From 387f42af5c6147a16af52cae4d50acaa3e6753a3 Mon Sep 17 00:00:00 2001
From: lxm <1367240116@qq.com>
Date: Fri, 11 Aug 2023 18:29:22 +0800
Subject: [PATCH 1/7] COCO Model mAP eVAL

---
 CMakeLists.txt                                |   3 +
 .../test/eval_mAP_on_coco_val/coco_eval.py    |  32 +++++
 .../eval_mAP_on_coco_val.cpp                  | 100 ++++++++++++++
 .../test/eval_mAP_on_coco_val/gt2cocojson.py  | 124 ++++++++++++++++++
 .../test/eval_mAP_on_coco_val/pd2cocojson.py  |  66 ++++++++++
 5 files changed, 325 insertions(+)
 create mode 100644 samples/test/eval_mAP_on_coco_val/coco_eval.py
 create mode 100644 samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
 create mode 100644 samples/test/eval_mAP_on_coco_val/gt2cocojson.py
 create mode 100644 samples/test/eval_mAP_on_coco_val/pd2cocojson.py

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e35dffe..d19ed0c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -269,6 +269,9 @@ target_link_libraries(GimbalUdpDetectionInfoSender sv_world)
 add_executable(EvalFpsOnVideo samples/test/eval_fps_on_video.cpp)
 target_link_libraries(EvalFpsOnVideo sv_world)
 
