mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
fix:切换了resnet34模型文件，注释掉了有果无果判别模型部分，添加部分函数注释
This commit is contained in:
TG
parent
28f81a135a
commit
b0cf31fd60
@ -12,12 +12,14 @@ import joblib
|
||||
import logging
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from torchvision import transforms
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
#图像分类网络所需库，实际并未使用分类网络
|
||||
# import torch
|
||||
# import torch.nn as nn
|
||||
# from torchvision import transforms
|
||||
|
||||
|
||||
#番茄RGB处理模型
|
||||
class Tomato:
|
||||
def __init__(self):
|
||||
''' 初始化 Tomato 类。'''
|
||||
@ -233,6 +235,7 @@ class Tomato:
|
||||
img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv)
|
||||
return img_filled
|
||||
|
||||
#百香果RGB处理模型
|
||||
class Passion_fruit:
|
||||
def __init__(self, hue_value=37, hue_delta=10, value_target=25, value_delta=10):
|
||||
# 初始化常用参数
|
||||
@ -315,6 +318,7 @@ class Passion_fruit:
|
||||
return np.zeros_like(rgb_img)
|
||||
return result
|
||||
|
||||
#糖度预测模型
|
||||
class Spec_predict(object):
|
||||
def __init__(self, load_from=None, debug_mode=False):
|
||||
self.debug_mode = debug_mode
|
||||
@ -350,6 +354,7 @@ class Spec_predict(object):
|
||||
data_y = self.model.predict(data_x)
|
||||
return data_y[0]
|
||||
|
||||
#数据处理模型
|
||||
class Data_processing:
|
||||
def __init__(self):
|
||||
pass
|
||||
@ -520,222 +525,170 @@ class Data_processing:
|
||||
return diameter, weigth, number_defects, total_pixels, rp
|
||||
|
||||
|
||||
#下面封装的是ResNet18和ResNet34的网络模型构建
|
||||
#原定用于构建RGB图像有果无果判断，后续发现存在纰漏，暂时搁置并未实际使用
|
||||
|
||||
class BasicBlock(nn.Module):
|
||||
'''
|
||||
BasicBlock for ResNet18 and ResNet34
|
||||
|
||||
'''
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
|
||||
kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channel)
|
||||
self.relu = nn.ReLU()
|
||||
self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
|
||||
kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(out_channel)
|
||||
self.downsample = downsample
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
"""
|
||||
注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。
|
||||
但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，
|
||||
这么做的好处是能够在top1上提升大概0.5%的准确率。
|
||||
可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch
|
||||
"""
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, in_channel, out_channel, stride=1, downsample=None,
|
||||
groups=1, width_per_group=64):
|
||||
super(Bottleneck, self).__init__()
|
||||
|
||||
width = int(out_channel * (width_per_group / 64.)) * groups
|
||||
|
||||
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
|
||||
kernel_size=1, stride=1, bias=False) # squeeze channels
|
||||
self.bn1 = nn.BatchNorm2d(width)
|
||||
# -----------------------------------------
|
||||
self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
|
||||
kernel_size=3, stride=stride, bias=False, padding=1)
|
||||
self.bn2 = nn.BatchNorm2d(width)
|
||||
# -----------------------------------------
|
||||
self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,
|
||||
kernel_size=1, stride=1, bias=False) # unsqueeze channels
|
||||
self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet(nn.Module):
|
||||
'''
|
||||
ResNet18 and ResNet34
|
||||
'''
|
||||
def __init__(self,
|
||||
block,
|
||||
blocks_num,
|
||||
num_classes=1000,
|
||||
include_top=True,
|
||||
groups=1,
|
||||
width_per_group=64):
|
||||
super(ResNet, self).__init__()
|
||||
self.include_top = include_top
|
||||
self.in_channel = 64
|
||||
|
||||
self.groups = groups
|
||||
self.width_per_group = width_per_group
|
||||
|
||||
self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
|
||||
padding=3, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(self.in_channel)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
self.layer1 = self._make_layer(block, 64, blocks_num[0])
|
||||
self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
|
||||
self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
|
||||
self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
|
||||
if self.include_top:
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1)
|
||||
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
|
||||
def _make_layer(self, block, channel, block_num, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.in_channel != channel * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(channel * block.expansion))
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.in_channel,
|
||||
channel,
|
||||
