Chengfang.LU/HSAP
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation

Browse Source 
Major changes:
- New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind
- 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理
- Data catalog with charts (DMS/ADAS/Lane 3-tab view)
- Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance
- Audit enhancements: batch operations, rejection categories, Feishu notifications
- Operation audit log (操作日志)
- World model simulation studio (仿真工坊)
- Dataset version management with snapshots and diff
- ADAS 7-class dataset integration (138K images organized + compressed)
- User management with Feishu integration and pagination
- CRUD/search/filter on all pages, card layout redesign
- PIL-optimized image overlay rendering
- Auto-snapshot on build, in_review workflow stage
- Removed embedded algorithm code (now in workspace)
This commit is contained in:
Chengfang Lu
2026-06-03 11:40:21 +08:00
parent 7c43b44c57
commit e72bc061c5
5487 changed files with 979207 additions and 6197 deletions
Download Patch File Download Diff File Expand all files Collapse all files

79
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/CMakeLists.txt Executable file
View File

@@ -0,0 +1,79 @@
# Thanks for the contribution of zchrissirhcz imzhuo@foxmail.com
cmake_minimum_required(VERSION 3.1)
project(culane_evaluator)
set(CMAKE_CXX_STANDARD 11)
add_definitions(
-DCPU_ONLY
)
set(SRC_LST
${CMAKE_SOURCE_DIR}/src/counter.cpp
${CMAKE_SOURCE_DIR}/src/evaluate.cpp
${CMAKE_SOURCE_DIR}/src/lane_compare.cpp
${CMAKE_SOURCE_DIR}/src/spline.cpp
)
set(HDR_LST
${CMAKE_SOURCE_DIR}/include/counter.hpp
${CMAKE_SOURCE_DIR}/include/hungarianGraph.hpp
${CMAKE_SOURCE_DIR}/include/lane_compare.hpp
${CMAKE_SOURCE_DIR}/include/spline.hpp
)
if (CMAKE_SYSTEM_NAME MATCHES "Windows")
list(APPEND SRC_LST ${CMAKE_SOURCE_DIR}/getopt/getopt.c)
list(APPEND HDR_LST ${CMAKE_SOURCE_DIR}/getopt/getopt.h)
endif()
add_executable(${PROJECT_NAME}
${SRC_LST}
${HDR_LST}
)
set(dep_libs "")
#--- OpenCV
# You may switch different version of OpenCV like this:
# set(OpenCV_DIR "/usr/local/opencv-4.3.0" CACHE PATH "")
find_package(OpenCV REQUIRED
COMPONENTS core highgui imgproc imgcodecs
)
if(NOT OpenCV_FOUND) # if not OpenCV 4.x/3.x, then imgcodecs are not found
find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
endif()
list(APPEND dep_libs
PUBLIC ${OpenCV_LIBS}
)
#--- OpenMP
find_package(OpenMP)
if(NOT TARGET OpenMP::OpenMP_CXX AND (OpenMP_CXX_FOUND OR OPENMP_FOUND))
target_compile_options(${PROJECT_NAME} PRIVATE ${OpenMP_CXX_FLAGS})
endif()
if(OpenMP_CXX_FOUND OR OPENMP_FOUND)
message(STATUS "Building with OpenMP")
if(OpenMP_CXX_FOUND)
list(APPEND dep_libs PUBLIC OpenMP::OpenMP_CXX)
else()
list(APPEND dep_libs PRIVATE "${OpenMP_CXX_FLAGS}")
endif()
endif()
set(dep_incs ${CMAKE_SOURCE_DIR}/include)
if (CMAKE_SYSTEM_NAME MATCHES "Windows")
list(APPEND dep_incs "${CMAKE_SOURCE_DIR}/getopt")
endif()
# --- target config with include dirs / libs
target_link_libraries(${PROJECT_NAME}
${dep_libs}
)
target_include_directories(${PROJECT_NAME}
PUBLIC ${dep_incs}
)

50
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/Makefile Executable file
View File

@@ -0,0 +1,50 @@
PROJECT_NAME:= evaluate
# config ----------------------------------
INCLUDE_DIRS := include
LIBRARY_DIRS := lib
# You may switch different versions of opencv like this:
# export PKG_CONFIG_PATH=/usr/local/opencv-4.1.1/lib/pkgconfig:$PKG_CONFIG_PATH
# then use `pkg-config opencv4 --cflags --libs` since `opencv4.pc` is found
COMMON_FLAGS := -DCPU_ONLY
CXXFLAGS := -std=c++11 -fopenmp #`pkg-config --cflags opencv`
LDFLAGS := -fopenmp -Wl,-rpath,./lib #`pkg-config --libs opencv`
BUILD_DIR := build
# make rules -------------------------------
CXX ?= g++
BUILD_DIR ?= ./build
LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
CXXFLAGS += $(COMMON_FLAGS) $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
LDFLAGS += $(COMMON_FLAGS) $(foreach includedir,$(LIBRARY_DIRS),-L$(includedir)) $(foreach library,$(LIBRARIES),-l$(library))
SRC_DIRS += $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
CXX_SRCS += $(shell find src/ -name "*.cpp")
CXX_TARGETS:=$(patsubst %.cpp, $(BUILD_DIR)/%.o, $(CXX_SRCS))
ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)))
.PHONY: all
all: $(PROJECT_NAME)
.PHONY: $(ALL_BUILD_DIRS)
$(ALL_BUILD_DIRS):
@mkdir -p $@
$(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)
@echo "CXX" $<
@$(CXX) $(CXXFLAGS) -c -o $@ $<
$(PROJECT_NAME): $(CXX_TARGETS)
@echo "CXX/LD" $@
@$(CXX) -o $@ $^ $(LDFLAGS)
.PHONY: clean
clean:
@rm -rf $(CXX_TARGETS)
@rm -rf $(PROJECT_NAME)
@rm -rf $(BUILD_DIR)

23
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/calTotal.m Executable file
View File

@@ -0,0 +1,23 @@
%% Calculate overall Fmeasure from each scenarios
clc; clear; close all;
allFile = 'output/vgg_SCNN_DULR_w9_iou0.5.txt';
all = textread(allFile,'%s');
TP = 0;
FP = 0;
FN = 0;
for i=1:9
tpline = (i-1)*14+4;
tp = str2double(all(tpline));
fp = str2double(all(tpline+2));
fn = str2double(all(tpline+4));
TP = TP + tp;
FP = FP + fp;
FN = FN + fn;
end
P = TP/(TP + FP)
R = TP/(TP + FN)
F = 2*P*R/(P + R)*100

201
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/getopt/LICENSE.md Executable file
View File

@@ -0,0 +1,201 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

51
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/getopt/getopt.c Executable file
View File

@@ -0,0 +1,51 @@
/* *****************************************************************
*
* Copyright 2016 Microsoft
*
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
******************************************************************/
#include "getopt.h"
#include <windows.h>
char* optarg = NULL;
int optind = 1;
int getopt(int argc, char *const argv[], const char *optstring)
{
if ((optind >= argc) || (argv[optind][0] != '-') || (argv[optind][0] == 0))
{
return -1;
}
int opt = argv[optind][1];
const char *p = strchr(optstring, opt);
if (p == NULL)
{
return '?';
}
if (p[1] == ':')
{
optind++;
if (optind >= argc)
{
return '?';
}
optarg = argv[optind];
optind++;
}
return opt;
}

36
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/getopt/getopt.h Executable file
View File

@@ -0,0 +1,36 @@
/* *****************************************************************
*
* Copyright 2016 Microsoft
*
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
******************************************************************/
#ifndef GETOPT_H__
#define GETOPT_H__
#ifdef __cplusplus
extern "C" {
#endif
extern char *optarg;
extern int optind;
int getopt(int argc, char *const argv[], const char *optstring);
#ifdef __cplusplus
}
#endif
#endif

3
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/getopt/readme.txt Executable file
View File

@@ -0,0 +1,3 @@
For windows build, `getopt.c` and `getopt.h` are required.
They are taken from the [iotivity](https://github.com/iotivity/iotivity) open source project, under Apache LICENSE 2.0.

