NanjingForestryUniversity/supermachine--tomato-passion_fruit
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refactor:修改代码结构，将process_data函数从main.py移动至classfier.py的Data_processing类下

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TG 2024-06-26 10:03:25 +08:00
parent 53d7f680e0
commit 6a304ceb7c
3 changed files with 70 additions and 69 deletions
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70
20240529RGBtest3/classifer.py
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@ -13,6 +13,7 @@ import logging
import random
import numpy as np
from PIL import Image
from utils import Pipe
from sklearn.ensemble import RandomForestRegressor
#图像分类网络所需库，实际并未使用分类网络
# import torch
@ -585,9 +586,9 @@ class Data_processing:
long_axis, short_axis = self.analyze_ellipse(contour_mask)
#重量单位为g，加上了一点随机数
weight_real = self.weight_estimates(long_axis, short_axis)
print(f'真实重量：{weight_real}')
# print(f'真实重量：{weight_real}')
weight = (weight_real * 2) + random.randint(0, 30)
print(f'估算重量：{weight}')
# print(f'估算重量：{weight}')
if weight > 255:
weight = weight_real
@ -606,6 +607,71 @@ class Data_processing:
rp = cv2.cvtColor(np.ones((613, 800, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
return diameter, weight, number_defects, total_pixels, rp
def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool:
"""
处理指令
:param cmd: 指令类型
:param images: 图像数据列表
:param spec: 光谱数据
:param detector: 模型
:return: 是否处理成功
"""
# pipe = Pipe()
diameter_axis_list = []
max_defect_num = 0 # 初始化最大缺陷数量为0
max_total_defect_area = 0 # 初始化最大总像素数为0
for i, img in enumerate(images):
if cmd == 'TO':
# 番茄
diameter, green_percentage, number_defects, total_pixels, rp = seif.analyze_tomato(img)
if i <= 2:
diameter_axis_list.append(diameter)
max_defect_num = max(max_defect_num, number_defects)
max_total_defect_area = max(max_total_defect_area, total_pixels)
if i == 1:
rp_result = rp
gp = round(green_percentage, 2)
elif cmd == 'PF':
# 百香果
diameter, weight, number_defects, total_pixels, rp = seif.analyze_passion_fruit(img)
if i <= 2:
diameter_axis_list.append(diameter)
max_defect_num = max(max_defect_num, number_defects)
max_total_defect_area = max(max_total_defect_area, total_pixels)
if i == 1:
rp_result = rp
weight = weight
else:
logging.error(f'错误指令，指令为{cmd}')
return False
diameter = round(sum(diameter_axis_list) / 3, 2)
if cmd == 'TO':
brix = 0
weight = 0
# print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{gp};'
# f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight,
defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
return response
elif cmd == 'PF':
green_percentage = 0
brix = detector.predict(spec)
if diameter == 0:
brix = 0
# print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{green_percentage};'
# f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter,
weight=weight,
defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
return response
# #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

66
20240529RGBtest3/main.py
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@ -18,68 +18,6 @@ import time
def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: Pipe, detector: Spec_predict) -> bool:
"""
处理指令
:param cmd: 指令类型
:param images: 图像数据列表
:param spec: 光谱数据
:param detector: 模型
:return: 是否处理成功
"""
diameter_axis_list = []
max_defect_num = 0 # 初始化最大缺陷数量为0
max_total_defect_area = 0 # 初始化最大总像素数为0
for i, img in enumerate(images):
if cmd == 'TO':
# 番茄
diameter, green_percentage, number_defects, total_pixels, rp = dp.analyze_tomato(img)
if i <= 2:
diameter_axis_list.append(diameter)
max_defect_num = max(max_defect_num, number_defects)
max_total_defect_area = max(max_total_defect_area, total_pixels)
if i == 1:
rp_result = rp
gp = round(green_percentage, 2)
elif cmd == 'PF':
# 百香果
diameter, weight, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img)
if i <= 2:
diameter_axis_list.append(diameter)
max_defect_num = max(max_defect_num, number_defects)
max_total_defect_area = max(max_total_defect_area, total_pixels)
if i == 1:
rp_result = rp
weight = weight
else:
logging.error(f'错误指令，指令为{cmd}')
return False
diameter = round(sum(diameter_axis_list) / 3, 2)
if cmd == 'TO':
brix = 0
weight = 0
# print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{gp};'
# f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight,
defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
return response
elif cmd == 'PF':
green_percentage = 0
brix = detector.predict(spec)
if diameter == 0:
brix = 0
# print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{green_percentage};'
# f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, weight=weight,
defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
return response
def main(is_debug=False):
file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato.log'), encoding='utf-8')
file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
@ -165,8 +103,8 @@ def main(is_debug=False):
#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()
response = dp.process_data(cmd, images, spec, pipe, detector)
# end_time3 = time.time()
# 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}')

3
20240529RGBtest3/utils.py
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@ -241,9 +241,6 @@ class Pipe:
return True
def create_file(file_name):
"""
创建文件