[Model] Add python API for the detection result and modify YOLOv7 docs

examples/vision/detection/paddledetection/coco_label_list.txt

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

examples/vision/detection/yolov7/README.md

@@ -18,8 +18,8 @@ wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
 # 导出onnx格式文件 (Tips: 对应 YOLOv7 release v0.1 代码)
 python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
 
-# 如果您的代码版本中有支持NMS的ONNX文件导出，请使用如下命令导出ONNX文件(请暂时不要使用 "--end2end"，我们后续将支持带有NMS的ONNX模型的部署)
-python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
+# 如果您的代码版本中有支持NMS的ONNX文件导出，请使用如下命令导出ONNX文件，并且参考`yolov7end2end_ort` 或 `yolov7end2end_trt`示例使用
+python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
 
 
 ```

examples/vision/detection/yolov7/README_EN.md

@@ -3,7 +3,7 @@
 # YOLOv7 Prepare the model for Deployment
 
 - YOLOv7 deployment is based on [YOLOv7](https://github.com/WongKinYiu/yolov7/tree/v0.1) branching code, and [COCO Pre-Trained Models](https://github.com/WongKinYiu/yolov7/releases/tag/v0.1).
-  
+
   - （1）The *.pt provided by the [Official Library](https://github.com/WongKinYiu/yolov7/releases/tag/v0.1) can be deployed after the [export ONNX model](#export ONNX model) operation; *.trt and *.pose models do not support deployment.
   - （2）As for YOLOv7 model trained on customized data, please follow the operations guidelines in [Export ONNX model](#Export-ONNX-Model) and then refer to [Detailed Deployment Tutorials](#Detailed-Deployment-Tutorials) to complete the deployment.
 
@@ -16,8 +16,8 @@ wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
 # Export onnx file (Tips: in accordance with YOLOv7 release v0.1 code)
 python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
 
-# If your code supports exporting ONNX files with NMS, please use the following command to export ONNX files (do not use "--end2end" for now. We will support deployment of ONNX models with NMS in the future)
-python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
+# If your code supports exporting ONNX files with NMS, please use the following command to export ONNX files, then refer to the example of `yolov7end2end_ort` or `yolov7end2end_ort`
+python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
 ```
 
 ## Download the pre-trained ONNX model

fastdeploy/vision/visualize/visualize_pybind.cc

@@ -26,6 +26,17 @@ void BindVisualize(pybind11::module& m) {
           vision::Mat(vis_im).ShareWithTensor(&out);
           return TensorToPyArray(out);
         })
+      .def("vis_detection_for_label",
+        [](pybind11::array& im_data, vision::DetectionResult& result,
+           std::vector<std::string>& labels,
+           float score_threshold, int line_size, float font_size) {
+          auto im = PyArrayToCvMat(im_data);
+          auto vis_im = vision::VisDetection(im, result, labels, score_threshold, 
+                                             line_size, font_size);
+          FDTensor out;
+          vision::Mat(vis_im).ShareWithTensor(&out);
+          return TensorToPyArray(out);
+        })
       .def("vis_face_detection",
            [](pybind11::array& im_data, vision::FaceDetectionResult& result,
               int line_size, float font_size) {

python/fastdeploy/vision/visualize/__init__.py

@@ -20,11 +20,16 @@
 
 def vis_detection(im_data,
                   det_result,
+                  labels=None,
                   score_threshold=0.0,
                   line_size=1,
                   font_size=0.5):
-    return C.vision.vis_detection(im_data, det_result, score_threshold,
-                                  line_size, font_size)
+    if labels:
+        return C.vision.vis_detection_for_label(
+            im_data, det_result, labels, score_threshold, line_size, font_size)
+    else:
+        return C.vision.vis_detection(im_data, det_result, score_threshold,
+                                      line_size, font_size)
 
 
 def vis_keypoint_detection(im_data, keypoint_det_result, conf_threshold=0.5):