├── CMakeLists.txt
├── LICENSE
├── README.md
├── docker
├── .env
├── docker-compose.development.yml
├── docker-compose.yml
├── dockerfile
└── ros_entrypoint.sh
├── include
├── sgm_gpu
│ ├── configuration.h
│ ├── cost_aggregation.h
│ ├── costs.h
│ ├── hamming_cost.h
│ ├── left_right_consistency.h
│ ├── median_filter.h
│ ├── sgm_gpu.h
│ └── util.h
└── sgm_gpu_node.h
├── launch
└── test.launch
├── package.xml
├── src
├── costs.cu
├── hamming_cost.cu
├── left_right_consistency.cu
├── median_filter.cu
├── sgm_gpu.cu
├── sgm_gpu_node.cpp
└── sgm_gpu_node_main.cpp
└── test_input.bag
/CMakeLists.txt:
--------------------------------------------------------------------------------
1 | # CMake>=3.8 supports CUDA C++ as intrinsically supported language
2 | cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
3 | project(sgm_gpu CUDA CXX)
4 |
5 | find_package(catkin REQUIRED COMPONENTS
6 | cv_bridge
7 | image_geometry
8 | image_transport
9 | message_filters
10 | roscpp
11 | sensor_msgs
12 | stereo_msgs
13 | )
14 | find_package(OpenCV REQUIRED)
15 |
16 | set(CUDA_NVCC_FLAGS
17 | ${CUDA_NVCC_FLAGS};
18 | -O3
19 | -gencode=arch=compute_30,code=sm_30
20 | -gencode=arch=compute_35,code=sm_35
21 | -gencode=arch=compute_50,code=sm_50
22 | -gencode=arch=compute_52,code=sm_52
23 | -gencode=arch=compute_61,code=sm_61
24 | )
25 |
26 | catkin_package(
27 | CATKIN_DEPENDS cv_bridge image_geometry roscpp sensor_msgs stereo_msgs
28 | DEPENDS OpenCV
29 | INCLUDE_DIRS include
30 | LIBRARIES ${PROJECT_NAME}
31 | )
32 |
33 | include_directories(
34 | include
35 | ${catkin_INCLUDE_DIRS}
36 | )
37 |
38 | # Build lib${PROJECT_NAME}
39 | add_library(${PROJECT_NAME}
40 | src/sgm_gpu.cu
41 | src/costs.cu
42 | src/hamming_cost.cu
43 | src/left_right_consistency.cu
44 | src/median_filter.cu
45 | )
46 | add_dependencies(${PROJECT_NAME} ${catkin_EXPORTED_TARGETS})
47 | target_link_libraries(${PROJECT_NAME}
48 | ${catkin_LIBRARIES}
49 | ${OpenCV_LIBS}
50 | )
51 |
52 | # Build ${PROJECT_NAME}_node
53 | add_executable(${PROJECT_NAME}_node
54 | src/${PROJECT_NAME}_node.cpp
55 | src/${PROJECT_NAME}_node_main.cpp
56 | )
57 | add_dependencies(${PROJECT_NAME}_node
58 | ${PROJECT_NAME}
59 | ${catkin_EXPORTED_TARGETS}
60 | )
61 | target_link_libraries(${PROJECT_NAME}_node
62 | ${PROJECT_NAME}
63 | ${catkin_LIBRARIES}
64 | )
65 |
66 | # Install lib${PROJECT_NAME}
67 | install(TARGETS ${PROJECT_NAME}
68 | ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
69 | LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
70 | RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
71 | )
72 | install(DIRECTORY include/${PROJECT_NAME}/
73 | DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
74 | )
75 |
76 | # Install ${PROJECT_NAME}_node
77 | install(TARGETS ${PROJECT_NAME}_node
78 | RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
79 | )
80 |
81 | # Install launch, bag and document
82 | install(FILES test_input.bag README.md
83 | DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
84 | )
85 | install(DIRECTORY launch/
86 | DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
87 | )
88 |
89 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # A ROS package of Semi-Global Matching on the GPU
2 |
3 | `sgm_gpu` is a ROS package which contains a nodelet based on [Semi-Global Matching on the GPU by D. Hernandez-Juarez](https://github.com/dhernandez0/sgm) .
4 |
5 | ## Prerequisite
6 |
7 | ### Without Docker
8 |
9 | - [ROS Melodic Morenia](http://wiki.ros.org/melodic)
10 | - [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit)
11 |
12 | ### With Docker
13 |
14 | - Docker
15 | - Docker Compose
16 | - NVIDIA Container Toolkit
17 |
18 | ## Run
19 |
20 | ```
21 | $ cd /src
22 | $ git clone https://github.com/ActiveIntelligentSystemsLab/sgm_gpu_ros.git
23 | $ cd ..
24 | $ catkin_make
25 | $ roslaunch sgm_gpu test.launch
26 | ```
27 |
28 | ## Run with Docker
29 |
30 | ```
31 | $ git clone https://github.com/ActiveIntelligentSystemsLab/sgm_gpu_ros.git
32 | $ cd sgm_gpu_ros/docker
33 | $ xhost +local:root
34 | $ sudo docker compose up
35 | ```
36 |
37 | ## sgm_gpu_node
38 |
39 | A node calculates disparity from stereo image topic.
40 |
41 | ### Subscribed topics
42 |
43 | - `left_image` ([sensor_msgs/Image](http://docs.ros.org/api/sensor_msgs/html/msg/Image.html))
44 |
45 | Rectified image topic from left camera.
46 | Should be remapped.
47 |
48 | - `right_image` ([sensor_msgs/Image](http://docs.ros.org/api/sensor_msgs/html/msg/Image.html))
49 |
50 | Rectified image topic from right camera. Should be remapped.
51 |
52 | - `/camera_info` ([sensor_msgs/CameraInfo](http://docs.ros.org/api/sensor_msgs/html/msg/CameraInfo.html))
53 |
54 | Subscribed automatically based on topic of left_image.
55 |
56 | - `/camera_info` ([sensor_msgs/CameraInfo](http://docs.ros.org/api/sensor_msgs/html/msg/CameraInfo.html))
57 |
58 | Subscribed automatically based on topic of right_image.
59 |
60 | ### Published topic
61 |
62 | - `~disparity` ([stereo_msgs/DisparityImage](http://docs.ros.org/api/stereo_msgs/html/msg/DisparityImage.html))
63 |
64 | Disparity image computed by SGM
65 |
66 | ### Parameters
67 |
68 | - `~libsgm/p1` (int)
69 |
70 | Parameter used in SGM algorithm.
71 | See [SGM on GPU papar](https://www.sciencedirect.com/science/article/pii/S1877050916306561) and [SGM paper](https://ieeexplore.ieee.org/document/4359315) .
72 |
73 | Default value is `6` from [SGM on GPU](https://github.com/dhernandez0/sgm) .
74 |
75 | - `~libsgm/p2` (int)
76 |
77 | Parameter used in SGM algorithm.
78 | See [SGM on GPU papar](https://www.sciencedirect.com/science/article/pii/S1877050916306561) and [SGM paper](https://ieeexplore.ieee.org/document/4359315) .
79 |
80 | Default value is `96` from [SGM on GPU](https://github.com/dhernandez0/sgm) .
81 |
82 | - `~libsgm/check_consistency` (bool)
83 |
84 | Check left-right consistency if true.
85 |
86 | Default value is `true`.
87 |
88 | - `~image_transport` (string)
89 |
90 | See [image_transport](http://wiki.ros.org/image_transport)
91 |
92 | ### Limitations
93 |
94 | - Disparity range is `[0, 127]`
95 |
96 |
--------------------------------------------------------------------------------
/docker/.env:
--------------------------------------------------------------------------------
1 | COMPOSE_PROJECT_NAME=sgm_gpu
2 | #COMPOSE_FILE=docker-compose.yml:docker-compose.development.yml
3 |
4 |
--------------------------------------------------------------------------------
/docker/docker-compose.development.yml:
--------------------------------------------------------------------------------
1 | # Override for development
2 | # Just run ROS master and access interactive shell by docker-compose exec
3 | version: "2.3"
4 | services:
5 | master:
6 | command: roscore
7 |
8 |
--------------------------------------------------------------------------------
/docker/docker-compose.yml:
--------------------------------------------------------------------------------
1 | version: "2.3"
2 | services:
3 | master:
4 | build: .
