373 lines
12 KiB
Python
373 lines
12 KiB
Python
import torch
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from PIL import Image
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import torchvision.transforms as transforms
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from torch.autograd import Variable
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from torch import nn
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import matplotlib.pyplot as plt
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import cv2
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import time
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import os
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from model import resnet34
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import json
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import serial
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from datetime import datetime
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from scipy.optimize import curve_fit
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import numpy as np
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import re
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import json
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import Find_COM
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def get_picture(frame, typ=0, date=''): # 获取照片
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# 捕获一帧的数据
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# ret, frame = cap.read()
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if frame is None:
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print(frame)
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# if ret:
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# # 默认不阻塞
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# cv2.imshow("picture", frame)
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# 数据帧写入图片中
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label = "1"
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timeStamp = 1381419600
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if typ:
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image_name = f'{date}{int(time.time())}.jpg'
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else:
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image_name = f'{date}PH{int(time.time())}.jpg'
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# 照片存储位置
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filepath = "Input/" + image_name # 改成跟上面一样的位置
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str_name = filepath.replace('%s', label)
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cv2.imwrite(str_name, frame) # 将照片保存起来
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return image_name
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def start_move_1(ser): # 抽取原料
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# 注意：这里我们将控制器的模式切换成了20ml注射泵模式，由于丝杠的区别，这里设定的速度为实际速度（ml/min）的一半
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data = b"q1h12d" # 每分钟加样24ml
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ser.write(data)
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time.sleep(0.01)
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data = b"q4h0d" # 转0分钟
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ser.write(data)
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time.sleep(0.01)
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data = b"q5h30d" # 转30秒
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ser.write(data) # 合计抽取12ml
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time.sleep(0.01)
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data = b"q6h3d" # 抽取
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ser.write(data)
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time.sleep(30) # 等待抽取
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print('完成抽取')
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# ser.close()
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def start_move_2(ser): # 缓慢加样程序
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data = b"q1h1d" # 每分钟加样3ml，每秒0.1ml
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ser.write(data)
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time.sleep(0.01)
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data = b"q2h50d" # 每分钟加样6ml，每秒0.1ml
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ser.write(data)
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time.sleep(0.01)
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data = b"q4h30d" # 转0分钟
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ser.write(data)
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time.sleep(0.01)
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data = b"q5h0d" # 转1秒
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ser.write(data) # 合计进样12ml
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time.sleep(0.01)
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data = b"q6h2d" # 进样
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ser.write(data)
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time.sleep(1)
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# 注意，这里没有将阀门转回去，而是持续几秒钟滴加一次的状态
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# ser.close()
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def start_move_4(ser): # 缓慢加样程序0.2
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data = b"q1h6d" # 每分钟加样12ml，每秒0.2ml
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ser.write(data)
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time.sleep(0.01)
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data = b"q2h0d" # 每分钟加样6ml，每秒0.1ml
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ser.write(data)
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time.sleep(0.01)
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data = b"q4h30d" # 转30分钟
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ser.write(data)
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time.sleep(0.01)
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data = b"q5h0d" # 转0秒
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ser.write(data) # 持续进样
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time.sleep(0.01)
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data = b"q6h2d" # 进样
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ser.write(data)
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time.sleep(1)
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# 注意，这里没有将阀门转回去，而是持续几秒钟滴加一次的状态
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# ser.close()
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def start_move_3(ser): # 停止加酸程序
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data = b"q6h6d" # 停止指令
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ser.write(data)
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# 将阀门转回去
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ser.close()
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def read_number_new(filepath):
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from paddleocr import PaddleOCR, draw_ocr
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# 创建一个OCR实例，配置语言为中文
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ocr = PaddleOCR(use_angle_cls=True, lang="ch")
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# 对图片进行OCR识别
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img_path = filepath
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result = ocr.ocr(img_path, cls=True)
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print('-----------------------------------------')
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print(result)
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ans = []
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for line in result:
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if line:
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for line1 in line:
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# print(line1[-1])
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# print(line1[-1][0])
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try:
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ans.append(float(line1[-1][0]))
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except:
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continue
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print(ans)
