1089 lines
49 KiB
Python
1089 lines
49 KiB
Python
from PIL import Image
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from work_util import prepare_data,params
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from work_util.logger import logger
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import os,sys
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import xgboost as xgb
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import uuid
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from collections import defaultdict
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import copy
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import torch
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import numpy as np
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import pandas as pd
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import cv2 as cv
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import torch.nn.functional as F
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from joblib import dump, load
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from fastapi import HTTPException
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import pickle
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param = params.ModelParams()
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import cv2
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import traceback
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################################################### 图像类函数调用###########################################################################
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# 生成随机目录，确保文件处理不冲突
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def create_tmp_path(self, path):
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folder_name = str(uuid.uuid4())
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folder_path = os.path.join(path,'tmp', folder_name)
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os.makedirs(folder_path)
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return folder_path
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# 遍历图像，并保存返回
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def get_images(path):
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imglist = []
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lines = os.listdir(path)
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# print(lines)
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for ii, line in enumerate(lines):
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name = line
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_imagepath = os.path.join(path, name)
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assert os.path.isfile(_imagepath)
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image = Image.open(_imagepath)
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orininal_h = image.size[1]
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orininal_w = image.size[0]
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item = {"name": name, "orininal_h": orininal_h, "orininal_w": orininal_w, "image": image}
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imglist.append(item)
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# print("共监测到{}张原始图像和标签".format(len(imglist)))
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return imglist
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# 地貌识别——模型计算
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def dimaoshibie_cal(model,path,count=None, name_classes=None):
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image = Image.open(path)
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r_image, count_dict, classes_nums = model.detect_image(image, count=count, name_classes=name_classes)
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# 十一个分类的每个的像素点
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logger.info(count_dict)
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# 总像素点数量，对应十一个分类
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logger.info(classes_nums)
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return r_image, count_dict, classes_nums
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"""
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1. 图片分割成规定大小
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2. 遍历分割图片，进行预测
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3. 图片合并成大图
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4. 删除分割的小图片
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"""
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def dimaoshibie_pic(model,path,count=None, name_classes=None):
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single = prepare_data.cut_big_image(path)
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if single == False:
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return {
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"success": False,
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"msg": "图片大小不合规，请检查图片大小是否符合尺寸大于512*512，文件大小小于10MB",
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"data": None
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}
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else:
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file_directory = os.path.dirname(path)
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folder_path = os.path.join(file_directory,'ori')
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lines = os.listdir(folder_path)
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# 创建小图结果目录
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output_floder_path = os.path.join(file_directory,'binary')
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# 创建输出文件夹（如果不存在）
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os.makedirs(output_floder_path, exist_ok=True)
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total_piex = {
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"_background_" : 0, # Background (黑色)
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"Cropland" : 0, # Cropland (淡黄色)
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"Forest": 0, # Forest (深绿色)
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"Grass": 0, # Grass (浅绿色)
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"Shrub": 0, # Shrub (浅蓝绿色)
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"Wetland": 0, # Wetland (浅蓝色)
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"Water": 0, # Water (深蓝色)
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"Tundra": 0, # Tundra (土黄色)
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"Impervious surface": 0, # Impervious surface (红色)