5 | container_name: ${COMPOSE_PROJECT_NAME}_master
6 | command: roslaunch sgm_gpu test.launch
7 | runtime: nvidia
8 | environment:
9 | - DISPLAY
10 | - QT_X11_NO_MITSHM=1
11 | volumes:
12 | - /tmp/.X11-unix:/tmp/.X11-unix:rw
13 | - sgm_gpu_root:/root
14 |
15 | volumes:
16 | sgm_gpu_root:
17 |
18 |
--------------------------------------------------------------------------------
/docker/dockerfile:
--------------------------------------------------------------------------------
1 | FROM nvidia/cuda:11.4.3-cudnn8-devel-ubuntu18.04
2 |
3 | # Setup timezone
4 | RUN echo 'Etc/UTC' > /etc/timezone && \
5 | ln -s /usr/share/zoneinfo/Etc/UTC /etc/localtime && \
6 | apt-get update && \
7 | apt-get install -y tzdata
8 |
9 | # Install ROS
10 | RUN apt-get install -y dirmngr gnupg2 curl
11 | RUN sh -c 'echo "deb http://packages.ros.org/ros/ubuntu bionic main" > /etc/apt/sources.list.d/ros-latest.list'
12 | RUN curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | apt-key add -
13 | RUN apt-get update && \
14 | apt-get install --no-install-recommends -y \
15 | python-rosdep \
16 | python-rosinstall \
17 | python-vcstools \
18 | ros-melodic-perception
19 | RUN rosdep init
20 | RUN rosdep update
21 |
22 | # Install tools for development
23 | RUN apt-get update && \
24 | apt-get install -y vim byobu
25 |
26 | # Make catkin workspace
27 | RUN mkdir -p /root/catkin_ws/src
28 | WORKDIR /root/catkin_ws
29 | # Build sgm_gpu
30 | RUN git clone https://github.com/ActiveIntelligentSystemsLab/sgm_gpu_ros.git src/sgm_gpu_ros
31 | RUN bash -c "source /opt/ros/melodic/setup.bash && \
32 | catkin_make -DCMAKE_BUILD_TYPE=Release"
33 |
34 | # Load ROS environment at docker exec bash
35 | RUN echo "source /opt/ros/melodic/setup.bash" >> /root/.bashrc
36 | RUN echo "source /root/catkin_ws/devel/setup.bash" >> /root/.bashrc
37 |
38 | # Set entrypoint to load catkin_ws at docker run
39 | COPY ./ros_entrypoint.sh /
40 | RUN chmod +x /ros_entrypoint.sh
41 | ENTRYPOINT ["/ros_entrypoint.sh"]
42 | CMD [ "bash" ]
43 |
44 |
--------------------------------------------------------------------------------
/docker/ros_entrypoint.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | set -e
3 |
4 | # setup ros environment
5 | source "/opt/ros/melodic/setup.bash"
6 | source "/root/catkin_ws/devel/setup.bash"
7 | exec "$@"
8 |
--------------------------------------------------------------------------------
/include/sgm_gpu/configuration.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef SGM_GPU__CONFIGURATION_H_
18 | #define SGM_GPU__CONFIGURATION_H_
19 |
20 | #include
21 |
22 | #define MAX_DISPARITY 128
23 | #define CENSUS_WIDTH 9
24 | #define CENSUS_HEIGHT 7
25 |
26 | #define TOP (CENSUS_HEIGHT-1)/2
27 | #define LEFT (CENSUS_WIDTH-1)/2
28 |
29 | namespace sgm_gpu
30 | {
31 |
32 | typedef uint32_t cost_t;
33 |
34 | }
35 |
36 | #define COSTAGG_BLOCKSIZE GPU_THREADS_PER_BLOCK
37 | #define COSTAGG_BLOCKSIZE_HORIZ GPU_THREADS_PER_BLOCK
38 |
39 | #endif // SGM_GPU__CONFIGURATION_H_
40 |
--------------------------------------------------------------------------------
/include/sgm_gpu/cost_aggregation.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2019 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef COST_AGGREGATION_H_
18 | #define COST_AGGREGATION_H_
19 |
20 | #define ITER_COPY 0
21 | #define ITER_NORMAL 1
22 |
23 | #define MIN_COMPUTE 0
24 | #define MIN_NOCOMPUTE 1
25 |
26 | #define DIR_UPDOWN 0
27 | #define DIR_DOWNUP 1
28 | #define DIR_LEFTRIGHT 2
29 | #define DIR_RIGHTLEFT 3
30 |
31 | namespace sgm_gpu
32 | {
33 |
34 | template
35 | __device__ __forceinline__ void CostAggregationGenericIndexesIncrement(int *index, int *index_im, int *col, const int add_index, const int add_imindex) {
36 | *index += add_index;
37 | if(recompute || join_dispcomputation) {
38 | *index_im += add_imindex;
39 | if(recompute) {
40 | *col += add_col;
41 | }
42 | }
43 | }
44 |
45 | template
46 | __device__ __forceinline__ void CostAggregationDiagonalGenericIndexesIncrement(int *index, int *index_im, int *col, const int cols, const int initial_row, const int i, const int dis) {
47 | *col += add_index;
48 | if(add_index > 0 && *col >= cols) {
49 | *col = 0;
50 | } else if(*col < 0) {
51 | *col = cols-1;
52 | }
53 | *index = abs(initial_row-i)*cols*MAX_DISPARITY+*col*MAX_DISPARITY+dis;
54 | if(recompute || join_dispcomputation) {
55 | *index_im = abs(initial_row-i)*cols+*col;
56 | }
57 | }
58 |
59 | template
60 | __device__ __forceinline__ void CostAggregationGenericIteration(int index, int index_im, int col, uint32_t *old_values, int *old_value1, int *old_value2, int *old_value3, int *old_value4, uint32_t *min_cost, uint32_t *min_cost_p2, uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int p1_vector, const int p2_vector, const T *_d_transform0, const T *_d_transform1, const int lane, const int MAX_PAD, const int dis, T *rp0, T *rp1, T *rp2, T *rp3, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
61 | const T __restrict__ *d_transform0 = _d_transform0;
62 | const T __restrict__ *d_transform1 = _d_transform1;
63 | uint32_t costs, next_dis, prev_dis;
64 |
65 | if(iter_type == ITER_NORMAL) {
66 | // First shuffle
67 | int prev_dis1 = shfl_up_32(*old_value4, 1);
68 | if(lane == 0) {
69 | prev_dis1 = MAX_PAD;
70 | }
71 |
72 | // Second shuffle
73 | int next_dis4 = shfl_down_32(*old_value1, 1);
74 | if(lane == 31) {
75 | next_dis4 = MAX_PAD;
76 | }
77 |
78 | // Shift + rotate
79 | //next_dis = __funnelshift_r(next_dis4, *old_values, 8);
80 | next_dis = __byte_perm(*old_values, next_dis4, 0x4321);
81 | prev_dis = __byte_perm(*old_values, prev_dis1, 0x2104);
82 |
83 | next_dis = next_dis + p1_vector;
84 | prev_dis = prev_dis + p1_vector;
85 | }
86 | if(recompute) {
87 | const int dif = col - dis;
88 | if(dir_type == DIR_LEFTRIGHT) {
89 | if(lane == 0) {
90 | // lane = 0 is dis = 0, no need to subtract dis
91 | *rp0 = d_transform1[index_im];
92 | }
93 | } else if(dir_type == DIR_RIGHTLEFT) {
94 | // First iteration, load D pixels
95 | if(first_iteration) {
96 | const uint4 right = reinterpret_cast(&d_transform1[index_im-dis-3])[0];
97 | *rp3 = right.x;
98 | *rp2 = right.y;
99 | *rp1 = right.z;
100 | *rp0 = right.w;
101 | } else if(lane == 31 && dif >= 3) {
102 | *rp3 = d_transform1[index_im-dis-3];
103 | }
104 | } else {
105 | /*
106 | __shared__ T right_p[MAX_DISPARITY+32];
107 | const int warp_id = threadIdx.x / WARP_SIZE;
108 | if(warp_id < 5) {
109 | const int block_imindex = index_im - warp_id + 32;
110 | const int rp_index = warp_id*WARP_SIZE+lane;
111 | const int col_cpy = col-warp_id+32;
112 | right_p[rp_index] = ((col_cpy-(159-rp_index)) >= 0) ? ld_gbl_cs(&d_transform1[block_imindex-(159-rp_index)]) : 0;
113 | }*/
114 |
115 | __shared__ T right_p[128+32];
116 | const int warp_id = threadIdx.x / WARP_SIZE;
117 | const int block_imindex = index_im - warp_id + 2;
118 | const int rp_index = warp_id*WARP_SIZE+lane;
119 | const int col_cpy = col-warp_id+2;
120 | right_p[rp_index] = ((col_cpy-(129-rp_index)) >= 0) ? d_transform1[block_imindex-(129-rp_index)] : 0;
121 | right_p[rp_index+64] = ((col_cpy-(129-rp_index-64)) >= 0) ? d_transform1[block_imindex-(129-rp_index-64)] : 0;
122 | //right_p[rp_index+128] = ld_gbl_cs(&d_transform1[block_imindex-(129-rp_index-128)]);
123 | if(warp_id == 0) {
124 | right_p[128+lane] = ld_gbl_cs(&d_transform1[block_imindex-(129-lane)]);
125 | }
126 | __syncthreads();
127 |
128 | const int px = MAX_DISPARITY+warp_id-dis-1;
129 | *rp0 = right_p[px];
130 | *rp1 = right_p[px-1];
131 | *rp2 = right_p[px-2];
132 | *rp3 = right_p[px-3];
133 | }
134 | const T left_pixel = d_transform0[index_im];
135 | *old_value1 = popcount(left_pixel ^ *rp0);
136 | *old_value2 = popcount(left_pixel ^ *rp1);
137 | *old_value3 = popcount(left_pixel ^ *rp2);
138 | *old_value4 = popcount(left_pixel ^ *rp3);
139 | if(iter_type == ITER_COPY) {
140 | *old_values = uchars_to_uint32(*old_value1, *old_value2, *old_value3, *old_value4);
141 | } else {
142 | costs = uchars_to_uint32(*old_value1, *old_value2, *old_value3, *old_value4);
143 | }
144 | // Prepare for next iteration
145 | if(dir_type == DIR_LEFTRIGHT) {
146 | *rp3 = shfl_up_32(*rp3, 1);
147 | } else if(dir_type == DIR_RIGHTLEFT) {
148 | *rp0 = shfl_down_32(*rp0, 1);
149 | }
150 | } else {
151 | if(iter_type == ITER_COPY) {
152 | *old_values = ld_gbl_ca(reinterpret_cast(&d_cost[index]));
153 | } else {
154 | costs = ld_gbl_ca(reinterpret_cast(&d_cost[index]));
155 | }
156 | }
157 |
158 | if(iter_type == ITER_NORMAL) {
159 | const uint32_t min1 = __vminu4(*old_values, prev_dis);
160 | const uint32_t min2 = __vminu4(next_dis, *min_cost_p2);
161 | const uint32_t min_prev = __vminu4(min1, min2);
162 | *old_values = costs + (min_prev - *min_cost);
163 | }
164 | if(iter_type == ITER_NORMAL || !recompute) {
165 | uint32_to_uchars(*old_values, old_value1, old_value2, old_value3, old_value4);
166 | }
167 |
168 | if(join_dispcomputation) {
169 | const uint32_t L0_costs = *((uint32_t*) (d_L0+index));
170 | const uint32_t L1_costs = *((uint32_t*) (d_L1+index));
171 | const uint32_t L2_costs = *((uint32_t*) (d_L2+index));
172 | const uint32_t L3_costs = *((uint32_t*) (d_L3+index));
173 | const uint32_t L4_costs = *((uint32_t*) (d_L4+index));
174 | const uint32_t L5_costs = *((uint32_t*) (d_L5+index));
175 | const uint32_t L6_costs = *((uint32_t*) (d_L6+index));
176 |
177 | int l0_x, l0_y, l0_z, l0_w;
178 | int l1_x, l1_y, l1_z, l1_w;
179 | int l2_x, l2_y, l2_z, l2_w;
180 | int l3_x, l3_y, l3_z, l3_w;
181 | int l4_x, l4_y, l4_z, l4_w;