47
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/include/counter.hpp Executable file
View File

@@ -0,0 +1,47 @@
#ifndef COUNTER_HPP
#define COUNTER_HPP
#include "lane_compare.hpp"
#include "hungarianGraph.hpp"
#include <iostream>
#include <algorithm>
#include <tuple>
#include <vector>
#include <opencv2/core.hpp>
using namespace std;
using namespace cv;
// before coming to use functions of this class, the lanes should resize to im_width and im_height using resize_lane() in lane_compare.hpp
class Counter
{
public:
Counter(int _im_width, int _im_height, double _iou_threshold=0.4, int _lane_width=10):tp(0),fp(0),fn(0){
im_width = _im_width;
im_height = _im_height;
sim_threshold = _iou_threshold;
lane_compare = new LaneCompare(_im_width, _im_height, _lane_width, LaneCompare::IOU);
};
double get_precision(void);
double get_recall(void);
long getTP(void);
long getFP(void);
long getFN(void);
void setTP(long);
void setFP(long);
void setFN(long);
// direct add tp, fp, tn and fn
// first match with hungarian
tuple<vector<int>, long, long, long, long> count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes);
void makeMatch(const vector<vector<double> > &similarity, vector<int> &match1, vector<int> &match2);
private:
double sim_threshold;
int im_width;
int im_height;
long tp;
long fp;
long fn;
LaneCompare *lane_compare;
};
#endif

71
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/include/hungarianGraph.hpp Executable file
View File

@@ -0,0 +1,71 @@
#ifndef HUNGARIAN_GRAPH_HPP
#define HUNGARIAN_GRAPH_HPP
#include <vector>
using namespace std;
struct pipartiteGraph {
vector<vector<double> > mat;
vector<bool> leftUsed, rightUsed;
vector<double> leftWeight, rightWeight;
vector<int>rightMatch, leftMatch;
int leftNum, rightNum;
bool matchDfs(int u) {
leftUsed[u] = true;
for (int v = 0; v < rightNum; v++) {
if (!rightUsed[v] && fabs(leftWeight[u] + rightWeight[v] - mat[u][v]) < 1e-2) {
rightUsed[v] = true;
if (rightMatch[v] == -1 || matchDfs(rightMatch[v])) {
rightMatch[v] = u;
leftMatch[u] = v;
return true;
}
}
}
return false;
}
void resize(int leftNum, int rightNum) {
this->leftNum = leftNum;
this->rightNum = rightNum;
leftMatch.resize(leftNum);
rightMatch.resize(rightNum);
leftUsed.resize(leftNum);
rightUsed.resize(rightNum);
leftWeight.resize(leftNum);
rightWeight.resize(rightNum);
mat.resize(leftNum);
for (int i = 0; i < leftNum; i++) mat[i].resize(rightNum);
}
void match() {
for (int i = 0; i < leftNum; i++) leftMatch[i] = -1;
for (int i = 0; i < rightNum; i++) rightMatch[i] = -1;
for (int i = 0; i < rightNum; i++) rightWeight[i] = 0;
for (int i = 0; i < leftNum; i++) {
leftWeight[i] = -1e5;
for (int j = 0; j < rightNum; j++) {
if (leftWeight[i] < mat[i][j]) leftWeight[i] = mat[i][j];
}
}
for (int u = 0; u < leftNum; u++) {
while (1) {
for (int i = 0; i < leftNum; i++) leftUsed[i] = false;
for (int i = 0; i < rightNum; i++) rightUsed[i] = false;
if (matchDfs(u)) break;
double d = 1e10;
for (int i = 0; i < leftNum; i++) {
if (leftUsed[i] ) {
for (int j = 0; j < rightNum; j++) {
if (!rightUsed[j]) d = min(d, leftWeight[i] + rightWeight[j] - mat[i][j]);
}
}
}
if (d == 1e10) return ;
for (int i = 0; i < leftNum; i++) if (leftUsed[i]) leftWeight[i] -= d;
for (int i = 0; i < rightNum; i++) if (rightUsed[i]) rightWeight[i] += d;
}
}
}
};
#endif // HUNGARIAN_GRAPH_HPP

37
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/include/lane_compare.hpp Executable file
View File

@@ -0,0 +1,37 @@
#ifndef LANE_COMPARE_HPP
#define LANE_COMPARE_HPP
#include "spline.hpp"
#include <vector>
#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
using namespace std;
using namespace cv;
class LaneCompare{
public:
enum CompareMode{
IOU,
Caltech
};
LaneCompare(int _im_width, int _im_height, int _lane_width = 10, CompareMode _compare_mode = IOU){
im_width = _im_width;
im_height = _im_height;
compare_mode = _compare_mode;
lane_width = _lane_width;
}
double get_lane_similarity(const vector<Point2f> &lane1, const vector<Point2f> &lane2);
void resize_lane(vector<Point2f> &curr_lane, int curr_width, int curr_height);
private:
CompareMode compare_mode;
int im_width;
int im_height;
int lane_width;
Spline splineSolver;
};
#endif

28
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/include/spline.hpp Executable file
View File

@@ -0,0 +1,28 @@
#ifndef SPLINE_HPP
#define SPLINE_HPP
#include <vector>
#include <cstdio>
#include <math.h>
#include <opencv2/core.hpp>
using namespace cv;
using namespace std;
struct Func {
double a_x;
double b_x;
double c_x;
double d_x;
double a_y;
double b_y;
double c_y;
double d_y;
double h;
};
class Spline {
public:
vector<Point2f> splineInterpTimes(const vector<Point2f> &tmp_line, int times);
vector<Point2f> splineInterpStep(vector<Point2f> tmp_line, double step);
vector<Func> cal_fun(const vector<Point2f> &point_v);
};
#endif

37
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/run-full.sh Executable file
View File

@@ -0,0 +1,37 @@
root=../../
data_dir=${root}data/CULane/
exp=vgg_SCNN_DULR_w9
detect_dir=${root}tools/prob2lines/output/${exp}/
w_lane=30;
iou=0.5; # Set iou to 0.3 or 0.5
im_w=1640
im_h=590
frame=1
list0=${data_dir}list/test_split/test0_normal.txt
list1=${data_dir}list/test_split/test1_crowd.txt
list2=${data_dir}list/test_split/test2_hlight.txt
list3=${data_dir}list/test_split/test3_shadow.txt
list4=${data_dir}list/test_split/test4_noline.txt
list5=${data_dir}list/test_split/test5_arrow.txt
list6=${data_dir}list/test_split/test6_curve.txt
list7=${data_dir}list/test_split/test7_cross.txt
list8=${data_dir}list/test_split/test8_night.txt
out0=./output/out0_normal.txt
out1=./output/out1_crowd.txt
out2=./output/out2_hlight.txt
out3=./output/out3_shadow.txt
out4=./output/out4_noline.txt
out5=./output/out5_arrow.txt
out6=./output/out6_curve.txt
out7=./output/out7_cross.txt
out8=./output/out8_night.txt
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list0 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out0
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list1 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out1
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list2 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out2
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list3 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out3
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list4 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out4
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list5 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out5
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list6 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out6
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list7 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out7
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list8 -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out8
cat ./output/out*.txt>./output/${exp}_iou${iou}_split.txt

12
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/run-lite.sh Executable file
View File

@@ -0,0 +1,12 @@
root=../../
data_dir=${root}data/CULane/
exp=vgg_SCNN_DULR_w9
detect_dir=${root}tools/prob2lines/output/${exp}/
w_lane=30;
iou=0.5; # Set iou to 0.3 or 0.5
im_w=1640
im_h=590
frame=1
list=${data_dir}list/test.txt
out=./output/${exp}_iou${iou}.txt
./evaluate -a $data_dir -d $detect_dir -i $data_dir -l $list -w $w_lane -t $iou -c $im_w -r $im_h -f $frame -o $out

134
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/src/counter.cpp Executable file
View File

