高端设计网站制作,网页qq登录记录网站,南京俄语网站建设,网站制作常见的问题文章目录 一、任务描述二、指标分析2.1 TP/FP/FN/TN2.2 精准率2.3 召回率 三、接口处理四、数据集处理五、开始计算指标五、实用工具5.1 移动文件5.2 可视化JSON标签5.3 可视化TXT标签 一、任务描述
通过给定的算法接口#xff0c;对算法的输出#xff08;置信度、检测框、告… 文章目录 一、任务描述二、指标分析2.1 TP/FP/FN/TN2.2 精准率2.3 召回率 三、接口处理四、数据集处理五、开始计算指标五、实用工具5.1 移动文件5.2 可视化JSON标签5.3 可视化TXT标签 一、任务描述
通过给定的算法接口对算法的输出置信度、检测框、告警、类别等进行数据处理结合原标签完成TP、FP、FN、TN、准确率、召回率的指标测试。
二、指标分析
2.1 TP/FP/FN/TN
TPTrue Positives真正例表示模型正确预测为正类的样本数量也就是将正例正确分类为正例的数量。
FPFalse Positives假正例表示模型错误预测为正类的样本数量也就是将负例错误分类为正例的数量。
FNFalse Negatives假负例表示模型错误预测为负类的样本数量也就是将正例错误分类为负例的数量。
TNTrue Negatives真负例表示模型正确预测为负类的样本数量也就是将负例正确分类为负例的数量。
2.2 精准率
精准率是指模型在所有被预测为正例的样本中真正为正例的比例。它的计算公式是Precision TP / (TP FP)。精准率衡量了模型的预测中有多少是真正的正例是一个关于假正例的指标。
2.3 召回率
召回率是指模型在所有实际正例中成功预测为正例的比例。它的计算公式是Recall TP / (TP FN)。召回率衡量了模型对于所有正例的识别能力是一个关于假负例的指标。
三、接口处理
import os, base64, json, time, requestsdef get_all_file(path):result_list []for root, dirs, files in os.walk(path):for file in files:if os.path.basename(file).__contains__(.jpg):result_list.append(os.path.join(root, file))return result_listdef pic2base64(img_path):if os.path.exists(img_path):encoded_base64 base64.b64encode(open(img_path, rb).read()).decode()return encoded_base64else:os.error(图片不存在请检查图片路径{}.format(img_path))def write_content_to_json(content, json_file_path):with open(json_file_path, w, encodingutf-8) as f:f.write(json.dumps(content, indent4, ensure_asciiFalse))if __name__ __main__:result {}fish_pic_path rD:\datasets\test_mission\fishing_test_dataurl_fish http://***.***.***.*:端口号/能力接口pic_list get_all_file(fish_pic_path)for pic in pic_list:image_base64 pic2base64(pic)payload {seqid: test1,timestamp: int(time.time()),image: image_base64# image: rf;base64,{image_base64}\alt\\}res requests.post(url_fish, jsonpayload)json_res res.json()print(json_res)if code in json_res and json_res[code] 6000:result[os.path.basename(pic)] json_res[data]write_content_to_json(result, result/fish_result.json)将所有的结果保存为JSON文档如下
四、数据集处理
数据集统一处理成
数据集如果是JSON格式通过下面的代码转换为TXT格式
import os
import numpy as np
import json
from glob import glob
import cv2
from sklearn.model_selection import train_test_split
from shutil import copyfile
import argparseobj_classes []# Labelme坐标到YOLO V5坐标的转换
def convert(size, box):dw 1. / (size[0])dh 1. / (size[1])x (box[0] box[1]) / 2.0 - 1y (box[2] box[3]) / 2.0 - 1w box[1] - box[0]h box[3] - box[2]x x * dww w * dwy y * dhh h * dhreturn (x, y, w, h)def create(yolo_labels_dir):if not os.path.exists(yolo_labels_dir):os.makedirs(yolo_labels_dir)# 样本转换
def convertToYolo5(fileList, output_dir, labelImg_path, unify_path):# 创建指定样本的父目录if not os.path.exists(output_dir):os.makedirs(output_dir)# 创建指定样本的images和labels子目录yolo_images_dir {}/images/.format(output_dir)yolo_labels_dir {}/labels/.format(output_dir)create(yolo_images_dir)create(yolo_labels_dir)create(unify_path)# 一个样本图片一个样本图片地转换for num,json_file_ in enumerate(fileList):# print(fileList,fileList)# 1. 生成YOLO样本图片# 构建json图片文件的全路径名imagePath labelImg_path / json_file_ .jpgprint(name,imagePath, json_file_)# print(labelme_path, labelme_path)# 构建Yolo图片文件的全路径名yolo_image_file_path yolo_images_dir {}.jpg.format(json_file_)print(yolo_image_file_path, yolo_image_file_path)# copy样本图片copyfile(imagePath, yolo_image_file_path)# 2. 