NanjingForestryUniversity/supermachine--tomato-passion_fruit
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fix:修改部分logging信息，添加部分功能块注释

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TG 2024-06-22 16:40:55 +08:00
parent ebf41ab0fe
commit 28f81a135a
2 changed files with 19 additions and 42 deletions
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27
20240529RGBtest3/classifer.py
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@ -4,20 +4,18 @@
# @File : classifer.py # @File : classifer.py
# @Software: PyCharm # @Software: PyCharm
import cv2
import numpy as np
import os import os
import cv2
import json
import utils import utils
from sklearn.ensemble import RandomForestRegressor
import joblib import joblib
import torch.nn as nn import logging
import torch import numpy as np
from PIL import Image from PIL import Image
from torchvision import transforms from torchvision import transforms
import numpy as np from sklearn.ensemble import RandomForestRegressor
import json import torch
import logging import torch.nn as nn
class Tomato: class Tomato:
@ -297,11 +295,11 @@ class Passion_fruit:
''' '''
# 检查 RGB 图像是否为空或全黑 # 检查 RGB 图像是否为空或全黑
if rgb_img is None or rgb_img.size == 0 or np.all(rgb_img == 0): if rgb_img is None or rgb_img.size == 0 or np.all(rgb_img == 0):
logging.error("RGB 图像为空或全黑，返回一个全黑RGB图像。") logging.warning("RGB 图像为空或全黑，返回一个全黑RGB图像。")
return np.zeros((100, 100, 3), dtype=np.uint8) if rgb_img is None else np.zeros_like(rgb_img) return np.zeros((100, 100, 3), dtype=np.uint8) if rgb_img is None else np.zeros_like(rgb_img)
# 检查二值图像是否为空或全黑 # 检查二值图像是否为空或全黑
if bin_img is None or bin_img.size == 0 or np.all(bin_img == 0): if bin_img is None or bin_img.size == 0 or np.all(bin_img == 0):
logging.error("二值图像为空或全黑，返回一个全黑RGB图像。") logging.warning("二值图像为空或全黑，返回一个全黑RGB图像。")
return np.zeros((100, 100, 3), dtype=np.uint8) if rgb_img is None else np.zeros_like(rgb_img) return np.zeros((100, 100, 3), dtype=np.uint8) if rgb_img is None else np.zeros_like(rgb_img)
# 转换二值图像为三通道 # 转换二值图像为三通道
try: try:
@ -359,7 +357,7 @@ class Data_processing:
def contour_process(self, image_array): def contour_process(self, image_array):
# 检查图像是否为空或全黑 # 检查图像是否为空或全黑
if image_array is None or image_array.size == 0 or np.all(image_array == 0): if image_array is None or image_array.size == 0 or np.all(image_array == 0):
logging.error("输入的图像为空或全黑，返回一个全黑图像。") logging.warning("输入的图像为空或全黑，返回一个全黑图像。")
return np.zeros_like(image_array) if image_array is not None else np.zeros((100, 100), dtype=np.uint8) return np.zeros_like(image_array) if image_array is not None else np.zeros((100, 100), dtype=np.uint8)
# 应用中值滤波 # 应用中值滤波
image_filtered = cv2.medianBlur(image_array, 5) image_filtered = cv2.medianBlur(image_array, 5)
@ -521,6 +519,10 @@ class Data_processing:
return diameter, weigth, number_defects, total_pixels, rp 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): class BasicBlock(nn.Module):
''' '''
BasicBlock for ResNet18 and ResNet34 BasicBlock for ResNet18 and ResNet34
@ -691,7 +693,6 @@ def resnetzy(num_classes=1000, include_top=True):
return ResNet(Bottleneck, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top) return ResNet(Bottleneck, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
class ImageClassifier: 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。 图像分类器，用于加载预训练的 ResNet 模型并进行图像分类。

34
20240529RGBtest3/utils.py
View File

@ -72,12 +72,12 @@ class Pipe:
result, data = win32file.ReadFile(rgb_receive, data_size, None) result, data = win32file.ReadFile(rgb_receive, data_size, None)
# 检查读取操作是否成功 # 检查读取操作是否成功
if result != 0: if result != 0:
print(f"读取失败，错误代码: {result}") logging.error(f"读取失败，错误代码: {result}")
return None return None
# 返回成功读取的数据 # 返回成功读取的数据
return data return data
except Exception as e: except Exception as e:
print(f"数据接收失败，错误原因: {e}") logging.error(f"数据接收失败，错误原因: {e}")
return None return None
def receive_spec_data(self, spec_receive): def receive_spec_data(self, spec_receive):
@ -89,12 +89,12 @@ class Pipe:
result, spec_data = win32file.ReadFile(spec_receive, data_size, None) result, spec_data = win32file.ReadFile(spec_receive, data_size, None)
# 检查读取操作是否成功 # 检查读取操作是否成功
if result != 0: if result != 0:
print(f"读取失败，错误代码: {result}") logging.error(f"读取失败，错误代码: {result}")
return None return None
# 返回成功读取的数据 # 返回成功读取的数据
return spec_data return spec_data
except Exception as e: except Exception as e:
print(f"数据接收失败，错误原因: {e}") logging.error(f"数据接收失败，错误原因: {e}")
return None return None
def parse_img(self, data: bytes) -> (str, any): def parse_img(self, data: bytes) -> (str, any):
@ -232,41 +232,17 @@ class Pipe:
try: try:
win32file.WriteFile(self.rgb_send, send_message) win32file.WriteFile(self.rgb_send, send_message)
# time.sleep(0.01) # time.sleep(0.01)
print('发送成功') # print('发送成功')
# print(len(send_message), len(img_bytes))
# print(len(send_message))
except Exception as e: except Exception as e:
logging.error(f'发送指令失败，错误类型：{e}') logging.error(f'发送指令失败，错误类型：{e}')
return False return False
# end_time = time.time() # end_time = time.time()
# print(f'发送时间：{end_time - start_time}秒') # print(f'发送时间：{end_time - start_time}秒')
return True return True
def mkdir_if_not_exist(dir_name, is_delete=False):
"""
创建文件夹
:param dir_name: 文件夹
:param is_delete: 是否删除
:return: 是否成功
"""
try:
if is_delete:
if os.path.exists(dir_name):
shutil.rmtree(dir_name)
print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name)
return True
except Exception as e:
print('[Exception] %s' % e)
return False
def create_file(file_name): def create_file(file_name):
""" """