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"Bareland": 0, # Bareland (灰色)
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"Ice/snow": 0 # Ice/snow (浅天蓝色)
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}
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for ii, line in enumerate(lines):
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_imagepath = os.path.join(folder_path, line)
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r_image, count_dict, classes_nums = dimaoshibie_cal(model,_imagepath,count, name_classes)
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final_path = os.path.join(output_floder_path,line)
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# 保存融合后的图像
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r_image.save(final_path) # 替换为你想保存的路径
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for key in count_dict:
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if key in total_piex:
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total_piex[key] += count_dict[key]
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# 将像素分类，存储
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# 创建一个新的字典，用于存储中文键
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translated_total_piex = {}
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for key, value in total_piex.items():
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if key in param.dmsb_type:
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translated_total_piex[param.dmsb_type[key]] = value
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output_file_path = os.path.join(file_directory,'result.txt')
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# 写入字典到文本文件
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with open(output_file_path, 'w') as file:
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for key, value in translated_total_piex.items():
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file.write(f"{key}: {value}\n")
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# 图片合并
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status = prepare_data.merge_pic_binary(path,['binary'])
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if status['status'] == False:
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return {
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"success": False,
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"msg": "图片合并失败，请稍后再试",
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"data": None
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}
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target_path = status['path']
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# 临时文件删除
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tmp = prepare_data.delete_folder(folder_path)
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if tmp==False:
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return {
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"success": False,
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"msg": "临时文件删除失败",
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"data": None
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}
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tmp = prepare_data.delete_folder(output_floder_path)
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if tmp==False:
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return {
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"success": False,
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"msg": "临时文件删除失败",
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"data": None
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}
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# 返回图片地址
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return {"status":True, "reason":target_path}
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"""
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计算每一类地貌的面积大小
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1、读取前面的图片分割结果
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2、计算每一类的图像像素点数量
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3、计算每一类的像素点面积
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"""
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def dimaoshibie_area(path,scale,colors=None):
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file_directory = os.path.dirname(path)
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file_name = os.path.basename(path)
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file_path = os.path.join(file_directory,"merge_binary_" + file_name)
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if not os.path.exists(file_path):
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return {"status": False, "reason": "没有找到对应文件，请先进行图像分割", "result":""}
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image = Image.open(path)
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try:
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# 如果文件存在，读取图片
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# logger.info(file_path)
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image = Image.open(file_path)
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image = image.convert('RGB')
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color_count = defaultdict(int)
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# 遍历图像中的每个像素
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for x in range(image.width):
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for y in range(image.height):
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pixel_color = image.getpixel((x, y))
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if pixel_color in colors:
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color_count[pixel_color] += 1
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else:
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color_count[pixel_color] += 1
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for k,v in color_count.items():
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color_count[k] = v * scale
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# logger.info(color_count)
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result = {color: color_count[color] for color in colors}
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return {"status": True, "reason": result}
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except Exception as e:
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return {"status": False, "reason": str(e)}
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"""
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1. 图片分割成规定大小
