281 lines
9.9 KiB
Python
281 lines
9.9 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 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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import builtins
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class MAT:
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def __init__(self, videoSourceIndex=0, weights_path = "resnet34-1Net.pth", json_path = 'class_indices.json', classes = 2):
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print('实验初始化中')
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self.data_root = os.getcwd()
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self.videoSourceIndex = videoSourceIndex # 摄像机编号
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self.cap = cv2.VideoCapture(videoSourceIndex, cv2.CAP_DSHOW) # 打开摄像头
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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self.port = Find_COM.list_ch340_ports()[0] # 串口名
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self.pump_ser = serial.Serial(self.port, 9600) # 初始化串口
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self.usb_port = Find_COM.list_USB_ports() # 串口名
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if self.usb_port:
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self.usb_ser = serial.Serial(self.usb_port, 115200) # 初始化串口
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self.classes = classes
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self.total_volume = 0 # 记录总体积
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self.now_volume = 0 # 记录当前注射泵内体积
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self.volume_list = [] # 记录体积变化
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self.voltage_list = [] # 记录电位变化（如有需要）
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self.color_list = [] # 记录颜色变化
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self.start_time = time.time() # 记录实验开始时间
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self.weights_path = os.path.join(self.data_root, weights_path) # 权重文件路径
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self.json_path = os.path.join(self.data_root, json_path) # 类别文件路径
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# 将开始时间转化为年月日时分秒的格式，后续文件命名都已此命名
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self.formatted_time = datetime.fromtimestamp(self.start_time).strftime('%Y%m%d_%H%M%S')
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print("实验开始于", self.formatted_time)
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def get_picture(self, frame, typ=0, date=''): # 拍摄照片并保存
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if frame is None:
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print(frame)
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image_name = f'{date}_{self.total_volume}.jpg' # 照片保存在Input文件夹下，以开始时间+体积数的方式命名
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filepath = os.path.join(self.data_root, "Input", image_name)
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str_name = filepath.replace('%s', '1')
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cv2.imwrite(str_name, frame)
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return image_name
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def start_move_1(self): # 抽料程序
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data = b"q1h24d" # *2
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self.pump_ser.write(data)
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time.sleep(0.01)
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data = b"q2h0d"
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self.pump_ser.write(data)
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time.sleep(0.01)
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data = b"q4h0d"
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self.pump_ser.write(data)
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time.sleep(0.01)
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data = b"q5h15d"
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self.pump_ser.write(data)
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time.sleep(0.01)
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data = b"q6h3d"
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self.pump_ser.write(data)
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time.sleep(15)
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print('完成抽取')
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def start_move_2(self, speed=0.1): # 进料程序
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# 计算单次滴定体积并传输至控制器
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speed_min = speed * 30
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speed_min_int = int(speed_min)
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speed_min_float = int((speed_min - speed_min_int) * 100)
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# print(speed_min_int, speed_min_float)
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data = f"q1h{speed_min_int}d"
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self.pump_ser.write(data.encode('ascii'))
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time.sleep(0.01)
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data = f"q2h{speed_min_float}d"
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self.pump_ser.write(data.encode('ascii'))
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time.sleep(0.01)
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data = b"q4h0d"
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self.pump_ser.write(data)
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time.sleep(0.01)
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data = b"q5h1d"
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self.pump_ser.write(data)
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time.sleep(0.01)
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# 进料
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data = b"q6h2d"
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self.pump_ser.write(data)
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time.sleep(1)
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def start_move_3(self): # 进料急停
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data = b"q6h6d"
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self.pump_ser.write(data)
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def voltage(self): # 测量电位
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self.usb_ser.write("VOL|\n".encode())
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time.sleep(0.1)
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while True:
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response = self.usb_ser.readline().decode().strip()
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if response:
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try:
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return float(response)
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except:
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return 0
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@staticmethod
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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(self):
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x = self.volume_list
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y = self.voltage_list
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z = self.color_list
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fig, ax1 = plt.subplots()
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plt.title("titration curve")
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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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ax2 = ax1.twinx()
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color = 'tab:blue'
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ax2.set_ylabel('color', color=color)
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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])
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ax2.set_yticklabels(['yellow', 'orange'])
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ax2.spines['right'].set_position(('outward', 60))