182 | int l5_x, l5_y, l5_z, l5_w;
183 | int l6_x, l6_y, l6_z, l6_w;
184 |
185 | uint32_to_uchars(L0_costs, &l0_x, &l0_y, &l0_z, &l0_w);
186 | uint32_to_uchars(L1_costs, &l1_x, &l1_y, &l1_z, &l1_w);
187 | uint32_to_uchars(L2_costs, &l2_x, &l2_y, &l2_z, &l2_w);
188 | uint32_to_uchars(L3_costs, &l3_x, &l3_y, &l3_z, &l3_w);
189 | uint32_to_uchars(L4_costs, &l4_x, &l4_y, &l4_z, &l4_w);
190 | uint32_to_uchars(L5_costs, &l5_x, &l5_y, &l5_z, &l5_w);
191 | uint32_to_uchars(L6_costs, &l6_x, &l6_y, &l6_z, &l6_w);
192 |
193 | const uint16_t val1 = l0_x + l1_x + l2_x + l3_x + l4_x + l5_x + l6_x + *old_value1;
194 | const uint16_t val2 = l0_y + l1_y + l2_y + l3_y + l4_y + l5_y + l6_y + *old_value2;
195 | const uint16_t val3 = l0_z + l1_z + l2_z + l3_z + l4_z + l5_z + l6_z + *old_value3;
196 | const uint16_t val4 = l0_w + l1_w + l2_w + l3_w + l4_w + l5_w + l6_w + *old_value4;
197 |
198 | int min_idx1 = dis;
199 | uint16_t min1 = val1;
200 | if(val1 > val2) {
201 | min1 = val2;
202 | min_idx1 = dis+1;
203 | }
204 |
205 | int min_idx2 = dis+2;
206 | uint16_t min2 = val3;
207 | if(val3 > val4) {
208 | min2 = val4;
209 | min_idx2 = dis+3;
210 | }
211 |
212 | uint16_t minval = min1;
213 | int min_idx = min_idx1;
214 | if(min1 > min2) {
215 | minval = min2;
216 | min_idx = min_idx2;
217 | }
218 |
219 | const int min_warpindex = warpReduceMinIndex(minval, min_idx);
220 | if(lane == 0) {
221 | d_disparity[index_im] = min_warpindex;
222 | }
223 |
224 | // Save smoothed cost to obtain right disparity
225 | d_s[index] = val1;
226 | d_s[index+1] = val2;
227 | d_s[index+2] = val3;
228 | d_s[index+3] = val4;
229 | } else {
230 | st_gbl_cs(reinterpret_cast(&d_L[index]), *old_values);
231 | }
232 | if(min_type == MIN_COMPUTE) {
233 | int min_cost_scalar = min(min(*old_value1, *old_value2), min(*old_value3, *old_value4));
234 | *min_cost = uchar_to_uint32(warpReduceMin(min_cost_scalar));
235 | *min_cost_p2 = *min_cost + p2_vector;
236 | }
237 | }
238 |
239 | template
240 | __device__ __forceinline__ void CostAggregationGeneric(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int initial_row, const int initial_col, const int max_iter, const int cols, int add_index, const T *_d_transform0, const T *_d_transform1, const int add_imindex, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
241 | const int lane = threadIdx.x % WARP_SIZE;
242 | const int dis = 4*lane;
243 | int index = initial_row*cols*MAX_DISPARITY+initial_col*MAX_DISPARITY+dis;
244 | int col, index_im;
245 | if(recompute || join_dispcomputation) {
246 | if(recompute) {
247 | col = initial_col;
248 | }
249 | index_im = initial_row*cols+initial_col;
250 | }
251 |
252 | const int MAX_PAD = UCHAR_MAX-P1;
253 | const uint32_t p1_vector = uchars_to_uint32(P1, P1, P1, P1);
254 | const uint32_t p2_vector = uchars_to_uint32(P2, P2, P2, P2);
255 | int old_value1;
256 | int old_value2;
257 | int old_value3;
258 | int old_value4;
259 | uint32_t min_cost, min_cost_p2, old_values;
260 | T rp0, rp1, rp2, rp3;
261 |
262 | if(recompute) {
263 | if(dir_type == DIR_LEFTRIGHT) {
264 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
265 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
266 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
267 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
268 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
269 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
270 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
271 | for(int i = 4; i < max_iter-3; i+=4) {
272 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
273 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
274 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
275 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
276 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
277 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
278 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
279 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
280 | }
281 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
282 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
283 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
284 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
285 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
286 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
287 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
288 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
289 | } else if(dir_type == DIR_RIGHTLEFT) {
290 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
291 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
292 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
293 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
294 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
295 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
296 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
297 | for(int i = 4; i < max_iter-3; i+=4) {
298 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
299 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
300 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
301 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
302 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
303 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
304 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
305 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
306 | }
307 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
308 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
309 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
310 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp1, &rp2, &rp3, &rp0, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
311 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
312 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp2, &rp3, &rp0, &rp1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
313 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
314 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp3, &rp0, &rp1, &rp2, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
315 | } else {
316 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
317 | for(int i = 1; i < max_iter; i++) {
318 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
319 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
320 | }
321 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
322 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
323 | }
324 | } else {
325 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
326 |
327 | for(int i = 1; i < max_iter; i++) {
328 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
329 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
330 | }
331 | CostAggregationGenericIndexesIncrement(&index, &index_im, &col, add_index, add_imindex);
332 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
333 | }
334 | }
335 |
336 | template
337 | __device__ __forceinline__ void CostAggregationDiagonalGeneric(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int initial_row, const int initial_col, const int max_iter, const int col_nomin, const int col_copycost, const int cols, const T *_d_transform0, const T *_d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
338 | const int lane = threadIdx.x % WARP_SIZE;
339 | const int dis = 4*lane;
340 | int col = initial_col;
341 | int index = initial_row*cols*MAX_DISPARITY+initial_col*MAX_DISPARITY+dis;
342 | int index_im;
343 | if(recompute || join_dispcomputation) {
344 | index_im = initial_row*cols+col;
345 | }
346 | const int MAX_PAD = UCHAR_MAX-P1;
347 | const uint32_t p1_vector = uchars_to_uint32(P1, P1, P1, P1);
348 | const uint32_t p2_vector = uchars_to_uint32(P2, P2, P2, P2);
349 | int old_value1;
350 | int old_value2;
351 | int old_value3;
352 | int old_value4;
353 | uint32_t min_cost, min_cost_p2, old_values;
354 | T rp0, rp1, rp2, rp3;
355 |
356 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
357 | for(int i = 1; i < max_iter; i++) {
358 | CostAggregationDiagonalGenericIndexesIncrement(&index, &index_im, &col, cols, initial_row, i, dis);
359 | if(col == col_copycost) {
360 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
361 | } else {
362 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
363 | }
364 | }
365 |
366 | CostAggregationDiagonalGenericIndexesIncrement(&index, &index_im, &col, cols, max_iter, initial_row, dis);
367 | if(col == col_copycost) {
368 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
369 | } else {
370 | CostAggregationGenericIteration(index, index_im, col, &old_values, &old_value1, &old_value2, &old_value3, &old_value4, &min_cost, &min_cost_p2, d_cost, d_L, d_s, p1_vector, p2_vector, _d_transform0, _d_transform1, lane, MAX_PAD, dis, &rp0, &rp1, &rp2, &rp3, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
371 | }
372 | }
373 |
374 | template
375 |
376 | __global__ void CostAggregationKernelDiagonalDownUpRightLeft(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
377 | const int initial_col = cols - (blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE)) - 1;
378 | if(initial_col < cols) {
379 | const int initial_row = rows-1;
380 | const int add_index = -1;
381 | const int col_nomin = 0;
382 | const int col_copycost = cols-1;
383 | const int max_iter = rows-1;
384 | const bool recompute = false;
385 | const bool join_dispcomputation = false;
386 |
387 | CostAggregationDiagonalGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, col_nomin, col_copycost, cols, d_transform0, d_transform1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