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2. 遍历分割图片，进行预测
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3. 图片合并成大图
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4. 删除分割的小图片
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"""
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def roof_pic(net,path,palette):
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single = prepare_data.cut_big_image(path)
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logger.info("图片分割完成")
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if single == False:
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return {"status":False, "reason":"图片大小不合规，请检查图片大小是否符合尺寸大于512*512，文件大小小于10MB"}
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else:
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file_directory = os.path.dirname(path)
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folder_path = os.path.join(file_directory,'ori')
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# 创建小图结果目录
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output_floder_path = os.path.join(file_directory,'binary')
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# 创建输出文件夹（如果不存在）
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os.makedirs(output_floder_path, exist_ok=True)
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imglist = get_images(folder_path)
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assert len(imglist) != 0
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for i in imglist:
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image = i["image"]
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name = i["name"]
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# orininal_w = i["orininal_w"]
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# orininal_h = i["orininal_h"]
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# old_img = copy.deepcopy(image)
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imaged = cv.resize(np.array(image), dsize=(512, 512), interpolation=cv.INTER_LINEAR)
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image_data = np.expand_dims(
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np.transpose(prepare_data.roof_pv_preprocess_input(np.array(imaged, np.float32), md=False), (2, 0, 1)), 0)
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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image = images.to(device="cuda", dtype=torch.float32)
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model = net.to(device="cuda")
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out = model(image) # batch_size, 2, 512, 512
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if isinstance(out, list) or isinstance(out, tuple): # 可能有多个输出（这里把辅助解码头的也输出的所以是多个）
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out = out[0] # 就取第一个
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out = out[0] # 去掉batch
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pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
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result = pr.argmax(axis=-1)
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# 结果图
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output_im = Image.fromarray(np.uint8(result)).convert('P')
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output_im.putpalette(palette)
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tmp_path_binary= os.path.join(output_floder_path,name)
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output_im.save(tmp_path_binary)
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# 图片合并
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status = prepare_data.merge_pic_binary(path,['binary'])
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if status['status'] == False:
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return {"status":False, "reason":"图片合并失败"}
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target_path = status['path']
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# 临时文件删除
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tmp = prepare_data.delete_folder(folder_path)
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if tmp==False:
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return {"status":False, "reason":"临时文件删除失败"}
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tmp = prepare_data.delete_folder(output_floder_path)
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if tmp==False:
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return {"status":False, "reason":"临时文件删除失败"}
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# 返回图片地址
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return {"status":True, "reason":target_path}
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"""
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计算每一类地貌的面积大小
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||
1、读取前面的图片分割结果
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2、计算每一类的图像像素点数量
|
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3、计算每一类的像素点面积
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"""
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def roof_area(path,scale,colors=None):
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file_directory = os.path.dirname(path)
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file_name = os.path.basename(path)
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file_path = os.path.join(file_directory,"merge_binary_" + file_name)
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if not os.path.exists(file_path):
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return {"status": False, "reason": "没有找到对应文件，请先进行图像分割", "result":""}
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image = Image.open(path)
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try:
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# 如果文件存在，读取图片
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image = Image.open(file_path)
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image = image.convert('RGB')
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color_count = defaultdict(int)
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# 遍历图像中的每个像素
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for x in range(image.width):
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for y in range(image.height):
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pixel_color = image.getpixel((x, y))
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if pixel_color in colors:
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color_count[pixel_color] += 1
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for k,v in color_count.items():