生成YOLO样本标签# 构建json标签文件的全路径名labelme_path_ labelImg_path.split(image)[0]json_filename labelme_path_ json\\ json_file_ .json# 构建Yolo标签文件的全路径名yolo_label_file_path yolo_labels_dir {}.txt.format(json_file_)txt_label_file_path unify_path /{}.txt.format(json_file_)# 创建新的Yolo标签文件yolo_label_file open(yolo_label_file_path, w)txt_label_file open(txt_label_file_path, w)# 获取当前图片的Json标签文件json_obj json.load(open(json_filename, r, encodingutf-8))# 获取当前图片的长度、宽度信息height json_obj[imageHeight]width json_obj[imageWidth]# 依次读取json文件中所有目标的shapes信息for shape in json_obj[shapes]:# 获取shape中的物体分类信息label shape[label]if label not in [car_red,car_orange,car_green,car_blue,car_black,car_white,car_purple,car_grey,car_silvery,car grey,car orange,car black,car,car blue,car purple,car white,car silvery,car green,car_yellow,car red]:if label Fengtain or label FengTain or label FengtTian or label Fengtian:label FengTianif (label not in obj_classes):obj_classes.append(label)# 获取shape中的物体坐标信息if (shape[shape_type] rectangle):points np.array(shape[points])xmin min(points[:, 0]) if min(points[:, 0]) 0 else 0xmax max(points[:, 0]) if max(points[:, 0]) 0 else 0ymin min(points[:, 1]) if min(points[:, 1]) 0 else 0ymax max(points[:, 1]) if max(points[:, 1]) 0 else 0# 对坐标信息进行合法性检查if xmax xmin:passelif ymax ymin:passelse:# Labelme坐标转换成YOLO V5坐标bbox_labelme_float (float(xmin), float(xmax), float(ymin), float(ymax))bbox_yolo_normalized convert((width, height), bbox_labelme_float)# 把分类标签转换成分类idclass_id obj_classes.index(label)# 生成YOLO V5的标签文件yolo_label_file.write(str(class_id) .join([str(a) for a in bbox_yolo_normalized]) \n)# 保存为统一的位置txt_label_file.write(str(class_id) .join([str(a) for a in bbox_yolo_normalized]) \n)yolo_label_file.close()txt_label_file.close()def check_output_directory(output):# 创建保存输出图片的目录save_path output /is_exists os.path.exists(save_path)if is_exists:print(Warning: path of %s already exist, please remove it firstly by manual % save_path)# shutil.rmtree(save_path) # 避免误删除已有的文件return # print(create output path %s % save_path)os.makedirs(save_path)return save_pathdef create_yolo_dataset_cfg(output_dir, label_class[]):# 创建文件data_cfg_file open(output_dir /data.yaml, w)# 创建文件内容data_cfg_file.write(train: ../train/images\n)data_cfg_file.write(val: ../valid/images\n)data_cfg_file.write(test: ../test/images\n)data_cfg_file.write(\n)data_cfg_file.write(# Classes\n)data_cfg_file.write(nc: %s\n % len(label_class))data_cfg_file.write(names: )i 0for label in label_class:if (i 0):data_cfg_file.write([)else:data_cfg_file.write(, )if (i % 10 0):data_cfg_file.write(\n )i 1data_cfg_file.write( label )data_cfg_file.write(] # class names)data_cfg_file.close()# 关闭文件def labelImg2yolo(input, output, unify_path):outputdir_root output /labelImg_path inputprint(labelImg_path)labelImg_path_imagepath labelImg_path \\ imageprint(labelImg_path_imagepath)json_path labelImg_path\\jsonprint(json_path)print(**100)# 1.获取input目录中所有的json标签文件全路径名files glob(json_path /*.json)print(files)# 2.获取所有标签文件的短文件名称files [i.replace(\\, /).split(/)[-1].split(.json)[0] for i in files]print(files)# 3. 按比例随机切分数据集获取训练集样本train_files, valid_test_files train_test_split(files, test_size0.2, random_state55)# 4. 按比例随机切分数据集获取验证集和测试集样本valid_files, test_files train_test_split(valid_test_files, test_size0.1, random_state55)# 5. 构建YOLO数据集目录train_path outputdir_root /trainvalid_path outputdir_root /validtest_path outputdir_root /test# 6. 