pref: 注册时直接传入任务类
feat: 分任务设置检测计数值
This commit is contained in:
201
utils.py
201
utils.py
@@ -1,49 +1,176 @@
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from enum import Enum
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import numpy as np
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# 根据标签修改
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# class tlabel(Enum):
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# BBLOCK = 5 # 蓝色方块
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# RBLOCK = 2 # 红色方块
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# HOSPITAL = 3 # 医院
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# BBALL = 4 # 蓝球
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# YBALL = 5 # 黄球
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# TOWER = 6 # 通信塔
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# RBALL = 7 # 红球
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# BASKET = 8 # 球筐
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# MARKL = 9 # 指向标
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# MARKR = 10 # 指向标
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# SPILLAR = 11 # 小柱体 (红色)
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# MPILLAR = 12 # 中柱体 (蓝色)
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# LPILLAR = 13 # 大柱体 (红色)
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# SIGN = 14 # 文字标牌
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# TARGET = 15 # 目标靶
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# SHELTER = 16 # 停车区
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# BASE = 17 # 基地
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class tlabel(Enum):
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BBLOCK = 1 # 蓝色方块
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RBLOCK = 2 # 红色方块
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HOSPITAL = 3 # 医院
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BBALL = 4 # 蓝球
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YBALL = 5 # 黄球
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TOWER = 6 # 通信塔
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RBALL = 7 # 红球
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BASKET = 8 # 球筐
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MARKL = 9 # 指向标
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MARKR = 10 # 指向标
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SPILLAR = 11 # 小柱体 (红色)
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MPILLAR = 12 # 中柱体 (蓝色)
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LPILLAR = 13 # 大柱体 (红色)
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SIGN = 14 # 文字标牌
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TARGET = 15 # 目标靶
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SHELTER = 16 # 停车区
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BASE = 17 # 基地
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TOWER = 0
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SIGN = 1
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SHELTER = 2
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HOSPITAL = 3
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BASKET = 4
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BASE = 5
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YBALL = 6
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SPILLER = 7
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RMARK = 8
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RBLOCK = 9
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RBALL = 10
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MPILLER = 11
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LPILLER = 12
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LMARK = 13
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BBLOCK = 14
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BBALL = 15
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test_resp = {
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'code': 0,
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'data': np.array([
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[4., 0.97192055, 26.64415, 228.26755, 170.16872, 357.6216],
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[4., 0.97049206, 474.0152, 251.2854, 612.91644, 381.6831],
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[5., 0.972649, 250.84174, 238.43622, 378.115, 367.34906]
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])
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}
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test1_resp = {
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'code': 0,
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'data': np.array([])
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}
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class label_filter:
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def __init__(self, list_src):
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def __init__(self, socket, threshold=0.6):
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self.num = 0
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self.pos = []
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self.list = list_src # 获取目标检测输出的接口 (含标签，位置，置信度)
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# TODO 添加置信度阈值
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pass
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self.socket = socket
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self.threshold = threshold
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self.img_size = (320, 240)
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def get_resp(self):
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self.socket.send_string('')
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response = self.socket.recv_pyobj()
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return response
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def switch_camera(self,camera_id):
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if camera_id == 1 or camera_id == 2:
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self.socket.send_string(f'{camera_id}')
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response = self.socket.recv_pyobj()
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return response
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def filter_box(self,data):
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if len(data) > 0:
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expect_boxes = (data[:, 1] > self.threshold) & (data[:, 0] > -1)
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np_boxes = data[expect_boxes, :]
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results = [
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[
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item[0], # 'label':
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item[1], # 'score':
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item[2], # 'xmin':
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item[3], # 'ymin':
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item[4], # 'xmax':
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item[5] # 'ymax':
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]
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for item in np_boxes
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]
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if len(results) > 0:
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return True, np.array(results)
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return False, None
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def get(self, tlabel):
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# TODO 循环查找匹配的标签值
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# TODO 返回对应标签的个数，以及坐标列表
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return self.num, self.pos