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color_count[k] = v * scale
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result = {color: color_count[color] for color in colors}
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return {"status": True, "reason":result}
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except Exception as e:
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return {"status": False, "reason": str(e)}
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def roof_area_roofpv(path,scale,colors=None):
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file_directory = os.path.dirname(path)
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file_name = os.path.basename(path)
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file_path = os.path.join(file_directory,"merge_roofpv_binary_"+file_name)
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if not os.path.exists(file_path):
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return {"status": False, "reason": "没有找到对应文件，请先进行图像分割", "result":""}
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image = Image.open(path)
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try:
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# 如果文件存在，读取图片
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image = Image.open(file_path)
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image = image.convert('RGB')
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color_count = defaultdict(int)
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# 遍历图像中的每个像素
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for x in range(image.width):
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for y in range(image.height):
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pixel_color = image.getpixel((x, y))
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if pixel_color in colors:
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color_count[pixel_color] += 1
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for k,v in color_count.items():
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color_count[k] = v * scale
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result = {color: color_count[color] for color in colors}
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return {"status": True, "reason":result}
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except Exception as e:
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return {"status": False, "reason": str(e)}
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"""
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1. 图片分割成规定大小
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||
2. 遍历分割图片，进行预测
|
||
3. 图片合并成大图
|
||
4. 删除分割的小图片
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"""
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def roofpv_pic(net_roof,net_pv,path,palette):
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single = prepare_data.cut_big_image(path)
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logger.info("图片分割完成")
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if single == False:
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return {"status":False, "reason":"图片大小不合规，请检查图片大小是否符合尺寸大于512*512，文件大小小于10MB"}
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else:
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file_directory = os.path.dirname(path)
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folder_path = os.path.join(file_directory,'ori')
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# 创建小图结果目录
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output_floder_path_roof = os.path.join(file_directory, 'roof_binary')
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output_floder_path_pv = os.path.join(file_directory, 'pv_binary')
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# 创建输出文件夹（如果不存在）
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os.makedirs(output_floder_path_roof, exist_ok=True)
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os.makedirs(output_floder_path_pv, exist_ok=True)
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imglist = get_images(folder_path)
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assert len(imglist) != 0
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for i in imglist:
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image = i["image"]
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name = i["name"]
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# orininal_w = i["orininal_w"]
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# orininal_h = i["orininal_h"]
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# old_img = copy.deepcopy(image)
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imaged = cv.resize(np.array(image), dsize=(512, 512), interpolation=cv.INTER_LINEAR)
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image_data = np.expand_dims(
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np.transpose(prepare_data.roof_pv_preprocess_input(np.array(imaged, np.float32), md=False), (2, 0, 1)), 0)
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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image = images.to(device="cuda", dtype=torch.float32)
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model = net_roof.to(device="cuda")
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out = model(image) # batch_size, 2, 512, 512
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if isinstance(out, list) or isinstance(out, tuple): # 可能有多个输出（这里把辅助解码头的也输出的所以是多个）
|
||
out = out[0] # 就取第一个
|
||
out = out[0] # 去掉batch
|
||
pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
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result = pr.argmax(axis=-1)
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||
# 结果图
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||
output_im = Image.fromarray(np.uint8(result)).convert('P')
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output_im.putpalette(palette)
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tmp_path_binary= os.path.join(output_floder_path_roof,name)
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output_im.save(tmp_path_binary)
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||
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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image = images.to(device="cuda", dtype=torch.float32)
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model = net_pv.to(device="cuda")
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out = model(image) # batch_size, 2, 512, 512
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if isinstance(out, list) or isinstance(out, tuple): # 可能有多个输出（这里把辅助解码头的也输出的所以是多个）