@@ -0,0 +1,134 @@
/*************************************************************************
> File Name: counter.cpp
> Author: Xingang Pan, Jun Li
> Mail: px117@ie.cuhk.edu.hk
> Created Time: Thu Jul 14 20:23:08 2016
************************************************************************/
#include "counter.hpp"
double Counter::get_precision(void)
{
cerr<<"tp: "<<tp<<" fp: "<<fp<<" fn: "<<fn<<endl;
if(tp+fp == 0)
{
cerr<<"no positive detection"<<endl;
return -1;
}
return tp/double(tp + fp);
}
double Counter::get_recall(void)
{
if(tp+fn == 0)
{
cerr<<"no ground truth positive"<<endl;
return -1;
}
return tp/double(tp + fn);
}
long Counter::getTP(void)
{
return tp;
}
long Counter::getFP(void)
{
return fp;
}
long Counter::getFN(void)
{
return fn;
}
void Counter::setTP(long value)
{
tp = value;
}
void Counter::setFP(long value)
{
fp = value;
}
void Counter::setFN(long value)
{
fn = value;
}
tuple<vector<int>, long, long, long, long> Counter::count_im_pair(const vector<vector<Point2f> > &anno_lanes, const vector<vector<Point2f> > &detect_lanes)
{
vector<int> anno_match(anno_lanes.size(), -1);
vector<int> detect_match;
if(anno_lanes.empty())
{
return make_tuple(anno_match, 0, detect_lanes.size(), 0, 0);
}
if(detect_lanes.empty())
{
return make_tuple(anno_match, 0, 0, 0, anno_lanes.size());
}
// hungarian match first
// first calc similarity matrix
vector<vector<double> > similarity(anno_lanes.size(), vector<double>(detect_lanes.size(), 0));
for(int i=0; i<anno_lanes.size(); i++)
{
const vector<Point2f> &curr_anno_lane = anno_lanes[i];
for(int j=0; j<detect_lanes.size(); j++)
{
const vector<Point2f> &curr_detect_lane = detect_lanes[j];
similarity[i][j] = lane_compare->get_lane_similarity(curr_anno_lane, curr_detect_lane);
}
}
makeMatch(similarity, anno_match, detect_match);
int curr_tp = 0;
// count and add
for(int i=0; i<anno_lanes.size(); i++)
{
if(anno_match[i]>=0 && similarity[i][anno_match[i]] > sim_threshold)
{
curr_tp++;
}
else
{
anno_match[i] = -1;
}
}
int curr_fn = anno_lanes.size() - curr_tp;
int curr_fp = detect_lanes.size() - curr_tp;
return make_tuple(anno_match, curr_tp, curr_fp, 0, curr_fn);
}
void Counter::makeMatch(const vector<vector<double> > &similarity, vector<int> &match1, vector<int> &match2) {
int m = similarity.size();
int n = similarity[0].size();
pipartiteGraph gra;
bool have_exchange = false;
if (m > n) {
have_exchange = true;
swap(m, n);
}
gra.resize(m, n);
for (int i = 0; i < gra.leftNum; i++) {
for (int j = 0; j < gra.rightNum; j++) {
if(have_exchange)
gra.mat[i][j] = similarity[j][i];
else
gra.mat[i][j] = similarity[i][j];
}
}
gra.match();
match1 = gra.leftMatch;
match2 = gra.rightMatch;
if (have_exchange) swap(match1, match2);
}

302
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/src/evaluate.cpp Executable file
View File

@@ -0,0 +1,302 @@
/*************************************************************************
> File Name: evaluate.cpp
> Author: Xingang Pan, Jun Li
> Mail: px117@ie.cuhk.edu.hk
> Created Time: 2016年07月14日 星期四 18时28分45秒
************************************************************************/
#include "counter.hpp"
#include "spline.hpp"
#if __linux__
#include <unistd.h>
#elif _MSC_VER
#include "getopt.h"
#endif
#include <iostream>
#include <fstream>
#include <sstream>
#include <cstdlib>
#include <string>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
void help(void)
{
cout<<"./evaluate [OPTIONS]"<<endl;
cout<<"-h : print usage help"<<endl;
cout<<"-a : directory for annotation files (default: /data/driving/eval_data/anno_label/)"<<endl;
cout<<"-d : directory for detection files (default: /data/driving/eval_data/predict_label/)"<<endl;
cout<<"-i : directory for image files (default: /data/driving/eval_data/img/)"<<endl;
cout<<"-l : list of images used for evaluation (default: /data/driving/eval_data/img/all.txt)"<<endl;
cout<<"-w : width of the lanes (default: 10)"<<endl;
cout<<"-t : threshold of iou (default: 0.4)"<<endl;
cout<<"-c : cols (max image width) (default: 1920)"<<endl;
cout<<"-r : rows (max image height) (default: 1080)"<<endl;
cout<<"-s : show visualization"<<endl;
cout<<"-f : start frame in the test set (default: 1)"<<endl;
}
void read_lane_file(const string &file_name, vector<vector<Point2f> > &lanes);
void visualize(string &full_im_name, vector<vector<Point2f> > &anno_lanes, vector<vector<Point2f> > &detect_lanes, vector<int> anno_match, int width_lane);
int main(int argc, char **argv)
{
// process params
string anno_dir = "/data/driving/eval_data/anno_label/";
string detect_dir = "/data/driving/eval_data/predict_label/";
string im_dir = "/data/driving/eval_data/img/";
string list_im_file = "/data/driving/eval_data/img/all.txt";
string output_file = "./output.txt";
int width_lane = 10;
double iou_threshold = 0.4;
int im_width = 1920;
int im_height = 1080;
int oc;
bool show = false;
int frame = 1;
while((oc = getopt(argc, argv, "ha:d:i:l:w:t:c:r:sf:o:")) != -1)
{
switch(oc)
{
case 'h':
help();
return 0;
case 'a':
anno_dir = optarg;
break;
case 'd':
detect_dir = optarg;
break;
case 'i':
im_dir = optarg;
break;
case 'l':
list_im_file = optarg;
break;
case 'w':
width_lane = atoi(optarg);
break;
case 't':
iou_threshold = atof(optarg);
break;
case 'c':
im_width = atoi(optarg);
break;
case 'r':
im_height = atoi(optarg);
break;
case 's':
show = true;
break;
case 'f':
frame = atoi(optarg);
break;
case 'o':
output_file = optarg;
break;
}
}
cout<<"------------Configuration---------"<<endl;
cout<<"anno_dir: "<<anno_dir<<endl;
cout<<"detect_dir: "<<detect_dir<<endl;
cout<<"im_dir: "<<im_dir<<endl;
cout<<"list_im_file: "<<list_im_file<<endl;
cout<<"width_lane: "<<width_lane<<endl;
cout<<"iou_threshold: "<<iou_threshold<<endl;
cout<<"im_width: "<<im_width<<endl;
cout<<"im_height: "<<im_height<<endl;
cout<<"-----------------------------------"<<endl;
cout<<"Evaluating the results..."<<endl;
// this is the max_width and max_height
if(width_lane<1)
{
cerr<<"width_lane must be positive"<<endl;
help();
return 1;
}
ifstream ifs_im_list(list_im_file, ios::in);
if(ifs_im_list.fail())
{
cerr<<"Error: file "<<list_im_file<<" not exist!"<<endl;
return 1;
}
Counter counter(im_width, im_height, iou_threshold, width_lane);
vector<int> anno_match;
string sub_im_name;
// pre-load filelist
vector<string> filelists;
while (getline(ifs_im_list, sub_im_name)) {
filelists.push_back(sub_im_name);
}
ifs_im_list.close();
vector<tuple<vector<int>, long, long, long, long>> tuple_lists;
tuple_lists.resize(filelists.size());
#pragma omp parallel for
for (int i = 0; i < filelists.size(); i++)
{
auto sub_im_name = filelists[i];
string full_im_name = im_dir + sub_im_name;
string sub_txt_name = sub_im_name.substr(0, sub_im_name.find_last_of(".")) + ".lines.txt";
string anno_file_name = anno_dir + sub_txt_name;
string detect_file_name = detect_dir + sub_txt_name;
vector<vector<Point2f> > anno_lanes;
vector<vector<Point2f> > detect_lanes;
read_lane_file(anno_file_name, anno_lanes);
read_lane_file(detect_file_name, detect_lanes);
//cerr<<count<<": "<<full_im_name<<endl;
tuple_lists[i] = counter.count_im_pair(anno_lanes, detect_lanes);
if (show)
{
auto anno_match = get<0>(tuple_lists[i]);
visualize(full_im_name, anno_lanes, detect_lanes, anno_match, width_lane);
waitKey(0);
}
}
long tp = 0, fp = 0, tn = 0, fn = 0;
for (auto result: tuple_lists) {
tp += get<1>(result);
fp += get<2>(result);
// tn = get<3>(result);
fn += get<4>(result);
}
counter.setTP(tp);
counter.setFP(fp);
counter.setFN(fn);
double precision = counter.get_precision();
double recall = counter.get_recall();
double F = 2 * precision * recall / (precision + recall);
cerr<<"finished process file"<<endl;
cout<<"precision: "<<precision<<endl;
cout<<"recall: "<<recall<<endl;
cout<<"Fmeasure: "<<F<<endl;
cout<<"----------------------------------"<<endl;
ofstream ofs_out_file;
ofs_out_file.open(output_file, ios::out);
ofs_out_file<<"file: "<<output_file<<endl;
ofs_out_file<<"tp: "<<counter.getTP()<<" fp: "<<counter.getFP()<<" fn: "<<counter.getFN()<<endl;
ofs_out_file<<"precision: "<<precision<<endl;
ofs_out_file<<"recall: "<<recall<<endl;
ofs_out_file<<"Fmeasure: "<<F<<endl<<endl;
ofs_out_file.close();
return 0;
}
void read_lane_file(const string &file_name, vector<vector<Point2f> > &lanes)
{
lanes.clear();
ifstream ifs_lane(file_name, ios::in);
if(ifs_lane.fail())
{
return;
}
string str_line;
while(getline(ifs_lane, str_line))
{
vector<Point2f> curr_lane;
stringstream ss;
ss<<str_line;
double x,y;
while(ss>>x>>y)
{
curr_lane.push_back(Point2f(x, y));
}
lanes.push_back(curr_lane);
}
ifs_lane.close();
}
void visualize(string &full_im_name, vector<vector<Point2f> > &anno_lanes, vector<vector<Point2f> > &detect_lanes, vector<int> anno_match, int width_lane)
{
Mat img = imread(full_im_name, 1);
Mat img2 = imread(full_im_name, 1);
vector<Point2f> curr_lane;
vector<Point2f> p_interp;
Spline splineSolver;
Scalar color_B = Scalar(255, 0, 0);
Scalar color_G = Scalar(0, 255, 0);
Scalar color_R = Scalar(0, 0, 255);
Scalar color_P = Scalar(255, 0, 255);
Scalar color;
for (int i=0; i<anno_lanes.size(); i++)
{
curr_lane = anno_lanes[i];
if(curr_lane.size() == 2)
{
p_interp = curr_lane;
}
else
{
p_interp = splineSolver.splineInterpTimes(curr_lane, 50);
}
if (anno_match[i] >= 0)
{
color = color_G;
}
else
{
color = color_G;
}
for (int n=0; n<p_interp.size()-1; n++)
{
line(img, p_interp[n], p_interp[n+1], color, width_lane);
line(img2, p_interp[n], p_interp[n+1], color, 2);
}
}
bool detected;
for (int i=0; i<detect_lanes.size(); i++)
{
detected = false;
curr_lane = detect_lanes[i];
if(curr_lane.size() == 2)
{
p_interp = curr_lane;
}
else
{
p_interp = splineSolver.splineInterpTimes(curr_lane, 50);
}
for (int n=0; n<anno_lanes.size(); n++)
{
if (anno_match[n] == i)
{
detected = true;
break;
}
}
if (detected == true)
{
color = color_B;
}
else
{
color = color_R;
}
for (int n=0; n<p_interp.size()-1; n++)
{
line(img, p_interp[n], p_interp[n+1], color, width_lane);
line(img2, p_interp[n], p_interp[n+1], color, 2);
}
}
namedWindow("visualize", 1);
imshow("visualize", img);
namedWindow("visualize2", 1);
imshow("visualize2", img2);
}

