Version 5.7.1

添加了 `conf_thres` 和 `iou_thres` 的设置方法，在初始化识别方法时可以添加。

README.md

@@ -1,3 +1,6 @@
+
+
+
 # Introduction
 
 YoloV5 作为 YoloV4 之后的改进型，在算法上做出了优化，检测的性能得到了一定的提升。其特点之一就是权重文件非常的小，可以在一些配置更低的移动设备上运行，且提高速度的同时准确度更高。本次使用的是最新推出的 YoloV5 Version7 版本。
@@ -55,7 +58,7 @@ import torch
 
 if __name__ == '__main__':
     cap = cv2.VideoCapture(0)
-    a = yolo_detectAPI.DetectAPI(weights='last.pt')  # 你要使用的模型的路径
+    a = yolo_detectAPI.DetectAPI(weights='last.pt', conf_thres=0.5, iou_thres=0.5)  # 你要使用的模型的路径
     with torch.no_grad():
         while True:
             rec, img = cap.read()
@@ -65,7 +68,7 @@ if __name__ == '__main__':
             for cls, (x1, y1, x2, y2), conf in result[0][1]:
                 print(names[cls], x1, y1, x2, y2, conf)  # 识别物体种类、左上角x坐标、左上角y轴坐标、右下角x轴坐标、右下角y轴坐标，置信度
 
-            cv2.imshow("vedio", img)
+            cv2.imshow("video", img)
 
             if cv2.waitKey(1) == ord('q'):
                 break
@@ -97,3 +100,12 @@ if __name__ == '__main__':
 https://github.com/ultralytics/yolov5/releases/tag/v7.0
 https://blog.csdn.net/weixin_51331359/article/details/126012620
 https://blog.csdn.net/CharmsLUO/article/details/123422822
+
+# Update Version 5.7.1 2023-03-29
+添加了 `conf_thres` 和 `iou_thres` 的设置方法，在初始化识别方法时可以添加。
+```python
+yolo_detectAPI.DetectAPI(weights='last.pt', conf_thres=0.5, iou_thres=0.5)
+```
+`iou_thres` 过大容易出现一个目标多个检测框；
+
+`iou_thres` 过小容易出现检测结果少的问题。

setup.py

@@ -3,17 +3,19 @@
 
 setup(
     name='yolo_detectAPI',
-    version='5.7',
+    version='5.7.1',
     description='Detect API',
-    long_description='This is a API for yolov5 version7 detect.py',
+    long_description='This is a API for yolov5 version7 detect.py, new-version 5.7.1 allow user set <conf_thres> and '
+                     '<iou_thres>',
     license='GPL Licence',
     author='Da Kuang',
     author_email='[email protected]',
     py_modeles = '__init__.py',
     packages=find_packages(),
     pakages=['yolo_detectAPI'],
     include_package_data=True,
-    python_requires='>=3.8',
+    readme = 'README.md',
+    python_requires='>=3.7',
     url = 'http://blogs.kd-mercury.xyz/',
     install_requires=['matplotlib>=3.2.2', 'numpy>=1.18.5', 'opencv-python>=4.1.1',
                       'Pillow>=7.1.2', 'PyYAML>=5.3.1', 'requests>=2.23.0', 'scipy>=1.4.1',
@@ -22,10 +24,11 @@
                       'ipython>=8.3.0', 'psutil>=5.9.4'],
     data_files=['export.py'],
     classifiers=[
+        "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "License :: OSI Approved :: GNU General Public License (GPL)",
-        "Development Status :: 4 - Beta"
+        "Development Status :: 3 - Alpha"
     ],
     scripts=[],
 )

yolo_detectAPI/__init__.py

@@ -1,4 +1,4 @@
-#-*- coding:utf-8 -*-
+# -*- coding:utf-8 -*-
 import os
 import random
 import sys
@@ -53,8 +53,20 @@ def __init__(self, weights='weights/last.pt',
 
 
 class DetectAPI:
-    def __init__(self, weights, imgsz=640):
+    def __init__(self, weights, imgsz=640, conf_thres=None, iou_thres=None):
+        """
+        Init Detect API
+        Args:
+            weights: model
+            imgsz: default 640
+            conf_thres: 用于物体的识别率，object置信度阈值 默认0.25，大于此准确率才会显示识别结果
+            iou_thres: 用于去重，做nms的iou阈值 默认0.45，数值越小去重程度越高
+        """
         self.opt = YoloOpt(weights=weights, imgsz=imgsz)
+        if conf_thres is not None:
+            self.opt.conf_thres = conf_thres
+        if iou_thres is not None:
+            self.opt.iou_thres = iou_thres
         weights = self.opt.weights
         imgsz = self.opt.imgsz
 