|
||
out = out[0] # 就取第一个
|
||
out = out[0] # 去掉batch
|
||
pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
|
||
result = pr.argmax(axis=-1)
|
||
# 结果图
|
||
output_im = Image.fromarray(np.uint8(result)).convert('P')
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output_im.putpalette(palette)
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tmp_path_binary= os.path.join(output_floder_path_pv,name)
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output_im.save(tmp_path_binary)
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||
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||
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# 图片合并
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||
sataus = prepare_data.merge_pic_binary(path,['roof_binary','pv_binary'])
|
||
if sataus['status'] == False:
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return {"status":False, "reason":"图片合并失败"}
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target_path = sataus['path']
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# 临时文件删除
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||
tmp = prepare_data.delete_folder(folder_path)
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if tmp==False:
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return {"status":False, "reason":"临时文件删除失败"}
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tmp = prepare_data.delete_folder(output_floder_path_roof)
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if tmp==False:
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return {"status":False, "reason":"临时文件删除失败"}
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tmp = prepare_data.delete_folder(output_floder_path_pv)
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if tmp==False:
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return {"status":False, "reason":"临时文件删除失败"}
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# 返回图片地址
|
||
return {"status":True, "reason":target_path}
|
||
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||
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# 甲烷预测
|
||
# def start_predict_endpoint(ch4_model_flow,ch4_model_gas,data_path, start_index, end_index, type, is_show):
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||
# try:
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||
# data = pd.read_csv(data_path)
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||
# data['date_time'] = pd.to_datetime(data['date_time'])
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||
# max_date = data['date_time'].max()
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||
# min_date = data['date_time'].min()
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||
# start_index = pd.to_datetime(start_index)
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||
# end_index = pd.to_datetime(end_index)
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||
# if max_date < end_index :
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# return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
# if min_date > start_index :
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||
# return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
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||
# end_index_dt = pd.to_datetime(end_index)
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||
# end_index_plus_one_hour = end_index_dt + pd.Timedelta(hours=1)
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||
# filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
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||
# if is_show:
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||
# if len(filtered_data) < 96:
|
||
# return {"reason": "日期填写错误，截取步长应该超过96个步长","status": False}
|
||
# if max_date < end_index_plus_one_hour:
|
||
# return {"reason": "选择显示真实值，需要保留最终后四节点作为展示信息，请调整结束日期","status": False}
|
||
# else:
|
||
# if len(filtered_data) < 96:
|
||
# return {"reason": "上传文件中有效信息长度应大于96","status": False}
|
||
|
||
# train_data = prepare_data.get_pred_data(data,start_index,end_index)
|
||
# del train_data['date_time']
|
||
# train_data = np.array(train_data.values)
|
||
# train_data = xgb.DMatrix(train_data)
|
||
# target = None
|
||
# if type == 1: # 流量
|
||
# target = "Nm3d-1-ch4"
|
||
# result = ch4_model_flow.predict(train_data)
|
||
# else: # 气相
|
||
# target = "S_gas_ch4"
|
||
# result = ch4_model_gas.predict(train_data)
|
||
# if is_show:
|
||
# history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index_plus_one_hour)]
|
||
# cols = ['date_time']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# total_rows = len(history)
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# history.loc[total_rows - 4:total_rows - 1, 'pred_data'] = result[0]
|
||
# else:
|
||
# history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# cols = ['date_time']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# last_date = history['date_time'].iloc[-1]
|
||
# # 创建新的日期和对应的值
|
||
# new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(4)]
|
||
# new_data = pd.DataFrame({
|
||
# 'date_time': new_dates,
|
||
# 'true_data':[0,0,0,0],
|
||
# 'pred_data': result[0]
|
||
# })
|
||
# history = pd.concat([history, new_data], ignore_index=True)
|
||
|
||
# return {"status": True,"reason":history.to_dict(orient='records')}
|
||
# except Exception as e:
|
||
# return{"reason": str(e),"status":False}
|
||
|
||
|
||
def start_predict_endpoint(ch4_model_flow,ch4_model_gas,data_path, start_index, end_index, type, is_show):
|
||
try:
|
||
data = pd.read_csv(data_path)
|
||
data['date_time'] = pd.to_datetime(data['date_time'])
|
||
max_date = data['date_time'].max()
|
||
min_date = data['date_time'].min()
|
||
start_index = pd.to_datetime(start_index)
|
||
end_index = pd.to_datetime(end_index)
|
||
if max_date < end_index :
|
||
return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
if min_date > start_index :
|
||
return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
|
||
end_index_dt = pd.to_datetime(end_index)
|
||
end_index_plus_one_hour = end_index_dt + pd.Timedelta(hours=1)
|
||
filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
if is_show:
|
||
if len(filtered_data) < 96:
|
||
return {"reason": "日期填写错误，截取步长应该超过96个步长","status": False}
|
||
if max_date < end_index_plus_one_hour:
|
||
return {"reason": "选择显示真实值，需要保留最终后四节点作为展示信息，请调整结束日期","status": False}
|
||
else:
|
||
if len(filtered_data) < 96:
|
||
return {"reason": "上传文件中有效信息长度应大于96","status": False}
|
||
|
||
train_data = prepare_data.get_pred_data(data,start_index,end_index)
|
||
del train_data['date_time']
|
||
train_data = np.array(train_data.values)
|
||
train_data = xgb.DMatrix(train_data)
|
||
target = None
|
||
if type == 1: # 流量
