feat: 增加部分任务

utils.py

@@ -1,24 +1,9 @@
+
 from enum import Enum
 import numpy as np
-# 根据标签修改
-# class tlabel(Enum):
-#     BBLOCK   = 5    # 蓝色方块
-#     RBLOCK   = 2    # 红色方块
-#     HOSPITAL = 3    # 医院
-#     BBALL    = 4    # 蓝球
-#     YBALL    = 5    # 黄球
-#     TOWER    = 6    # 通信塔
-#     RBALL    = 7    # 红球
-#     BASKET   = 8    # 球筐
-#     MARKL    = 9    # 指向标
-#     MARKR    = 10   # 指向标
-#     SPILLAR  = 11   # 小柱体 (红色)
-#     MPILLAR  = 12   # 中柱体 (蓝色)
-#     LPILLAR  = 13   # 大柱体 (红色)
-#     SIGN     = 14   # 文字标牌
-#     TARGET   = 15   # 目标靶
-#     SHELTER  = 16   # 停车区
-#     BASE     = 17   # 基地
+
+lane_error = 0
+
 class tlabel(Enum):
     TOWER = 0
     SIGN = 1
@@ -36,6 +21,9 @@ class tlabel(Enum):
     LMARK = 13
     BBLOCK = 14
     BBALL = 15
+'''
+description: label_filter 的测试数据
+'''
 test_resp = {
     'code': 0,
     'data': np.array([
@@ -48,6 +36,9 @@ test1_resp = {
     'code': 0,
     'data': np.array([])
 }
+'''
+description: yolo 目标检测标签过滤器，需要传入连接到 yolo server 的 socket 对象
+'''
 class label_filter:
     def __init__(self, socket, threshold=0.6):
         self.num = 0
@@ -55,16 +46,33 @@ class label_filter:
         self.socket = socket
         self.threshold = threshold
         
-        self.img_size = (320, 240)
+        self.img_size = (320, 240) 
+    '''
+    description: 向 yolo server 请求目标检测数据
+    param {*} self
+    return {*}
+    '''    
     def get_resp(self):
         self.socket.send_string('')
         response = self.socket.recv_pyobj()
         return response
+    '''
+    description: 切换 yolo server 视频源 在分叉路口时目标检测需要使用前摄
+    param {*} self
+    param {*} camera_id 1 或者 2 字符串
+    return {*}
+    '''    
     def switch_camera(self,camera_id):
         if camera_id == 1 or camera_id == 2:
             self.socket.send_string(f'{camera_id}')
             response = self.socket.recv_pyobj()
             return response
+    '''
+    description: 对模型推理推理结果使用 threshold 过滤 默认阈值为 0.5
+    param {*} self
+    param {*} data get_resp 返回的数据
+    return {bool,array}
+    '''    
     def filter_box(self,data):
         if len(data) > 0:
             expect_boxes = (data[:, 1] > self.threshold) & (data[:, 0] > -1)
@@ -83,10 +91,15 @@ class label_filter:
             if len(results) > 0:
                 return True, np.array(results)
         return False, None
+    '''
+    description: 根据传入的标签过滤，返回该标签的个数以及 box
+    param {*} self
+    param {*} tlabel
+    return {int, array}
+    '''    
     def get(self, tlabel):
         # 循环查找匹配的标签值
         # 返回对应标签的个数，以及坐标列表
-        # TODO  self.filter_box none judge
         response = self.get_resp()
         if response['code'] == 0:
             ret, results = self.filter_box(response['data'])
@@ -97,8 +110,38 @@ class label_filter:
                 self.pos = boxes[:, 2:] # [[x1 y1 x2 y2]]
                 return self.num, self.pos
         return 0, []
+    '''
+    description: 
+    param {*} self
+    param {*} tlabel_list
+    return {*}
+    '''    
+    def get_mult(self, tlabel_list):
+        response = self.get_resp()
+        if response['code'] == 0:
+            ret, results = self.filter_box(response['data'])
+            target_counts = len(tlabel_list)
+            counts = 0
+            if ret:
+                for tlabel in tlabel_list:
+                    expect_boxes = (results[:, 0] == tlabel.value)
+                    has_true = np.any(expect_boxes)
+                    if has_true:
+                        counts += 1
+                    else:
+                        return False, []
+                if counts == target_counts:
+                    return True, counts
+                return False, []
+            return False, []    
+        return False, []
+    '''
+    description: 判断传入的标签是否存在，存在返回 True
+    param {*} self
+    param {*} tlabel
+    return {bool}
+    '''    
     def find(self, tlabel):
-        # 遍历返回的列表，有对应标签则返回 True
         response = self.get_resp()
         if response['code'] == 0:
             ret, results = self.filter_box(response['data'])
@@ -108,6 +151,12 @@ class label_filter:
                 if len(boxes) != 0:
                     return True 
         return False
+    '''
+    description: 根据传入的标签，
+    param {*} self
+    param {*} tlabel
+    return {*}
+    '''    
     def aim_left(self, tlabel):
         # 如果标签存在，则返回列表中位置最靠左的目标框和中心的偏移值
         response = self.get_resp()
@@ -151,7 +200,7 @@ class label_filter:
                 center_x_values = np.abs(boxes[:, 2] + boxes[:, 4] - self.img_size[0])
                 center_x_index = np.argmin(center_x_values)
                 error = (boxes[center_x_index][4] + boxes[center_x_index][2] - self.img_size[0]) / 2
-                return (True, error+15)
+                return (True, error)
         return (False, 0)
 
 # class Calibrate: