge_samplerates() or ff_merge_formats() returned NULL
while freeing one of its arguments: Said freeing does not change
the local variables of can_merge_formats().

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 21:27:52 +02:00
Andreas Rheinhardt
9d1bf9cffe avfilter/formats: Simplify cleanup for ff_merge_* functions
Now that the output's refs-array is only allocated once, it is NULL in
any error case and therefore needn't be freed at all; Instead an
av_assert1() has been added to guarantee it to be NULL.

Furthermore, it is unnecessary to av_freep(&ptr) when ptr == NULL.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 21:26:45 +02:00
Andreas Rheinhardt
195a25a7ab avfilter/formats: Leave lists' ownership unchanged upon merge failure
ff_merge_formats(), ff_merge_samplerates() and ff_merge_channel_layouts()
share common semantics: If merging succeeds, a non-NULL pointer is
returned and both input lists (of type AVFilterFormats resp.
AVFilterChannelLayouts) are to be treated as if they had been freed;
the owners of the input parameters (if any) become owners of the
returned list. If merging does not succeed, NULL is returned and both
input lists are supposed to be unchanged.

The problem is that the functions did not abide by these semantics:
In case of reallocation failure, it is possible for these functions
to return NULL after having already freed one of the two input list.
This happens because sometimes the refs-array of the destined output
gets reallocated twice to its final size and if the second of these
reallocations fails, the first of the two inputs has already been freed
and its refs updated to point to the destined output which in this case
will be freed immediately so that all of the already updated pointers
are now dangling. This leads to use-after-frees and memory corruptions
lateron (when these owners get cleaned up, the lists they own get
unreferenced). Should the input lists don't have owners at all, the
caller (namely can_merge_formats() in avfiltergraph.c) thinks that both
the input lists are unchanged and need to be freed, leading to a double
free.

The solution to this is simple: Don't reallocate twice; do it just once.
This also saves a reallocation.

This commit fixes the issue behind Coverity issue #1452636. It might
also make Coverity realize that the issue has been fixed.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 21:22:53 +02:00
Andreas Rheinhardt
ae5026c905 avfilter/formats: Schedule avfilter_make_format64_list() for removal
Despite its name, this function is not part of the public API, as
formats.h, the header containing its declaration, is a private header.
The formats API was once public API, but that changed long ago
(b74a1da49d, the commit scheduling it to
become private, is from 2012). That avfilter_make_format64_list() was
forgotten is probably a result of the confusion resulting from the
libav-ffmpeg split.

Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 21:10:59 +02:00
Andreas Rheinhardt
2e0cf4de01 avfilter/avf_showcqt: Mark arrays as static const
Reviewed-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 21:08:16 +02:00
Andreas Rheinhardt
efbe58ceb6 avfilter/formats: Remove ff_make_formatu64_list()
It is unused since 8cbb055760 and it
actually coincides with avfilter_make_format64_list().

Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-12 20:47:59 +02:00
Guo, Yejun
0a51abe8ab dnn: add backend options when load the model
different backend might need different options for a better performance,
so, add the parameter into dnn interface, as a preparation.

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
2020-08-12 15:43:40 +08:00
Mingyu Yin
4ed6bca4ae dnn_backend_native_layer_mathunary: add round support
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-08-12 10:30:46 +08:00
Paul B Mahol
4dbb75c437 avfilter/vf_xfade: check that fps between inputs are valid 2020-08-11 10:57:04 +02:00
Ting Fu
91efc41a69 dnn/native: add native support for avg_pool
Not support pooling strides in channel dimension yet.

Signed-off-by: Ting Fu <ting.fu@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-08-10 16:37:39 +08:00
Mingyu Yin
fab00b0ae0 dnn_backend_native_layer_mathunary: add floor support
It can be tested with the model generated with below python script:

import tensorflow as tf
import os
import numpy as np
import imageio
from tensorflow.python.framework import graph_util
name = 'floor'

pb_file_path = os.getcwd()
if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)):
    os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))

with tf.Session(graph=tf.Graph()) as sess:
    in_img = imageio.imread('detection.jpg')
    in_img = in_img.astype(np.float32)
    in_data = in_img[np.newaxis, :]
    input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
    y_ = tf.math.floor(input_x*255)/255
    y = tf.identity(y_, name='dnn_out')
    sess.run(tf.global_variables_initializer())
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])

    with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f:
        f.write(constant_graph.SerializeToString())

    print("model.pb generated, please in ffmpeg path use\n \n \
    python tools/python/convert.py {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))

    output = sess.run(y, feed_dict={ input_x: in_data})
    imageio.imsave("out.jpg", np.squeeze(output))

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n  \
    or\n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n  \
    or \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))

Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
2020-08-07 10:34:22 +08:00
Mingyu Yin
9fbdd5454b dnn_backend_native_layer_mathunary: add ceil support
It can be tested with the model generated with below python script:

import tensorflow as tf
import os
import numpy as np
import imageio
from tensorflow.python.framework import graph_util
name = 'ceil'

pb_file_path = os.getcwd()
if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)):
    os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))

with tf.Session(graph=tf.Graph()) as sess:
    in_img = imageio.imread('detection.jpg')
    in_img = in_img.astype(np.float32)
    in_data = in_img[np.newaxis, :]
    input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
    y = tf.math.ceil( input_x, name='dnn_out')
    sess.run(tf.global_variables_initializer())
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])

    with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f:
        f.write(constant_graph.SerializeToString())

    print("model.pb generated, please in ffmpeg path use\n \n \
    python tools/python/convert.py ceil_savemodel/model.pb --outdir=ceil_savemodel/ \n \n \
    to generate model.model\n")

    output = sess.run(y, feed_dict={ input_x: in_data})
    imageio.imsave("out.jpg", np.squeeze(output))

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 ceil_savemodel/tensorflow_out.md5\n \n \
    to generate output result of tensorflow model\n")

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 ceil_savemodel/native_out.md5\n \n \
    to generate output result of native model\n")

Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-08-04 19:56:54 +08:00
Andreas Rheinhardt
8129c32e48 avcodec, avfilter, avformat: Remove redundant avpriv_align_put_bits
flush_put_bits() already fills the bitstream with zeroes, so it is
unnecessary to align the bitstream before.

Reviewed-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
2020-08-01 18:56:32 +02:00
Paul B Mahol
17f6bd6e58 avfilter/vf_xfade: add hblur transition 2020-07-21 23:29:55 +02:00
Paul B Mahol
c4c989c7ca avfilter/vf_yaepblur: fix naming of filter class 2020-07-20 16:12:56 +02:00
Paul B Mahol
fa8345cf05 avfilter/vf_bilateral: simplify code a little
Make alpha_ calculation faster.
2020-07-18 10:22:12 +02:00
Paul B Mahol
3a37aa597f avfilter/avf_showwavespic: add filter mode 2020-07-17 14:26:48 +02:00
Paul B Mahol
d363afb30e avfilter/vf_tinterlace: fix mergex2, first frame is always considered odd 2020-07-17 13:53:55 +02:00
Paul B Mahol
24fea4d09b avfilter/vf_tinterlace: use frame counter from lavfi
Remove internal counter.
2020-07-17 13:53:55 +02:00
leozhang
fe591393cd avfilter/vf_bilateral: remove useless memcpy
Signed-off-by: leozhang <leozhang@qiyi.com>
2020-07-17 13:53:22 +02:00
Paul B Mahol
241cdded0f avfilter/vf_bilateral: stop using sigmaS as percent of width/height 2020-07-17 13:53:22 +02:00
James Almer
320694ff84 x86/vf_blend: fix warnings about trailing empty parameters
Finishes fixing ticket #8771

Signed-off-by: James Almer <jamrial@gmail.com>
2020-07-12 11:30:23 -03:00
Jun Zhao
04037e2966 lavfi/setpts: fix setpts/asetpts option dump error
fix the command ffmpeg -h filter=setpts/asetpts both dump the expr
option with "FVA" flags.

Reviewed-by: Paul B Mahol <onemda@gmail.com>
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
2020-07-12 08:11:42 +08:00
Ben Clayton
4dab04622a libavfilter/glslang: Remove unused header
The <glslang/Include/revision.h> include was not used, and revision.h has
been removed from glslang master.
See: https://github.com/KhronosGroup/glslang/pull/2277
2020-07-11 13:01:33 +01:00
Paul B Mahol
6f84e92172 avfilter/vf_chromanr: move thres calculation to filter_frame() 2020-07-10 23:09:19 +02:00
Limin Wang
f9277cd796 avfilter/vf_showinfo: add dump_s12m_timecode() helper function
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
2020-07-08 23:14:04 +08:00
Limin Wang
3ede8acba6 avfilter/vf_showinfo: check sd->size before reference the sd->data
Or it'll cause null pointer dereference if size < sizeof(uint32_t), also
in case tc[0] > 3, the code will report error directly.

Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
2020-07-08 23:12:48 +08:00
Paul B Mahol
6cdddb773f avfilter: add chromanr video filter 2020-07-08 15:23:43 +02:00
Valery Kot
855d51bf48 avfilter/vf_edgedetect: properly implement double_threshold()
Important part of this algorithm is the double threshold step: pixels
above "high" threshold being kept, pixels below "low" threshold dropped,
pixels in between (weak edges) are kept if they are neighboring "high"
pixels.

The weak edge check uses a neighboring context and should not be applied
on the plane's border. The condition was incorrect and has been fixed in
the commit.