388 | }
389 | }
390 |
391 | template
392 | __global__ void CostAggregationKernelDiagonalDownUpLeftRight(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
393 | const int initial_col = cols - (blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE)) - 1;
394 | if(initial_col >= 0) {
395 | const int initial_row = rows-1;
396 | const int add_index = 1;
397 | const int col_nomin = cols-1;
398 | const int col_copycost = 0;
399 | const int max_iter = rows-1;
400 | const bool recompute = false;
401 | const bool join_dispcomputation = false;
402 |
403 | CostAggregationDiagonalGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, col_nomin, col_copycost, cols, d_transform0, d_transform1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
404 | }
405 | }
406 |
407 | template
408 |
409 | __global__ void CostAggregationKernelDiagonalUpDownRightLeft(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
410 | const int initial_col = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
411 | if(initial_col < cols) {
412 | const int initial_row = 0;
413 | const int add_index = -1;
414 | const int col_nomin = 0;
415 | const int col_copycost = cols-1;
416 | const int max_iter = rows-1;
417 | const bool recompute = false;
418 | const bool join_dispcomputation = true;
419 |
420 | CostAggregationDiagonalGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, col_nomin, col_copycost, cols, d_transform0, d_transform1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
421 | }
422 | }
423 |
424 | template
425 |
426 | __global__ void CostAggregationKernelDiagonalUpDownLeftRight(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
427 | const int initial_col = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
428 | if(initial_col < cols) {
429 | const int initial_row = 0;
430 | const int add_index = 1;
431 | const int col_nomin = cols-1;
432 | const int col_copycost = 0;
433 | const int max_iter = rows-1;
434 | const bool recompute = false;
435 | const bool join_dispcomputation = false;
436 |
437 | CostAggregationDiagonalGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, col_nomin, col_copycost, cols, d_transform0, d_transform1, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
438 | }
439 | }
440 |
441 | template
442 |
443 | __global__ void CostAggregationKernelLeftToRight(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
444 | const int initial_row = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
445 | if(initial_row < rows) {
446 | const int initial_col = 0;
447 | const int add_index = MAX_DISPARITY;
448 | const int add_imindex = 1;
449 | const int max_iter = cols-1;
450 | const int add_col = 1;
451 | const bool recompute = true;
452 | const bool join_dispcomputation = false;
453 |
454 | CostAggregationGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, cols, add_index, d_transform0, d_transform1, add_imindex, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
455 | }
456 | }
457 |
458 | template
459 |
460 | __global__ void CostAggregationKernelRightToLeft(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
461 | const int initial_row = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
462 | if(initial_row < rows) {
463 | const int initial_col = cols-1;
464 | const int add_index = -MAX_DISPARITY;
465 | const int add_imindex = -1;
466 | const int max_iter = cols-1;
467 | const int add_col = -1;
468 | const bool recompute = true;
469 | const bool join_dispcomputation = false;
470 |
471 | CostAggregationGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, cols, add_index, d_transform0, d_transform1, add_imindex, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
472 | }
473 | }
474 |
475 | template
476 | __global__ void CostAggregationKernelDownToUp(uint8_t* d_cost, uint8_t *d_L, uint16_t *d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
477 | const int initial_col = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
478 | if(initial_col < cols) {
479 | const int initial_row = rows-1;
480 | const int add_index = -cols*MAX_DISPARITY;
481 | const int add_imindex = -cols;
482 | const int max_iter = rows-1;
483 | const int add_col = 0;
484 | const bool recompute = false;
485 | const bool join_dispcomputation = false;
486 |
487 | CostAggregationGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, cols, add_index, d_transform0, d_transform1, add_imindex, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
488 | }
489 | }
490 |
491 | template
492 | //__launch_bounds__(64, 16)
493 | __global__ void CostAggregationKernelUpToDown(uint8_t* d_cost, uint8_t *d_L, uint16_t* d_s, const int P1, const int P2, const int rows, const int cols, const T *d_transform0, const T *d_transform1, uint8_t* __restrict__ d_disparity, const uint8_t* d_L0, const uint8_t* d_L1, const uint8_t* d_L2, const uint8_t* d_L3, const uint8_t* d_L4, const uint8_t* d_L5, const uint8_t* d_L6) {
494 | const int initial_col = blockIdx.x*(blockDim.x/WARP_SIZE) + (threadIdx.x / WARP_SIZE);
495 | if(initial_col < cols) {
496 | const int initial_row = 0;
497 | const int add_index = cols*MAX_DISPARITY;
498 | const int add_imindex = cols;
499 | const int max_iter = rows-1;
500 | const int add_col = 0;
501 | const bool recompute = false;
502 | const bool join_dispcomputation = false;
503 |
504 | CostAggregationGeneric(d_cost, d_L, d_s, P1, P2, initial_row, initial_col, max_iter, cols, add_index, d_transform0, d_transform1, add_imindex, d_disparity, d_L0, d_L1, d_L2, d_L3, d_L4, d_L5, d_L6);
505 | }
506 | }
507 |
508 | } // namespace sgm_gpu
509 |
510 | #endif /* COST_AGGREGATION_H_ */
511 |
--------------------------------------------------------------------------------
/include/sgm_gpu/costs.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 | #ifndef SGM_GPU__COSTS_H_
17 | #define SGM_GPU__COSTS_H_
18 |
19 | #include
20 | #include "sgm_gpu/configuration.h"
21 |
22 | namespace sgm_gpu
23 | {
24 |
25 | __global__ void CenterSymmetricCensusKernelSM2(const uint8_t *im, const uint8_t *im2, cost_t *transform, cost_t *transform2, const uint32_t rows, const uint32_t cols);
26 |
27 | }
28 |
29 | #endif // SGM_GPU__COSTS_H_
30 |
31 |
--------------------------------------------------------------------------------
/include/sgm_gpu/hamming_cost.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef SGM_GPU__HAMMING_COST_H_
18 | #define SGM_GPU__HAMMING_COST_H_
19 |
20 | #include "sgm_gpu/configuration.h"
21 | #include "sgm_gpu/util.h"
22 | #include
23 |
24 | namespace sgm_gpu
25 | {
26 |
27 | __global__ void
28 | HammingDistanceCostKernel(const cost_t *d_transform0, const cost_t *d_transform1, uint8_t *d_cost, const int rows, const int cols );
29 |
30 | }
31 |
32 | #endif // SGM_GPU__HAMMING_COST_H_
33 |
34 |
--------------------------------------------------------------------------------
/include/sgm_gpu/left_right_consistency.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef SGM_GPU__LEFT_RIGHT_CONSISTENCY_H_
18 | #define SGM_GPU__LEFT_RIGHT_CONSISTENCY_H_
19 |
20 | #include
21 |
22 | namespace sgm_gpu
23 | {
24 |
25 | __global__ void ChooseRightDisparity(uint8_t *right_disparity, const uint16_t *smoothed_cost, const uint32_t rows, const uint32_t cols);
26 | __global__ void LeftRightConsistencyCheck(uint8_t *disparity, const uint8_t *disparity_right, const uint32_t rows, const uint32_t cols);
27 |
28 | }
29 |
30 | #endif // SGM_GPU__LEFT_RIGHT_CONSISTENCY_H_
31 |
32 |
--------------------------------------------------------------------------------
/include/sgm_gpu/median_filter.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef SGM_GPU__MEDIAN_FILTER_H_
18 | #define SGM_GPU__MEDIAN_FILTER_H_
19 |
20 | #include
21 |
22 | namespace sgm_gpu
23 | {
24 |
25 | __global__ void MedianFilter3x3(const uint8_t* __restrict__ d_input, uint8_t* __restrict__ d_out, const uint32_t rows, const uint32_t cols);
26 |
27 | template
28 | __inline__ __device__ void MedianFilter(const T* __restrict__ d_input, T* __restrict__ d_out, const uint32_t rows, const uint32_t cols) {
29 | const uint32_t idx = blockIdx.x*blockDim.x+threadIdx.x;
30 | const uint32_t row = idx / cols;
31 | const uint32_t col = idx % cols;
32 | T window[n*n];
33 | int half = n/2;
34 |
35 | if(row >= half && col >= half && row < rows-half && col < cols-half) {
36 | for(uint32_t i = 0; i < n; i++) {
37 | for(uint32_t j = 0; j < n; j++) {
38 | window[i*n+j] = d_input[(row-half+i)*cols+col-half+j];
39 | }
40 | }
41 |
42 | for(uint32_t i = 0; i < (n*n/2)+1; i++) {
43 | uint32_t min_idx = i;
44 | for(uint32_t j = i+1; j < n*n; j++) {
45 | if(window[j] < window[min_idx]) {
46 | min_idx = j;
47 | }
48 | }
49 | const T tmp = window[i];
50 | window[i] = window[min_idx];
51 | window[min_idx] = tmp;
52 | }
53 | d_out[idx] = window[n*n/2];
54 | } else if(row < rows && col < cols) {
55 | d_out[idx] = d_input[idx];
56 | }
57 | }
58 |
59 | } // namespace sgm_gpu
60 |
61 | #endif // SGM_GPU__MEDIAN_FILTER_H_
62 |
63 |
--------------------------------------------------------------------------------
/include/sgm_gpu/sgm_gpu.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 | #ifndef SGM_GPU__SGM_GPU_H_
17 | #define SGM_GPU__SGM_GPU_H_
18 |
19 | #include "sgm_gpu/configuration.h"
20 |
21 | #include
22 | #include
23 | #include
24 | #include
25 |
26 | #include
27 |
28 | namespace sgm_gpu
29 | {
30 |
31 | class SgmGpu
32 | {
33 | private:
34 | std::shared_ptr private_node_handle_;
35 | /**
36 | * @brief Parameter used in SGM algorithm
37 | *
38 | * See SGM paper.