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if not ans:
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ans.append(10)
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return ans
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# 定义反正切函数
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def poly_func(x, a, b, c, d):
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return a * np.tanh(d * x + b) + c
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def line_chart(date="1", volume_list=[], voltage_list=[], color_list=[]):
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x = volume_list
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y = voltage_list
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z = color_list
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# '''
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fig, ax1 = plt.subplots()
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plt.title("titration curve")
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# 绘制第一个Y轴的数据，绘制电位曲线
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color = 'tab:red'
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ax1.set_xlabel('value')
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ax1.set_ylabel('voltage', color=color)
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ax1.plot(x, y, color=color, antialiased=True)
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ax1.tick_params(axis='y', labelcolor=color)
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# 创建一个共享X轴的第二个Y轴，绘制颜色曲线
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ax2 = ax1.twinx()
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color = 'tab:blue'
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ax2.set_ylabel('color', color=color)
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print(x,z)
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ax2.plot(x, z, color=color)
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ax2.tick_params(axis='y', labelcolor=color)
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ax2.set_yticks([0, 1]) # 设置Y轴的刻度位置
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ax2.set_yticklabels(['yellow', 'orange']) # 设置Y轴的刻度标签
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ax2.spines['right'].set_position(('outward', 60)) # 将第三个Y轴向右移动
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# ax2.tick_params(axis='y', labelcolor='none')
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try:
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# 初始参数估计
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popt, pcov = curve_fit(poly_func, x, y, p0=[max(y)*3/4, -max(x), max(y), 1.5])
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# 打印最优参数
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print("最优参数:", popt)
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print(f'电位突跃点：{-popt[1]/popt[3]:.3f}')
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# print(max(x))
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x_d = np.arange(0, max(x), 0.05)
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# 使用拟合得到的参数计算二阶导数
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y_fit = poly_func(x_d, *popt)
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# 计算一阶微商（即电位对体积的导数）
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dE_dV = np.gradient(y_fit)
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# 计算二阶微商
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d2E_dV2 = np.gradient(dE_dV)
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y2 = d2E_dV2.tolist()
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# y2 = dE_dV.tolist()
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# y2 = y_fit
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# 创建一个共享X轴的第3个Y轴，绘制二阶导
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ax3 = ax1.twinx()
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color = 'tab:green'
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ax3.set_ylabel('2nd Derivative', color=color)
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ax3.plot(x_d, y2, color=color)
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# ax3.plot(x_d, y_fit, color=color)
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ax3.tick_params(axis='y', labelcolor=color)
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ax3.grid(True, linestyle='--', linewidth=0.5, color='gray', axis='both')
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# 画出电位突变点
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x_d, y_d = -popt[1]/popt[3], 0.0
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ax3.plot(x_d, y_d, 'ro') # 'ro' 表示红色圆圈，'r' 表示红色，'o' 表示圆圈4\4
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# 标注坐标
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ax3.annotate(f'({x_d:.2f})', # 标注的文本，使用格式化字符串显示坐标
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xy=(x_d, y_d), # 标注指向的点
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color='red', # 标注文本的颜色
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xytext=(x_d-1, y_d + max(y2)/10) # 标注文本的位置，这里相对于点的位置稍微偏移
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)
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# 画出视觉突变点
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x_c, y_c = x[xz] - 0.025, 0.0
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ax3.plot(x_c, y_c, 'bo') # 'bo' 表示蓝色圆圈，'b' 表示蓝色，'o' 表示圆圈4\4
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# 标注坐标
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ax3.annotate(f'({x_c:.2f})', # 标注的文本，使用格式化字符串显示坐标
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xy=(x_c, y_c), # 标注指向的点
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color='blue', # 标注文本的颜色
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xytext=(x_c - 1, y_c - max(y2) / 10) # 标注文本的位置，这里相对于点的位置稍微偏移
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)
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print(f"视觉突跃点：{x_c:.3f}")
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# '''
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except Exception as e:
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print(e)
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pass
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fig.tight_layout() # 自动调整子图参数, 使之填充整个图像区域
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plt.savefig(f'Output/{date}.png')
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# plt.savefig('O1.png')
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plt.show()
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plt.pause(1)
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plt.close()
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def predictor(im_file, device):
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# 使用PIL库打开图片
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image = Image.open(im_file)
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# 定义图片预处理流程
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data_transform = transforms.Compose(
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[
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# 调整图片大小为256x256
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transforms.Resize(256),
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# 从中心裁剪出224x224大小的图片
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transforms.CenterCrop(224),
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# 将图片转换为PyTorch的Tensor格式
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transforms.ToTensor(),
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# 对图片进行归一化，使用ImageNet的均值和标准差
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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# [N, C, H, W]
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img = data_transform(image)
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# expand batch dimension
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img = torch.unsqueeze(img, dim=0)
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# 定义模型分类文件的路径
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json_path = './class_indices.json'
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with open(json_path, "r") as f:
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class_indict = json.load(f)
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# create model
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model = resnet34(num_classes=2).to(device) # 根据分类数量修改