Signed-off-by: Andriy Gelman <andriy.gelman@gmail.com>
Reviewed-by: Andriy Gelman <andriy.gelman@gmail.com>
2020-07-06 23:20:53 -04:00
Reimar Döffinger
584f396132 dnn_backend_native: Add overflow check for length calculation.
We should not silently allocate an incorrect sized buffer.
Fixes trac issue #8718.

Signed-off-by: Reimar Döffinger <Reimar.Doeffinger@gmx.de>
Reviewed-by: Michael Niedermayer <michael@niedermayer.cc>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-07-06 20:22:30 +08:00
Ting Fu
c0cdeea0ee dnn_backend_native_layer_mathunary: add atanh support
It can be tested with the model generated with below python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpeg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]

x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')

please uncomment the part you want to test

x_sinh_1 = tf.sinh(x)
x_out = tf.divide(x_sinh_1, 1.176) # sinh(1.0)

x_cosh_1 = tf.cosh(x)
x_out = tf.divide(x_cosh_1, 1.55) # cosh(1.0)

x_tanh_1 = tf.tanh(x)
x__out = tf.divide(x_tanh_1, 0.77) # tanh(1.0)

x_asinh_1 = tf.asinh(x)
x_out = tf.divide(x_asinh_1, 0.89) # asinh(1.0/1.1)

x_acosh_1 = tf.add(x, 1.1)
x_acosh_2 = tf.acosh(x_acosh_1) # accept (1, inf)
x_out = tf.divide(x_acosh_2, 1.4) # acosh(2.1)

x_atanh_1 = tf.divide(x, 1.1)
x_atanh_2 = tf.atanh(x_atanh_1) # accept (-1, 1)
x_out = tf.divide(x_atanh_2, 1.55) # atanhh(1.0/1.1)

y = tf.identity(x_out, name='dnn_out') #please only preserve the x_out you want to test

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, '.', 'image_process.pb', as_text=False)

print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")

output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))

Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Ting Fu
cd2e3a864d dnn_backend_native_layer_mathunary: add acosh support
Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Ting Fu
9d14b38d9d dnn_backend_native_layer_mathunary: add asinh support
Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Ting Fu
ea71e731f4 dnn_backend_native_layer_mathunary: add tanh support
Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Ting Fu
62fc7e3035 dnn_backend_native_layer_mathunary: add cosh support
Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Ting Fu
91b4037101 dnn_backend_native_layer_mathunary: add sinh support
Signed-off-by: Ting Fu <ting.fu@intel.com>
2020-07-06 12:45:14 +08:00
Limin Wang
49054fe94c FATE: fix colorbalance fate test failed on x86_32
floating point precision will cause rgb*max generate different value on
x86_32 and x86_64. have pass fate test on x86_32 and x86_64 by using
lrintf to get the nearest integral value for rgb * max before av_clip.

Reviewed-by:   Paul B Mahol <onemda@gmail.com>
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
2020-07-02 21:12:37 +08:00
Guo, Yejun
9bcf2aa477 vf_dnn_processing.c: add dnn backend openvino
We can try with the srcnn model from sr filter.
1) get srcnn.pb model file, see filter sr
2) convert srcnn.pb into openvino model with command:
python mo_tf.py --input_model srcnn.pb --data_type=FP32 --input_shape [1,960,1440,1] --keep_shape_ops

See the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer
We'll see srcnn.xml and srcnn.bin at current path, copy them to the
directory where ffmpeg is.

I have also uploaded the model files at https://github.com/guoyejun/dnn_processing/tree/master/models

3) run with openvino backend:
ffmpeg -i input.jpg -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.jpg
(The input.jpg resolution is 720*480)

Also copy the logs on my skylake machine (4 cpus) locally with openvino backend
and tensorflow backend. just for your information.

$ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=tensorflow:model=srcnn.pb:input=x:output=y -y srcnn.tf.mp4
…
frame=  343 fps=2.1 q=31.0 Lsize=    2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.0706x
video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.517637%
[aac @ 0x2f5db80] Qavg: 454.353
real    2m46.781s
user    9m48.590s
sys     0m55.290s

$ time ./ffmpeg -i 480p.mp4 -vf format=yuv420p,scale=w=iw*2:h=ih*2,dnn_processing=dnn_backend=openvino:model=srcnn.xml:input=x:output=srcnn/Maximum -y srcnn.ov.mp4
…
frame=  343 fps=4.0 q=31.0 Lsize=    2172kB time=00:00:11.76 bitrate=1511.9kbits/s speed=0.137x
video:1973kB audio:187kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.517640%
[aac @ 0x31a9040] Qavg: 454.353
real    1m25.882s
user    5m27.004s
sys     0m0.640s

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2020-07-02 09:56:55 +08:00
Guo, Yejun
ff37ebaf30 dnn: add openvino as one of dnn backend
OpenVINO is a Deep Learning Deployment Toolkit at
https://github.com/openvinotoolkit/openvino, it supports CPU, GPU
and heterogeneous plugins to accelerate deep learning inferencing.

