添加注释

xznsh_paddle_scrfd/d_face.py

@@ -116,7 +116,7 @@ class SCRFD():
         return srcimg
 
 def face_detection(onnxmodel,image,crop_image,x1,y1,x2,y2,face_flag,count):
-
+    # 输入为crop目标检测bbox
     mynet = SCRFD(onnxmodel, confThreshold=0.8, nmsThreshold=0.5)
     outimg= mynet.detect(crop_image,face_flag,count)
     image[y1:y2,x1:x2] = outimg

xznsh_paddle_scrfd/d_people.py

@@ -62,12 +62,14 @@ def analysis_video(source_path,output_path,people_modle_path,face_modle_path,act
                     # 过滤置信度0.5以下目标
                     if confidence < 0.5:
                         continue
+                    # 当类别为巡检
                     if c.int() == 1:
                         if XJ_dict['head'] == 0 :
                             XJ_dict['head'] = count
                         else:
                             XJ_dict['tail'] = count
                         crop_img = frame[b_i[1]:b_i[3],b_i[0]:b_i[2]]
+                        # 人脸检测
                         frame = face_detection(face_modle_path,frame,crop_img,b_i[0],b_i[1],b_i[2],b_i[3],face_flag,count)
                     annotator.box_label(b, model_coco.names[int(c)]+str(confidence),(0,0,255))
 
@@ -77,7 +79,6 @@ def analysis_video(source_path,output_path,people_modle_path,face_modle_path,act
                 for box_a in boxes_a:                             
                     b_a = box_a.xyxy[0]  # get box coordinates in (x1,y1,x2,y2) format  #tensor([ 677.5757,  147.2737, 1182.3381,  707.2565])
                     c_a = box_a.cls  # tensor([0.])
-                    
                     confidence_a = float(box_a.conf)
                     confidence_a = round(confidence_a, 2)
                     # 过滤置信度0.5以下目标
@@ -87,7 +88,6 @@ def analysis_video(source_path,output_path,people_modle_path,face_modle_path,act
                     if c_a.int() == 1:
                         action_flag["action"] += 1
                         action_flag["action_frame"].append(count)
-
                     annotator_a.box_label(b_a, action_model.names[int(c_a)]+str(confidence_a),(255,0,0))
                     
             annotated_a_frame_coco = annotator_a.result()
@@ -98,10 +98,11 @@ def analysis_video(source_path,output_path,people_modle_path,face_modle_path,act
     
     cap.release()
     output_movie.release()
-
+    # 计算巡检时长
     diff = round((XJ_dict["tail"]-XJ_dict["head"])/fps,2)
     fina_frame = [round(_ /fps,2) for _ in face_flag["frame"]] 
     s = ', '.join(map(str, fina_frame))
+    # 拉门时间
     action_frame = [round(_ /fps,2) for _ in action_flag["action_frame"]] 
     s_action = ', '.join(map(str, action_frame))