downsample=downsample,
|
||||
stride=stride,
|
||||
groups=self.groups,
|
||||
width_per_group=self.width_per_group))
|
||||
self.in_channel = channel * block.expansion
|
||||
|
||||
for _ in range(1, block_num):
|
||||
layers.append(block(self.in_channel,
|
||||
channel,
|
||||
groups=self.groups,
|
||||
width_per_group=self.width_per_group))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
if self.include_top:
|
||||
x = self.avgpool(x)
|
||||
x = torch.flatten(x, 1)
|
||||
x = self.fc(x)
|
||||
|
||||
return x
|
||||
|
||||
def resnet18(num_classes=1000, include_top=True):
|
||||
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
|
||||
|
||||
def resnetzy(num_classes=1000, include_top=True):
|
||||
return ResNet(Bottleneck, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
|
||||
|
||||
|
||||
class ImageClassifier:
|
||||
'''
|
||||
图像分类器，用于加载预训练的 ResNet 模型并进行图像分类。
|
||||
'''
|
||||
def __init__(self, model_path, class_indices_path, device=None):
|
||||
if device is None:
|
||||
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
else:
|
||||
self.device = device
|
||||
|
||||
# 加载类别索引
|
||||
assert os.path.exists(class_indices_path), f"File: '{class_indices_path}' does not exist."
|
||||
with open(class_indices_path, "r") as json_file:
|
||||
self.class_indict = json.load(json_file)
|
||||
|
||||
# 创建模型并加载权重
|
||||
self.model = resnetzy(num_classes=len(self.class_indict)).to(self.device)
|
||||
assert os. path.exists(model_path), f"File: '{model_path}' does not exist."
|
||||
self.model.load_state_dict(torch.load(model_path, map_location=self.device))
|
||||
self.model.eval()
|
||||
|
||||
# 设置图像转换
|
||||
self.transform = transforms.Compose([
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||
])
|
||||
|
||||
def predict(self, image_np):
|
||||
'''
|
||||
对图像进行分类预测。
|
||||
:param image_np:
|
||||
:return:
|
||||
'''
|
||||
# 将numpy数组转换为图像
|
||||
image = Image.fromarray(image_np.astype('uint8'), 'RGB')
|
||||
image = self.transform(image).unsqueeze(0).to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
output = self.model(image).cpu()
|
||||
predict = torch.softmax(output, dim=1)
|
||||
predict_cla = torch.argmax(predict, dim=1).numpy()
|
||||
|
||||
# return self.class_indict[str(predict_cla[0])]
|
||||
return predict_cla[0]
|
||||
# #下面封装的是ResNet18和ResNet34的网络模型构建
|
||||
# #原定用于构建RGB图像有果无果判断，后续发现存在纰漏，暂时搁置并未实际使用
|
||||
# class BasicBlock(nn.Module):
|
||||
# '''
|
||||
# BasicBlock for ResNet18 and ResNet34
|
||||
#
|
||||
# '''
|
||||
# expansion = 1
|
||||
#
|
||||
# def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
|
||||
# super(BasicBlock, self).__init__()
|
||||
# self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
|
||||
# kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
# self.bn1 = nn.BatchNorm2d(out_channel)
|
||||
# self.relu = nn.ReLU()
|
||||
# self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
|
||||
# kernel_size=3, stride=1, padding=1, bias=False)
|
||||
# self.bn2 = nn.BatchNorm2d(out_channel)
|
||||
# self.downsample = downsample
|
||||
#
|
||||
# def forward(self, x):
|
||||
# identity = x
|
||||
# if self.downsample is not None:
|
||||
# identity = self.downsample(x)
|
||||
#
|
||||
# out = self.conv1(x)
|
||||
# out = self.bn1(out)
|
||||
# out = self.relu(out)
|
||||
#
|
||||
# out = self.conv2(out)
|
||||
# out = self.bn2(out)
|
||||
#
|
||||
# out += identity
|
||||
# out = self.relu(out)
|
||||
#
|
||||
# return out
|
||||
#
|
||||
# class ResNet(nn.Module):
|
||||
# '''
|
||||
# ResNet18 and ResNet34
|
||||
# '''
|
||||
# def __init__(self,
|
||||
# block,
|
||||
# blocks_num,
|
||||
# num_classes=1000,
|
||||
# include_top=True,
|
||||
# groups=1,
|
||||
# width_per_group=64):
|
||||
# super(ResNet, self).__init__()
|
||||
# self.include_top = include_top
|
||||
# self.in_channel = 64
|
||||
#
|
||||
# self.groups = groups
|
||||
# self.width_per_group = width_per_group
|
||||
#
|
||||
# self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
|
||||
# padding=3, bias=False)
|
||||
# self.bn1 = nn.BatchNorm2d(self.in_channel)
|
||||
# self.relu = nn.ReLU(inplace=True)
|
||||
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
# self.layer1 = self._make_layer(block, 64, blocks_num[0])
|
||||
# self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
|
||||
# self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
|
||||
# self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
|
||||
# if self.include_top:
|
||||
# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1)