+add_executable(EvalModelOnCocoVal samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp)
+target_link_libraries(EvalModelOnCocoVal sv_world)
+
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/samples/calib)
 add_executable(CameraCalibrarion samples/calib/calibrate_camera_charuco.cpp)
 target_link_libraries(CameraCalibrarion ${OpenCV_LIBS})
diff --git a/samples/test/eval_mAP_on_coco_val/coco_eval.py b/samples/test/eval_mAP_on_coco_val/coco_eval.py
new file mode 100644
index 0000000..25f90af
--- /dev/null
+++ b/samples/test/eval_mAP_on_coco_val/coco_eval.py
@@ -0,0 +1,32 @@
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+import os
+
+if __name__ == '__main__':
+  path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+  pred_json = 'pd_coco.json'
+  anno_json = path + '/val2017/gt_coco.json'
+
+  # use COCO API to load forecast results and annotations
+  cocoGt = COCO(anno_json)
+  cocoDt = cocoGt.loadRes(pred_json)
+
+  # create COCO eval object
+  cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
+
+  # assessment
+  cocoEval.evaluate()
+  cocoEval.accumulate()
+  cocoEval.summarize()
+
+
+  # save results
+  with open('coco_eval.txt', 'w') as f:
+    f.write(str(cocoEval.stats))
+
+
+
+
+
+
+
diff --git a/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp b/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
new file mode 100644
index 0000000..dc31f8a
--- /dev/null
+++ b/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
@@ -0,0 +1,100 @@
+#include <iostream>
+#include <string>
+// 包含SpireCV SDK头文件
+#include <sv_world.h>
+
+using namespace std;
+using namespace cv;
+
+//extract name
+std::string GetImageFileName(const std::string& imagePath) {
+    size_t lastSlash = imagePath.find_last_of("/\\");
+    if (lastSlash == std::string::npos) {
+        return imagePath;
+    } else {
+        std::string fileName = imagePath.substr(lastSlash + 1);
+        size_t lastDot = fileName.find_last_of(".");
+        if (lastDot != std::string::npos) {
+            return fileName.substr(0, lastDot);
+        }
+        return fileName;
+    }
+}
+
+
+int main(int argc, char *argv[])
+{
+  // 实例化 通用目标 检测器类
+  sv::CommonObjectDetector cod;
+  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
+
+  //load data
+  string val_path = sv::get_home() + "/SpireCV/val2017/val2017"; 
+  vector<string> val_image;
+  glob(val_path, val_image, false);
+  if (val_image.size() == 0)
+  { 
+    printf("val_image error!!!\n");
+    exit(1);
+  }
+
+  //preds folder
+
+  std::string folder = sv::get_home() + "/SpireCV/val2017/preds";
+  int checkStatus = std::system(("if [ -d \"" + folder + "\" ]; then echo; fi").c_str());
+  if(checkStatus == 0)
+  {
+    int removeStatus = std::system(("rm -rf \"" + folder + "\"").c_str());
+    if(removeStatus != 0)
+    {
+      printf("remove older preds folder error!!!\n");
+      exit(1);
+    }
+  } 
+
+  int status = std::system(("mkdir \""+folder+"\"").c_str());
+  if(status != 0)
+  {
+    printf("create preds folder error!!!\n");
+    exit(1);
+  }
+
+
+  for (int i = 0; i < val_image.size(); i++) {
+
+    //create pred file
+    std::string val_image_name = GetImageFileName(val_image[i]);
+    std::string filename = folder+ val_image_name + ".txt";
+    std::ofstream file(filename);
+    file.is_open();
+    file<<std::fixed<<std::setprecision(6);
+
+    // 实例化SpireCV的 单帧检测结果 接口类 TargetsInFrame
+    sv::TargetsInFrame tgts(i);
+    cv::Mat img = imread(val_image[i]);
+
+    int rows = img.rows;
+    int cols = img.cols;
+   
+    // 执行通用目标检测
+    cod.detect(img, tgts);
+    // 可视化检测结果，叠加到img上
+    sv::drawTargetsInFrame(img, tgts);
+    
+    // reslusts
+    for (int j = 0; j < tgts.targets.size(); j++)
+    {
+        sv::Box b;
+        tgts.targets[j].getBox(b);
+        file<<tgts.targets[j].category_id<<" "<<(float)(b.x1+b.x2)/(2*cols)<<" "<<(float)(b.y1+b.y2)/(2*rows)<<" "<<(float)(b.x2-b.x1)/cols<<" "<<(float)(b.y2-b.y1)/rows<<" "<<(float)tgts.targets[j].score<<"\n";
+    }
+    file.close();
+    
+    cv::imshow("image", img);
+    cv::waitKey(500);
+    img.release();
+    cv::destroyAllWindows();
+    }
+  return 0;
+}
diff --git a/samples/test/eval_mAP_on_coco_val/gt2cocojson.py b/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
new file mode 100644
index 0000000..46a66aa
--- /dev/null
+++ b/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
@@ -0,0 +1,124 @@
+# -*- coding: UTF-8 -*-
+"""
+  @Author: lxm
+  
+  @version V1.0
+"""
+import datetime
+import json
+import os
+import cv2
+
+# 将yolo格式的数据集转换成coco格式的数据集
+
+# 读取文件夹下的所有文件
+path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+images_path = path+'/val2017/val2017'
+labels_path = path+'/val2017/labels'
+
+coco_json_save = path + '/val2017/gt_coco.json'
+
+
+# 创建coco格式的json文件
+coco_json = {
+	'info': {
+		"description": "COCOVal Dataset",
+		"url": "www.amov.com",
+		"version": "1.0",
+		"year": 2023,
+		"contributor": "lxm",
+		"date_created": datetime.datetime.utcnow().isoformat(' ')
+	},
+	"licenses": [
+		{
+			"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/",
+			"id": 1,
+			"name": "Attribution-NonCommercial-ShareAlike License"
+		}
+	],
+	'images': [],
+	'annotations': [],
+	'categories': []
+}
+
+
+# COCO classes
+classes = ["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"]
+
+# 创建coco格式的json文件
+for i, c in enumerate(classes):
+	coco_json['categories'].append({'id': i + 1, 'name': c, 'supercategory': c})
+
+
+
+
+
+
+# 读取images文件夹下的所有文件
+images = os.listdir(images_path)
+for image in images:
+	# 获取图片名和后缀
+	image_name, image_suffix = os.path.splitext(image)
+	# 获取图片的宽和高
+	image_path = images_path + '/' + image
+	img = cv2.imread(image_path)
+	height, width, _ = img.shape
+	# 添加图片信息
+	coco_json['images'].append({
+		'id': int(image_name),
+		'file_name': image,
+		'width': width,
+		'height': height,
+		'date_captured': datetime.datetime.utcnow().isoformat(' '),
+		'license': 1
+	})
+	
+	# 读取图片对应的标签文件
+	label_path = labels_path + '/' + image_name + '.txt'
+	if not os.path.exists(label_path):
+		continue
+	with open(label_path, 'r') as f:
+		labels = f.readlines()
+		labels = [l.strip() for l in labels]
+		for j, label in enumerate(labels):
+			label = label.split(' ')
+			# 获取类别id
+			category_id = int(label[0])
+			# 将yolo格式的数据转换成coco格式的数据
+			x = float(label[1]) * width
+			y = float(label[2]) * height
+			w = float(label[3]) * width
+			h = float(label[4]) * height
+			xmin = x - w / 2
+			ymin = y - h / 2
+			xmax = x + w / 2
+			ymax = y + h / 2
+			# 添加bbox信息
+			# 添加bbox信息
+			coco_json['annotations'].append({
+				'image_id': int(image_name),
+				'category_id': category_id + 1,
+				'bbox': [xmin, ymin, w, h],
+				'id': len(coco_json['annotations']),
+				'area': w * h,
+				'iscrowd': 0,
+				'segmentation': [],
+				'attributes': ""
+			})
+
+# 保存json文件
+with open(coco_json_save, 'w') as f:
+	json.dump(coco_json, f, indent=2)
+
+print(len(coco_json['images']), len(coco_json['annotations']), len(coco_json['categories']), 'Done!')
+
diff --git a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
new file mode 100644
index 0000000..4c5d890
--- /dev/null
+++ b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
@@ -0,0 +1,66 @@
+import datetime
+import json
+import os
+import cv2
+
+# revert prediction results to coco_json 
+path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+# all files dir
+images_path = path+'/val2017/val2017'
+preds_path = path+'/val2017/preds'
+
+coco_json_save ='pd_coco.json'
+
+# config coco_json
+coco_json = []
+
+# load images dir
+images = os.listdir(images_path)
+for image in images:
+  print(image)
+  # get image name
+  image_name, image_suffix = os.path.splitext(image)
+  # get image W and H
+  image_path = images_path + '/' + image
+  img = cv2.imread(image_path)
+  height, width, _ = img.shape
+
+  # read pred's txt
+  pred_path = preds_path + '/' + image_name + '.txt'
+  if not os.path.exists(pred_path):
+    continue
+  with open(pred_path, 'r') as f:
+    preds = f.readlines()
+    preds = [l.strip() for l in preds]
+    for j,pred in enumerate(preds):
+      pred = pred.split(' ')
+      category_id = int(pred[0])
+      x = float(pred[1]) * width
+      y = float(pred[2]) * height
+      w = float(pred[3]) * width
+      h = float(pred[4]) * height
+      xmin = x - w / 2
+      ymin = y - h / 2
+      xmax = x + w / 2
+      ymax = y + h / 2
+			
+      coco_json.append({
+        'image_id': int(image_name),
+        'category_id': category_id + 1,
+        'bbox': [xmin, ymin, w, h],
+        'score': float(pred[5]),
+        'area': w * h})
+
+# save json
+with open(os.path.join(coco_json_save), 'w') as f:
+  json.dump(coco_json, f, indent=2)
+
+print(len(coco_json), 'Done!')
+
+
+
+
+
+
+
+