Please refer to https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md
to build openvino (c library is built at the same time). Please add
option -DENABLE_MKL_DNN=ON for cmake to enable CPU path. The header
files and libraries are installed to /usr/local/deployment_tools/inference_engine/
with default options on my system.

To build FFmpeg with openvion, take my system as an example, run with:
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/deployment_tools/inference_engine/lib/intel64/:/usr/local/deployment_tools/inference_engine/external/tbb/lib/
$ ../ffmpeg/configure --enable-libopenvino --extra-cflags=-I/usr/local/deployment_tools/inference_engine/include/ --extra-ldflags=-L/usr/local/deployment_tools/inference_engine/lib/intel64
$ make

Here are the features provided by OpenVINO inference engine:
- support more DNN model formats
It supports TensorFlow, Caffe, ONNX, MXNet and Kaldi by converting them
into OpenVINO format with a python script. And torth model
can be first converted into ONNX and then to OpenVINO format.

see the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer/mo.py
which also does some optimization at model level.

- optimize at inference stage
It optimizes for X86 CPUs with SSE, AVX etc.

It also optimizes based on OpenCL for Intel GPUs.
(only Intel GPU supported becuase Intel OpenCL extension is used for optimization)

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2020-07-02 09:36:34 +08:00
Paul B Mahol
cca982ee01 avfilter/vf_colorbalance: remove wrong addition 2020-06-29 14:52:37 +02:00
Limin Wang
12c42c709e avfilter/vf_showinfo: add a \n for end of ERROR and WARNNING log
Note for info level, one extra \n will be print after the log.

Reviewed-by:   Paul B Mahol <onemda@gmail.com>
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
2020-06-28 09:00:28 +08:00
exwm
32d6fe23b6 avfilter/zoompan: add in_time variable
Currently, the zoompan filter exposes a 'time' variable (missing from docs) for use in
the 'zoom', 'x', and 'y' expressions. This variable is perhaps better named
'out_time' as it represents the timestamp in seconds of each output frame
produced by zoompan. This patch adds aliases 'out_time' and 'ot' for 'time'.

This patch also adds an 'in_time' (alias 'it') variable that provides access
to the timestamp in seconds of each input frame to the zoompan filter.
This helps to design zoompan filters that depend on the input video timestamps.
For example, it makes it easy to zoom in instantly for only some portion of a video.
Both the 'out_time' and 'in_time' variables have been added in the documentation
for zoompan.

Example usage of 'in_time' in the zoompan filter to zoom in 2x for the
first second of the input video and 1x for the rest:
    zoompan=z='if(between(in_time,0,1),2,1):d=1'

V2: Fix zoompan filter documentation stating that the time variable
would be NAN if the input timestamp is unknown.

V3: Add 'it' alias for 'in_time. Add 'out_time' and 'ot' aliases for 'time'.
Minor corrections to zoompan docs.

Signed-off-by: exwm <thighsman@protonmail.com>
2020-06-25 10:27:07 +02:00
Ting Fu
13f5613e68 dnn_backend_native_layer_mathunary: add atan support
It can be tested with the model generated with below python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpeg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]

x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
x1 = tf.atan(x)
x2 = tf.divide(x1, 3.1416/4) # pi/4
y = tf.identity(x2, 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, '.', 'image_process.pb', as_text=False)

print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")

output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))

Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
2020-06-25 08:41:50 +08:00
Ting Fu
461485feac dnn_backend_native_layer_mathunary: add acos support
It can be tested with the model generated with below python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpeg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]

x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
x1 = tf.acos(x)
x2 = tf.divide(x1, 3.1416/2) # pi/2
y = tf.identity(x2, 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, '.', 'image_process.pb', as_text=False)

print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")

output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))

Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
2020-06-25 08:41:50 +08:00
Ting Fu
486c0c419d dnn_backend_native_layer_mathunary: add asin support
It can be tested with the model generated with below python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpeg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]

x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
x1 = tf.asin(x)
x2 = tf.divide(x1, 3.1416/2) # pi/2
y = tf.identity(x2, 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, '.', 'image_process.pb', as_text=False)

print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")

output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))

Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
2020-06-25 08:41:50 +08:00
Paul B Mahol
ce297b44d3 avfilter/vf_v360: do not ignore return value of allocate_plane() 2020-06-23 21:55:40 +02:00
Paul B Mahol
00a5df71ad avfilter/vf_v360: add orthographic projection support 2020-06-23 16:00:02 +02:00