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592 Commits

Author SHA1 Message Date
Michael Niedermayer
8fbcc546b8 tools/target_dem_fuzzer: Consider it an EIO when reading position wraps around 64bit 
Fixes: signed integer overflow: 9223372036854775807 + 564 cannot be represented in type 'long'
Fixes: 26494/clusterfuzz-testcase-minimized-ffmpeg_dem_VOC_fuzzer-576754158849228
Fixes: 26549/clusterfuzz-testcase-minimized-ffmpeg_dem_AVS_fuzzer-4844306424397824
FIxes: 26875/clusterfuzz-testcase-minimized-ffmpeg_dem_C93_fuzzer-5996226782429184

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-11-07 20:50:33 +01:00
Michael Niedermayer
0d395767ec tools/target_dec_fuzzer: adjust threshold for wmalossless 
Fixes: Timeout (>30sec -> 0.5sec)
Fixes: 26351/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_WMALOSSLESS_fuzzer-5191487740182528

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-25 09:49:56 +01:00
Michael Niedermayer
6e64d89410 tools/target_dem_fuzzer: Limit max blocks 
With a IO block size of 1 byte potentially megabytes are quite slow to read, thus
limit the number

Fixes: 26511/clusterfuzz-testcase-minimized-ffmpeg_dem_NUV_fuzzer-5679249073373184
Fixes: 26517/clusterfuzz-testcase-minimized-ffmpeg_dem_XMV_fuzzer-6316634501021696
Fixes: 26518/clusterfuzz-testcase-minimized-ffmpeg_dem_WSVQA_fuzzer-485568285324083
Fixes: 26525/clusterfuzz-testcase-minimized-ffmpeg_dem_MSNWC_TCP_fuzzer-5121987011411968
Fixes: 26538/clusterfuzz-testcase-minimized-ffmpeg_dem_DHAV_fuzzer-5441800598454272
Fixes: OOM
Fixes: Timeout

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-24 22:38:55 +02:00
Michael Niedermayer
c5b8f2321c tools/target_dec_fuzzer: Adjust threshold for opus 
Fixes: Timeout (12sec -> 3sec)
Fixes: 24549/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_LIBOPUS_fuzzer-6211170349088768

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-23 10:07:57 +02:00
James Almer
3e4214109a tools/target_dec_fuzzer: remove calls to avcodec_register*() 
They are no longer needed.

Signed-off-by: James Almer <jamrial@gmail.com>
 2020-10-19 12:07:49 -03:00
James Almer
a8a1a58af3 tools/target_dem_fuzzer: switch to the iterate API 
Signed-off-by: James Almer <jamrial@gmail.com>
 2020-10-19 11:54:46 -03:00
Michael Niedermayer
c0c6b68ddb tools/target_dec_fuzzer: assume that discarded audio packets produced max samples 
We do not know how many samples these produce as its not exported.
Alternatively we could export that but as long as its not we better
assume its more than 0 as otherwise the thresholds would not work

Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-18 21:27:23 +02:00
Michael Niedermayer
c062dd74d7 tools/target_dec_fuzzer: Correct maxsamples_per_frame if maxsamples has been changed 
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-18 21:27:23 +02:00
Michael Niedermayer
af701196ec tools/target_dem_fuzzer: Set format independent of c 
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-16 14:56:24 +02:00
Michael Niedermayer
d40679d89c Add support for building fuzzer tools for an individual demuxer 
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-10-12 21:01:48 +02:00
Mingyu Yin
ad2546e3b3 dnn/native: add native support for dense 
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
 2020-09-29 14:19:55 +08:00
Michael Niedermayer
5dae33bb39 tools/target_dec_fuzzer: Adjust VQA threshold 
Fixes: Timeout (169sec -> 9sec)
Fixes: 23745/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_VQA_fuzzer-5638172179693568

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-09-19 00:40:56 +02:00
Michael Niedermayer
e3af2a0756 tools:target_dem_fuzzer: Split into a fuzzer fuzzing at the protocol level and one fuzzing a fixed demuxer input 
This should improve coverage and should improve the efficiency of seed files

Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-09-13 10:40:02 +02:00
Michael Niedermayer
a12864938d tools/target_dec_fuzzer: Adjust threshold for WMV3IMAGE 
Fixes: Timeout (1131sec -> 1sec)
Fixes: 24727/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_WMV3IMAGE_fuzzer-5754167793287168

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-09-07 23:05:25 +02:00
Mingyu Yin
3477feb643 dnn_backend_native_layer_mathbinary: add floormod support 
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
 2020-08-24 09:09:11 +08:00
Michael Niedermayer
d08c3f56ec tools/target_dec_fuzzer: Adjust threshold for DST 
Fixes: Timeout (too long -> 3sec)
Fixes: 24239/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_DST_fuzzer-5189061015502848

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Reviewed-by: Peter Ross <pross@xvid.org>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-08-18 14:56:04 +02:00
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
Michael Niedermayer
4b7189848f tools/target_dec_fuzzer: Adjust threshold for AGM 
Fixes: Timeout (142sec -> 2sec)
Fixes: 24426/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_AGM_fuzzer-5639724379930624

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-08-11 14:21:56 +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
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
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
Michael Niedermayer
0b182ff66d tools/target_dec_fuzzer: Adjust threshold for lagarith 
Fixes: Timeout (3minute 49 sec -> 3sec)
Fixes: 22020/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_LAGARITH_fuzzer-5708544679870464