From 5b66234c72904bce1dbb3c67fac0b7e131b20db4 Mon Sep 17 00:00:00 2001
From: lxm <1367240116@qq.com>
Date: Fri, 11 Aug 2023 18:33:17 +0800
Subject: [PATCH 2/7] COCO Model mAP Eval

---
 samples/test/eval_mAP_on_coco_val/coco_eval.py   | 7 -------
 samples/test/eval_mAP_on_coco_val/gt2cocojson.py | 1 -
 samples/test/eval_mAP_on_coco_val/pd2cocojson.py | 8 --------
 3 files changed, 16 deletions(-)

diff --git a/samples/test/eval_mAP_on_coco_val/coco_eval.py b/samples/test/eval_mAP_on_coco_val/coco_eval.py
index 25f90af..9412ddd 100644
--- a/samples/test/eval_mAP_on_coco_val/coco_eval.py
+++ b/samples/test/eval_mAP_on_coco_val/coco_eval.py
@@ -23,10 +23,3 @@ if __name__ == '__main__':
   # save results
   with open('coco_eval.txt', 'w') as f:
     f.write(str(cocoEval.stats))
-
-
-
-
-
-
-
diff --git a/samples/test/eval_mAP_on_coco_val/gt2cocojson.py b/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
index 46a66aa..c3ff5a4 100644
--- a/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
+++ b/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
@@ -121,4 +121,3 @@ with open(coco_json_save, 'w') as f:
 	json.dump(coco_json, f, indent=2)
 