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# load model weights
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weights_path = "./resnet34-1Net.pth" # 根据实际需要使用的模型名称修改
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assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
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model.load_state_dict(torch.load(weights_path, map_location=device))
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# prediction
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model.eval()
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with torch.no_grad():
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# predict class
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output = torch.squeeze(model(img.to(device))).cpu()
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# 对预测结果进行softmax，得到每个类别的概率
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predict = torch.softmax(output, dim=0)
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# 找到概率最大的类别的索引
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predict_cla = torch.argmax(predict).numpy()
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# 根据索引从类别字典中获取类别名称
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class_a = "{}".format(class_indict[str(predict_cla)])
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# 格式化概率值，保留三位小数
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prob_a = "{:.3}".format(predict[predict_cla].numpy())
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# 将概率值转换为浮点数
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prob_b = float(prob_a)
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# 打印预测的类别和概率
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print(class_a)
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print(prob_b)
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return class_a, prob_b
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def voltage(ser):
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# data = "VOL|" # 每分钟加样12ml，每秒0.2ml
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ser.write("VOL|\n".encode())
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time.sleep(0.1) # 等待设备响应
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while True:
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# 读取响应
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response = ser.readline().decode().strip()
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if response:
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# print(f"设备响应: {response}")
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# if "END" in response:
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# break
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try:
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return float(response)
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except:
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pass
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def main():
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# port = "COM11" # 串口名，根据实际情况修改
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port = Find_COM.list_ch340_ports()[0] # 串口名，根据实际情况修改
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baudrate = 9600 # 波特率，根据实际情况修改
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pump_ser = serial.Serial(port, baudrate)
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# port_USB = []
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port_USB = Find_COM.list_USB_ports() # 串口名，根据实际情况修改
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if port_USB:
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USB_ser = serial.Serial(port_USB[0], baudrate=115200, timeout=1)
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# print(voltage(USB_ser))
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videoSourceIndex = 0 # 摄像机编号，请根据自己的情况调整
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cap = cv2.VideoCapture(videoSourceIndex, cv2.CAP_DSHOW) # 打开摄像头
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# 是否用GPU
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# 循环开始之前需要一个变量来记录初始状态 比如说就叫color_type
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total_volume = 0
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now_volume = 0
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volume_list = []
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voltage_list = []
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color_list = []
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start_time = time.time()
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# 将时间戳转换为datetime对象
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dt_object = datetime.fromtimestamp(start_time)
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# 格式化datetime对象为字符串，该时间用于保存图像名称
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formatted_time = dt_object.strftime('%Y%m%d_%H%M%S')
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print("实验开始于", formatted_time)
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n = 10
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total_n = n
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start_move_2(pump_ser)
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while True:
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total_volume += 1
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volume_list.append(total_volume)
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# 读取图片
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ret, frame = cap.read()
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name = get_picture(frame, 0, formatted_time)
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# 图片完整路径
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im_file = 'Input/' + name
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cv2.imshow('Color', frame)
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cv2.waitKey(1)
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class_a ,prob_b =predictor(im_file,device)
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volume_list.append(total_volume)
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if port_USB:
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voltage_list.append(voltage(USB_ser))
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if class_a == "orange" and prob_b > 0.5: # 判断终点
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# 如果判断为终点
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# 使用两个空列表用来记录后续五次的判断结果
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start_move_3(pump_ser)
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print('----->>Visual Endpoint<<-----')
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print(volume_list[-1])
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print(im_file)
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color_list.append(1)
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break
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color_list.append(0)
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print(total_volume)
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print(volume_list)
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print(voltage_list)
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print(color_list)
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with open(f'Output/{formatted_time}.json', 'w') as f:
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# 使用json.dump()将列表保存到文件
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json.dump({"volume_list": volume_list, 'voltage_list': voltage_list, 'color_list': color_list}, f)
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# 关闭串口
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pump_ser.close()
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if port_USB:
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USB_ser.close()
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line_chart(formatted_time, volume_list = volume_list, voltage_list = voltage_list, color_list = color_list)
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if __name__ == "__main__":
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import warnings
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# 忽略所有警告
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warnings.filterwarnings('ignore')
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main()
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