39 | */
40 | uint8_t p1_, p2_;
41 | /**
42 | * @brief Enable/disable left-right consistency check
43 | */
44 | bool check_consistency_;
45 |
46 | // Memory for disparity computation
47 | // d_: for device
48 | uint8_t *d_im0_;
49 | uint8_t *d_im1_;
50 | uint32_t *d_transform0_;
51 | uint32_t *d_transform1_;
52 | uint8_t *d_cost_;
53 | uint8_t *d_disparity_;
54 | uint8_t *d_disparity_filtered_uchar_;
55 | uint8_t *d_disparity_right_;
56 | uint8_t *d_disparity_right_filtered_uchar_;
57 | uint8_t *d_L0_;
58 | uint8_t *d_L1_;
59 | uint8_t *d_L2_;
60 | uint8_t *d_L3_;
61 | uint8_t *d_L4_;
62 | uint8_t *d_L5_;
63 | uint8_t *d_L6_;
64 | uint8_t *d_L7_;
65 | uint16_t *d_s_;
66 |
67 | bool memory_allocated_;
68 |
69 | uint32_t cols_, rows_;
70 |
71 | void allocateMemory(uint32_t cols, uint32_t rows);
72 | void freeMemory();
73 |
74 | /**
75 | * @brief Resize images to be width and height divisible by 4 for limit of CUDA code
76 | */
77 | void resizeToDivisibleBy4(cv::Mat& left_image, cv::Mat& right_image);
78 |
79 | void convertToMsg(
80 | const cv::Mat_& disparity,
81 | const sensor_msgs::CameraInfo& left_camera_info,
82 | const sensor_msgs::CameraInfo& right_camera_info,
83 | stereo_msgs::DisparityImage& disparity_msg
84 | );
85 |
86 | public:
87 | /**
88 | * @brief Constructor which use namespace /libsgm_gpu for ROS param
89 | */
90 | SgmGpu(const ros::NodeHandle& parent_node_handle);
91 | ~SgmGpu();
92 |
93 | bool computeDisparity(
94 | const sensor_msgs::Image& left_image,
95 | const sensor_msgs::Image& right_image,
96 | const sensor_msgs::CameraInfo& left_camera_info,
97 | const sensor_msgs::CameraInfo& right_camera_info,
98 | stereo_msgs::DisparityImage& disparity_msg
99 | );
100 | };
101 |
102 | } // namespace sgm_gpu
103 |
104 | #endif // SGM_GPU__SGM_GPU_H_
105 |
106 |
--------------------------------------------------------------------------------
/include/sgm_gpu/util.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #ifndef SGM_GPU__UTIL_H_
18 | #define SGM_GPU__UTIL_H_
19 |
20 | #include
21 | #include
22 | #include
23 |
24 | #define FERMI false
25 |
26 | #define GPU_THREADS_PER_BLOCK_FERMI 256
27 | #define GPU_THREADS_PER_BLOCK_MAXWELL 64
28 |
29 | /* Defines related to GPU Architecture */
30 | #if FERMI
31 | #define GPU_THREADS_PER_BLOCK GPU_THREADS_PER_BLOCK_FERMI
32 | #else
33 | #define GPU_THREADS_PER_BLOCK GPU_THREADS_PER_BLOCK_MAXWELL
34 | #endif
35 |
36 | #define WARP_SIZE 32
37 |
38 | namespace sgm_gpu
39 | {
40 |
41 | static void CheckCudaErrorAux (const char *, unsigned, const char *, cudaError_t);
42 | #define CUDA_CHECK_RETURN(value) CheckCudaErrorAux(__FILE__,__LINE__, #value, value)
43 |
44 | /**
45 | * Check the return value of the CUDA runtime API call and exit
46 | * the application if the call has failed.
47 | */
48 | static void CheckCudaErrorAux (const char *file, unsigned line, const char *statement, cudaError_t err) {
49 | if (err == cudaSuccess)
50 | return;
51 | std::cerr << statement<<" returned " << cudaGetErrorString(err) << "("<>8) & 0xff;
169 | *u2 = __byte_perm(s, 0, 0x4441);
170 | //*u3 = (s>>16) & 0xff;
171 | *u3 = __byte_perm(s, 0, 0x4442);
172 | //*u4 = s>>24;
173 | *u4 = __byte_perm(s, 0, 0x4443);
174 | }
175 |
176 | __device__ __forceinline__ uint32_t uchars_to_uint32(int u1, int u2, int u3, int u4) {
177 | //return u1 | (u2<<8) | (u3<<16) | (u4<<24);
178 | //return __byte_perm(u1, u2, 0x7740) + __byte_perm(u3, u4, 0x4077);
179 | return u1 | (u2<<8) | __byte_perm(u3, u4, 0x4077);
180 | }
181 |
182 | __device__ __forceinline__ uint32_t uchar_to_uint32(int u1) {
183 | return __byte_perm(u1, u1, 0x0);
184 | }
185 |
186 | __device__ __forceinline__ unsigned int vcmpgeu4(unsigned int a, unsigned int b) {
187 | unsigned int r, c;
188 | c = a-b;
189 | asm ("prmt.b32 %0,%1,0,0xba98;" : "=r"(r) : "r"(c));// build mask from msbs
190 | return r; // byte-wise unsigned gt-eq comparison with mask result
191 | }
192 |
193 | __device__ __forceinline__ unsigned int vminu4(unsigned int a, unsigned int b) {
194 | unsigned int r, s;
195 | s = vcmpgeu4 (b, a);// mask = 0xff if a >= b
196 | r = a & s; // select a when b >= a
197 | s = b & ~s; // select b when b < a
198 | r = r | s; // combine byte selections
199 | return r;
200 | }
201 |
202 | __device__ __forceinline__ void print_uchars(const char* str, const uint32_t s) {
203 | int u1, u2, u3, u4;
204 | uint32_to_uchars(s, &u1, &u2, &u3, &u4);
205 | printf("%s: %d %d %d %d\n", str, u1, u2, u3, u4);
206 | }
207 |
208 | template
209 | __device__ __forceinline__ int popcount(T n) {
210 | #if CSCT or CSCT_RECOMPUTE
211 | return __popc(n);
212 | #else
213 | return __popcll(n);
214 | #endif
215 | }
216 |
217 | __inline__ __device__ uint8_t minu8_index4(int *min_idx, const uint8_t val1, const int dis, const uint8_t val2, const int dis2, const uint8_t val3, const int dis3, const uint8_t val4, const int dis4) {
218 | int min_idx1 = dis;
219 | uint8_t min1 = val1;
220 | if(val1 > val2) {
221 | min1 = val2;
222 | min_idx1 = dis2;
223 | }
224 |
225 | int min_idx2 = dis3;
226 | uint8_t min2 = val3;
227 | if(val3 > val4) {
228 | min2 = val4;
229 | min_idx2 = dis4;
230 | }
231 |
232 | uint8_t minval = min1;
233 | *min_idx = min_idx1;
234 | if(min1 > min2) {
235 | minval = min2;
236 | *min_idx = min_idx2;
237 | }
238 | return minval;
239 | }
240 |
241 | __inline__ __device__ uint8_t minu8_index8(int *min_idx, const uint8_t val1, const int dis, const uint8_t val2, const int dis2, const uint8_t val3, const int dis3, const uint8_t val4, const int dis4, const uint8_t val5, const int dis5, const uint8_t val6, const int dis6, const uint8_t val7, const int dis7, const uint8_t val8, const int dis8) {
242 | int min_idx1, min_idx2;
243 | uint8_t minval1, minval2;
244 |
245 | minval1 = minu8_index4(&min_idx1, val1, dis, val2, dis2, val3, dis3, val4, dis4);
246 | minval2 = minu8_index4(&min_idx2, val5, dis5, val6, dis6, val7, dis7, val8, dis8);
247 |
248 | *min_idx = min_idx1;
249 | uint8_t minval = minval1;
250 | if(minval1 > minval2) {
251 | *min_idx = min_idx2;
252 | minval = minval2;
253 | }
254 | return minval;
255 | }
256 |
257 | __inline__ __device__ int warpReduceMinIndex2(int *val, int idx) {
258 | for(int d = 1; d < WARP_SIZE; d *= 2) {
259 | int tmp = shfl_xor_32(*val, d);
260 | int tmp_idx = shfl_xor_32(idx, d);
261 | if(*val > tmp) {
262 | *val = tmp;
263 | idx = tmp_idx;
264 | }
265 | }
266 | return idx;
267 | }
268 |
269 | __inline__ __device__ int warpReduceMinIndex(int val, int idx) {
270 | for(int d = 1; d < WARP_SIZE; d *= 2) {
271 | int tmp = shfl_xor_32(val, d);
272 | int tmp_idx = shfl_xor_32(idx, d);
273 | if(val > tmp) {
274 | val = tmp;
275 | idx = tmp_idx;
276 | }
277 | }
278 | return idx;
279 | }
280 |
281 | __inline__ __device__ int warpReduceMin(int val) {
282 | val = min(val, shfl_xor_32(val, 1));
283 | val = min(val, shfl_xor_32(val, 2));
284 | val = min(val, shfl_xor_32(val, 4));
285 | val = min(val, shfl_xor_32(val, 8));
286 | val = min(val, shfl_xor_32(val, 16));
287 | return val;
288 | }
289 |
290 | __inline__ __device__ int blockReduceMin(int val) {
291 | static __shared__ int shared[WARP_SIZE]; // Shared mem for WARP_SIZE partial sums
292 | const int lane = threadIdx.x % WARP_SIZE;
293 | const int wid = threadIdx.x / WARP_SIZE;
294 |
295 | val = warpReduceMin(val); // Each warp performs partial reduction
296 |
297 | if (lane==0) shared[wid]=val; // Write reduced value to shared memory
298 |
299 | __syncthreads(); // Wait for all partial reductions
300 |
301 | //read from shared memory only if that warp existed
302 | val = (threadIdx.x < blockDim.x / warpSize) ? shared[lane] : INT_MAX;
303 |
304 | if (wid==0) val = warpReduceMin(val); //Final reduce within first warp
305 |
306 | return val;
307 | }
308 |
309 | __inline__ __device__ int blockReduceMinIndex(int val, int idx) {
310 | static __shared__ int shared_val[WARP_SIZE]; // Shared mem for WARP_SIZE partial mins
311 | static __shared__ int shared_idx[WARP_SIZE]; // Shared mem for WARP_SIZE indexes
312 | const int lane = threadIdx.x % WARP_SIZE;
313 | const int wid = threadIdx.x / WARP_SIZE;
314 |
315 | idx = warpReduceMinIndex2(&val, idx); // Each warp performs partial reduction
316 |
317 | if (lane==0) {
318 | shared_val[wid]=val;
319 | shared_idx[wid]=idx;
320 | }
321 |
322 | __syncthreads(); // Wait for all partial reductions
323 |
324 | //read from shared memory only if that warp existed