 print(len(coco_json['images']), len(coco_json['annotations']), len(coco_json['categories']), 'Done!')
-
diff --git a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
index 4c5d890..05f002a 100644
--- a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
+++ b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
@@ -56,11 +56,3 @@ with open(os.path.join(coco_json_save), 'w') as f:
   json.dump(coco_json, f, indent=2)
 
 print(len(coco_json), 'Done!')
-
-
-
-
-
-
-
-

From 7a01223db47bd9d1b714e8f6a1a94f7db5853bf1 Mon Sep 17 00:00:00 2001
From: jario-jin <jariof@foxmail.com>
Date: Fri, 11 Aug 2023 11:27:38 +0000
Subject: [PATCH 3/7] update samples/test/eval_mAP_on_coco_val/coco_eval.py.

Signed-off-by: jario-jin <jariof@foxmail.com>
---
 .../test/eval_mAP_on_coco_val/coco_eval.py    | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/samples/test/eval_mAP_on_coco_val/coco_eval.py b/samples/test/eval_mAP_on_coco_val/coco_eval.py
index 9412ddd..7ff2303 100644
--- a/samples/test/eval_mAP_on_coco_val/coco_eval.py
+++ b/samples/test/eval_mAP_on_coco_val/coco_eval.py
@@ -3,23 +3,23 @@ from pycocotools.cocoeval import COCOeval
 import os
 
 if __name__ == '__main__':
-  path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
-  pred_json = 'pd_coco.json'
-  anno_json = path + '/val2017/gt_coco.json'
+    path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+    pred_json = 'pd_coco.json'
+    anno_json = path + '/val2017/gt_coco.json'
 
-  # use COCO API to load forecast results and annotations
-  cocoGt = COCO(anno_json)
-  cocoDt = cocoGt.loadRes(pred_json)
+    # use COCO API to load forecast results and annotations
+    cocoGt = COCO(anno_json)
+    cocoDt = cocoGt.loadRes(pred_json)
 
-  # create COCO eval object
-  cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
+    # create COCO eval object
+    cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
 
-  # assessment
-  cocoEval.evaluate()
-  cocoEval.accumulate()
-  cocoEval.summarize()
+    # assessment
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    cocoEval.summarize()
 
 
-  # save results
-  with open('coco_eval.txt', 'w') as f:
-    f.write(str(cocoEval.stats))
+    # save results
+    with open('coco_eval.txt', 'w') as f:
+        f.write(str(cocoEval.stats))

From cf7fad3b2b1dda84d3f4bf27102043cd393b5ba5 Mon Sep 17 00:00:00 2001
From: jario-jin <jariof@foxmail.com>
Date: Fri, 11 Aug 2023 11:31:51 +0000
Subject: [PATCH 4/7] update samples/test/eval_mAP_on_coco_val/pd2cocojson.py.

Signed-off-by: jario-jin <jariof@foxmail.com>
---
 .../test/eval_mAP_on_coco_val/pd2cocojson.py  | 66 +++++++++----------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
index 05f002a..3d74acb 100644
--- a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
+++ b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
@@ -17,42 +17,42 @@ coco_json = []
 # load images dir
 images = os.listdir(images_path)
 for image in images:
-  print(image)
-  # get image name
-  image_name, image_suffix = os.path.splitext(image)
-  # get image W and H
-  image_path = images_path + '/' + image
-  img = cv2.imread(image_path)
-  height, width, _ = img.shape
+    print(image)
+    # get image name
+    image_name, image_suffix = os.path.splitext(image)
+    # get image W and H
+    image_path = images_path + '/' + image
+    img = cv2.imread(image_path)
+    height, width, _ = img.shape
 