73
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/src/lane_compare.cpp Executable file
View File

@@ -0,0 +1,73 @@
/*************************************************************************
> File Name: lane_compare.cpp
> Author: Xingang Pan, Jun Li
> Mail: px117@ie.cuhk.edu.hk
> Created Time: Fri Jul 15 10:26:32 2016
************************************************************************/
#include "lane_compare.hpp"
double LaneCompare::get_lane_similarity(const vector<Point2f> &lane1, const vector<Point2f> &lane2)
{
if(lane1.size()<2 || lane2.size()<2)
{
cerr<<"lane size must be greater or equal to 2"<<endl;
return 0;
}
Mat im1 = Mat::zeros(im_height, im_width, CV_8UC1);
Mat im2 = Mat::zeros(im_height, im_width, CV_8UC1);
// draw lines on im1 and im2
vector<Point2f> p_interp1;
vector<Point2f> p_interp2;
if(lane1.size() == 2)
{
p_interp1 = lane1;
}
else
{
p_interp1 = splineSolver.splineInterpTimes(lane1, 50);
}
if(lane2.size() == 2)
{
p_interp2 = lane2;
}
else
{
p_interp2 = splineSolver.splineInterpTimes(lane2, 50);
}
Scalar color_white = Scalar(1);
for(int n=0; n<p_interp1.size()-1; n++)
{
line(im1, p_interp1[n], p_interp1[n+1], color_white, lane_width);
}
for(int n=0; n<p_interp2.size()-1; n++)
{
line(im2, p_interp2[n], p_interp2[n+1], color_white, lane_width);
}
double sum_1 = cv::sum(im1).val[0];
double sum_2 = cv::sum(im2).val[0];
double inter_sum = cv::sum(im1.mul(im2)).val[0];
double union_sum = sum_1 + sum_2 - inter_sum;
double iou = inter_sum / union_sum;
return iou;
}
// resize the lane from Size(curr_width, curr_height) to Size(im_width, im_height)
void LaneCompare::resize_lane(vector<Point2f> &curr_lane, int curr_width, int curr_height)
{
if(curr_width == im_width && curr_height == im_height)
{
return;
}
double x_scale = im_width/(double)curr_width;
double y_scale = im_height/(double)curr_height;
for(int n=0; n<curr_lane.size(); n++)
{
curr_lane[n] = Point2f(curr_lane[n].x*x_scale, curr_lane[n].y*y_scale);
}
}

178
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/culane/src/spline.cpp Executable file
View File

