Difference between revisions of "Sirius"
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== Progress == | == Progress == | ||
=== Assignment 1 === | === Assignment 1 === | ||
| − | == Vehicle detection and tracking (Rosario A. Cali)== | + | |
| + | ---- | ||
| + | |||
| + | === Vehicle detection and tracking (Rosario A. Cali)=== | ||
The source code for this project can be found alongside its references and test run results [https://github.com/RosarioAleCali/DPS915_Final_Project/tree/master/vehicle_detection here].<br> | The source code for this project can be found alongside its references and test run results [https://github.com/RosarioAleCali/DPS915_Final_Project/tree/master/vehicle_detection here].<br> | ||
The program uses [https://www.ffmpeg.org/ FFmpeg] to extract frames from a video and then each frame is analyzed to detect if any cars are present in the frame or not.<br> | The program uses [https://www.ffmpeg.org/ FFmpeg] to extract frames from a video and then each frame is analyzed to detect if any cars are present in the frame or not.<br> | ||
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For me the most important thing is to solve the problem regardless of the tools used and I think that reimplementing everything from scratch using OpenCV and CUDA is a viable solution. | For me the most important thing is to solve the problem regardless of the tools used and I think that reimplementing everything from scratch using OpenCV and CUDA is a viable solution. | ||
| − | == Boxblur on an image using opencv C++ Library == | + | === Boxblur on an image using opencv C++ Library === |
=== Assignment 2 === | === Assignment 2 === | ||
| + | |||
| + | ---- | ||
| + | |||
=== Assignment 3 === | === Assignment 3 === | ||
| + | |||
| + | ---- | ||
Revision as of 14:39, 4 March 2018
Contents
Sirius
Team Members
Progress
Assignment 1
Vehicle detection and tracking (Rosario A. Cali)
The source code for this project can be found alongside its references and test run results here.
The program uses FFmpeg to extract frames from a video and then each frame is analyzed to detect if any cars are present in the frame or not.
The analysis on each frame is done by using the Dlib Library that performs a Convolutional Neural Network based vehicle detector on each frame.
When a car is found, a rectangle will be drawn around the car and a label, identifying the front or the rear of a car, will be attached to it.
When running the application, long processing times were expected but the actual results were really bad - a lot worst than what we were expecting.
Only one test was fully run using a 10 seconds long video. We extracted the video at 25fps resulting with 251 frames with a resolution of 854 x 480 pixels.
The elapsed time for the application, using a 10 seconds long video, was of 21.02 minutes.
Here's an extract from the Flat Profile:
Flat profile:
Each sample counts as 0.01 seconds.
% cumulative self self total
time seconds seconds calls ms/call ms/call name
89.19 1124.65 1124.65 1757 640.09 640.09 dlib::enable_if_c<(dlib::ma::matrix_is_vector<dlib::matrix_op<dlib::op_pointer_to_mat<float> >, void>::value==(false))&&(dlib::ma::matrix_is_vector<dlib::matrix_op<dlib::op_trans<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > >, void>::value==(false)), void>::type dlib::default_matrix_multiply<dlib::assignable_ptr_matrix<float>, dlib::matrix_op<dlib::op_pointer_to_mat<float> >, dlib::matrix_op<dlib::op_trans<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > > >(dlib::assignable_ptr_matrix<float>&, dlib::matrix_op<dlib::op_pointer_to_mat<float> > const&, dlib::matrix_op<dlib::op_trans<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > > const&)
10.15 1252.68 128.03 1693 75.62 75.62 dlib::cpu::img2col(dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout>&, dlib::tensor const&, long, long, long, long, long, long, long)
0.16 1254.75 2.07 8218 0.25 0.25 dlib::enable_if_c<(dlib::is_grayscale_image<dlib::const_sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > >::value&&dlib::is_grayscale_image<dlib::sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > >::value)&&dlib::images_have_same_pixel_types<dlib::const_sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> >, dlib::sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > >::value, void>::type dlib::resize_image<dlib::const_sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> >, dlib::sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > >(dlib::const_sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> > const&, dlib::sub_image_proxy<dlib::matrix<float, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> >&, dlib::interpolate_bilinear)
0.16 1256.77 2.02 1506 1.34 1.34 dlib::cpu::affine_transform_conv(dlib::tensor&, dlib::tensor const&, dlib::tensor const&, dlib::tensor const&)
0.12 1258.24 1.47 1506 0.98 0.98 dlib::tt::relu(dlib::tensor&, dlib::tensor const&)
0.08 1259.22 0.99 1757 0.56 0.56 dlib::cpu::add(float, dlib::tensor&, float, dlib::tensor const&)
0.05 1259.88 0.66 844 0.78 1.03 dlib::image_display::draw(dlib::canvas const&) const
0.02 1260.17 0.29 detect_vehicles()
0.02 1260.39 0.22 251 0.88 0.88 void dlib::png_loader::get_image<dlib::matrix<dlib::rgb_pixel, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> >(dlib::matrix<dlib::rgb_pixel, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout>&) const
0.02 1260.58 0.19 347936511 0.00 0.00 dlib::enable_if_c<dlib::pixel_traits<dlib::canvas::pixel>::rgb&&dlib::pixel_traits<dlib::rgb_alpha_pixel>::rgb_alpha, void>::type dlib::assign_pixel_helpers::assign<dlib::canvas::pixel, dlib::rgb_alpha_pixel>(dlib::canvas::pixel&, dlib::rgb_alpha_pixel const&)
0.01 1260.73 0.15 251 0.60 0.60 void dlib::input_rgb_image_pyramid<dlib::pyramid_down<6u> >::to_tensor<dlib::matrix<dlib::rgb_pixel, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> const*>(dlib::matrix<dlib::rgb_pixel, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> const*, dlib::matrix<dlib::rgb_pixel, 0l, 0l, dlib::memory_manager_stateless_kernel_1<char>, dlib::row_major_layout> const*, dlib::resizable_tensor&) const
0.01 1260.80 0.07 844 0.08 1.11 dlib::drawable_window::paint(dlib::canvas const&)The full Flat profile, together with the Call Graph, can be found on the link provided above.
As we can tell from the profile, the application takes a really long time to process and it is kind of hard to tell how to optimize the code since the Dlib library is what is taking up most of the time.
There must be a way to optimize this application, but as of today (March 4, 2018) I am not sure which path to take.
For me the most important thing is to solve the problem regardless of the tools used and I think that reimplementing everything from scratch using OpenCV and CUDA is a viable solution.
