452 lines
18 KiB
Python
452 lines
18 KiB
Python
import numpy as np
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import pandas as pd
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import copy
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import cv2 as cv
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from tqdm import tqdm
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import os
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import torch
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import torch.nn.functional as F
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from PIL import Image
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import uuid
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from pathlib import Path
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import shutil
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from work_util.logger import logger
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from work_util import params
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import itertools
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################################################### 图像类函数调用###########################################################################
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"""将图像按512*512进行裁剪"""
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def cut_big_image(path):
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file_name = os.path.basename(path)
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file_directory = os.path.dirname(path)
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output_floder_normal = os.path.join(file_directory,'ori')
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# 创建输出文件夹（如果不存在）
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os.makedirs(output_floder_normal, exist_ok=True)
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# 打开图像
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image = Image.open(path)
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width, height = image.size
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# 定义块的大小
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block_size = 512
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if (width < block_size) or (height < block_size):
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return False
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file_size = os.path.getsize(path)
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# 将最大大小转换为字节
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max_size_mb = 100
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max_size_bytes = max_size_mb * 1024 * 1024 # 10MB = 10 * 1024 * 1024 bytes
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# 判断文件大小是否超过最大限制
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if file_size > max_size_bytes:
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return False
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# 计算需要的块数
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num_blocks_x = (width + block_size - 1) // block_size # 向上取整
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num_blocks_y = (height + block_size - 1) // block_size # 向上取整
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# 裁剪并保存图像块
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for i in range(num_blocks_x):
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for j in range(num_blocks_y):
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# 计算裁剪区域
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left = i * block_size
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upper = j * block_size
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right = min(left + block_size, width)
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lower = min(upper + block_size, height)
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# 裁剪图像
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block = image.crop((left, upper, right, lower))
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# 创建一个新的512x512白色图像
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new_block = Image.new('RGB', (block_size, block_size), (255, 255, 255))
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# 将裁剪的图像粘贴到白色图像上
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new_block.paste(block, (0, 0))
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# 保存图像块
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block_filename = f'block_{i}_{j}.png'
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new_block.save(os.path.join(output_floder_normal, block_filename))
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logger.info("裁剪完成，图像块已保存。")
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return True
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"""屋顶光伏的图像预处理"""
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def roof_pv_preprocess_input(image, md=False):
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mean = (0.231, 0.217, 0.22)
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std = (0.104, 0.086, 0.085)
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if md:
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image /= 255.0
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image -= mean
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image /= std
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return image
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else:
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image /= 255.0
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return image
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"""用于图像合并"""
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def merge_pic_binary(path, tpye_list):
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try:
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image = Image.open(path)
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width, height = image.size
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file_name = os.path.basename(path)
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file_directory = os.path.dirname(path)
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path_list = []
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for name in tpye_list:
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file_name_ = "merge_" +name+ "_" + file_name
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input_folder = os.path.join(file_directory,name)
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# 获取所有小图片文件名
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image_files = [f for f in os.listdir(input_folder) if f.endswith('.png')]
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image_files.sort() # 按文件名排序，确保按顺序合并
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# 假设小图的尺寸是512x512
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block_size = 512
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# 计算需要的块数
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num_blocks_x = (width + block_size - 1) // block_size # 向上取整
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num_blocks_y = (height + block_size - 1) // block_size # 向上取整
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# 创建一个新的空白图像，用于合并块
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merged_image = Image.new('RGB', (width, height))
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# 遍历所有块并将它们粘贴到合并图像中
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for i in range(num_blocks_x):
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for j in range(num_blocks_y):
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# 计算块的文件名
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block_filename = f'block_{i}_{j}.png'
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block_path = os.path.join(input_folder, block_filename)
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logger.info(block_path)
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# 打开块图像
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if os.path.exists(block_path):
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block = Image.open(block_path)
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# 计算粘贴位置
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left = i * block_size
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upper = j * block_size
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merged_image.paste(block, (left, upper))
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# 保存合并后的图像
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target = os.path.join(file_directory,file_name_)
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merged_image.save(target) # 替换为你想保存的路径
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path_list.append(target)
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logger.info("合并完成，保存为:%s", target)
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return {"status":True , "path": path_list}
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except Exception as e:
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logger.error("发生错误:", e)
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return {"status":True , "path": None}
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"""删除文件夹"""
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def delete_folder(folder_path):
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try:
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if os.path.exists(folder_path):
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shutil.rmtree(folder_path)
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return True
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else:
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return False
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except Exception as e:
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logger.error(f"删除文件夹时发生错误: {e}")
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return False
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# 将二值图与原始图合并
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def merge_final(path_list):