@@ -0,0 +1,178 @@
#include <vector>
#include <iostream>
#include "spline.hpp"
using namespace std;
using namespace cv;
vector<Point2f> Spline::splineInterpTimes(const vector<Point2f>& tmp_line, int times) {
vector<Point2f> res;
if(tmp_line.size() == 2) {
double x1 = tmp_line[0].x;
double y1 = tmp_line[0].y;
double x2 = tmp_line[1].x;
double y2 = tmp_line[1].y;
for (int k = 0; k <= times; k++) {
double xi = x1 + double((x2 - x1) * k) / times;
double yi = y1 + double((y2 - y1) * k) / times;
res.push_back(Point2f(xi, yi));
}
}
else if(tmp_line.size() > 2)
{
vector<Func> tmp_func;
tmp_func = this->cal_fun(tmp_line);
if (tmp_func.empty()) {
cout << "in splineInterpTimes: cal_fun failed" << endl;
return res;
}
for(int j = 0; j < tmp_func.size(); j++)
{
double delta = tmp_func[j].h / times;
for(int k = 0; k < times; k++)
{
double t1 = delta*k;
double x1 = tmp_func[j].a_x + tmp_func[j].b_x*t1 + tmp_func[j].c_x*pow(t1,2) + tmp_func[j].d_x*pow(t1,3);
double y1 = tmp_func[j].a_y + tmp_func[j].b_y*t1 + tmp_func[j].c_y*pow(t1,2) + tmp_func[j].d_y*pow(t1,3);
res.push_back(Point2f(x1, y1));
}
}
res.push_back(tmp_line[tmp_line.size() - 1]);
}
else {
cerr << "in splineInterpTimes: not enough points" << endl;
}
return res;
}
vector<Point2f> Spline::splineInterpStep(vector<Point2f> tmp_line, double step) {
vector<Point2f> res;
/*
if (tmp_line.size() == 2) {
double x1 = tmp_line[0].x;
double y1 = tmp_line[0].y;
double x2 = tmp_line[1].x;
double y2 = tmp_line[1].y;
for (double yi = std::min(y1, y2); yi < std::max(y1, y2); yi += step) {
double xi;
if (yi == y1) xi = x1;
else xi = (x2 - x1) / (y2 - y1) * (yi - y1) + x1;
res.push_back(Point2f(xi, yi));
}
}*/
if (tmp_line.size() == 2) {
double x1 = tmp_line[0].x;
double y1 = tmp_line[0].y;
double x2 = tmp_line[1].x;
double y2 = tmp_line[1].y;
tmp_line[1].x = (x1 + x2) / 2;
tmp_line[1].y = (y1 + y2) / 2;
tmp_line.push_back(Point2f(x2, y2));
}
if (tmp_line.size() > 2) {
vector<Func> tmp_func;
tmp_func = this->cal_fun(tmp_line);
double ystart = tmp_line[0].y;
double yend = tmp_line[tmp_line.size() - 1].y;
bool down;
if (ystart < yend) down = 1;
else down = 0;
if (tmp_func.empty()) {
cerr << "in splineInterpStep: cal_fun failed" << endl;
}
for(int j = 0; j < tmp_func.size(); j++)
{
for(double t1 = 0; t1 < tmp_func[j].h; t1 += step)
{
double x1 = tmp_func[j].a_x + tmp_func[j].b_x*t1 + tmp_func[j].c_x*pow(t1,2) + tmp_func[j].d_x*pow(t1,3);
double y1 = tmp_func[j].a_y + tmp_func[j].b_y*t1 + tmp_func[j].c_y*pow(t1,2) + tmp_func[j].d_y*pow(t1,3);
res.push_back(Point2f(x1, y1));
}
}
res.push_back(tmp_line[tmp_line.size() - 1]);
}
else {
cerr << "in splineInterpStep: not enough points" << endl;
}
return res;
}
vector<Func> Spline::cal_fun(const vector<Point2f> &point_v)
{
vector<Func> func_v;
int n = point_v.size();
if(n<=2) {
cout << "in cal_fun: point number less than 3" << endl;
return func_v;
}
func_v.resize(point_v.size()-1);
vector<double> Mx(n);
vector<double> My(n);
vector<double> A(n-2);
vector<double> B(n-2);
vector<double> C(n-2);
vector<double> Dx(n-2);
vector<double> Dy(n-2);
vector<double> h(n-1);
//vector<func> func_v(n-1);
for(int i = 0; i < n-1; i++)
{
h[i] = sqrt(pow(point_v[i+1].x - point_v[i].x, 2) + pow(point_v[i+1].y - point_v[i].y, 2));
}
for(int i = 0; i < n-2; i++)
{
A[i] = h[i];
B[i] = 2*(h[i]+h[i+1]);
C[i] = h[i+1];
Dx[i] = 6*( (point_v[i+2].x - point_v[i+1].x)/h[i+1] - (point_v[i+1].x - point_v[i].x)/h[i] );
Dy[i] = 6*( (point_v[i+2].y - point_v[i+1].y)/h[i+1] - (point_v[i+1].y - point_v[i].y)/h[i] );
}
//TDMA
C[0] = C[0] / B[0];
Dx[0] = Dx[0] / B[0];
Dy[0] = Dy[0] / B[0];
for(int i = 1; i < n-2; i++)
{
double tmp = B[i] - A[i]*C[i-1];
C[i] = C[i] / tmp;
Dx[i] = (Dx[i] - A[i]*Dx[i-1]) / tmp;
Dy[i] = (Dy[i] - A[i]*Dy[i-1]) / tmp;
}
Mx[n-2] = Dx[n-3];
My[n-2] = Dy[n-3];
for(int i = n-4; i >= 0; i--)
{
Mx[i+1] = Dx[i] - C[i]*Mx[i+2];
My[i+1] = Dy[i] - C[i]*My[i+2];
}
Mx[0] = 0;
Mx[n-1] = 0;
My[0] = 0;
My[n-1] = 0;
for(int i = 0; i < n-1; i++)
{
func_v[i].a_x = point_v[i].x;
func_v[i].b_x = (point_v[i+1].x - point_v[i].x)/h[i] - (2*h[i]*Mx[i] + h[i]*Mx[i+1]) / 6;
func_v[i].c_x = Mx[i]/2;
func_v[i].d_x = (Mx[i+1] - Mx[i]) / (6*h[i]);
func_v[i].a_y = point_v[i].y;
func_v[i].b_y = (point_v[i+1].y - point_v[i].y)/h[i] - (2*h[i]*My[i] + h[i]*My[i+1]) / 6;
func_v[i].c_y = My[i]/2;
func_v[i].d_y = (My[i+1] - My[i]) / (6*h[i]);
func_v[i].h = h[i];
}
return func_v;
}

244
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/eval_wrapper.py Executable file
View File