生成YOLO 训练、验证、测试数据集图片标签convertToYolo5(train_files, train_path, labelImg_path_imagepath, unify_path)convertToYolo5(valid_files, valid_path, labelImg_path_imagepath, unify_path)convertToYolo5(test_files, test_path, labelImg_path_imagepath, unify_path)print(**100)# 7. 创建YOLO数据集配置文件create_yolo_dataset_cfg(output, obj_classes)labelme_path inputprint(Classes:, obj_classes)print(Finished, output path , outputdir_root)return 0def parse_opt():# define argparse objectparser argparse.ArgumentParser()# input 包含两个文件夹 image和json分别存放了对应的文件parser.add_argument(--input, typestr, defaultrF:\python\mission\fish_power_test\data\test,helpThe input LabelImg directory)# output 存放保存的yolov5的训练数据分为train、val、test三个文件里面分别存放了对应的images和labels在train目录下还存放了yolov5加载数据集的yaml文件见data.yamlparser.add_argument(--output, typestr,defaultrF:\python\mission\fish_power_test\data\test/yolo_txt, helpThe output YOLO V5 directory)# 统一存放parser.add_argument(--unify_path, typestr,defaultrF:\python\mission\fish_power_test\data\test/txt, helpThe output txt directory)# parse arges from command lineopt parser.parse_args()print(input , opt.input)print(output , opt.output)print(unify_path , opt.unify_path)# return optreturn optdef main(opt):labelImg2yolo(**vars(opt))if __name__ __main__:opt parse_opt()main(opt)然后通过下面的代码将源标签的txt文档进行总结
# 将txt标签对应的原始的label和boxs写入到txt文档中
import cv2
import os# 读取txt文件信息
def read_list(txt_path):pos []with open(txt_path, r) as file_to_read:while True:lines file_to_read.readline() # 整行读取数据if not lines:break# 将整行数据分割处理如果分割符是空格括号里就不用传入参数如果是逗号 则传入‘字符。p_tmp [float(i) for i in lines.split( )]pos.append(p_tmp) # 添加新读取的数据passreturn posdef draw_box_in_single_image(image_path, txt_path, image_name):# 读取图像image cv2.imread(image_path)pos read_list(txt_path)reses []reses.append(image_name)for i in range(len(pos)):label classes[int(str(int(pos[i][0])))]print(label is label)# 将中心坐标、宽度和高度转换为 xywh 格式x_center int(pos[i][1] * image.shape[1])y_center int(pos[i][2] * image.shape[0])width int(pos[i][3] * image.shape[1])height int(pos[i][4] * image.shape[0])x_min x_center - width // 2y_min y_center - height // 2# 绘制矩形框cv2.rectangle(image, (x_min, y_min), (x_min width, y_min height), colores[int(str(int(pos[i][0])))], 2)cv2.putText(image, label, (x_min, y_min - 2), 0, 1, colores[int(str(int(pos[i][0])))], thickness2, lineTypecv2.LINE_AA)res [label, x_min, y_min, width, height]reses.append(res)# cv2.imshow(images, image)# cv2.waitKey(0)return resesif __name__ __main__:f open(result/ori_data.txt, w)img_folder data/test/imageimg_list [f for f in os.listdir(img_folder) if f.endswith(.jpg)]img_list.sort()label_folder data/test/txtlabel_list [f for f in os.listdir(label_folder) if f.endswith(.txt)]label_list.sort()classes {0: fishing, 1: no_fishing}colores [(0, 0, 255), (255, 0, 255)]for i in range(len(img_list)):image_path os.path.join(img_folder, img_list[i])txt_path os.path.join(label_folder, label_list[i])reses draw_box_in_single_image(image_path, txt_path, img_list[i])print(reses)f.write(str(reses))f.write(\n)f.close()五、开始计算指标
处理之后就可以得到检测后的fish_result.json和ori_data.txt。然后根据这两个文件来计算即可。
部分重要代码放在这里面链接
五、实用工具
5.1 移动文件
import os
import shutil# 源文件夹和目标文件夹的路径
source_folder fishing_test_data
destination_folder test/image# 确保目标文件夹存在如果不存在就创建它
if not os.path.exists(destination_folder):os.makedirs(destination_folder)# 遍历源文件夹中的文件