325 | val = (threadIdx.x < blockDim.x / WARP_SIZE) ? shared_val[lane] : INT_MAX;
326 | idx = (threadIdx.x < blockDim.x / WARP_SIZE) ? shared_idx[lane] : INT_MAX;
327 |
328 | if (wid==0) {
329 | idx = warpReduceMinIndex2(&val, idx); //Final reduce within first warp
330 | }
331 |
332 | return idx;
333 | }
334 |
335 |
336 | __inline__ __device__ bool blockAny(bool local_condition) {
337 | __shared__ bool conditions[WARP_SIZE];
338 | const int lane = threadIdx.x % WARP_SIZE;
339 | const int wid = threadIdx.x / WARP_SIZE;
340 |
341 | local_condition = __any_sync(0xffffffff, local_condition); // Each warp performs __any
342 |
343 | if (lane==0) {
344 | conditions[wid]=local_condition;
345 | }
346 |
347 | __syncthreads(); // Wait for all partial __any
348 |
349 | //read from shared memory only if that warp existed
350 | local_condition = (threadIdx.x < blockDim.x / WARP_SIZE) ? conditions[lane] : false;
351 |
352 | if (wid==0) {
353 | local_condition = __any_sync(0xffffffff, local_condition); //Final __any within first warp
354 | }
355 |
356 | return local_condition;
357 | }
358 |
359 | } //namespace sgm_gpu
360 |
361 | #endif // SGM_GPU__UTIL_H_
362 |
363 |
--------------------------------------------------------------------------------
/include/sgm_gpu_node.h:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 | #ifndef SGM_GPU__SGM_GPU_NODE_H_
17 | #define SGM_GPU__SGM_GPU_NODE_H_
18 |
19 | #include "sgm_gpu/sgm_gpu.h"
20 |
21 | #include
22 | #include
23 | #include
24 | #include
25 | #include
26 |
27 | namespace sgm_gpu
28 | {
29 |
30 | class SgmGpuNode
31 | {
32 | private:
33 | std::shared_ptr node_handle_;
34 | std::shared_ptr private_node_handle_;
35 |
36 | std::shared_ptr image_transport_;
37 |
38 | std::shared_ptr sgm_;
39 |
40 | image_transport::SubscriberFilter left_image_sub_;
41 | image_transport::SubscriberFilter right_image_sub_;
42 | message_filters::Subscriber left_info_sub_;
43 | message_filters::Subscriber right_info_sub_;
44 |
45 | using StereoSynchronizer = message_filters::TimeSynchronizer
46 | <
47 | sensor_msgs::Image, sensor_msgs::Image,
48 | sensor_msgs::CameraInfo, sensor_msgs::CameraInfo
49 | >;
50 | std::shared_ptr stereo_synchronizer_;
51 |
52 | ros::Publisher disparity_pub_;
53 |
54 | void stereoCallback(
55 | const sensor_msgs::ImageConstPtr &left_image_msg,
56 | const sensor_msgs::ImageConstPtr &right_image_msg,
57 | const sensor_msgs::CameraInfoConstPtr &left_info_msg,
58 | const sensor_msgs::CameraInfoConstPtr &right_info_msg
59 | );
60 |
61 | public:
62 | SgmGpuNode();
63 | };
64 |
65 | } // namespace sgm_gpu
66 |
67 | #endif
68 |
69 |
--------------------------------------------------------------------------------
/launch/test.launch:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
--------------------------------------------------------------------------------
/package.xml:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 | sgm_gpu
6 | 0.0.0
7 | A ROS package of Semi-Global Matching on the GPU
8 | Hironori Fujimoto
9 | GPL3
10 | https://github.com/ActiveIntelligentSystemsLab/sgm_gpu_ros
11 |
12 | catkin
13 | cv_bridge
14 | image_geometry
15 | image_transport
16 | message_filters
17 | roscpp
18 | sensor_msgs
19 | stereo_msgs
20 |
21 | libopencv-dev
22 | nvidia-cuda
23 | nvidia-cuda-dev
24 |
25 |
26 |
--------------------------------------------------------------------------------
/src/costs.cu:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2019 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu/costs.h"
18 | #include
19 |
20 | namespace sgm_gpu
21 | {
22 |
23 | __global__ void
24 | __launch_bounds__(1024, 2)
25 | CenterSymmetricCensusKernelSM2(const uint8_t *im, const uint8_t *im2, cost_t *transform, cost_t *transform2, const uint32_t rows, const uint32_t cols) {
26 | const int idx = blockIdx.x*blockDim.x+threadIdx.x;
27 | const int idy = blockIdx.y*blockDim.y+threadIdx.y;
28 |
29 | const int win_cols = (32+LEFT*2); // 32+4*2 = 40
30 | const int win_rows = (32+TOP*2); // 32+3*2 = 38
31 |
32 | __shared__ uint8_t window[win_cols*win_rows];
33 | __shared__ uint8_t window2[win_cols*win_rows];
34 |
35 | const int id = threadIdx.y*blockDim.x+threadIdx.x;
36 | const int sm_row = id / win_cols;
37 | const int sm_col = id % win_cols;
38 |
39 | const int im_row = blockIdx.y*blockDim.y+sm_row-TOP;
40 | const int im_col = blockIdx.x*blockDim.x+sm_col-LEFT;
41 | const bool boundaries = (im_row >= 0 && im_col >= 0 && im_row < rows && im_col < cols);
42 | window[sm_row*win_cols+sm_col] = boundaries ? im[im_row*cols+im_col] : 0;
43 | window2[sm_row*win_cols+sm_col] = boundaries ? im2[im_row*cols+im_col] : 0;
44 |
45 | // Not enough threads to fill window and window2
46 | const int block_size = blockDim.x*blockDim.y;
47 | if(id < (win_cols*win_rows-block_size)) {
48 | const int id = threadIdx.y*blockDim.x+threadIdx.x+block_size;
49 | const int sm_row = id / win_cols;
50 | const int sm_col = id % win_cols;
51 |
52 | const int im_row = blockIdx.y*blockDim.y+sm_row-TOP;
53 | const int im_col = blockIdx.x*blockDim.x+sm_col-LEFT;
54 | const bool boundaries = (im_row >= 0 && im_col >= 0 && im_row < rows && im_col < cols);
55 | window[sm_row*win_cols+sm_col] = boundaries ? im[im_row*cols+im_col] : 0;
56 | window2[sm_row*win_cols+sm_col] = boundaries ? im2[im_row*cols+im_col] : 0;
57 | }
58 |
59 | __syncthreads();
60 | cost_t census = 0;
61 | cost_t census2 = 0;
62 | if(idy < rows && idx < cols) {
63 | for(int k = 0; k < CENSUS_HEIGHT/2; k++) {
64 | for(int m = 0; m < CENSUS_WIDTH; m++) {
65 | const uint8_t e1 = window[(threadIdx.y+k)*win_cols+threadIdx.x+m];
66 | const uint8_t e2 = window[(threadIdx.y+2*TOP-k)*win_cols+threadIdx.x+2*LEFT-m];
67 | const uint8_t i1 = window2[(threadIdx.y+k)*win_cols+threadIdx.x+m];
68 | const uint8_t i2 = window2[(threadIdx.y+2*TOP-k)*win_cols+threadIdx.x+2*LEFT-m];
69 |
70 | const int shft = k*CENSUS_WIDTH+m;
71 | // Compare to the center
72 | cost_t tmp = (e1 >= e2);
73 | // Shift to the desired position
74 | tmp <<= shft;
75 | // Add it to its place
76 | census |= tmp;
77 | // Compare to the center
78 | cost_t tmp2 = (i1 >= i2);
79 | // Shift to the desired position
80 | tmp2 <<= shft;
81 | // Add it to its place
82 | census2 |= tmp2;
83 | }
84 | }
85 | if(CENSUS_HEIGHT % 2 != 0) {
86 | const int k = CENSUS_HEIGHT/2;
87 | for(int m = 0; m < CENSUS_WIDTH/2; m++) {
88 | const uint8_t e1 = window[(threadIdx.y+k)*win_cols+threadIdx.x+m];
89 | const uint8_t e2 = window[(threadIdx.y+2*TOP-k)*win_cols+threadIdx.x+2*LEFT-m];
90 | const uint8_t i1 = window2[(threadIdx.y+k)*win_cols+threadIdx.x+m];
91 | const uint8_t i2 = window2[(threadIdx.y+2*TOP-k)*win_cols+threadIdx.x+2*LEFT-m];
92 |
93 | const int shft = k*CENSUS_WIDTH+m;
94 | // Compare to the center
95 | cost_t tmp = (e1 >= e2);
96 | // Shift to the desired position
97 | tmp <<= shft;
98 | // Add it to its place
99 | census |= tmp;
100 | // Compare to the center
101 | cost_t tmp2 = (i1 >= i2);
102 | // Shift to the desired position
103 | tmp2 <<= shft;
104 | // Add it to its place
105 | census2 |= tmp2;
106 | }
107 | }
108 |
109 | transform[idy*cols+idx] = census;
110 | transform2[idy*cols+idx] = census2;
111 | }
112 | }
113 |
114 | } // namespace sgm_gpu
115 |
116 |
--------------------------------------------------------------------------------
/src/hamming_cost.cu:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2019 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu/hamming_cost.h"
18 |
19 | namespace sgm_gpu
20 | {
21 |
22 | //d_transform0, d_transform1, d_cost, rows, cols
23 | __global__ void
24 | HammingDistanceCostKernel ( const cost_t *d_transform0, const cost_t *d_transform1,
25 | uint8_t *d_cost, const int rows, const int cols ) {
26 | //const int Dmax= blockDim.x; // Dmax is CTA size
27 | const int y= blockIdx.x; // y is CTA Identifier
28 | const int THRid = threadIdx.x; // THRid is Thread Identifier
29 |
30 | __shared__ cost_t SharedMatch[2*MAX_DISPARITY];
31 | __shared__ cost_t SharedBase [MAX_DISPARITY];
32 |
33 | SharedMatch [MAX_DISPARITY+THRid] = d_transform1[y*cols+0]; // init position
34 |
35 | int n_iter = cols/MAX_DISPARITY;
36 | for (int ix=0; ix 0) {
54 | SharedMatch [THRid] = SharedMatch [THRid + MAX_DISPARITY];
55 | if(THRid < left) {
56 | SharedMatch [THRid+MAX_DISPARITY] = d_transform1 [y*cols+x+THRid];
57 | SharedBase [THRid] = d_transform0 [y*cols+x+THRid];
58 | }
59 |
60 | __syncthreads();
61 | for (int i=0; i.