-  # read pred's txt
-  pred_path = preds_path + '/' + image_name + '.txt'
-  if not os.path.exists(pred_path):
-    continue
-  with open(pred_path, 'r') as f:
-    preds = f.readlines()
-    preds = [l.strip() for l in preds]
-    for j,pred in enumerate(preds):
-      pred = pred.split(' ')
-      category_id = int(pred[0])
-      x = float(pred[1]) * width
-      y = float(pred[2]) * height
-      w = float(pred[3]) * width
-      h = float(pred[4]) * height
-      xmin = x - w / 2
-      ymin = y - h / 2
-      xmax = x + w / 2
-      ymax = y + h / 2
-			
-      coco_json.append({
-        'image_id': int(image_name),
-        'category_id': category_id + 1,
-        'bbox': [xmin, ymin, w, h],
-        'score': float(pred[5]),
-        'area': w * h})
+    # read pred's txt
+    pred_path = preds_path + '/' + image_name + '.txt'
+    if not os.path.exists(pred_path):
+        continue
+    with open(pred_path, 'r') as f:
+        preds = f.readlines()
+        preds = [l.strip() for l in preds]
+        for j, pred in enumerate(preds):
+            pred = pred.split(' ')
+            category_id = int(pred[0])
+            x = float(pred[1]) * width
+            y = float(pred[2]) * height
+            w = float(pred[3]) * width
+            h = float(pred[4]) * height
+            xmin = x - w / 2
+            ymin = y - h / 2
+            xmax = x + w / 2
+            ymax = y + h / 2
+                
+            coco_json.append({
+            'image_id': int(image_name),
+            'category_id': category_id + 1,
+            'bbox': [xmin, ymin, w, h],
+            'score': float(pred[5]),
+            'area': w * h})
 
 # save json
 with open(os.path.join(coco_json_save), 'w') as f:
-  json.dump(coco_json, f, indent=2)
+    json.dump(coco_json, f, indent=2)
 
 print(len(coco_json), 'Done!')

From 6d0f83b4d3bf7ccc4362887a476370efd589f526 Mon Sep 17 00:00:00 2001
From: lxm <1367240116@qq.com>
Date: Sun, 13 Aug 2023 16:05:24 +0800
Subject: [PATCH 5/7] COCO Model mAP Eval

---
 .../test/eval_mAP_on_coco_val/coco_eval.py    |  30 ++---
 .../eval_mAP_on_coco_val.cpp                  |   2 +-
 .../test/eval_mAP_on_coco_val/gt2cocojson.py  | 123 ------------------
 .../test/eval_mAP_on_coco_val/pd2cocojson.py  |  70 +++++-----
 4 files changed, 53 insertions(+), 172 deletions(-)
 delete mode 100644 samples/test/eval_mAP_on_coco_val/gt2cocojson.py

diff --git a/samples/test/eval_mAP_on_coco_val/coco_eval.py b/samples/test/eval_mAP_on_coco_val/coco_eval.py
index 7ff2303..81391d5 100644
--- a/samples/test/eval_mAP_on_coco_val/coco_eval.py
+++ b/samples/test/eval_mAP_on_coco_val/coco_eval.py
@@ -3,23 +3,23 @@ from pycocotools.cocoeval import COCOeval
 import os
 
 if __name__ == '__main__':
-    path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
-    pred_json = 'pd_coco.json'
-    anno_json = path + '/val2017/gt_coco.json'
+  path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+  pred_json = 'pd_coco.json'
+  anno_json = path + '/val2017/instances_val2017.json'
 
-    # use COCO API to load forecast results and annotations
-    cocoGt = COCO(anno_json)
-    cocoDt = cocoGt.loadRes(pred_json)
+  # use COCO API to load forecast results and annotations
+  cocoGt = COCO(anno_json)
+  cocoDt = cocoGt.loadRes(pred_json)
 
-    # create COCO eval object
-    cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
+  # create COCO eval object
+  cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
 
-    # assessment
-    cocoEval.evaluate()
-    cocoEval.accumulate()
-    cocoEval.summarize()
+  # assessment
+  cocoEval.evaluate()
+  cocoEval.accumulate()
+  cocoEval.summarize()
 
 
-    # save results
-    with open('coco_eval.txt', 'w') as f:
-        f.write(str(cocoEval.stats))
+  # save results
+  with open('coco_eval.txt', 'w') as f:
+    f.write(str(cocoEval.stats))
diff --git a/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp b/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
index dc31f8a..5ec4514 100644
--- a/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
+++ b/samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
@@ -65,7 +65,7 @@ int main(int argc, char *argv[])
 
