mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
242bb9a71b
fix:修复在20240627test4中的classifier.py的analyze_tomato函数中white_defect的函数忘记传递两个阈值量的错误；修复analyze_tomato函数中的叶片实例分割存在的问题，解决由于变量污染引起的分割错误；
89 lines
2.2 KiB
Python
89 lines
2.2 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/7/16 下午8:58
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# @Author : GG
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# @File : pf_zz_test.py
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# @Software: PyCharm
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# -*- coding: utf-8 -*-
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# @Time : 2024/7/7 下午4:33
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# @Author : TG
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# @File : totest.py
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# @Software: PyCharm
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import time
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import cv2
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import numpy as np
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import os
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import pandas as pd
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from PIL import Image
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import re
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from classifer import ImageClassifier
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from config import Config as setting
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def tryint(s):
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try:
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return int(s)
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except ValueError:
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return s
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def natural_sort_key(s):
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return [tryint(c) for c in re.split('([0-9]+)', s)]
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# "0": "De" 褶皱
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# "1": "N" 正常
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def main():
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image_dir = r'D:\project\20240714Actual_deployed\zz_test\TEST'
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pf_zz = ImageClassifier(model_path=setting.imgclassifier_model_path,
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class_indices_path=setting.imgclassifier_class_indices_path) # 假设 TOSEG 是已定义好的类，可以处理图片分割
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# 获取所有.bmp文件，并进行自然排序
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rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.bmp')]
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rgb_files.sort(key=natural_sort_key)
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# 准备保存到 Excel 的数据
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records = []
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for idx, image_path in enumerate(rgb_files):
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img = cv2.imread(image_path, cv2.IMREAD_COLOR)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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t = time.time()
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result = pf_zz.predict(img)
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e = time.time()
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process_time = (e - t) * 1000
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print(f'第{idx + 1}张图时间：{process_time}')
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print(f'结果：{result}')
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## 控制台显示识别结果
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# records.append(result)
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# print(f'识别为正常未褶皱的数量：{sum(records)}')
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## 将结果及原始文件信息写入excel
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# 获取原始文件名
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original_filename = os.path.splitext(os.path.basename(image_path))[0]
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# 添加记录到列表
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records.append({
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"图片序号": idx + 1,
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"图片名": original_filename,
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"识别结果(0为褶皱，1为正常）": result,
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"处理时间(ms)": process_time
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})
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# 创建 DataFrame 并写入 Excel 文件
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df = pd.DataFrame(records)
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df.to_excel(r'./zz_result.xlsx', index=False)
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if __name__ == '__main__':
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main()
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