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try:
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popt, pcov = curve_fit(self.poly_func, x, y, p0=[max(y) * 3 / 4, -max(x), max(y), 1.5])
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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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x_d = np.arange(0, max(x), 0.05)
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y_fit = self.poly_func(x_d, *popt)
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dE_dV = np.gradient(y_fit)
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d2E_dV2 = np.gradient(dE_dV)
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y2 = d2E_dV2.tolist()
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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.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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x_d, y_d = -popt[1] / popt[3], 0.0
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ax3.plot(x_d, y_d, 'ro')
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ax3.annotate(f'({x_d:.2f})', xy=(x_d, y_d), color='red', xytext=(x_d - 1, y_d + max(y2) / 10))
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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()
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plt.savefig(f'Output/{self.formatted_time}.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(self, im_file): # 预测分类
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image = Image.open(im_file)
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data_transform = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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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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img = data_transform(image)
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img = torch.unsqueeze(img, dim=0)
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with open(self.json_path, "r") as f:
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class_indict = json.load(f)
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model = resnet34(num_classes=self.classes).to(self.device)
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assert os.path.exists(self.weights_path), "file: '{}' dose not exist.".format(self.weights_path)
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model.load_state_dict(torch.load(self.weights_path, map_location=self.device))
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model.eval()
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with torch.no_grad():
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output = torch.squeeze(model(img.to(self.device))).cpu()
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predict = torch.softmax(output, dim=0)
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predict_cla = torch.argmax(predict).numpy()
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class_a = "{}".format(class_indict[str(predict_cla)])
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prob_a = "{:.3}".format(predict[predict_cla].numpy())
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prob_b = float(prob_a)
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print('class_:',class_a)
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print('prob_:',prob_b)
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return class_a, prob_b
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def __del__(self):
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# 绘制滴定曲线
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# self.line_chart()
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# 关闭串口和摄像头
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self.pump_ser.close()
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self.cap.release()
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cv2.destroyAllWindows()
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print("Experiment finished.")
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def run(self,quick_speed = 0.2, slow_speed = 0.05,switching_point = 5, end_kind = 'orange', end_prob =0.5):
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n = 1
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total_n = n
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while True:
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if self.now_volume <= 0:
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self.start_move_1() # 抽取12ml
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self.now_volume += 12
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if self.total_volume < switching_point: # 每次加0.2ml
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speed = quick_speed
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self.start_move_2(speed)
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self.total_volume += speed
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self.now_volume -= speed
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else:
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speed = slow_speed
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self.start_move_2(speed) # 每次加0.05ml
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self.total_volume += speed
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self.now_volume -= speed
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self.total_volume = round(self.total_volume, 3)
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# 读取图片
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ret, frame = self.cap.read()
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if not ret:
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print("Failed to capture frame from camera.")
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break
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name = self.get_picture(frame, 0, self.formatted_time)
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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 = self.predictor(im_file)
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self.volume_list.append(self.total_volume)
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# 如果有电压测量设备，可以在这里读取电压
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# self.voltage_list.append(self.voltage())
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if class_a == end_kind and prob_b > end_prob: # 判断终点
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print('----->>Visual Endpoint<<-----')
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print(f"Total Volume: {self.total_volume} ml")
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print(f"Image File: {im_file}")
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self.color_list.append(1)
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break
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else:
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self.color_list.append(0)
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print(f"Current Total Volume: {self.total_volume} ml")
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print("Volume List:", self.volume_list)
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print("Voltage List:", self.voltage_list)
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print("Color List:", self.color_list)
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# 保存实验数据到JSON文件
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with builtins.open(f'Output/{self.formatted_time}.json', 'w') as f:
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json.dump(
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{"volume_list": self.volume_list, 'voltage_list': self.voltage_list, 'color_list': self.color_list},
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f)
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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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# 创建MAT类的实例并运行
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mat = MAT(videoSourceIndex = 0, weights_path = "resnet34-1Net.pth", json_path = 'class_indices.json', classes = 2)
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mat.run(quick_speed = 0.2, slow_speed = 0.05, switching_point = 5, end_kind = 'orange', end_prob = 0.5)
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