     //create pred file
     std::string val_image_name = GetImageFileName(val_image[i]);
-    std::string filename = folder+ val_image_name + ".txt";
+    std::string filename = folder+"/"+ val_image_name + ".txt";
     std::ofstream file(filename);
     file.is_open();
     file<<std::fixed<<std::setprecision(6);
diff --git a/samples/test/eval_mAP_on_coco_val/gt2cocojson.py b/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
deleted file mode 100644
index c3ff5a4..0000000
--- a/samples/test/eval_mAP_on_coco_val/gt2cocojson.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# -*- coding: UTF-8 -*-
-"""
-  @Author: lxm
-  
-  @version V1.0
-"""
-import datetime
-import json
-import os
-import cv2
-
-# 将yolo格式的数据集转换成coco格式的数据集
-
-# 读取文件夹下的所有文件
-path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
-images_path = path+'/val2017/val2017'
-labels_path = path+'/val2017/labels'
-
-coco_json_save = path + '/val2017/gt_coco.json'
-
-
-# 创建coco格式的json文件
-coco_json = {
-	'info': {
-		"description": "COCOVal Dataset",
-		"url": "www.amov.com",
-		"version": "1.0",
-		"year": 2023,
-		"contributor": "lxm",
-		"date_created": datetime.datetime.utcnow().isoformat(' ')
-	},
-	"licenses": [
-		{
-			"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/",
-			"id": 1,
-			"name": "Attribution-NonCommercial-ShareAlike License"
-		}
-	],
-	'images': [],
-	'annotations': [],
-	'categories': []
-}
-
-
-# COCO classes
-classes = ["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"]
-
-# 创建coco格式的json文件
-for i, c in enumerate(classes):
-	coco_json['categories'].append({'id': i + 1, 'name': c, 'supercategory': c})
-
-
-
-
-
-
-# 读取images文件夹下的所有文件
-images = os.listdir(images_path)
-for image in images:
-	# 获取图片名和后缀
-	image_name, image_suffix = os.path.splitext(image)
-	# 获取图片的宽和高
-	image_path = images_path + '/' + image
-	img = cv2.imread(image_path)
-	height, width, _ = img.shape
-	# 添加图片信息
-	coco_json['images'].append({
-		'id': int(image_name),
-		'file_name': image,
-		'width': width,
-		'height': height,
-		'date_captured': datetime.datetime.utcnow().isoformat(' '),
-		'license': 1
-	})
-	
-	# 读取图片对应的标签文件
-	label_path = labels_path + '/' + image_name + '.txt'
-	if not os.path.exists(label_path):
-		continue
-	with open(label_path, 'r') as f:
-		labels = f.readlines()
-		labels = [l.strip() for l in labels]
-		for j, label in enumerate(labels):
-			label = label.split(' ')
-			# 获取类别id
-			category_id = int(label[0])
-			# 将yolo格式的数据转换成coco格式的数据
-			x = float(label[1]) * width
-			y = float(label[2]) * height
-			w = float(label[3]) * width
-			h = float(label[4]) * height
-			xmin = x - w / 2
-			ymin = y - h / 2
-			xmax = x + w / 2
-			ymax = y + h / 2
-			# 添加bbox信息
-			# 添加bbox信息
-			coco_json['annotations'].append({
-				'image_id': int(image_name),
-				'category_id': category_id + 1,
-				'bbox': [xmin, ymin, w, h],
-				'id': len(coco_json['annotations']),
-				'area': w * h,
-				'iscrowd': 0,
-				'segmentation': [],
-				'attributes': ""
-			})
-
-# 保存json文件
-with open(coco_json_save, 'w') as f:
-	json.dump(coco_json, f, indent=2)
-
-print(len(coco_json['images']), len(coco_json['annotations']), len(coco_json['categories']), 'Done!')
diff --git a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
index 3d74acb..fa0c48b 100644
--- a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
+++ b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
@@ -14,45 +14,49 @@ coco_json_save ='pd_coco.json'
 # config coco_json
 coco_json = []
 
+# remap the id of the coco dataset
+id_map = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}
+reid_mp = { value: key for key, value in id_map.items()}
+
 # load images dir
 images = os.listdir(images_path)
 for image in images:
-    print(image)
-    # get image name
-    image_name, image_suffix = os.path.splitext(image)
-    # get image W and H
-    image_path = images_path + '/' + image
-    img = cv2.imread(image_path)
-    height, width, _ = img.shape
+  print(image)
+  # get image name
+  image_name, image_suffix = os.path.splitext(image)
+  # get image W and H
+  image_path = images_path + '/' + image
+  img = cv2.imread(image_path)
+  height, width, _ = img.shape
 
