diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_press.md b/deploy/pipeline/docs/tutorials/ppvehicle_press.md index d44c4b58eac722b969b2929689a50c15b6441a70..acf9dc9095f070896e0f76343eb52c118f16f081 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_press.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_press.md @@ -29,7 +29,7 @@ LANE_SEG: lane_seg_config: deploy/pipeline/config/lane_seg_config.yml #车道线提取配置文件 model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/pp_lite_stdc2_bdd100k.zip #模型文件路径 ``` -[车道线配置文件](../../config/lane_seg.yml)中与车道线提取相关的参数如下： +[车道线配置文件](../../config/lane_seg_config.yml)中与车道线提取相关的参数如下： ``` type: PLSLaneseg #选择分割模型 @@ -107,7 +107,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv 3 虚线 车辆压线分析过滤虚线类； -2.车道线通过对分割结果聚类得到，且默认过滤水平方向车道线，若不过滤可在[车道线配置文件](../../config/lane_seg.yml)修改`filter_flag`参数； +2.车道线通过对分割结果聚类得到，且默认过滤水平方向车道线，若不过滤可在[车道线配置文件](../../config/lane_seg_config.yml)修改`filter_flag`参数； 3.车辆压线判断条件：车辆的检测框底边线与车道线是否有交点； diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_press_en.md b/deploy/pipeline/docs/tutorials/ppvehicle_press_en.md index 8825bfab5563400ce42682b75433a0246fb85bc1..93b93a5e626307d5962fdb296c81966d5f5afd19 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_press_en.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_press_en.md @@ -30,7 +30,7 @@ LANE_SEG: lane_seg_config: deploy/pipeline/config/lane_seg_config.yml #lane line seg config file model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/pp_lite_stdc2_bdd100k.zip #model path ``` -The parameters related to Lane line segmentation in [lane line seg config file](../../config/lane_seg.yml)is as follows: +The parameters related to Lane line segmentation in [lane line seg config file](../../config/lane_seg_config.yml)is as follows: ``` type: PLSLaneseg #Select segmentation Model @@ -107,7 +107,7 @@ The result is shown as follow: 3 Dashed line Lane line recognition filtering Dashed lines; -2.Lane lines are obtained by clustering segmentation results, and the horizontal lane lines are filtered by default. If not, you can modify the `filter_flag` in [lane line seg config file](../../config/lane_seg.yml); +2.Lane lines are obtained by clustering segmentation results, and the horizontal lane lines are filtered by default. If not, you can modify the `filter_flag` in [lane line seg config file](../../config/lane_seg_config.yml); 3.Judgment conditions for vehicle line pressing: whether there is intersection between the bottom edge line of vehicle detection frame and lane line; diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_retrograde.md b/deploy/pipeline/docs/tutorials/ppvehicle_retrograde.md index fa45436a2f158ae07d4507e59b4580545d8260b2..57ba922ff35c63b4a74a51af6c7defef994878fe 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_retrograde.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_retrograde.md @@ -38,7 +38,7 @@ VEHICLE_RETROGRADE: #车辆静止 fence_line: [] #车道中间线坐标，格式[x1,y1,x2,y2] 且y2>y1。若为空，由程序根据车流方向自动判断 ``` -[车道线配置文件](../../config/lane_seg.yml)中与车道线提取相关的参数如下： +[车道线配置文件](../../config/lane_seg_config.yml)中与车道线提取相关的参数如下： ``` type: PLSLaneseg #选择分割模型 @@ -113,7 +113,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv 3 虚线 车辆逆行分析过滤虚线类； -3.车道线通过对分割结果聚类得到，且默认过滤水平方向车道线，若不过滤可在[车道线配置文件](../../config/lane_seg.yml)修改`filter_flag`参数; +3.车道线通过对分割结果聚类得到，且默认过滤水平方向车道线，若不过滤可在[车道线配置文件](../../config/lane_seg_config.yml)修改`filter_flag`参数; 4.车辆逆行判断默认过滤水平方向车辆，若不过滤可在[配置文件](../../config/infer_cfg_ppvehicle.yml)修改`filter_horizontal_flag`参数; diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_retrograde_en.md b/deploy/pipeline/docs/tutorials/ppvehicle_retrograde_en.md index 16c94492e9e8112f0beae69cc6243e6a8171e046..457f84bfa2084075c9396f95868b29f51b45e61f 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_retrograde_en.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_retrograde_en.md @@ -38,7 +38,7 @@ VEHICLE_RETROGRADE: move_scale: 0.01 #Filter the threshold value of stationary vehicles. If the vehicle moving pixel is greater than the image diagonal * move_scale, the vehicle is considered moving, otherwise, the vehicle is stationary fence_line: [] #Lane centerline coordinates, format[x1,y1,x2,y2] and y2>y1. If it is empty, the program will automatically judge according to the direction of traffic flow ``` -The parameters related to Lane line segmentation in [lane line seg config file](../../config/lane_seg.yml)is as follows: +The parameters related to Lane line segmentation in [lane line seg config file](../../config/lane_seg_config.yml)is as follows: ``` type: PLSLaneseg #Select segmentation Model @@ -111,7 +111,7 @@ The result is shown as follow: 3 Dashed line Lane line recognition filtering Dashed lines; -3.Lane lines are obtained by clustering segmentation results, and the horizontal lane lines are filtered by default. If not, you can modify the `filter_flag` in [lane line seg config file](../../config/lane_seg.yml); +3.Lane lines are obtained by clustering segmentation results, and the horizontal lane lines are filtered by default. If not, you can modify the `filter_flag` in [lane line seg config file](../../config/lane_seg_config.yml); 4.The vehicles in the horizontal direction are filtered by default when judging the vehicles in the reverse direction. If not, you can modify the `filter_horizontal_flag` in [config file](../../config/infer_cfg_ppvehicle.yml);