@@ -73,7 +85,7 @@ def __init__(self, weights, imgsz=640):
 
         # 不使用半精度
         if self.half:
-            self.model.half() # switch to FP16
+            self.model.half()  # switch to FP16
 
         # read names and colors
         self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
@@ -88,7 +100,7 @@ def detect(self, source):
         # 直接从 source 加载数据
         dataset = LoadImages(source)
         # 源程序通过路径加载数据，现在 source 就是加载好的数据，因此 LoadImages 就要重写
-        bs = 1 # set batch size
+        bs = 1  # set batch size
 
         # 保存的路径
         vid_path, vid_writer = [None] * bs, [None] * bs
@@ -113,14 +125,15 @@ def detect(self, source):
 
                 # NMS
                 with dt[2]:
-                    pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, self.opt.classes, self.opt.agnostic_nms, max_det=2)
+                    pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, self.opt.classes,
+                                               self.opt.agnostic_nms, max_det=2)
 
                 # Process predictions
                 # 处理每一张图片
                 det = pred[0]  # API 一次只处理一张图片，因此不需要 for 循环
                 im0 = im0s.copy()  # copy 一个原图片的副本图片
                 result_txt = []  # 储存检测结果，每新检测出一个物品，长度就加一。
-                                 # 每一个元素是列表形式，储存着 类别，坐标，置信度
+                # 每一个元素是列表形式，储存着 类别，坐标，置信度
                 # 设置图片上绘制框的粗细，类别名称
                 annotator = Annotator(im0, line_width=3, example=str(self.names))
                 if len(det):
@@ -137,6 +150,7 @@ def detect(self, source):
                 result.append((im0, result_txt))  # 对于每张图片，返回画完框的图片，以及该图片的标签列表。
             return result, self.names
 
+
 if __name__ == '__main__':
     cap = cv2.VideoCapture(0)
     a = DetectAPI(weights='weights/last.pt')
@@ -155,4 +169,4 @@ def detect(self, source):
             cv2.imshow("vedio", img)
 
             if cv2.waitKey(1) == ord('q'):
-                break
+                break

yolo_detectAPI/setup.py

@@ -0,0 +1,34 @@
+import os
+from setuptools import setup, find_packages
+
+setup(
+    name='yolo_detectAPI',
+    version='5.7.1',
+    description='Detect API',
+    long_description='This is a API for yolov5 version7 detect.py, new-version 5.7.1 allow user set <conf_thres> and '
+                     '<iou_thres>',
+    license='GPL Licence',
+    author='Da Kuang',
+    author_email='[email protected]',
+    py_modeles = '__init__.py',
+    packages=find_packages(),
+    pakages=['yolo_detectAPI'],
+    include_package_data=True,
+    readme = 'README.md',
+    python_requires='>=3.7',
+    url = 'http://blogs.kd-mercury.xyz/',
+    install_requires=['matplotlib>=3.2.2', 'numpy>=1.18.5', 'opencv-python>=4.1.1',
+                      'Pillow>=7.1.2', 'PyYAML>=5.3.1', 'requests>=2.23.0', 'scipy>=1.4.1',
+                      'thop>=0.1.1', 'torch>=1.7.0', 'torchvision>=0.8.1', 'tqdm>=4.64.0',
+                      'tensorboard>=2.4.1', 'pandas>=1.1.4', 'seaborn>=0.11.0',
+                      'ipython>=8.3.0', 'psutil>=5.9.4'],
+    data_files=['export.py'],
+    classifiers=[
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "License :: OSI Approved :: GNU General Public License (GPL)",
+        "Development Status :: 3 - Alpha"
+    ],
+    scripts=[],
+)