-    # read pred's txt
-    pred_path = preds_path + '/' + image_name + '.txt'
-    if not os.path.exists(pred_path):
-        continue
-    with open(pred_path, 'r') as f:
-        preds = f.readlines()
-        preds = [l.strip() for l in preds]
-        for j, pred in enumerate(preds):
-            pred = pred.split(' ')
-            category_id = int(pred[0])
-            x = float(pred[1]) * width
-            y = float(pred[2]) * height
-            w = float(pred[3]) * width
-            h = float(pred[4]) * height
-            xmin = x - w / 2
-            ymin = y - h / 2
-            xmax = x + w / 2
-            ymax = y + h / 2
-                
-            coco_json.append({
-            'image_id': int(image_name),
-            'category_id': category_id + 1,
-            'bbox': [xmin, ymin, w, h],
-            'score': float(pred[5]),
-            'area': w * h})
+  # read pred's txt
+  pred_path = preds_path + '/' + image_name + '.txt'
+  if not os.path.exists(pred_path):
+    continue
+  with open(pred_path, 'r') as f:
+    preds = f.readlines()
+    preds = [l.strip() for l in preds]
+    for j,pred in enumerate(preds):
+      pred = pred.split(' ')
+      category_id = int(pred[0])
+      x = float(pred[1]) * width
+      y = float(pred[2]) * height
+      w = float(pred[3]) * width
+      h = float(pred[4]) * height
+      xmin = x - w / 2
+      ymin = y - h / 2
+      xmax = x + w / 2
+      ymax = y + h / 2
+			
+      coco_json.append({
+        'image_id': int(image_name),
+        'category_id': int(reid_mp[category_id]),
+        'bbox': [xmin, ymin, w, h],
+        'score': float(pred[5]),
+        'area': w * h})
 
 # save json
 with open(os.path.join(coco_json_save), 'w') as f:
-    json.dump(coco_json, f, indent=2)
+  json.dump(coco_json, f, indent=2)
 
 print(len(coco_json), 'Done!')

From 5c30296b734c1a6fa4ed74b664f411469e17e4f4 Mon Sep 17 00:00:00 2001
From: jario-jin <jariof@foxmail.com>
Date: Mon, 14 Aug 2023 01:53:16 +0000
Subject: [PATCH 6/7] update samples/test/eval_mAP_on_coco_val/coco_eval.py.

Signed-off-by: jario-jin <jariof@foxmail.com>
---
 .../test/eval_mAP_on_coco_val/coco_eval.py    | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/samples/test/eval_mAP_on_coco_val/coco_eval.py b/samples/test/eval_mAP_on_coco_val/coco_eval.py
index 81391d5..b534d29 100644
--- a/samples/test/eval_mAP_on_coco_val/coco_eval.py
+++ b/samples/test/eval_mAP_on_coco_val/coco_eval.py
@@ -3,23 +3,23 @@ from pycocotools.cocoeval import COCOeval
 import os
 
 if __name__ == '__main__':
-  path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
-  pred_json = 'pd_coco.json'
-  anno_json = path + '/val2017/instances_val2017.json'
+    path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
+    pred_json = 'pd_coco.json'
+    anno_json = path + '/val2017/instances_val2017.json'
 
-  # use COCO API to load forecast results and annotations
-  cocoGt = COCO(anno_json)
-  cocoDt = cocoGt.loadRes(pred_json)
+    # use COCO API to load forecast results and annotations
+    cocoGt = COCO(anno_json)
+    cocoDt = cocoGt.loadRes(pred_json)
 
-  # create COCO eval object
-  cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
+    # create COCO eval object
+    cocoEval = COCOeval(cocoGt, cocoDt,'bbox')
 
-  # assessment
-  cocoEval.evaluate()
-  cocoEval.accumulate()
-  cocoEval.summarize()
+    # assessment
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    cocoEval.summarize()
 
 
-  # save results
-  with open('coco_eval.txt', 'w') as f:
-    f.write(str(cocoEval.stats))
+    # save results
+    with open('coco_eval.txt', 'w') as f:
+        f.write(str(cocoEval.stats))

From 1859cd66eb33ba3c792a0fc78beab1b2f2110c03 Mon Sep 17 00:00:00 2001
From: jario-jin <jariof@foxmail.com>
Date: Mon, 14 Aug 2023 01:57:52 +0000
Subject: [PATCH 7/7] update samples/test/eval_mAP_on_coco_val/pd2cocojson.py.