for filename in os.listdir(source_folder):# 检查文件扩展名是否为图片格式可以根据需要添加其他格式if filename.endswith((.jpg, .png, .jpeg)):# 构建源文件的完整路径和目标文件的完整路径source_file_path os.path.join(source_folder, filename)destination_file_path os.path.join(destination_folder, filename)# 移动文件shutil.move(source_file_path, destination_file_path)print(fMoved: {filename} to {destination_folder})
5.2 可视化JSON标签
# -*- coding: utf-8 -*-
import cv2
import os
import json
import shutil
import numpy as np
from pathlib import Path
from glob import globid2cls {0: fishing, 1: no_fishing}
cls2id {fishing: 0, no_fishing: 1}
id2color {fishing: (0, 255, 0), no_fishing: (0, 255, 255)}# 支持中文路径
def cv_imread(filePath):cv_img cv2.imdecode(np.fromfile(filePath, dtypenp.uint8), flagscv2.IMREAD_COLOR)return cv_imgdef get_labelme_info(label_file):anno json.load(open(label_file, r, encodingutf-8))shapes anno[shapes]image_path os.path.basename(anno[imagePath])labels []for s in shapes:pts s[points]x1, y1 pts[0]x2, y2 pts[1]color id2color[s[label]]labels.append([color, x1, y1, x2, y2])return labels, image_pathdef vis_labelme(labelme_label_dir, save_dirres/):labelme_label_dir str(Path(labelme_label_dir)) /save_dir str(Path(save_dir)) /if not os.path.exists(save_dir):os.makedirs(save_dir)json_files glob(labelme_label_dir *.json)for ijf, jf in enumerate(json_files):print(ijf 1, /, len(json_files), jf)filename os.path.basename(jf).rsplit(., 1)[0]labels, image_path get_labelme_info(jf)image cv_imread(labelme_label_dir image_path)for label in labels:color label[0]x1, y1, x2, y2 label[1:]x1, y1, x2, y2 int(x1), int(y1), int(x2), int(y2)cv2.rectangle(image, (x1, y1), (x2, y2), color, 3)# 显示图片# cv2.imshow(filename, image)# cv2.waitKey(0)# 支持中文路径保存图片cv2.imencode(os.path.splitext(image_path)[-1], image)[1].tofile(save_dir image_path)print(Completed!)if __name__ __main__:root_dir rD:\Python\money\data\testsave_dir rD:\Python\money\data\test/tvis_labelme(root_dir, save_dir)5.3 可视化TXT标签
import cv2
import os# 读取txt文件信息
def read_list(txt_path):pos []with open(txt_path, r) as file_to_read:while True:lines file_to_read.readline() # 整行读取数据if not lines:break# 将整行数据分割处理如果分割符是空格括号里就不用传入参数如果是逗号 则传入‘字符。p_tmp [float(i) for i in lines.split( )]pos.append(p_tmp) # 添加新读取的数据# Efield.append(E_tmp)passreturn pos# txt转换为box
def convert(size, box):xmin (box[1] - box[3] / 2.) * size[1]xmax (box[1] box[3] / 2.) * size[1]ymin (box[2] - box[4] / 2.) * size[0]ymax (box[2] box[4] / 2.) * size[0]box (int(xmin), int(ymin), int(xmax), int(ymax))return boxdef draw_box_in_single_image(image_path, txt_path):# 读取图像image cv2.imread(image_path)pos read_list(txt_path)for i in range(len(pos)):label classes[int(str(int(pos[i][0])))]print(label is label)box convert(image.shape, pos[i])image cv2.rectangle(image,(box[0], box[1]),(box[2],box[3]),colores[int(str(int(pos[i][0])))],2)cv2.putText(image, label,(box[0],box[1]-2), 0, 1, colores[int(str(int(pos[i][0])))], thickness2, lineTypecv2.LINE_AA)cv2.imshow(images, image)cv2.waitKey(0)if __name__ __main__:img_folder rF:\python\mission\fish_power_test\data\test\yolo_txt\train\imagesimg_list os.listdir(img_folder)img_list.sort()label_folder rF:\python\mission\fish_power_test\data\test\yolo_txt\train/labelslabel_list os.listdir(label_folder)label_list.sort()classes {0: fishing, 1: no_fishing}colores [(0,0,255),(255,0,255)]for i in range(len(img_list)):image_path img_folder \\ img_list[i]txt_path label_folder \\ label_list[i]draw_box_in_single_image(image_path, txt_path)