|
||
target = "Nm3d-1-ch4"
|
||
result = ch4_model_flow.predict(train_data)
|
||
else: # 气相
|
||
target = "S_gas_ch4"
|
||
result = ch4_model_gas.predict(train_data)
|
||
if is_show:
|
||
"""两字典，历史+真实，预测"""
|
||
history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index_plus_one_hour)]
|
||
cols = ['date_time']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
last_date = history['date_time'].iloc[-5]
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(4)]
|
||
new_data = pd.DataFrame({
|
||
'date_time': new_dates,
|
||
target: result[0]
|
||
})
|
||
|
||
else:
|
||
history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
history.reset_index(drop=True, inplace=True)
|
||
cols = ['date_time']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
|
||
last_date = history['date_time'].iloc[-1]
|
||
# 创建新的日期和对应的值
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(4)]
|
||
new_data = pd.DataFrame({
|
||
'date_time': new_dates,
|
||
target: result[0]
|
||
})
|
||
|
||
|
||
return {"status": True,"reason":[history.to_dict(orient='records'),new_data.to_dict(orient='records')]}
|
||
except Exception as e:
|
||
return{"reason": str(e),"status":False}
|
||
|
||
|
||
|
||
# 光伏出力预测 - 预测长度96
|
||
|
||
def start_pvelectric_predict_endpoint(pvfd_model,data_path, start_index, end_index, is_show):
|
||
try:
|
||
data = pd.read_csv(data_path)
|
||
data['date_time'] = pd.to_datetime(data['date_time'])
|
||
max_date = data['date_time'].max()
|
||
min_date = data['date_time'].min()
|
||
start_index = pd.to_datetime(start_index)
|
||
end_index = pd.to_datetime(end_index)
|
||
if max_date < end_index :
|
||
return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
if min_date > start_index :
|
||
return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
|
||
end_index_dt = pd.to_datetime(end_index)
|
||
end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=24)
|
||
filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
if is_show:
|
||
if len(filtered_data) < 192:
|
||
return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False}
|
||
if max_date < end_index_plus_one_day:
|
||
return {"reason": "选择显示真实值，需要保留最终一天数据96点作为展示信息，请调整结束日期","status": False}
|
||
else:
|
||
if len(filtered_data) < 192:
|
||
return {"reason": "上传文件中有效信息长度应大于192","status": False}
|
||
|
||
predictions = pvfd_model.run_inference(filtered_data)
|
||
if predictions['status'] == True:
|
||
predictions_value = np.array(predictions['reason']).flatten()
|
||
predictions_value = [max(0, x) for x in predictions_value]
|
||
target = "power"
|
||
if is_show:
|
||
history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index_plus_one_day)]
|
||
cols = ['date_time']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
last_date = history['date_time'].iloc[-97]
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
|
||
new_data = pd.DataFrame({
|
||
'date_time': new_dates,
|
||
target: predictions_value
|
||
})
|
||
else:
|
||
history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
history.reset_index(drop=True, inplace=True)
|
||
cols = ['date_time']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
last_date = history['date_time'].iloc[-1]
|
||
# 创建新的日期和对应的值
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
|
||
new_data = pd.DataFrame({
|
||
'date_time': new_dates,
|
||
target: predictions_value
|
||
})
|
||
|
||
return {"status": True,"reason":[history.to_dict(orient='records'),new_data.to_dict(orient='records')]}
|
||
except Exception as e:
|
||
return{"reason": str(e),"status":False}
|
||
# def start_pvelectric_predict_endpoint(pvfd_model,data_path, start_index, end_index, is_show):
|
||
# try:
|
||
# data = pd.read_csv(data_path)
|
||
# data['date_time'] = pd.to_datetime(data['date_time'])
|
||
# max_date = data['date_time'].max()
|
||
# min_date = data['date_time'].min()
|
||
# start_index = pd.to_datetime(start_index)
|
||
# end_index = pd.to_datetime(end_index)
|
||
# if max_date < end_index :
|
||
# return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
# if min_date > start_index :
|
||
# return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
|
||
# end_index_dt = pd.to_datetime(end_index)
|
||
# end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=24)
|
||
# filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
# if is_show:
|
||
# if len(filtered_data) < 192:
|
||
# return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False}
|
||
# if max_date < end_index_plus_one_day:
|
||
# return {"reason": "选择显示真实值，需要保留最终一天数据96点作为展示信息，请调整结束日期","status": False}
|
||
# else:
|
||
# if len(filtered_data) < 192:
|
||
# return {"reason": "上传文件中有效信息长度应大于192","status": False}
|
||
|
||
# predictions = pvfd_model.run_inference(filtered_data)
|
||
# if predictions['status'] == True:
|
||
# predictions_value = np.array(predictions['reason']).flatten()
|
||
# predictions_value = [max(0, x) for x in predictions_value]
|
||
# target = "power"
|
||
# if is_show:
|
||
# history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index_plus_one_day)]
|
||
# cols = ['date_time']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# total_rows = len(history)
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# history.loc[total_rows - 96:total_rows - 1, 'pred_data'] = predictions_value
|
||
# else:
|
||
# history = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# cols = ['date_time']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# last_date = history['date_time'].iloc[-1]
|
||
# # 创建新的日期和对应的值
|
||
# new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
|
||
# new_data = pd.DataFrame({
|
||
# 'date_time': new_dates,
|
||
# 'true_data':[0]*96,
|
||
# 'pred_data': predictions_value
|
||
# })
|
||
# history = pd.concat([history, new_data], ignore_index=True)
|
||
|
||
# return {"status": True,"reason":history.to_dict(orient='records')}
|
||
# except Exception as e:
|
||
# return{"reason": str(e),"status":False}
|
||
|
||
|
||
|
||
# 风力发电出力预测 - 预测长度12
|
||
def start_wind_electric_predict_endpoint(windfd_model,data_path, start_index, end_index, is_show):
|
||
# try:
|
||
data = pd.read_csv(data_path)
|
||
data['date'] = pd.to_datetime(data['date'])
|
||
max_date = data['date'].max()
|
||
min_date = data['date'].min()
|
||
start_index = pd.to_datetime(start_index)
|
||
end_index = pd.to_datetime(end_index)
|
||
if max_date < end_index :
|
||
return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
if min_date > start_index :
|
||
return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
|
||
end_index_dt = pd.to_datetime(end_index)
|
||
end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=3)
|
||
filtered_data = data[(data['date'] >= start_index) & (data['date'] <= end_index)]
|
||
if is_show:
|
||
if len(filtered_data) < 192:
|
||