Signed-off-by: jario-jin <jariof@foxmail.com>
---
 .../test/eval_mAP_on_coco_val/pd2cocojson.py  | 72 +++++++++----------
 1 file changed, 36 insertions(+), 36 deletions(-)

diff --git a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
index fa0c48b..7373110 100644
--- a/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
+++ b/samples/test/eval_mAP_on_coco_val/pd2cocojson.py
@@ -6,57 +6,57 @@ import cv2
 # revert prediction results to coco_json 
 path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
 # all files dir
-images_path = path+'/val2017/val2017'
-preds_path = path+'/val2017/preds'
+images_path = path + '/val2017/val2017'
+preds_path = path + '/val2017/preds'
 
-coco_json_save ='pd_coco.json'
+coco_json_save = 'pd_coco.json'
 
 # config coco_json
 coco_json = []
 
 # remap the id of the coco dataset
 id_map = {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 13: 11, 14: 12, 15: 13, 16: 14, 17: 15, 18: 16, 19: 17, 20: 18, 21: 19, 22: 20, 23: 21, 24: 22, 25: 23, 27: 24, 28: 25, 31: 26, 32: 27, 33: 28, 34: 29, 35: 30, 36: 31, 37: 32, 38: 33, 39: 34, 40: 35, 41: 36, 42: 37, 43: 38, 44: 39, 46: 40, 47: 41, 48: 42, 49: 43, 50: 44, 51: 45, 52: 46, 53: 47, 54: 48, 55: 49, 56: 50, 57: 51, 58: 52, 59: 53, 60: 54, 61: 55, 62: 56, 63: 57, 64: 58, 65: 59, 67: 60, 70: 61, 72: 62, 73: 63, 74: 64, 75: 65, 76: 66, 77: 67, 78: 68, 79: 69, 80: 70, 81: 71, 82: 72, 84: 73, 85: 74, 86: 75, 87: 76, 88: 77, 89: 78, 90: 79}
-reid_mp = { value: key for key, value in id_map.items()}
+reid_mp = {value: key for key, value in id_map.items()}
 
 # load images dir
 images = os.listdir(images_path)
 for image in images:
-  print(image)
-  # get image name
-  image_name, image_suffix = os.path.splitext(image)
-  # get image W and H
-  image_path = images_path + '/' + image
-  img = cv2.imread(image_path)
-  height, width, _ = img.shape
+    print(image)
+    # get image name
+    image_name, image_suffix = os.path.splitext(image)
+    # get image W and H
+    image_path = images_path + '/' + image
+    img = cv2.imread(image_path)
+    height, width, _ = img.shape
 
-  # read pred's txt
-  pred_path = preds_path + '/' + image_name + '.txt'
-  if not os.path.exists(pred_path):
-    continue
-  with open(pred_path, 'r') as f:
-    preds = f.readlines()
-    preds = [l.strip() for l in preds]
-    for j,pred in enumerate(preds):
-      pred = pred.split(' ')
-      category_id = int(pred[0])
-      x = float(pred[1]) * width
-      y = float(pred[2]) * height
-      w = float(pred[3]) * width
-      h = float(pred[4]) * height
-      xmin = x - w / 2
-      ymin = y - h / 2
-      xmax = x + w / 2
-      ymax = y + h / 2
+    # read pred's txt
+    pred_path = preds_path + '/' + image_name + '.txt'
+    if not os.path.exists(pred_path):
+        continue
+    with open(pred_path, 'r') as f:
+        preds = f.readlines()
+        preds = [l.strip() for l in preds]
+        for j, pred in enumerate(preds):
+            pred = pred.split(' ')
+            category_id = int(pred[0])
+            x = float(pred[1]) * width
+            y = float(pred[2]) * height
+            w = float(pred[3]) * width
+            h = float(pred[4]) * height
+            xmin = x - w / 2
+            ymin = y - h / 2
+            xmax = x + w / 2
+            ymax = y + h / 2
 			
-      coco_json.append({
-        'image_id': int(image_name),
-        'category_id': int(reid_mp[category_id]),
-        'bbox': [xmin, ymin, w, h],
-        'score': float(pred[5]),
-        'area': w * h})
+            coco_json.append({
+                'image_id': int(image_name),
+                'category_id': int(reid_mp[category_id]),
+                'bbox': [xmin, ymin, w, h],
+                'score': float(pred[5]),
+                'area': w * h})
 
 # save json
 with open(os.path.join(coco_json_save), 'w') as f:
-  json.dump(coco_json, f, indent=2)
+    json.dump(coco_json, f, indent=2)
 
 print(len(coco_json), 'Done!')