|
||||
# self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
#
|
||||
# for m in self.modules():
|
||||
# if isinstance(m, nn.Conv2d):
|
||||
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
#
|
||||
# def _make_layer(self, block, channel, block_num, stride=1):
|
||||
# downsample = None
|
||||
# if stride != 1 or self.in_channel != channel * block.expansion:
|
||||
# downsample = nn.Sequential(
|
||||
# nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
# nn.BatchNorm2d(channel * block.expansion))
|
||||
#
|
||||
# layers = []
|
||||
# layers.append(block(self.in_channel,
|
||||
# channel,
|
||||
# downsample=downsample,
|
||||
# stride=stride,
|
||||
# groups=self.groups,
|
||||
# width_per_group=self.width_per_group))
|
||||
# self.in_channel = channel * block.expansion
|
||||
#
|
||||
# for _ in range(1, block_num):
|
||||
# layers.append(block(self.in_channel,
|
||||
# channel,
|
||||
# groups=self.groups,
|
||||
# width_per_group=self.width_per_group))
|
||||
#
|
||||
# return nn.Sequential(*layers)
|
||||
#
|
||||
# def forward(self, x):
|
||||
# x = self.conv1(x)
|
||||
# x = self.bn1(x)
|
||||
# x = self.relu(x)
|
||||
# x = self.maxpool(x)
|
||||
#
|
||||
# x = self.layer1(x)
|
||||
# x = self.layer2(x)
|
||||
# x = self.layer3(x)
|
||||
# x = self.layer4(x)
|
||||
#
|
||||
# if self.include_top:
|
||||
# x = self.avgpool(x)
|
||||
# x = torch.flatten(x, 1)
|
||||
# x = self.fc(x)
|
||||
#
|
||||
# return x
|
||||
#
|
||||
# def resnet18(num_classes=1000, include_top=True):
|
||||
# return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
|
||||
#
|
||||
# def resnet34(num_classes=1000, include_top=True):
|
||||
# return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
|
||||
#
|
||||
# #图像有无果判别模型
|
||||
# class ImageClassifier:
|
||||
# '''
|
||||
# 图像分类器，用于加载预训练的 ResNet 模型并进行图像分类。
|
||||
# '''
|
||||
# def __init__(self, model_path, class_indices_path, device=None):
|
||||
# if device is None:
|
||||
# self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
# else:
|
||||
# self.device = device
|
||||
#
|
||||
# # 加载类别索引
|
||||
# assert os.path.exists(class_indices_path), f"File: '{class_indices_path}' does not exist."
|
||||
# with open(class_indices_path, "r") as json_file:
|
||||
# self.class_indict = json.load(json_file)
|
||||
#
|
||||
# # 创建模型并加载权重
|
||||
# self.model = resnet34(num_classes=len(self.class_indict)).to(self.device)
|
||||
# assert os. path.exists(model_path), f"File: '{model_path}' does not exist."
|
||||
# self.model.load_state_dict(torch.load(model_path, map_location=self.device))
|
||||
# self.model.eval()
|
||||
#
|
||||
# # 设置图像转换
|
||||
# self.transform = transforms.Compose([
|
||||
# transforms.Resize(256),
|
||||
# transforms.CenterCrop(224),
|
||||
# transforms.ToTensor(),
|
||||
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||
# ])
|
||||
#
|
||||
# def predict(self, image_np):
|
||||
# '''
|
||||
# 对图像进行分类预测。
|
||||
# :param image_np:
|
||||
# :return:
|
||||
# '''
|
||||
# # 将numpy数组转换为图像
|
||||
# image = Image.fromarray(image_np.astype('uint8'), 'RGB')
|
||||
# image = self.transform(image).unsqueeze(0).to(self.device)
|
||||
#
|
||||
# with torch.no_grad():
|
||||
# output = self.model(image).cpu()
|
||||
# predict = torch.softmax(output, dim=1)
|
||||
# predict_cla = torch.argmax(predict, dim=1).numpy()
|
||||
#
|
||||
# # return self.class_indict[str(predict_cla[0])]
|
||||
# return predict_cla[0]
|
||||
@ -6,11 +6,10 @@
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
import cv2
|
||||
|
||||
from root_dir import ROOT_DIR
|
||||
from classifer import Spec_predict, Data_processing, ImageClassifier
|
||||
from classifer import Spec_predict, Data_processing
|
||||
# from classifer import ImageClassifier
|
||||
import logging
|
||||
from utils import Pipe
|
||||
import numpy as np
|
||||
@ -83,12 +82,14 @@ def main(is_debug=False):
|
||||
logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
|
||||
handlers=[file_handler, console_handler],
|
||||
level=logging.DEBUG)
|
||||
#模型加载
|
||||
detector = Spec_predict(ROOT_DIR/'models'/'passion_fruit_2.joblib')
|
||||
classifier = ImageClassifier(ROOT_DIR/'models'/'resnet18_0616.pth', ROOT_DIR/'models'/'class_indices.json')
|
||||
# classifier = ImageClassifier(ROOT_DIR/'models'/'resnet34_0619.pth', ROOT_DIR/'models'/'class_indices.json')
|
||||
dp = Data_processing()
|
||||
print('系统初始化中...')
|
||||
#模型预热
|
||||
_ = detector.predict(np.ones((30, 30, 224), dtype=np.uint16))
|
||||
_ = classifier.predict(np.ones((224, 224, 3), dtype=np.uint8))
|
||||
# _ = classifier.predict(np.ones((224, 224, 3), dtype=np.uint8))
|
||||
# _, _, _, _, _ =dp.analyze_tomato(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\tomato_img\bad\71.bmp'))
|
||||
# _, _, _, _, _ = dp.analyze_passion_fruit(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img\38.bmp'))
|
||||
print('系统初始化完成')
|
||||
@ -99,30 +100,33 @@ def main(is_debug=False):
|
||||
pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
|
||||
rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
|
||||
# 预热循环，只处理cmd为'YR'的数据
|
||||
# 当接收到的第一个指令预热命令时，结束预热循环
|
||||
while True:
|
||||
start_time00 = time.time()
|
||||
# start_time00 = time.time()
|
||||
data = pipe.receive_rgb_data(rgb_receive)
|
||||
cmd, _ = pipe.parse_img(data)
|
||||
end_time00 = time.time()
|
||||
print(f'接收预热数据时间：{(end_time00 - start_time00) * 1000}毫秒')
|
||||
# end_time00 = time.time()
|
||||
# print(f'接收预热数据时间：{(end_time00 - start_time00) * 1000}毫秒')
|
||||
if cmd == 'YR':
|
||||
break # 当接收到的不是预热命令时，结束预热循环
|
||||
q = 1
|
||||
break
|
||||
#主循环
|
||||
# q = 1
|
||||
while True:
|
||||
start_time = time.time()
|
||||
#RGB图像部分
|
||||
# start_time = time.time()
|
||||
images = []
|
||||
cmd = None
|
||||
for i in range(5):