@@ -0,0 +1,244 @@
from data.dataloader import get_test_loader
from evaluation.tusimple.lane import LaneEval
from utils.dist_utils import is_main_process, dist_print, get_rank, get_world_size, dist_tqdm, synchronize
import os, json, torch, scipy
import numpy as np
import platform
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def generate_lines(out, shape, names, output_path, griding_num, localization_type='abs', flip_updown=False):
col_sample = np.linspace(0, shape[1] - 1, griding_num)
col_sample_w = col_sample[1] - col_sample[0]
for j in range(out.shape[0]):
out_j = out[j].data.cpu().numpy()
if flip_updown:
out_j = out_j[:, ::-1, :]
if localization_type == 'abs':
out_j = np.argmax(out_j, axis=0)
out_j[out_j == griding_num] = -1
out_j = out_j + 1
elif localization_type == 'rel':
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
idx = np.arange(griding_num) + 1
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
out_j = np.argmax(out_j, axis=0)
loc[out_j == griding_num] = 0
out_j = loc
else:
raise NotImplementedError
name = names[j]
line_save_path = os.path.join(output_path, name[:-3] + 'lines.txt')
save_dir, _ = os.path.split(line_save_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(line_save_path, 'w') as fp:
for i in range(out_j.shape[1]):
if np.sum(out_j[:, i] != 0) > 2:
for k in range(out_j.shape[0]):
if out_j[k, i] > 0:
fp.write(
'%d %d ' % (int(out_j[k, i] * col_sample_w * 1640 / 800) - 1, int(590 - k * 20) - 1))
fp.write('\n')
def run_test(net, data_root, exp_name, work_dir, griding_num, use_aux, distributed, batch_size=8, test_list=None):
# torch.backends.cudnn.benchmark = True
output_path = os.path.join(work_dir, exp_name)
if not os.path.exists(output_path) and is_main_process():
os.mkdir(output_path)
synchronize()
loader = get_test_loader(batch_size, data_root, 'CULane', distributed, test_list=test_list)
# import pdb;pdb.set_trace()
for i, data in enumerate(dist_tqdm(loader)):
imgs, names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs)
if len(out) == 2 and use_aux:
out, seg_out = out
generate_lines(out,imgs[0,0].shape,names,output_path,griding_num,localization_type = 'rel',flip_updown = True)
def generate_tusimple_lines(out,shape,griding_num,localization_type='rel'):
out = out.data.cpu().numpy()
out_loc = np.argmax(out,axis=0)
if localization_type == 'rel':
prob = scipy.special.softmax(out[:-1, :, :], axis=0)
idx = np.arange(griding_num)
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
loc[out_loc == griding_num] = griding_num
out_loc = loc
lanes = []
for i in range(out_loc.shape[1]):
out_i = out_loc[:,i]
lane = [int(round((loc + 0.5) * 1280.0 / (griding_num - 1))) if loc != griding_num else -2 for loc in out_i]
lanes.append(lane)
return lanes
def run_test_tusimple(net, data_root, work_dir, exp_name, griding_num, use_aux, distributed, batch_size=1, test_list=None):
output_path = os.path.join(work_dir,exp_name+'.%d.txt'% get_rank())
fp = open(output_path,'w')
loader = get_test_loader(batch_size, data_root, 'Tusimple', distributed, test_list=test_list)
for i,data in enumerate(dist_tqdm(loader)):
imgs,names = data
# imgs = imgs.cuda()
imgs = imgs.to(device)
with torch.no_grad():
out = net(imgs)
if len(out) == 2 and use_aux:
out = out[0]
for i,name in enumerate(names):
tmp_dict = {}
tmp_dict['lanes'] = generate_tusimple_lines(out[i],imgs[0,0].shape,griding_num)
tmp_dict['h_samples'] = [160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,
270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420,
430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580,
590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710]
tmp_dict['raw_file'] = name
tmp_dict['run_time'] = 10
json_str = json.dumps(tmp_dict)
fp.write(json_str+'\n')
fp.close()
def combine_tusimple_test(work_dir,exp_name):
size = get_world_size()
all_res = []
for i in range(size):
output_path = os.path.join(work_dir,exp_name+'.%d.txt'% i)
with open(output_path, 'r') as fp:
res = fp.readlines()
all_res.extend(res)
names = set()
all_res_no_dup = []
for i, res in enumerate(all_res):
pos = res.find('clips')
name = res[pos:].split('\"')[0]
if name not in names:
names.add(name)
all_res_no_dup.append(res)
output_path = os.path.join(work_dir,exp_name+'.txt')
with open(output_path, 'w') as fp:
fp.writelines(all_res_no_dup)
def eval_lane(net, dataset, data_root, work_dir, griding_num, use_aux, distributed, test_list=None, skip_eval=False):
net.eval()
if dataset == 'CULane':
run_test(net, data_root, 'culane_eval_tmp', work_dir, griding_num, use_aux, distributed, test_list=test_list)
synchronize() # wait for all results
if is_main_process():
res = call_culane_eval(data_root, 'culane_eval_tmp', work_dir)
TP,FP,FN = 0,0,0
for k, v in res.items():
val = float(v['Fmeasure']) if 'nan' not in v['Fmeasure'] else 0
val_tp,val_fp,val_fn = int(v['tp']),int(v['fp']),int(v['fn'])
TP += val_tp
FP += val_fp
FN += val_fn
dist_print(k,val)
P = TP * 1.0/(TP + FP)
R = TP * 1.0/(TP + FN)
F = 2*P*R/(P + R)
dist_print(F)
synchronize()
elif dataset == 'Tusimple':
exp_name = 'tusimple_eval_tmp'
run_test_tusimple(net, data_root, work_dir, exp_name, griding_num, use_aux, distributed, test_list=test_list)
synchronize() # wait for all results
if is_main_process():
pred_path = os.path.join(work_dir, exp_name + '.0.txt')
label_json = os.path.join(data_root, 'test_label.json')
if skip_eval or not os.path.isfile(label_json):
dist_print('skip TuSimple metrics (no test_label.json); predictions:', pred_path)
else:
res = LaneEval.bench_one_submit(pred_path, label_json)
res = json.loads(res)
for r in res:
dist_print(r['name'], r['value'])
synchronize()
def read_helper(path):
lines = open(path, 'r').readlines()[1:]
lines = ' '.join(lines)
values = lines.split(' ')[1::2]
keys = lines.split(' ')[0::2]
keys = [key[:-1] for key in keys]
res = {k : v for k,v in zip(keys,values)}
return res
def call_culane_eval(data_dir, exp_name,output_path):
if data_dir[-1] != '/':
data_dir = data_dir + '/'
detect_dir=os.path.join(output_path,exp_name)+'/'
w_lane=30
iou=0.5; # Set iou to 0.3 or 0.5
im_w=1640
im_h=590
frame=1
list0 = os.path.join(data_dir,'list/test_split/test0_normal.txt')
list1 = os.path.join(data_dir,'list/test_split/test1_crowd.txt')
list2 = os.path.join(data_dir,'list/test_split/test2_hlight.txt')
list3 = os.path.join(data_dir,'list/test_split/test3_shadow.txt')
list4 = os.path.join(data_dir,'list/test_split/test4_noline.txt')
list5 = os.path.join(data_dir,'list/test_split/test5_arrow.txt')
list6 = os.path.join(data_dir,'list/test_split/test6_curve.txt')
list7 = os.path.join(data_dir,'list/test_split/test7_cross.txt')
list8 = os.path.join(data_dir,'list/test_split/test8_night.txt')
if not os.path.exists(os.path.join(output_path,'txt')):
os.mkdir(os.path.join(output_path,'txt'))
out0 = os.path.join(output_path,'txt','out0_normal.txt')
out1=os.path.join(output_path,'txt','out1_crowd.txt')
out2=os.path.join(output_path,'txt','out2_hlight.txt')
out3=os.path.join(output_path,'txt','out3_shadow.txt')
out4=os.path.join(output_path,'txt','out4_noline.txt')
out5=os.path.join(output_path,'txt','out5_arrow.txt')
out6=os.path.join(output_path,'txt','out6_curve.txt')
out7=os.path.join(output_path,'txt','out7_cross.txt')
out8=os.path.join(output_path,'txt','out8_night.txt')
eval_cmd = './evaluation/culane/evaluate'
if platform.system() == 'Windows':
eval_cmd = eval_cmd.replace('/', os.sep)
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list0,w_lane,iou,im_w,im_h,frame,out0))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list0,w_lane,iou,im_w,im_h,frame,out0))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list1,w_lane,iou,im_w,im_h,frame,out1))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list1,w_lane,iou,im_w,im_h,frame,out1))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list2,w_lane,iou,im_w,im_h,frame,out2))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list2,w_lane,iou,im_w,im_h,frame,out2))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list3,w_lane,iou,im_w,im_h,frame,out3))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list3,w_lane,iou,im_w,im_h,frame,out3))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list4,w_lane,iou,im_w,im_h,frame,out4))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list4,w_lane,iou,im_w,im_h,frame,out4))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list5,w_lane,iou,im_w,im_h,frame,out5))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list5,w_lane,iou,im_w,im_h,frame,out5))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list6,w_lane,iou,im_w,im_h,frame,out6))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list6,w_lane,iou,im_w,im_h,frame,out6))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list7,w_lane,iou,im_w,im_h,frame,out7))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list7,w_lane,iou,im_w,im_h,frame,out7))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list8,w_lane,iou,im_w,im_h,frame,out8))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list8,w_lane,iou,im_w,im_h,frame,out8))
res_all = {}
res_all['res_normal'] = read_helper(out0)
res_all['res_crowd']= read_helper(out1)
res_all['res_night']= read_helper(out8)
res_all['res_noline'] = read_helper(out4)
res_all['res_shadow'] = read_helper(out3)
res_all['res_arrow']= read_helper(out5)
res_all['res_hlight'] = read_helper(out2)
res_all['res_curve']= read_helper(out6)
res_all['res_cross']= read_helper(out7)
return res_all

238
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/eval_wrapper0.py Executable file
View File