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu/left_right_consistency.h"
18 | #include "sgm_gpu/configuration.h"
19 |
20 | namespace sgm_gpu
21 | {
22 |
23 | __global__ void ChooseRightDisparity(uint8_t *right_disparity, const uint16_t *smoothed_cost, const uint32_t rows, const uint32_t cols) {
24 | const int x = blockIdx.x*blockDim.x+threadIdx.x;
25 | const int y = blockIdx.y*blockDim.y+threadIdx.y;
26 |
27 | if (x >= cols || y >= rows)
28 | return;
29 |
30 | int min_cost_disparity = 0;
31 | uint16_t min_cost = smoothed_cost[(y*cols + x)*MAX_DISPARITY + min_cost_disparity];
32 |
33 | for (int d = 1; d < MAX_DISPARITY; d++) {
34 | if (x + d >= cols)
35 | break;
36 | uint16_t tmp_cost = smoothed_cost[(y*cols + (x+d))*MAX_DISPARITY + d];
37 | if (tmp_cost < min_cost) {
38 | min_cost = tmp_cost;
39 | min_cost_disparity = d;
40 | }
41 | }
42 |
43 | right_disparity[y*cols+x] = min_cost_disparity;
44 | }
45 |
46 | __global__ void LeftRightConsistencyCheck(uint8_t* disparity, const uint8_t* disparity_right, uint32_t rows, uint32_t cols)
47 | {
48 | const int x = blockIdx.x*blockDim.x+threadIdx.x;
49 | const int y = blockIdx.y*blockDim.y+threadIdx.y;
50 |
51 | if (x >= cols || y >= rows)
52 | return;
53 |
54 | const int x_right = x - disparity[y*cols + x];
55 |
56 | if (x_right < 0) {
57 | disparity[y*cols + x] = 255;
58 | return;
59 | }
60 |
61 | int diff = disparity[y*cols + x] - disparity_right[y*cols + x_right];
62 | diff = diff < 0 ? diff * -1 : diff;
63 | if (diff > 1) {
64 | disparity[y*cols + x] = 255;
65 | }
66 | }
67 |
68 | } // namespace sgm_gpu
69 |
70 |
--------------------------------------------------------------------------------
/src/median_filter.cu:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2019 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu/median_filter.h"
18 |
19 | namespace sgm_gpu
20 | {
21 |
22 | __global__ void MedianFilter3x3(const uint8_t* __restrict__ d_input, uint8_t* __restrict__ d_out, const uint32_t rows, const uint32_t cols) {
23 | MedianFilter<3>(d_input, d_out, rows, cols);
24 | }
25 |
26 | }
27 |
--------------------------------------------------------------------------------
/src/sgm_gpu.cu:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 | #include "sgm_gpu/sgm_gpu.h"
17 |
18 | #include "sgm_gpu/costs.h"
19 | #include "sgm_gpu/hamming_cost.h"
20 | #include "sgm_gpu/median_filter.h"
21 | #include "sgm_gpu/cost_aggregation.h"
22 | #include "sgm_gpu/left_right_consistency.h"
23 |
24 | #include
25 | #include
26 |
27 | namespace sgm_gpu
28 | {
29 | // Variables which have CUDA-related type are put here
30 | // in order to include sgm_gpu.h from non-CUDA package
31 | cudaStream_t stream1_;
32 | cudaStream_t stream2_;
33 | cudaStream_t stream3_;
34 |
35 | dim3 BLOCK_SIZE_;
36 | dim3 grid_size_;
37 |
38 | SgmGpu::SgmGpu(const ros::NodeHandle& parent_node_handle)
39 | : memory_allocated_(false), cols_(0), rows_(0)
40 | {
41 | private_node_handle_.reset(
42 | new ros::NodeHandle(parent_node_handle, "libsgm_gpu")
43 | );
44 | // Get parameters used in SGM algorithm
45 | p1_ = static_cast(private_node_handle_->param("p1", 6));
46 | p2_ = static_cast(private_node_handle_->param("p2", 96));
47 | check_consistency_ = private_node_handle_->param("check_consistency", true);
48 |
49 | // Create streams
50 | cudaStreamCreate(&stream1_);
51 | cudaStreamCreate(&stream2_);
52 | cudaStreamCreate(&stream3_);
53 | }
54 |
55 | SgmGpu::~SgmGpu()
56 | {
57 | freeMemory();
58 |
59 | cudaStreamDestroy(stream1_);
60 | cudaStreamDestroy(stream2_);
61 | cudaStreamDestroy(stream3_);
62 | }
63 |
64 | void SgmGpu::allocateMemory(uint32_t cols, uint32_t rows)
65 | {
66 | freeMemory();
67 |
68 | cols_ = cols;
69 | rows_ = rows;
70 |
71 | int total_pixel = cols_ * rows_;
72 | cudaMalloc((void **)&d_im0_, sizeof(uint8_t) * total_pixel);
73 | cudaMalloc((void **)&d_im1_, sizeof(uint8_t) * total_pixel);
74 |
75 | cudaMalloc((void **)&d_transform0_, sizeof(cost_t) * total_pixel);
76 | cudaMalloc((void **)&d_transform1_, sizeof(cost_t) * total_pixel);
77 |
78 | int cost_volume_size = total_pixel * MAX_DISPARITY;
79 | cudaMalloc((void **)&d_cost_, sizeof(uint8_t) * cost_volume_size);
80 |
81 | cudaMalloc((void **)&d_L0_, sizeof(uint8_t) * cost_volume_size);
82 | cudaMalloc((void **)&d_L1_, sizeof(uint8_t) * cost_volume_size);
83 | cudaMalloc((void **)&d_L2_, sizeof(uint8_t) * cost_volume_size);
84 | cudaMalloc((void **)&d_L3_, sizeof(uint8_t) * cost_volume_size);
85 | cudaMalloc((void **)&d_L4_, sizeof(uint8_t) * cost_volume_size);
86 | cudaMalloc((void **)&d_L5_, sizeof(uint8_t) * cost_volume_size);
87 | cudaMalloc((void **)&d_L6_, sizeof(uint8_t) * cost_volume_size);
88 | cudaMalloc((void **)&d_L7_, sizeof(uint8_t) * cost_volume_size);
89 |
90 | cudaMalloc((void **)&d_s_, sizeof(uint16_t) * cost_volume_size);
91 |
92 | cudaMalloc((void **)&d_disparity_, sizeof(uint8_t) * total_pixel);
93 | cudaMalloc((void **)&d_disparity_filtered_uchar_, sizeof(uint8_t) * total_pixel);
94 | cudaMalloc((void **)&d_disparity_right_, sizeof(uint8_t) * total_pixel);
95 | cudaMalloc((void **)&d_disparity_right_filtered_uchar_, sizeof(uint8_t) * total_pixel);
96 |
97 | memory_allocated_ = true;
98 | }
99 |
100 | void SgmGpu::freeMemory() {
101 | if (!memory_allocated_)
102 | return;
103 |
104 | cudaFree(d_im0_);
105 | cudaFree(d_im1_);
106 | cudaFree(d_transform0_);
107 | cudaFree(d_transform1_);
108 | cudaFree(d_L0_);
109 | cudaFree(d_L1_);
110 | cudaFree(d_L2_);
111 | cudaFree(d_L3_);
112 | cudaFree(d_L4_);
113 | cudaFree(d_L5_);
114 | cudaFree(d_L6_);
115 | cudaFree(d_L7_);
116 | cudaFree(d_disparity_);
117 | cudaFree(d_disparity_filtered_uchar_);
118 | cudaFree(d_disparity_right_);
119 | cudaFree(d_disparity_right_filtered_uchar_);
120 | cudaFree(d_cost_);
121 | cudaFree(d_s_);
122 |
123 | memory_allocated_ = false;
124 | }
125 |
126 |
127 | bool SgmGpu::computeDisparity(
128 | const sensor_msgs::Image& left_image,
129 | const sensor_msgs::Image& right_image,
130 | const sensor_msgs::CameraInfo& left_camera_info,
131 | const sensor_msgs::CameraInfo& right_camera_info,
132 | stereo_msgs::DisparityImage& disparity_msg
133 | )
134 | {
135 | if (left_image.width != right_image.width || left_image.height != right_image.height)
136 | {
137 | ROS_ERROR_STREAM_NAMED("libsgm_gpu",
138 | "Image dimension of left and right are not same: \n" <<
139 | "Left: " << left_image.width << "x" << left_image.height << "\n" <<
140 | "Right: " << right_image.width << "x" << right_image.height
141 | );
142 | return false;
143 | }
144 |
145 | if (left_image.encoding != right_image.encoding)
146 | {
147 | ROS_ERROR_STREAM_NAMED("libsgm_gpu",
148 | "Image encoding of left and right are not same: \n" <<
149 | "Left: " << left_image.encoding << "\n" <<
150 | "Right: " << right_image.encoding
151 | );
152 | return false;
153 | }
154 |
155 | // Convert to 8 bit grayscale image
156 | cv_bridge::CvImagePtr left_mono8 = cv_bridge::toCvCopy(
157 | left_image,
158 | sensor_msgs::image_encodings::MONO8
159 | );
160 | cv_bridge::CvImagePtr right_mono8 = cv_bridge::toCvCopy(
161 | right_image,
162 | sensor_msgs::image_encodings::MONO8
163 | );
164 |
165 | // Resize images to their width and height divisible by 4 for limit of CUDA code
166 | resizeToDivisibleBy4(left_mono8->image, right_mono8->image);
167 |
168 | // Reallocate memory if needed
169 | bool size_changed = (
170 | cols_ != left_mono8->image.cols ||
171 | rows_ != left_mono8->image.rows
172 | );
173 | if (!memory_allocated_ || size_changed)
174 | allocateMemory(left_mono8->image.cols, left_mono8->image.rows);
175 |
176 | // Copy image to GPU device
177 | size_t mono8_image_size = left_mono8->image.total() * sizeof(uint8_t);
178 | cudaMemcpyAsync(d_im0_, left_mono8->image.ptr(),
179 | mono8_image_size, cudaMemcpyHostToDevice, stream1_);
180 | cudaMemcpyAsync(d_im1_, right_mono8->image.ptr(),
181 | mono8_image_size, cudaMemcpyHostToDevice, stream1_);
182 |
183 | BLOCK_SIZE_.x = 32;
184 | BLOCK_SIZE_.y = 32;
185 |
186 | grid_size_.x = (cols_ + BLOCK_SIZE_.x-1) / BLOCK_SIZE_.x;
187 | grid_size_.y = (rows_ + BLOCK_SIZE_.y-1) / BLOCK_SIZE_.y;
188 |
189 | CenterSymmetricCensusKernelSM2<<>>(d_im0_, d_im1_, d_transform0_, d_transform1_, rows_, cols_);
190 |
191 | cudaStreamSynchronize(stream1_);
192 | HammingDistanceCostKernel<<>>(d_transform0_, d_transform1_, d_cost_, rows_, cols_);
193 |
194 | const int PIXELS_PER_BLOCK = COSTAGG_BLOCKSIZE/WARP_SIZE;