|
||||
start_time1 = time.time()
|
||||
for _ in range(5):
|
||||
# start_time1 = time.time()
|
||||
data = pipe.receive_rgb_data(rgb_receive)
|
||||
end_time10 = time.time()
|
||||
# end_time10 = time.time()
|
||||
# print(f'接收第{q}组第{i}份RGB数据时间：{(end_time10 - start_time1) * 1000}毫秒')
|
||||
|
||||
start_time11 = time.time()
|
||||
# start_time11 = time.time()
|
||||
cmd, img = pipe.parse_img(data)
|
||||
end_time1 = time.time()
|
||||
# end_time1 = time.time()
|
||||
# print(f'解析第{q}组第{i}份RGB数据时间：{(end_time1 - start_time11) * 1000}毫秒')
|
||||
print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒')
|
||||
# print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒')
|
||||
|
||||
# 使用分类器进行预测
|
||||
# prediction = classifier.predict(img)
|
||||
@ -140,24 +144,24 @@ def main(is_debug=False):
|
||||
if cmd not in ['TO', 'PF', 'YR', 'KO']:
|
||||
logging.error(f'错误指令，指令为{cmd}')
|
||||
continue
|
||||
|
||||
#Spec数据部分
|
||||
spec = None
|
||||
if cmd == 'PF':
|
||||
start_time2 = time.time()
|
||||
# start_time2 = time.time()
|
||||
spec_data = pipe.receive_spec_data(spec_receive)
|
||||
print(f'接收第{q}组光谱数据长度：{len(spec_data)}')
|
||||
# print(f'接收第{q}组光谱数据长度：{len(spec_data)}')
|
||||
_, spec = pipe.parse_spec(spec_data)
|
||||
print(f'处理第{q}组光谱数据长度：{len(spec)}')
|
||||
print(spec.shape)
|
||||
print(f'解析第{q}组光谱数据时间：{(time.time() - start_time2) * 1000}毫秒')
|
||||
end_time2 = time.time()
|
||||
print(f'接收第{q}组光谱数据时间：{(end_time2 - start_time2) * 1000}毫秒')
|
||||
|
||||
start_time3 = time.time()
|
||||
# print(f'处理第{q}组光谱数据长度：{len(spec)}')
|
||||
# print(spec.shape)
|
||||
# print(f'解析第{q}组光谱数据时间：{(time.time() - start_time2) * 1000}毫秒')
|
||||
# end_time2 = time.time()
|
||||
# print(f'接收第{q}组光谱数据时间：{(end_time2 - start_time2) * 1000}毫秒')
|
||||
#数据处理部分
|
||||
# start_time3 = time.time()
|
||||
if images: # 确保images不为空
|
||||
response = process_data(cmd, images, spec, dp, pipe, detector)
|
||||
end_time3 = time.time()
|
||||
print(f'第{q}组处理时间：{(end_time3 - start_time3) * 1000}毫秒')
|
||||
# print(f'第{q}组处理时间：{(end_time3 - start_time3) * 1000}毫秒')
|
||||
if response:
|
||||
logging.info(f'处理成功，响应为: {response}')
|
||||
else:
|
||||
@ -165,10 +169,14 @@ def main(is_debug=False):
|
||||
else:
|
||||
logging.error("没有有效的图像进行处理")
|
||||
|
||||
end_time = time.time()
|
||||
print(f'第{q}组全流程时间：{(end_time - start_time) * 1000}毫秒')
|
||||
q += 1
|
||||
# end_time = time.time()
|
||||
# print(f'第{q}组全流程时间：{(end_time - start_time) * 1000}毫秒')
|
||||
# q += 1
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
'''
|
||||
python与qt采用windows下的命名管道进行通信，数据流按照约定的通信协议进行
|
||||
数据处理逻辑为：连续接收5张RGB图，然后根据解析出的指令部分决定是否接收一张光谱图，然后进行处理，最后将处理得到的指标结果进行编码回传
|
||||
'''
|
||||
main(is_debug=False)
|
||||
|
||||
BIN
20240529RGBtest3/models/resnet34_0619.pth
Normal file
BIN
20240529RGBtest3/models/resnet34_0619.pth
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user