@@ -0,0 +1,238 @@
from data.dataloader import get_test_loader
from evaluation.tusimple.lane import LaneEval
from utils.dist_utils import is_main_process, dist_print, get_rank, get_world_size, dist_tqdm, synchronize
import os, json, torch, scipy
import numpy as np
import platform
def generate_lines(out, shape, names, output_path, griding_num, localization_type='abs', flip_updown=False):
col_sample = np.linspace(0, shape[1] - 1, griding_num)
col_sample_w = col_sample[1] - col_sample[0]
for j in range(out.shape[0]):
out_j = out[j].data.cpu().numpy()
if flip_updown:
out_j = out_j[:, ::-1, :]
if localization_type == 'abs':
out_j = np.argmax(out_j, axis=0)
out_j[out_j == griding_num] = -1
out_j = out_j + 1
elif localization_type == 'rel':
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
idx = np.arange(griding_num) + 1
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
out_j = np.argmax(out_j, axis=0)
loc[out_j == griding_num] = 0
out_j = loc
else:
raise NotImplementedError
name = names[j]
line_save_path = os.path.join(output_path, name[:-3] + 'lines.txt')
save_dir, _ = os.path.split(line_save_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(line_save_path, 'w') as fp:
for i in range(out_j.shape[1]):
if np.sum(out_j[:, i] != 0) > 2:
for k in range(out_j.shape[0]):
if out_j[k, i] > 0:
fp.write(
'%d %d ' % (int(out_j[k, i] * col_sample_w * 1640 / 800) - 1, int(590 - k * 20) - 1))
fp.write('\n')
def run_test(net, data_root, exp_name, work_dir, griding_num, use_aux,distributed, batch_size=8):
# torch.backends.cudnn.benchmark = True
output_path = os.path.join(work_dir, exp_name)
if not os.path.exists(output_path) and is_main_process():
os.mkdir(output_path)
synchronize()
loader = get_test_loader(batch_size, data_root, 'CULane', distributed)
# import pdb;pdb.set_trace()
for i, data in enumerate(dist_tqdm(loader)):
imgs, names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs)
if len(out) == 2 and use_aux:
out, seg_out = out
generate_lines(out,imgs[0,0].shape,names,output_path,griding_num,localization_type = 'rel',flip_updown = True)
def generate_tusimple_lines(out,shape,griding_num,localization_type='rel'):
out = out.data.cpu().numpy()
out_loc = np.argmax(out,axis=0)
if localization_type == 'rel':
prob = scipy.special.softmax(out[:-1, :, :], axis=0)
idx = np.arange(griding_num)
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
loc[out_loc == griding_num] = griding_num
out_loc = loc
lanes = []
for i in range(out_loc.shape[1]):
out_i = out_loc[:,i]
lane = [int(round((loc + 0.5) * 1280.0 / (griding_num - 1))) if loc != griding_num else -2 for loc in out_i]
lanes.append(lane)
return lanes
def run_test_tusimple(net,data_root,work_dir,exp_name,griding_num,use_aux, distributed,batch_size = 8):
output_path = os.path.join(work_dir,exp_name+'.%d.txt'% get_rank())
fp = open(output_path,'w')
loader = get_test_loader(batch_size,data_root,'Tusimple', distributed)
for i,data in enumerate(dist_tqdm(loader)):
imgs,names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs)
if len(out) == 2 and use_aux:
out = out[0]
for i,name in enumerate(names):
tmp_dict = {}
tmp_dict['lanes'] = generate_tusimple_lines(out[i],imgs[0,0].shape,griding_num)
tmp_dict['h_samples'] = [160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,
270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420,
430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580,
590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710]
tmp_dict['raw_file'] = name
tmp_dict['run_time'] = 10
json_str = json.dumps(tmp_dict)
fp.write(json_str+'\n')
fp.close()
def combine_tusimple_test(work_dir,exp_name):
size = get_world_size()
all_res = []
for i in range(size):
output_path = os.path.join(work_dir,exp_name+'.%d.txt'% i)
with open(output_path, 'r') as fp:
res = fp.readlines()
all_res.extend(res)
names = set()
all_res_no_dup = []
for i, res in enumerate(all_res):
pos = res.find('clips')
name = res[pos:].split('\"')[0]
if name not in names:
names.add(name)
all_res_no_dup.append(res)
output_path = os.path.join(work_dir,exp_name+'.txt')
with open(output_path, 'w') as fp:
fp.writelines(all_res_no_dup)
def eval_lane(net, dataset, data_root, work_dir, griding_num, use_aux, distributed):
net.eval()
if dataset == 'CULane':
run_test(net,data_root, 'culane_eval_tmp', work_dir, griding_num, use_aux, distributed)
synchronize() # wait for all results
if is_main_process():
res = call_culane_eval(data_root, 'culane_eval_tmp', work_dir)
TP,FP,FN = 0,0,0
for k, v in res.items():
val = float(v['Fmeasure']) if 'nan' not in v['Fmeasure'] else 0
val_tp,val_fp,val_fn = int(v['tp']),int(v['fp']),int(v['fn'])
TP += val_tp
FP += val_fp
FN += val_fn
dist_print(k,val)
P = TP * 1.0/(TP + FP)
R = TP * 1.0/(TP + FN)
F = 2*P*R/(P + R)
dist_print(F)
synchronize()
elif dataset == 'Tusimple':
exp_name = 'tusimple_eval_tmp'
run_test_tusimple(net, data_root, work_dir, exp_name, griding_num, use_aux, distributed)
synchronize() # wait for all results
if is_main_process():
combine_tusimple_test(work_dir,exp_name)
res = LaneEval.bench_one_submit(os.path.join(work_dir,exp_name + '.txt'),os.path.join(data_root,'test_label.json'))
res = json.loads(res)
for r in res:
dist_print(r['name'], r['value'])
synchronize()
def read_helper(path):
lines = open(path, 'r').readlines()[1:]
lines = ' '.join(lines)
values = lines.split(' ')[1::2]
keys = lines.split(' ')[0::2]
keys = [key[:-1] for key in keys]
res = {k : v for k,v in zip(keys,values)}
return res
def call_culane_eval(data_dir, exp_name,output_path):
if data_dir[-1] != '/':
data_dir = data_dir + '/'
detect_dir=os.path.join(output_path,exp_name)+'/'
w_lane=30
iou=0.5; # Set iou to 0.3 or 0.5
im_w=1640
im_h=590
frame=1
list0 = os.path.join(data_dir,'list/test_split/test0_normal.txt')
list1 = os.path.join(data_dir,'list/test_split/test1_crowd.txt')
list2 = os.path.join(data_dir,'list/test_split/test2_hlight.txt')
list3 = os.path.join(data_dir,'list/test_split/test3_shadow.txt')
list4 = os.path.join(data_dir,'list/test_split/test4_noline.txt')
list5 = os.path.join(data_dir,'list/test_split/test5_arrow.txt')
list6 = os.path.join(data_dir,'list/test_split/test6_curve.txt')
list7 = os.path.join(data_dir,'list/test_split/test7_cross.txt')
list8 = os.path.join(data_dir,'list/test_split/test8_night.txt')
if not os.path.exists(os.path.join(output_path,'txt')):
os.mkdir(os.path.join(output_path,'txt'))
out0 = os.path.join(output_path,'txt','out0_normal.txt')
out1=os.path.join(output_path,'txt','out1_crowd.txt')
out2=os.path.join(output_path,'txt','out2_hlight.txt')
out3=os.path.join(output_path,'txt','out3_shadow.txt')
out4=os.path.join(output_path,'txt','out4_noline.txt')
out5=os.path.join(output_path,'txt','out5_arrow.txt')
out6=os.path.join(output_path,'txt','out6_curve.txt')
out7=os.path.join(output_path,'txt','out7_cross.txt')
out8=os.path.join(output_path,'txt','out8_night.txt')
eval_cmd = './evaluation/culane/evaluate'
if platform.system() == 'Windows':
eval_cmd = eval_cmd.replace('/', os.sep)
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list0,w_lane,iou,im_w,im_h,frame,out0))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list0,w_lane,iou,im_w,im_h,frame,out0))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list1,w_lane,iou,im_w,im_h,frame,out1))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list1,w_lane,iou,im_w,im_h,frame,out1))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list2,w_lane,iou,im_w,im_h,frame,out2))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list2,w_lane,iou,im_w,im_h,frame,out2))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list3,w_lane,iou,im_w,im_h,frame,out3))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list3,w_lane,iou,im_w,im_h,frame,out3))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list4,w_lane,iou,im_w,im_h,frame,out4))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list4,w_lane,iou,im_w,im_h,frame,out4))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list5,w_lane,iou,im_w,im_h,frame,out5))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list5,w_lane,iou,im_w,im_h,frame,out5))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list6,w_lane,iou,im_w,im_h,frame,out6))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list6,w_lane,iou,im_w,im_h,frame,out6))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list7,w_lane,iou,im_w,im_h,frame,out7))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list7,w_lane,iou,im_w,im_h,frame,out7))
# print('./evaluate -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(data_dir,detect_dir,data_dir,list8,w_lane,iou,im_w,im_h,frame,out8))
os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list8,w_lane,iou,im_w,im_h,frame,out8))
res_all = {}
res_all['res_normal'] = read_helper(out0)
res_all['res_crowd']= read_helper(out1)
res_all['res_night']= read_helper(out8)
res_all['res_noline'] = read_helper(out4)
res_all['res_shadow'] = read_helper(out3)
res_all['res_arrow']= read_helper(out5)
res_all['res_hlight'] = read_helper(out2)
res_all['res_curve']= read_helper(out6)
res_all['res_cross']= read_helper(out7)
return res_all

111
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/tusimple/lane.py Executable file
View File