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# 循环查找匹配的标签值
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# 返回对应标签的个数，以及坐标列表
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# TODO self.filter_box none judge
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response = self.get_resp()
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if response['code'] == 0:
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ret, results = self.filter_box(response['data'])
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if ret:
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expect_boxes = (results[:, 0] == tlabel.value)
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boxes = results[expect_boxes, :]
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self.num = len(boxes)
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self.pos = boxes[:, 2:] # [[x1 y1 x2 y2]]
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return self.num, self.pos
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return 0, []
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def find(self, tlabel):
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# TODO 遍历返回的列表，有对应标签则返回 True
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# 遍历返回的列表，有对应标签则返回 True
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response = self.get_resp()
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if response['code'] == 0:
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ret, results = self.filter_box(response['data'])
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if ret:
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expect_boxes = (results[:, 0] == tlabel.value)
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boxes = results[expect_boxes, :]
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if len(boxes) != 0:
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return True
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return False
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def aim_left(self, tlabel):
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# TODO 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
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error = 0
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return error
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# 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
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response = self.get_resp()
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if response['code'] == 0:
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ret, results = self.filter_box(response['data'])
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if ret:
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expect_boxes = (results[:, 0] == tlabel.value)
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boxes = results[expect_boxes, :]
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if len(boxes) == 0:
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return (False, )
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xmin_values = boxes[:, 2] # xmin
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xmin_index = np.argmin(xmin_values)
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error = (boxes[xmin_index][4] + boxes[xmin_index][2] - self.img_size[0]) / 2
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return (True, error)
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return (False, )
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def aim_right(self, tlabel):
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# TODO 如果标签存在，则返回列表中位置最靠右的目标框和中心的偏移值
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error = 0
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return error
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# 如果标签存在，则返回列表中位置最靠右的目标框和中心的偏移值
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response = self.get_resp()
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if response['code'] == 0:
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ret, results = self.filter_box(response['data'])
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if ret:
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expect_boxes = (results[:, 0] == tlabel.value)
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boxes = results[expect_boxes, :]
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if len(boxes) == 0:
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return (False, )
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xmax_values = boxes[:, 4] # xmax
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xmax_index = np.argmax(xmax_values)
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error = (boxes[xmax_index][4] + boxes[xmax_index][2] - self.img_size[0]) / 2
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return (True, error)
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return (False, )
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def aim_near(self, tlabel):
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# TODO 如果标签存在，则返回列表中位置最近的目标框和中心的偏移值
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error = 0
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return error
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# 如果标签存在，则返回列表中位置最近的目标框和中心的偏移值
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response = self.get_resp()
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if response['code'] == 0:
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ret, results = self.filter_box(response['data'])
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if ret:
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expect_boxes = (results[:, 0] == tlabel.value)
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boxes = results[expect_boxes, :]
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if len(boxes) == 0:
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return (False, 0)
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center_x_values = np.abs(boxes[:, 2] + boxes[:, 4] - self.img_size[0])
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center_x_index = np.argmin(center_x_values)
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error = (boxes[center_x_index][4] + boxes[center_x_index][2] - self.img_size[0]) / 2
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return (True, error+15)
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return (False, 0)
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# class Calibrate:
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# def __init__(self,by_cmd):
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# # 车控制对象初始化
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# self.by_cmd = by_cmd
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# def aim(self,error):
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# self.by_cmd.send_distance_x(error,)
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if __name__ == '__main__':
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obj = label_filter(None)
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# results = obj.filter_box(resp['data'])
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# expect_boxes = (results[:, 0] == tlabel.SPILLAR.value)
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# np_boxes = results[expect_boxes, :]
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# print(np_boxes[:, 2:])
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# print(len(np_boxes))
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print(obj.find(tlabel.BBALL))
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print(obj.aim_left(tlabel.BBALL))
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print(obj.aim_right(tlabel.BBALL))
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print(obj.aim_near(tlabel.BBALL))
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print(obj.get(tlabel.HOSPITAL))
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