add persn detect

README.md

 # YOLO V3
-物体检测，包括手部检测、人脸检测，因为数据集的独立所以分别为独立模型。  
+物体检测，包括手部检测、人脸检测、人检测，因为数据集的独立所以分别为3个独立模型。  
 
 ## 项目介绍    
 ### 1、手部检测  
@@ -14,6 +14,11 @@
 * 视频示例：    
 ![videoface](https://codechina.csdn.net/EricLee/yolo_v3/-/raw/master/samples/face.gif)    
 
+### 3、人检测  
+人检测示例如下 ：     
+* 视频示例：    
+![videoPerson](https://codechina.csdn.net/EricLee/yolo_v3/-/raw/master/samples/person.gif)    
+
 ## 项目配置  
 * 作者开发环境：  
 * Python 3.7  
@@ -25,16 +30,20 @@
 * [数据集下载地址(百度网盘 Password: c680 )](https://pan.baidu.com/s/1H0YH8jMEXeIcubLEv0W_yw)   
 
 ### 2、脸部检测数据集   
-该项目采用的是开源数据集 WIDERFACE，其下载地址为 http://shuoyang1213.me/WIDERFACE/  
+该项目采用的是开源数据集 WIDERFACE，其地址为 http://shuoyang1213.me/WIDERFACE/  
 ```  
 @inproceedings{yang2016wider,
-	Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
-	Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-	Title = {WIDER FACE: A Face Detection Benchmark},
-	Year = {2016}}
+Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+Title = {WIDER FACE: A Face Detection Benchmark},
+Year = {2016}}
 ```  
 * [该项目制作的训练集的数据集下载地址(百度网盘 Password: r77x )](https://pan.baidu.com/s/1Jsm1qPPzAW46LRW5nUClzQ)   
 
+### 3、人检测数据集     
+该项目采用的是开源数据集 COCO，其地址为 https://cocodataset.org/   
+* [该项目制作的训练集的数据集下载地址(百度网盘 Password:  )]()     
+
 ### 数据格式  
 size是全图分辨率， (x，y) 是目标物体中心对于全图的归一化坐标，w,h是目标物体边界框对于全图的归一化宽、高。   
 
@@ -65,6 +74,9 @@ Contextual Attention for Hand Detection in the Wild. S. Narasimhaswamy, Z. Wei,
 ### 2、脸部检测预训练模型    
 * [预训练模型下载地址(百度网盘 Password: l2a3 )](https://pan.baidu.com/s/1xVtZUMD94DiT9FQQ66xG1A)     
 
+### 3、人检测预训练模型    
+* [预训练模型下载地址(百度网盘 Password: ise9 )](https://pan.baidu.com/s/1mxiI-tOpE3sU-9TVPJmPWw)     
+
 
 ## 项目使用方法     
 

cfg/person.data

+cfg_model=yolo
+classes=1
+gpus = 0
+num_workers = 8
+batch_size = 8
+img_size = 416
+multi_scale = True
+epochs = 100
+train=./yolo_person_train/anno/train.txt
+valid=./yolo_person_train/anno/train.txt
+names=./cfg/person.names
+#finetune_model=./coco_model/yolov3_coco.pt
+finetune_model = ./weights-yolov3-person/latest_416.pt
+lr_step = 10,20,30
+lr0 = 0.0001

cfg/person.names

+Person

predict.py

@@ -93,7 +93,7 @@ def detect(
 
     colors = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) for v in range(1, num_classes + 1)][::-1]
 
-    video_capture = cv2.VideoCapture("./video/bean.mp4")
+    video_capture = cv2.VideoCapture("./video/bean_1.mp4")
     # url="http://admin:admin@192.168.43.1:8081"
     # video_capture=cv2.VideoCapture(url)
 
@@ -151,7 +151,7 @@ def detect(
 
                 # print(conf, cls_conf)
                 # xyxy = refine_hand_bbox(xyxy,im0.shape)
-                plot_one_box(xyxy, im0, label=label, color=(155,55,255),line_thickness = 3)
+                plot_one_box(xyxy, im0, label=label, color=(15,255,95),line_thickness = 3)
 
             s2 = time.time()
             print("detect time: {} \n".format(s2 - t))
@@ -177,8 +177,8 @@ def detect(
 
 if __name__ == '__main__':
 
-    voc_config = 'cfg/face.data' # 模型相关配置文件
-    model_path = './weights-yolov3-face/latest_416.pt' # 检测模型路径
+    voc_config = 'cfg/person.data' # 模型相关配置文件
+    model_path = './weights-yolov3-person/latest_416.pt' # 检测模型路径
     model_cfg = 'yolo' # yolo / yolo-tiny
 
     img_size = 416 # 图像尺寸

show_yolo_anno.py

@@ -12,8 +12,11 @@ if __name__ == "__main__":
     # path='./datasets_fusion_hand_train/anno/train.txt'
     # path_voc_names = './cfg/hand.names'
 
-    path='./yolo_widerface_open_train/anno/train.txt'
-    path_voc_names = './cfg/face.names'
+    # path='./yolo_widerface_open_train/anno/train.txt'
+    # path_voc_names = './cfg/face.names'
+
+    path='./yolo_person_train/anno/train.txt'
+    path_voc_names = './cfg/person.names'
 
     with open(path_voc_names, 'r') as f:
         label_map = f.readlines()

train.py

@@ -203,7 +203,8 @@ if __name__ == '__main__':
 
 
     # train(data_cfg="cfg/hand.data")
-    train(data_cfg = "cfg/face.data")
+    # train(data_cfg = "cfg/face.data")
+    train(data_cfg = "cfg/person.data")
 
 
     print('well done ~ ')