195 | const int PIXELS_PER_BLOCK_HORIZ = COSTAGG_BLOCKSIZE_HORIZ/WARP_SIZE;
196 |
197 | // Cost Aggregation
198 | CostAggregationKernelLeftToRight<<<(rows_+PIXELS_PER_BLOCK_HORIZ-1)/PIXELS_PER_BLOCK_HORIZ, COSTAGG_BLOCKSIZE_HORIZ, 0, stream2_>>>(d_cost_, d_L0_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
199 | CostAggregationKernelRightToLeft<<<(rows_+PIXELS_PER_BLOCK_HORIZ-1)/PIXELS_PER_BLOCK_HORIZ, COSTAGG_BLOCKSIZE_HORIZ, 0, stream3_>>>(d_cost_, d_L1_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
200 | CostAggregationKernelUpToDown<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L2_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
201 | CostAggregationKernelDownToUp<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L3_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
202 | CostAggregationKernelDiagonalDownUpLeftRight<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L4_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
203 | CostAggregationKernelDiagonalUpDownLeftRight<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L5_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
204 | CostAggregationKernelDiagonalDownUpRightLeft<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L6_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
205 | CostAggregationKernelDiagonalUpDownRightLeft<<<(cols_+PIXELS_PER_BLOCK-1)/PIXELS_PER_BLOCK, COSTAGG_BLOCKSIZE, 0, stream1_>>>(d_cost_, d_L7_, d_s_, p1_, p2_, rows_, cols_, d_transform0_, d_transform1_, d_disparity_, d_L0_, d_L1_, d_L2_, d_L3_, d_L4_, d_L5_, d_L6_);
206 |
207 | int total_pixel = rows_ * cols_;
208 | MedianFilter3x3<<<(total_pixel+MAX_DISPARITY-1)/MAX_DISPARITY, MAX_DISPARITY, 0, stream1_>>>(d_disparity_, d_disparity_filtered_uchar_, rows_, cols_);
209 |
210 | if (check_consistency_) {
211 | ChooseRightDisparity<<>>(d_disparity_right_, d_s_, rows_, cols_);
212 | MedianFilter3x3<<<(total_pixel+MAX_DISPARITY-1)/MAX_DISPARITY, MAX_DISPARITY, 0, stream1_>>>(d_disparity_right_, d_disparity_right_filtered_uchar_, rows_, cols_);
213 |
214 | LeftRightConsistencyCheck<<>>(d_disparity_filtered_uchar_, d_disparity_right_filtered_uchar_, rows_, cols_);
215 | }
216 | cudaError_t err = cudaGetLastError();
217 | if (err != cudaSuccess) {
218 | ROS_ERROR_NAMED("libsgm_gpu", "%s %d\n", cudaGetErrorString(err), err);
219 | return false;
220 | }
221 |
222 | cudaDeviceSynchronize();
223 | cv::Mat disparity(rows_, cols_, CV_8UC1);
224 | cudaMemcpy(disparity.data, d_disparity_filtered_uchar_, sizeof(uint8_t)*total_pixel, cudaMemcpyDeviceToHost);
225 |
226 | // Restore image size if resized to be divisible by 4
227 | if (cols_ != left_image.width || rows_ != left_image.height)
228 | {
229 | cv::Size input_size(left_image.width, left_image.height);
230 | cv::resize(disparity, disparity, input_size, 0, 0, cv::INTER_AREA);
231 | }
232 |
233 | convertToMsg(disparity, left_camera_info, right_camera_info, disparity_msg);
234 |
235 | return true;
236 | }
237 |
238 | void SgmGpu::resizeToDivisibleBy4(cv::Mat& left_image, cv::Mat& right_image)
239 | {
240 | bool need_resize = false;
241 | cv::Size original_size, resized_size;
242 |
243 | original_size = cv::Size(left_image.cols, left_image.rows);
244 | resized_size = original_size;
245 | if (original_size.width % 4 != 0)
246 | {
247 | need_resize = true;
248 | resized_size.width = (original_size.width / 4 + 1) * 4;
249 | }
250 | if (original_size.height % 4 != 0)
251 | {
252 | need_resize = true;
253 | resized_size.height = (original_size.height / 4 + 1) * 4;
254 | }
255 |
256 | if (need_resize)
257 | {
258 | cv::resize(left_image, left_image, resized_size, 0, 0, cv::INTER_LINEAR);
259 | cv::resize(right_image, right_image, resized_size, 0, 0, cv::INTER_LINEAR);
260 | }
261 | }
262 |
263 | void SgmGpu::convertToMsg(
264 | const cv::Mat_& disparity,
265 | const sensor_msgs::CameraInfo& left_camera_info,
266 | const sensor_msgs::CameraInfo& right_camera_info,
267 | stereo_msgs::DisparityImage& disparity_msg
268 | )
269 | {
270 | cv::Mat disparity_32f;
271 | disparity.convertTo(disparity_32f, CV_32F);
272 | cv_bridge::CvImage disparity_converter(
273 | left_camera_info.header,
274 | sensor_msgs::image_encodings::TYPE_32FC1,
275 | disparity_32f
276 | );
277 | disparity_converter.toImageMsg(disparity_msg.image);
278 |
279 | disparity_msg.header = left_camera_info.header;
280 |
281 | image_geometry::StereoCameraModel stereo_model;
282 | stereo_model.fromCameraInfo(left_camera_info, right_camera_info);
283 | disparity_msg.f = stereo_model.left().fx();
284 | disparity_msg.T = stereo_model.baseline();
285 |
286 | disparity_msg.min_disparity = 0.0;
287 | disparity_msg.max_disparity = MAX_DISPARITY;
288 | disparity_msg.delta_d = 1.0;
289 | }
290 |
291 | } // namespace sgm_gpu
292 |
--------------------------------------------------------------------------------
/src/sgm_gpu_node.cpp:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu_node.h"
18 |
19 | namespace sgm_gpu
20 | {
21 |
22 | SgmGpuNode::SgmGpuNode()
23 | {
24 | node_handle_.reset(new ros::NodeHandle());
25 | private_node_handle_.reset(new ros::NodeHandle("~"));
26 |
27 | image_transport_.reset(new image_transport::ImageTransport(*node_handle_));
28 |
29 | sgm_.reset(new SgmGpu(*private_node_handle_));
30 |
31 | disparity_pub_ = private_node_handle_->advertise("disparity", 1);
32 |
33 | // Subscribe left and right Image topic
34 | std::string left_base_topic = node_handle_->resolveName("left_image");
35 | std::string right_base_topic = node_handle_->resolveName("right_image");
36 | left_image_sub_.subscribe(*image_transport_, left_base_topic, 10);
37 | right_image_sub_.subscribe(*image_transport_, right_base_topic, 10);
38 |
39 | // Find CameraInfo topic from corresponded Image topic and subscribe it
40 | std::string left_info_topic = image_transport::getCameraInfoTopic(left_base_topic);
41 | std::string right_info_topic = image_transport::getCameraInfoTopic(right_base_topic);
42 | left_info_sub_.subscribe(*node_handle_, left_info_topic, 10);
43 | right_info_sub_.subscribe(*node_handle_, right_info_topic, 10);
44 |
45 | stereo_synchronizer_.reset(
46 | new StereoSynchronizer(left_image_sub_, right_image_sub_, left_info_sub_, right_info_sub_, 10)
47 | );
48 | stereo_synchronizer_->registerCallback(&SgmGpuNode::stereoCallback, this);
49 | }
50 |
51 | void SgmGpuNode::stereoCallback(
52 | const sensor_msgs::ImageConstPtr &left_image,
53 | const sensor_msgs::ImageConstPtr &right_image,
54 | const sensor_msgs::CameraInfoConstPtr &left_info,
55 | const sensor_msgs::CameraInfoConstPtr &right_info
56 | )
57 | {
58 | if (disparity_pub_.getNumSubscribers() == 0)
59 | return;
60 |
61 | stereo_msgs::DisparityImage disparity;
62 | sgm_->computeDisparity(*left_image, *right_image, *left_info, *right_info, disparity);
63 |
64 | disparity_pub_.publish(disparity);
65 | }
66 |
67 | } // namespace sgm_gpu
68 |
--------------------------------------------------------------------------------
/src/sgm_gpu_node_main.cpp:
--------------------------------------------------------------------------------
1 | /***********************************************************************
2 | Copyright (C) 2020 Hironori Fujimoto
3 |
4 | This program is free software: you can redistribute it and/or modify
5 | it under the terms of the GNU General Public License as published by
6 | the Free Software Foundation, either version 3 of the License, or
7 | (at your option) any later version.
8 |
9 | This program is distributed in the hope that it will be useful,
10 | but WITHOUT ANY WARRANTY; without even the implied warranty of
11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 | GNU General Public License for more details.
13 | You should have received a copy of the GNU General Public License
14 | along with this program. If not, see .
15 | ***********************************************************************/
16 |
17 | #include "sgm_gpu_node.h"
18 | #include
19 |
20 | int main(int argc, char** argv)
21 | {
22 | ros::init(argc, argv, "sgm_gpu_node");
23 | sgm_gpu::SgmGpuNode sgm_gpu;
24 | ros::spin();
25 |
26 | return 0;
27 | }
28 |
29 |
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/test_input.bag:
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https://raw.githubusercontent.com/ActiveIntelligentSystemsLab/sgm_gpu_ros/72920928827efa7b0aec9c77b6bfe749a667caf7/test_input.bag
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