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-06-11 14:59:04 +02:00
Michael Niedermayer
d3747f4431 tools/target_dem_fuzzer: Use file extensions listed in input formats 
This should make it easier for the fuzzer to fuzz formats being detected only by
file extension and thus increase coverage

Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-06-11 13:49:54 +02:00
Ting Fu
22d0860c13 dnn_backend_native_layer_mathunary: add tan support 
It can be tested with the model generated with below python scripy

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.multiply(x, 0.78)
x2 = tf.tan(x1)
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>
 2020-06-11 11:10:51 +08:00
Ting Fu
88fb494f42 dnn_backend_native_layer_mathunary: add cos support 
It can be tested with the model generated with below python scripy

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.multiply(x, 1.5)
x2 = tf.cos(x1)
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>
 2020-06-11 11:10:51 +08:00
Ting Fu
0b6d3f0d83 dnn_backend_native_layer_mathunary: add sin support 
It can be tested with the model file generated with below python scripy:

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.multiply(x, 3.14)
x2 = tf.sin(x1)
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>
 2020-06-11 11:10:51 +08:00
Michael Niedermayer
3371d0611f tools/target_dec_fuzzer: enable mjpeg for tiff or tdsc 
This is needed for fuzzing tiff/tdsc and should increase coverage

Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-06-08 20:45:56 +02:00
Michael Niedermayer
3e651eeac4 tools/target_dem_fuzzer: Implement AVSEEK_SIZE 
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-06-08 12:27:18 +02:00
Ting Fu
f73cc61bf5 dnn_backend_native_layer_mathunary: add abs support 
more math unary operations will be added here

It can be tested with the model file generated with below python scripy:

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.subtract(x, 0.5)
x2 = tf.abs(x1)
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>
 2020-05-28 11:04:21 +08:00
Michael Niedermayer
6d4fdb4f5a tools/target_dec_fuzzer: Adjust max_pixels for AV_CODEC_ID_HAP 
Fixes: Timeout (170sec -> 6sec)
Fixes: 20956/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_HAP_fuzzer-5713643025203200

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-05-27 23:52:46 +02:00
Michael Niedermayer
d6824ef905 tools/target_dec_fuzzer: Reduce maxpixels for HEVC 
high resolutions with only small blocks appear to be rather
slow with the fuzzer + sanitizers.
A solution which makes this run faster is welcome.

Fixes: Timeout (did not wait -> 17sec)
Fixes: 21006/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_HEVC_fuzzer-6002552539971584

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-05-27 23:52:46 +02:00
Michael Niedermayer
05d364dccc tools/target_dec_fuzzer: Do not test AV_CODEC_FLAG2_FAST with AV_CODEC_ID_H264 
This combination skips allocating large padding which can read out of array

Fixes: 20978/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_H264_fuzzer-5746381832847360

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-05-27 23:52:46 +02:00
Anton Khirnov
ea980d4162 fate: add tests for h264 and vp9 video enc parameters export 2020-05-25 11:59:45 +02:00
Anton Khirnov
bf80725352 lavc: rename bsf.h to bsf_internal.h 
This will allow adding a public header named bsf.h
 2020-05-22 14:38:57 +02:00
Michael Niedermayer
4f54982773 tools/target_dec_fuzzer: Adjust threshold for PNG and APNG 
Fixes: Timeout (84sec -> 2sec)
Fixes: 21127/clusterfuzz-testcase-minimized-ffmpeg_AV_CODEC_ID_APNG_fuzzer-5098412367413248

Found-by: continuous fuzzing process https://github.com/google/oss-fuzz/tree/master/projects/ffmpeg
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
 2020-05-10 01:09:13 +02:00
Guo, Yejun
71e28c5422 dnn/native: add native support for minimum 
it can be tested with model file generated with below python script:
import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpg')
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.minimum(0.7, x)
x2 = tf.maximum(x1, 0.4)
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: Guo, Yejun <yejun.guo@intel.com>
 2020-05-08 15:22:27 +08:00
Josh de Kock
d817b57d36 tools: fix const specifier for AVInputFormat 
Signed-off-by: Josh de Kock <josh@itanimul.li>
 2020-04-30 10:25:32 +01:00
Guo, Yejun
8ce9d88f93 dnn/native: add native support for divide 
it can be tested with model file generated with below python script:
import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpg')
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')
z1 = 2 / x
z2 = 1 / z1
z3 = z2 / 0.25 + 0.3
z4 = z3 - x * 1.5 - 0.3
y = tf.identity(z4, 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: Guo, Yejun <yejun.guo@intel.com>
 2020-04-22 13:15:00 +08:00
Guo, Yejun
ef79408e97 dnn/native: add native support for 'mul' 
it can be tested with model file generated from above python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpg')
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')
z1 = 0.5 + 0.3 * x
z2 = z1 * 4
z3 = z2 - x - 2.0
y = tf.identity(z3, 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: Guo, Yejun <yejun.guo@intel.com>
 2020-04-22 13:14:47 +08:00
Guo, Yejun
6aa7e07e7c dnn/native: add native support for 'add' 
It can be tested with the model file generated with below python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpg')
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')
z1 = 0.039 + x
z2 = x + 0.042
z3 = z1 + z2
z4 = z3 - 0.381
z5 = z4 - x
y = tf.math.maximum(z5, 0.0, 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: Guo, Yejun <yejun.guo@intel.com>
 2020-04-22 13:14:30 +08:00
Josh de Kock
39962072a8 tools: stop using deprecated av_codec_next() 
Signed-off-by: Josh de Kock <josh@itanimul.li>
 2020-04-20 15:08:20 +00:00