Fixes: CID1398579 Dereference before null check
Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Two modes are supported in guided filter, basic mode and fast mode.
Basic mode is the initial pushed guided filter without optimization.
Fast mode is implemented based on the basic one by sub-sampling method.
The sub-sampling ratio which can be defined by users controls the
algorithm complexity. The larger the sub-sampling ratio, the lower
the algorithm complexity.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Reviewed-by: Steven Liu <liuqi05@kuaishou.com>
duplicate ff_hex_to_data() function from avformat and rename it to
hex_to_data() as static function.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
CID: 1482090
there can return null from av_frame_get_side_data, and will use sd->data
after av_frame_get_side_data, so should check null return value.
Signed-off-by: Steven Liu <liuqi05@kuaishou.com>
Testing model is tensorflow offical model in github repo, please refer
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
to download the detect model as you need.
For example, local testing was carried on with 'ssd_mobilenet_v2_coco_2018_03_29.tar.gz', and
used one image of dog in
https://github.com/tensorflow/models/blob/master/research/object_detection/test_images/image1.jpg
Testing command is:
./ffmpeg -i image1.jpg -vf dnn_detect=dnn_backend=tensorflow:input=image_tensor:output=\
"num_detections&detection_scores&detection_classes&detection_boxes":model=ssd_mobilenet_v2_coco.pb,\
showinfo -f null -
We will see the result similar as below:
[Parsed_showinfo_1 @ 0x33e65f0] side data - detection bounding boxes:
[Parsed_showinfo_1 @ 0x33e65f0] source: ssd_mobilenet_v2_coco.pb
[Parsed_showinfo_1 @ 0x33e65f0] index: 0, region: (382, 60) -> (1005, 593), label: 18, confidence: 9834/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index: 1, region: (12, 8) -> (328, 549), label: 18, confidence: 8555/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index: 2, region: (293, 7) -> (682, 458), label: 1, confidence: 8033/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index: 3, region: (342, 0) -> (690, 325), label: 1, confidence: 5878/10000.
There are two boxes of dog with cores 94.05% & 93.45% and two boxes of person with scores 80.33% & 58.78%.
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Add examples on how to use this filter, and improve the code style.
Implement the slice-level parallelism for guided filter.
Add the basic version of guided filter.
Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Reviewed-by: Steven Liu <liuqi05@kuaishou.com>
classification is done on every detection bounding box in frame's side data,
which are the results of object detection (filter dnn_detect).
Please refer to commit log of dnn_detect for the material for detection,
and see below for classification.
- download material for classifcation:
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.bin
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.xml
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.label
- run command as:
./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,dnn_classify=dnn_backend=openvino:model=emotions-recognition-retail-0003.xml:input=data:output=prob_emotion:confidence=0.3:labels=emotions-recognition-retail-0003.label:target=face,showinfo -f null -
We'll see the detect&classify result as below:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] side data - detection bounding boxes:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] source: face-detection-adas-0001.xml, emotions-recognition-retail-0003.xml
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 0, region: (1005, 813) -> (1086, 905), label: face, confidence: 10000/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: happy, confidence: 6757/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 1, region: (888, 839) -> (967, 926), label: face, confidence: 6917/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: anger, confidence: 4320/10000.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Different function type of model requires different parameters, for
example, object detection detects lots of objects (cat/dog/...) in
the frame, and classifcation needs to know which object (cat or dog)
it is going to classify.
The current interface needs to add a new function with more parameters
to support new requirement, with this change, we can just add a new
struct (for example DNNExecClassifyParams) based on DNNExecBaseParams,
and so we can continue to use the current interface execute_model just
with params changed.
There's one task item for one function call from dnn interface,
there's one request item for one call to openvino. For classify,
one task might need multiple inference for classification on every
bounding box, so add InferenceItem.
please use tools/python/tf_sess_config.py to get the sess_config after that.
note the byte order of session config is in normal order.
bump the MICRO version for the config change.
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
Originally deprecated in 1296b1f6c0631ab79464e22d48a6a1548450b943;
scheduled again for removal in a991526832.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Signed-off-by: James Almer <jamrial@gmail.com>
avfilter_transform, avfilter_(add|sub|mult)_matrix are not part of the
public API (transform.h is not a public header), yet they are currently
exported because of their name. This commit changes this:
avfilter_transform is renamed to ff_affine_transform; the other
functions are just removed as they have never been used at all.
Found-by: Anton Khirnov <anton@khirnov.net>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Signed-off-by: James Almer <jamrial@gmail.com>