return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False}
|
||
if max_date < end_index_plus_one_day:
|
||
return {"reason": "选择显示真实值，需要保留最终一天数据12个点作为展示信息，请调整结束日期","status": False}
|
||
else:
|
||
if len(filtered_data) < 192:
|
||
return {"reason": "上传文件中有效信息长度应大于192","status": False}
|
||
|
||
predictions = windfd_model.run_inference(filtered_data)
|
||
if predictions['status'] == True:
|
||
predictions_value = np.array(predictions['reason']).flatten()
|
||
predictions_value = [max(0, x) for x in predictions_value]
|
||
target = "Power"
|
||
if is_show:
|
||
history = data[(data['date'] >= start_index) & (data['date'] <= end_index_plus_one_day)]
|
||
cols = ['date']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
last_date = history['date'].iloc[-13]
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(12)]
|
||
new_data = pd.DataFrame({
|
||
'date': new_dates,
|
||
target: predictions_value
|
||
})
|
||
else:
|
||
history = data[(data['date'] >= start_index) & (data['date'] <= end_index)]
|
||
history.reset_index(drop=True, inplace=True)
|
||
cols = ['date']
|
||
cols.append(target)
|
||
history = history[cols]
|
||
last_date = history['date'].iloc[-1]
|
||
# 创建新的日期和对应的值
|
||
new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(12)]
|
||
new_data = pd.DataFrame({
|
||
'date': new_dates,
|
||
target: predictions_value
|
||
})
|
||
history.rename(columns={target: 'power'}, inplace=True)
|
||
new_data.rename(columns={target: 'power'}, inplace=True)
|
||
return {"status": True,"reason":[history.to_dict(orient='records'),new_data.to_dict(orient='records')]}
|
||
# except Exception as e:
|
||
# return{"reason": str(e),"status":False}
|
||
# def start_wind_electric_predict_endpoint(windfd_model,data_path, start_index, end_index, is_show):
|
||
# # try:
|
||
# data = pd.read_csv(data_path)
|
||
# data['date'] = pd.to_datetime(data['date'])
|
||
# max_date = data['date'].max()
|
||
# min_date = data['date'].min()
|
||
# start_index = pd.to_datetime(start_index)
|
||
# end_index = pd.to_datetime(end_index)
|
||
# if max_date < end_index :
|
||
# return {"reason": "结束日期填写错误，超过上传数据最大日期","status": False}
|
||
# if min_date > start_index :
|
||
# return {"reason": "开始日期填写错误，小于上传数据最小日期","status": False}
|
||
# end_index_dt = pd.to_datetime(end_index)
|
||
# end_index_plus_one_day = end_index_dt + pd.Timedelta(hours=3)
|
||
# filtered_data = data[(data['date'] >= start_index) & (data['date'] <= end_index)]
|
||
# if is_show:
|
||
# if len(filtered_data) < 192:
|
||
# return {"reason": "日期填写错误，截取步长应该超过192个步长","status": False}
|
||
# if max_date < end_index_plus_one_day:
|
||
# return {"reason": "选择显示真实值，需要保留最终一天数据12个点作为展示信息，请调整结束日期","status": False}
|
||
# else:
|
||
# if len(filtered_data) < 192:
|
||
# return {"reason": "上传文件中有效信息长度应大于192","status": False}
|
||
|
||
# predictions = windfd_model.run_inference(filtered_data)
|
||
# if predictions['status'] == True:
|
||
# predictions_value = np.array(predictions['reason']).flatten()
|
||
# predictions_value = [max(0, x) for x in predictions_value]
|
||
# target = "Power"
|
||
# if is_show:
|
||
# history = data[(data['date'] >= start_index) & (data['date'] <= end_index_plus_one_day)]
|
||
# cols = ['date']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# total_rows = len(history)
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# history.loc[total_rows - 12:total_rows - 1, 'pred_data'] = predictions_value
|
||
# else:
|
||
# history = data[(data['date'] >= start_index) & (data['date'] <= end_index)]
|
||
# history.reset_index(drop=True, inplace=True)
|
||
# cols = ['date']
|
||
# cols.append(target)
|
||
# history = history[cols]
|
||
# history.rename(columns={target: 'true_data'}, inplace=True)
|
||
# history['pred_data'] = 0
|
||
# last_date = history['date'].iloc[-1]
|
||
# # 创建新的日期和对应的值
|
||
# new_dates = [last_date + pd.Timedelta(minutes=15 * (i + 1)) for i in range(12)]
|
||
# new_data = pd.DataFrame({
|
||
# 'date': new_dates,
|
||
# 'true_data':[0]*12,
|
||
# 'pred_data': predictions_value
|
||
# })
|
||
# history = pd.concat([history, new_data], ignore_index=True)
|
||
|
||
# return {"status": True,"reason":history.to_dict(orient='records')}
|
||
# # except Exception as e:
|
||
# # return{"reason": str(e),"status":False}
|
||
|
||
|
||
|
||
def pred_single_tar(test_content):
|
||
tar_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meirejie_model_list_tar:
|
||
model_path = os.path.join(current_directory,"meirejie",param.meirejie_model_dict[name])
|
||
tar_model = load(model_path)
|
||
pred = tar_model.predict(test_content)
|
||
tar_pred.append(pred[0])
|
||
result = [param.meirejie_tar_mae, param.meirejie_tar_r2,tar_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
|
||
|
||
|
||
def pred_single_gas(test_content):
|
||
gas_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meirejie_model_list_gas:
|
||
model_path = os.path.join(current_directory,"meirejie",param.meirejie_model_dict[name])
|
||
gas_model = load(model_path)
|
||
pred = gas_model.predict(test_content)
|
||
gas_pred.append(pred[0])
|
||
result = [param.meirejie_gas_mae, param.meirejie_gas_r2,gas_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
def pred_single_water(test_content):
|
||
water_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meirejie_model_list_water:
|
||
model_path = os.path.join(current_directory,"meirejie",param.meirejie_model_dict[name])
|
||
# print(model_path)
|
||
water_model = load(model_path)
|
||
pred = water_model.predict(test_content)
|
||
water_pred.append(pred[0])
|
||
result = [param.meirejie_water_mae, param.meirejie_water_r2,water_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
def pred_single_char(test_content):
|
||
char_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meirejie_model_list_char:
|
||
model_path = os.path.join(current_directory,"meirejie",param.meirejie_model_dict[name])
|
||
char_model = load(model_path)
|
||
pred = char_model.predict(test_content)
|
||
char_pred.append(pred[0])
|
||
result = [param.meirejie_char_mae, param.meirejie_char_r2,char_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
|
||
|
||
def choose_model_meirejie(name,data):
|
||
current_directory = os.getcwd()
|
||
model_path = os.path.join(current_directory,'meirejie',param.meirejie_model_dict[name])
|
||
model = load(model_path)
|
||
pred = model.predict(data)
|
||
return pred
|
||
|
||
def get_excel_tar(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Tar"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
del test_data['Tar']
|
||
pred = choose_model_meirejie(model_name,test_data)
|
||
test_data['tar_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
|
||
def get_excel_gas(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Gas"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