@@ -0,0 +1,111 @@
import os
import numpy as np
from sklearn.linear_model import LinearRegression
import json, shutil
class LaneEval(object):
lr = LinearRegression()
pixel_thresh = 20
pt_thresh = 0.85
@staticmethod
def get_angle(xs, y_samples):
xs, ys = xs[xs >= 0], y_samples[xs >= 0]
if len(xs) > 1:
LaneEval.lr.fit(ys[:, None], xs)
k = LaneEval.lr.coef_[0]
theta = np.arctan(k)
else:
theta = 0
return theta
@staticmethod
def line_accuracy(pred, gt, thresh):
pred = np.array([p if p >= 0 else -100 for p in pred])
gt = np.array([g if g >= 0 else -100 for g in gt])
return np.sum(np.where(np.abs(pred - gt) < thresh, 1., 0.)) / len(gt)
@staticmethod
def bench(pred, gt, y_samples, running_time):
if any(len(p) != len(y_samples) for p in pred):
raise Exception('Format of lanes error.')
if running_time > 200 or len(gt) + 2 < len(pred):
return 0., 0., 1.
angles = [LaneEval.get_angle(np.array(x_gts), np.array(y_samples)) for x_gts in gt]
threshs = [LaneEval.pixel_thresh / np.cos(angle) for angle in angles]
line_accs = []
fp, fn = 0., 0.
matched = 0.
for x_gts, thresh in zip(gt, threshs):
accs = [LaneEval.line_accuracy(np.array(x_preds), np.array(x_gts), thresh) for x_preds in pred]
max_acc = np.max(accs) if len(accs) > 0 else 0.
if max_acc < LaneEval.pt_thresh:
fn += 1
else:
matched += 1
line_accs.append(max_acc)
fp = len(pred) - matched
if len(gt) > 4 and fn > 0:
fn -= 1
s = sum(line_accs)
if len(gt) > 4:
s -= min(line_accs)
# print('s:', s, len(gt))
return s / max(min(4.0, len(gt)), 1.), fp / len(pred) if len(pred) > 0 else 0., fn / max(min(len(gt), 4.), 1.)
@staticmethod
def bench_one_submit(pred_file, gt_file):
print(pred_file,"//////////////////",gt_file)
try:
json_pred = [json.loads(line) for line in open(pred_file).readlines()]
except BaseException as e:
raise Exception('Fail to load json file of the prediction.')
json_gt = [json.loads(line) for line in open(gt_file).readlines()]
print("****************************************",len(json_gt), len(json_pred))
if len(json_gt) != len(json_pred):
# print(len(json_gt), json_pred)
raise Exception('We do not get the predictions of all the test tasks')
gts = {l['raw_file']: l for l in json_gt}
accuracy, fp, fn = 0., 0., 0.
for pred in json_pred:
if 'raw_file' not in pred or 'lanes' not in pred or 'run_time' not in pred:
raise Exception('raw_file or lanes or run_time not in some predictions.')
raw_file = pred['raw_file']
pred_lanes = pred['lanes']
run_time = pred['run_time']
if raw_file not in gts:
raise Exception('Some raw_file from your predictions do not exist in the test tasks.')
gt = gts[raw_file]
gt_lanes = gt['lanes']
y_samples = gt['h_samples']
try:
a, p, n = LaneEval.bench(pred_lanes, gt_lanes, y_samples, run_time)
except BaseException as e:
raise Exception('Format of lanes error.')
# if a <= 0.85:
print(raw_file, 'a: ', a)
# srcfile = "C:/data/Tusimple/test_set/" + raw_file
#fpath, fname = os.path.split(srcfile)
#shutil.copy(srcfile, './out/' + fname)
accuracy += a
fp += p
fn += n
num = len(gts)
# the first return parameter is the default ranking parameter
return json.dumps([
{'name': 'Accuracy', 'value': accuracy / num, 'order': 'desc'},
{'name': 'FP', 'value': fp / num, 'order': 'asc'},
{'name': 'FN', 'value': fn / num, 'order': 'asc'}
])
if __name__ == '__main__':
import sys
try:
if len(sys.argv) != 3:
raise Exception('Invalid input arguments')
print(LaneEval.bench_one_submit(sys.argv[1], sys.argv[2]))
except Exception as e:
print(e.message)
sys.exit(e.message)

102
algorithms/lane_ufld/code.embedded.bak/UFLD/evaluation/tusimple/lane0.py Executable file
View File

@@ -0,0 +1,102 @@
import numpy as np
from sklearn.linear_model import LinearRegression
import json
class LaneEval(object):
lr = LinearRegression()
pixel_thresh = 20
pt_thresh = 0.85
@staticmethod
def get_angle(xs, y_samples):
xs, ys = xs[xs >= 0], y_samples[xs >= 0]
if len(xs) > 1:
LaneEval.lr.fit(ys[:, None], xs)
k = LaneEval.lr.coef_[0]
theta = np.arctan(k)
else:
theta = 0
return theta
@staticmethod
def line_accuracy(pred, gt, thresh):
pred = np.array([p if p >= 0 else -100 for p in pred])
gt = np.array([g if g >= 0 else -100 for g in gt])
return np.sum(np.where(np.abs(pred - gt) < thresh, 1., 0.)) / len(gt)
@staticmethod
def bench(pred, gt, y_samples, running_time):
if any(len(p) != len(y_samples) for p in pred):
raise Exception('Format of lanes error.')
if running_time > 200 or len(gt) + 2 < len(pred):
return 0., 0., 1.
angles = [LaneEval.get_angle(np.array(x_gts), np.array(y_samples)) for x_gts in gt]
threshs = [LaneEval.pixel_thresh / np.cos(angle) for angle in angles]
line_accs = []
fp, fn = 0., 0.
matched = 0.
for x_gts, thresh in zip(gt, threshs):
accs = [LaneEval.line_accuracy(np.array(x_preds), np.array(x_gts), thresh) for x_preds in pred]
max_acc = np.max(accs) if len(accs) > 0 else 0.
if max_acc < LaneEval.pt_thresh:
fn += 1
else:
matched += 1
line_accs.append(max_acc)
fp = len(pred) - matched
if len(gt) > 4 and fn > 0:
fn -= 1
s = sum(line_accs)
if len(gt) > 4:
s -= min(line_accs)
return s / max(min(4.0, len(gt)), 1.), fp / len(pred) if len(pred) > 0 else 0., fn / max(min(len(gt), 4.) , 1.)
@staticmethod
def bench_one_submit(pred_file, gt_file):
try:
json_pred = [json.loads(line) for line in open(pred_file).readlines()]
except BaseException as e:
raise Exception('Fail to load json file of the prediction.')
json_gt = [json.loads(line) for line in open(gt_file).readlines()]
print(len(json_gt), len(json_pred))
if len(json_gt) != len(json_pred):
raise Exception('We do not get the predictions of all the test tasks')
gts = {l['raw_file']: l for l in json_gt}
accuracy, fp, fn = 0., 0., 0.
for pred in json_pred:
if 'raw_file' not in pred or 'lanes' not in pred or 'run_time' not in pred:
raise Exception('raw_file or lanes or run_time not in some predictions.')
raw_file = pred['raw_file']
pred_lanes = pred['lanes']
run_time = pred['run_time']
if raw_file not in gts:
raise Exception('Some raw_file from your predictions do not exist in the test tasks.')
gt = gts[raw_file]
gt_lanes = gt['lanes']
y_samples = gt['h_samples']
try:
a, p, n = LaneEval.bench(pred_lanes, gt_lanes, y_samples, run_time)
except BaseException as e:
raise Exception('Format of lanes error.')
accuracy += a
fp += p
fn += n
num = len(gts)
# the first return parameter is the default ranking parameter
return json.dumps([
{'name': 'Accuracy', 'value': accuracy / num, 'order': 'desc'},
{'name': 'FP', 'value': fp / num, 'order': 'asc'},
{'name': 'FN', 'value': fn / num, 'order': 'asc'}
])
if __name__ == '__main__':
import sys
try:
if len(sys.argv) != 3:
raise Exception('Invalid input arguments')
print(LaneEval.bench_one_submit(sys.argv[1], sys.argv[2]))
except Exception as e:
print(e.message)
sys.exit(e.message)