add safe hat pretrain model

README.md

@@ -90,7 +90,7 @@ h = h*dh
 * [预训练模型下载地址(百度网盘 Password: ise9 )](https://pan.baidu.com/s/1mxiI-tOpE3sU-9TVPJmPWw)     
 
 ### 4、安全帽检测预训练模型    
-* [预训练模型下载地址(百度网盘 Password:  )]()     
+* [预训练模型下载地址(百度网盘 Password: inu8 )](https://pan.baidu.com/s/1rWUEsPnOTdmfJW4xM8m3Eg)     
 
 ## 项目使用方法     
 

predict.py

@@ -60,6 +60,7 @@ def detect(
         img_size=416,
         conf_thres=0.5,
         nms_thres=0.5,
+        video_path = 0,
 ):
     classes = load_classes(parse_data_cfg(data_cfg)['names'])
     num_classes = len(classes)
@@ -93,7 +94,7 @@ 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_1.mp4")
+    video_capture = cv2.VideoCapture(video_path)
     # url="http://admin:admin@192.168.43.1:8081"
     # video_capture=cv2.VideoCapture(url)
 
@@ -151,7 +152,11 @@ def detect(
 
                 # print(conf, cls_conf)
                 # xyxy = refine_hand_bbox(xyxy,im0.shape)
-                plot_one_box(xyxy, im0, label=label, color=(15,255,95),line_thickness = 3)
+                xyxy = int(xyxy[0]),int(xyxy[1])+6,int(xyxy[2]),int(xyxy[3])
+                if int(cls) == 0:
+                    plot_one_box(xyxy, im0, label=label, color=(15,255,95),line_thickness = 3)
+                else:
+                    plot_one_box(xyxy, im0, label=label, color=(15,155,255),line_thickness = 3)
 
             s2 = time.time()
             print("detect time: {} \n".format(s2 - t))
@@ -177,20 +182,22 @@ def detect(
 
 if __name__ == '__main__':
 
-    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
+    voc_config = 'cfg/helmet.data' # 模型相关配置文件
+    model_path = './weights-yolov3-helmet/hat_416_epoch_410.pt' # 检测模型路径
+    model_cfg = 'yolo' # yolo / yolo-tiny 模型结构
+    video_path = "./video/hat5.mp4" # 测试视频
 
     img_size = 416 # 图像尺寸
-    conf_thres = 0.36# 检测置信度
-    nms_thres = 0.5 # nms 阈值
+    conf_thres = 0.5# 检测置信度
+    nms_thres = 0.6 # nms 阈值
 
-    with torch.no_grad():#设置无梯度运行
+    with torch.no_grad():#设置无梯度运行模型推理
         detect(
             model_path = model_path,
             cfg = model_cfg,
             data_cfg = voc_config,
-            img_size=img_size,
-            conf_thres=conf_thres,
-            nms_thres=nms_thres,
+            img_size = img_size,
+            conf_thres = conf_thres,
+            nms_thres = nms_thres,
+            video_path = video_path,
         )

train.py

@@ -204,7 +204,8 @@ if __name__ == '__main__':
 
     # train(data_cfg="cfg/hand.data")
     # train(data_cfg = "cfg/face.data")
-    train(data_cfg = "cfg/person.data")
+    # train(data_cfg = "cfg/person.data")
+    train(data_cfg = "cfg/helmet.data")
 
 
     print('well done ~ ')