del test_data['Gas']
|
||
pred = choose_model_meirejie(model_name,test_data)
|
||
test_data['gas_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
def get_excel_char(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Char"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
del test_data['Char']
|
||
pred = choose_model_meirejie(model_name,test_data)
|
||
test_data['char_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
def get_excel_water(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "V", "FC", "C", "H", "N" ,"S" ,"O" ,"H/C" ,"O/C" ,"N/C" ,"Rt" ,"Hr", "dp","T","Water"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
del test_data['Water']
|
||
pred = choose_model_meirejie(model_name,test_data)
|
||
test_data['water_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
|
||
|
||
|
||
#==========================================煤基炭材料====================================================
|
||
|
||
def pred_single_ssa(test_content):
|
||
char_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meijitancailiao_model_list_ssa:
|
||
model_path = os.path.join(current_directory,"meijitancailiao",param.meijitancailiao_model_dict[name])
|
||
ssa_model = load(model_path)
|
||
pred = ssa_model.predict(test_content)
|
||
char_pred.append(pred[0])
|
||
result = [param.meijitancailiao_ssa_mae, param.meijitancailiao_ssa_r2,char_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns_ssa)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
|
||
def pred_single_tpv(test_content):
|
||
char_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meijitancailiao_model_list_tpv:
|
||
model_path = os.path.join(current_directory,"meijitancailiao",param.meijitancailiao_model_dict[name])
|
||
tpv_model = load(model_path)
|
||
pred = tpv_model.predict(test_content)
|
||
char_pred.append(pred[0])
|
||
result = [param.meijitancailiao_tpv_mae, param.meijitancailiao_tpv_r2,char_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns_tpv)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
def pred_single_meitan(test_content):
|
||
char_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meijitancailiao_model_list_meitan:
|
||
model_path = os.path.join(current_directory,"meijitancailiao",param.meijitancailiao_model_dict[name])
|
||
meitan_model = load(model_path)
|
||
pred = meitan_model.predict(test_content)
|
||
char_pred.append(pred[0])
|
||
result = [param.meijitancailiao_meitan_mae, param.meijitancailiao_meitan_r2,char_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns_meitan)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
def pred_single_meiliqing(test_content):
|
||
char_pred = []
|
||
current_directory = os.getcwd()
|
||
for name in param.meijitancailiao_model_list_meiliqing:
|
||
model_path = os.path.join(current_directory,"meijitancailiao",param.meijitancailiao_model_dict[name])
|
||
meiliqing_model = load(model_path)
|
||
pred = meiliqing_model.predict(test_content)
|
||
char_pred.append(pred[0])
|
||
result = [param.meijitancailiao_meiliqing_mae, param.meijitancailiao_meiliqing_r2,char_pred]
|
||
# 创建 DataFrame
|
||
df = pd.DataFrame(result, index=param.index, columns=param.columns_meiliqing)
|
||
result = df.to_json(orient='index')
|
||
return result
|
||
|
||
def choose_model_meijitancailiao(name,data):
|
||
current_directory = os.getcwd()
|
||
model_path = os.path.join(current_directory,'meijitancailiao',param.meijitancailiao_model_dict[name])
|
||
model = load(model_path)
|
||
pred = model.predict(data)
|
||
return pred
|
||
|
||
|
||
def get_excel_ssa(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "VM", "K/C", "MM", "AT", "At", "Rt", "SSA"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
del test_data['SSA']
|
||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||
test_data['ssa_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
def get_excel_tpv(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["A", "VM", "K/C", "MM", "AT", "At", "Rt", "TPV"]
|
||
if list(test_data.columns) != expected_columns:
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
del test_data['TPV']
|
||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||
test_data['tpv_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
|
||
def get_excel_meitan(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
# raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["SSA", "TPV", "N", "O", "ID/IG", "J","C"]
|
||
if list(test_data.columns) != expected_columns:
|
||
# raise HTTPException(status_code=400, detail=f"文件列名不匹配，预期列名为: {expected_columns}")
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
del test_data['C']
|
||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||
test_data['C_pred'] = pred
|
||
# return test_data.to_dict(orient='records')
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
def get_excel_meiliqing(model_name,file_path):
|
||
if not file_path.endswith('.csv'):
|
||
return {"status":False, "reason":"上传文件类型有误，必须是 CSV 文件"}
|
||
test_data = pd.read_csv(file_path)
|
||
expected_columns = ["SSA", "TPV", "N", "O", "ID/IG", "J", "C"]
|
||
if list(test_data.columns) != expected_columns:
|
||
return {"status":False, "reason":f"文件列名不匹配，预期列名为: {expected_columns}"}
|
||
del test_data['C']
|
||
pred = choose_model_meijitancailiao(model_name,test_data)
|
||
test_data['C_pred'] = pred
|
||
return {"status":True, "reason":test_data.to_dict(orient='records')}
|
||
|
||
|
||
def pred_func(func_name, pred_data):
|
||
current_script_directory = os.getcwd()
|
||
|
||
ssa_model_path = os.path.join(current_script_directory,"meijitancailiao",param.simu_model_dict[func_name][0])
|
||
tpv_model_path = os.path.join(current_script_directory,"meijitancailiao",param.simu_model_dict[func_name][1])
|
||
|
||
|
||
ssa_model = load(ssa_model_path)
|
||
tpv_model = load(tpv_model_path)
|
||
# 使用模型预测
|
||
pred_data = pd.DataFrame(pred_data,columns=['A','VM','K/C','MM','AT','At','Rt'])
|
||
pred_ssa = ssa_model.predict(pred_data)
|
||
pred_tpv = tpv_model.predict(pred_data)
|
||
|
||
result = pd.DataFrame({
|
||
"SSA":pred_ssa,
|
||
"TPV":pred_tpv
|
||
})
|
||
|
||
result = pd.concat([pred_data,result], axis=1)
|
||
|
||
|
||
return result
|
||
|
||
|
||
|
||
def get_pic_url(path):
|
||
# 1. 移除本地根目录 `/root/app`
|
||
logger.info(path)
|
||
relative_path = path.replace("/root/app", "", 1)
|
||
# 2. 拼接服务器地址和端口
|
||
server = "http://124.16.151.196:13432"
|
||
# 3. 组合成完整 URL
|
||
url = f"{server}/files{relative_path}"
|
||
return url
|
||
|
||
def get_pic_path(url):
|
||
# 1. 移除协议和服务器地址
|
||
path_without_server = url.replace("http://124.16.151.196:13432", "")
|
||
|
||
# 2. 移除 /files 前缀
|
||
relative_path = path_without_server.replace("/files", "", 1)
|
||
|
||
# 3. 添加本地根目录
|
||
local_path = f"/root/app{relative_path}"
|
||
return local_path
|
||
|
||
|
||
|