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ori_path = path_list[1] # 原始图像
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binary_path = path_list[0] # 二值图像
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# 加载图像并确保它们是RGBA模式（如果需要透明度）
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ori_image = Image.open(ori_path).convert('RGBA')
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binary_image = Image.open(binary_path).convert('RGBA')
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# 确保两张图像的大小相同
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if ori_image.size != binary_image.size:
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raise ValueError("两张图像的尺寸必须相同！")
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# 设置混合比例，0.5表示两张图像各占50%
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alpha = 0.4
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# 融合图像
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blended_image = Image.blend(ori_image, binary_image, alpha)
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file_directory = os.path.dirname(ori_path)
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file_name = os.path.basename(ori_path)
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final_path = os.path.join(file_directory,'fusion_'+file_name)
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# 保存融合后的图像
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blended_image.save(final_path) # 替换为你想保存的路径
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def replace_substring(filename, old_substring, new_substring):
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# 使用 replace 方法进行替换
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new_filename = filename.replace(old_substring, new_substring)
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return new_filename
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# 将屋顶与光伏的二值图进行合并
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def merge_binary(path_list):
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# 打开二值图像
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image1_path = path_list[1] # 替换为你的屋顶图像路径
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image2_path = path_list[0] # 替换为你的光伏图像路径
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file_directory = os.path.dirname(image1_path)
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# 加载图像并确保它们是RGB模式
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image1 = Image.open(image1_path).convert('RGB')
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image2 = Image.open(image2_path).convert('RGB')
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# 创建一个新的图像，RGB模式，初始为黑色
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output_image = Image.new('RGB', image1.size, (0, 0, 0))
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# 获取像素数据
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pixels1 = image1.load()
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pixels2 = image2.load()
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output_pixels = output_image.load()
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# 遍历所有像素点
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for x in range(image1.width):
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for y in range(image1.height):
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# 检查两个图像在当前像素点上的颜色
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if pixels1[x, y] == (255, 0, 0) and pixels2[x, y] == (255, 0, 0): # 红色
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output_pixels[x, y] = (255, 0, 0) # 设置为红色
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else:
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output_pixels[x, y] = (0, 0, 0) # 设置为黑色
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# 保存输出图像
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old_substring = "_pv_"
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new_substring = "_roofpv_"
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new_filename = replace_substring(path_list[1], old_substring, new_substring)
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final_path = os.path.join(file_directory, new_filename)
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output_image.save(final_path) # 替换为你想保存的路径
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return final_path
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def show_data_pvfd(col):
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current_directory = os.getcwd()
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train_data_path = os.path.join(current_directory,"guangfufadian","datasets","station08.csv")
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data = pd.read_csv(train_data_path)
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# print(data.columns)
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cols = ['date_time']
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cols.append(col)
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# print(cols)
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result = data[cols]
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result = result.iloc[23362:23553]
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json_result = result.to_json(orient='records', lines=True)
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return json_result
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def show_testdata_pvfd(test_data_path):
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data = pd.read_csv(test_data_path)
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json_result = data.to_json(orient='records', lines=True)
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return json_result
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#预测数据补全
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def data_complement(data):
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current_length = len(data)
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target_length = 192
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rows_to_add = target_length - current_length
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first_row = data.iloc[0].copy()
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new_date_times = [first_row['date_time'] - pd.Timedelta(minutes=15 * (i + 1)) for i in range(rows_to_add)]
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new_rows = pd.DataFrame({
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'date_time': new_date_times[::-1], # 反转顺序，使得时间从最早到最晚
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'power': [first_row['power']] * rows_to_add # 复制 'Power' 列的值
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})
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df = pd.concat([new_rows, data], ignore_index=True)
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return df
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#模型结果输出与真实值合并,返回结果到前端
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def result_merge_guangfufadian(data_path, predictions):
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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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length = data.shape[0]
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if length >= 192+96:
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data = data.loc[-(192+96):]
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true_data = data[['date_time', 'power']]
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pred_time = np.array(true_data['date_time'].iloc[192:288])
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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elif length < 192:
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true_data = data[['date_time', 'power']]
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true_data = data_complement(true_data)
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last_row = data.iloc[-1].copy()
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pred_time = [last_row['date_time'] + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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else : # 小于192
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true_data = data[['date_time', 'power']]
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last_row = data.iloc[191].copy()
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pred_time = [last_row['date_time'] + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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pred_data['pred_power'] = pred_data['pred_power'].clip(lower=0)
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pred_data = pred_data.to_json(orient='records')
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true_data = true_data.to_json(orient='records')
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return pred_data, true_data
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""" 风力预测发电 """
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def show_data_windfd(col):
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current_directory = os.getcwd()
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train_data_path = os.path.join(current_directory,"fenglifadian","datasets","Wind_farm.csv")
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data = pd.read_csv(train_data_path)
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# print(data.columns)
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cols = ['date']
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cols.append(col)
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# print(cols)
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result = data[cols]
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result = result.iloc[21600:21600+96*2]
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json_result = result.to_json(orient='records', lines=True)
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return json_result
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def show_testdata_windfd(test_data_path):
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data = pd.read_csv(test_data_path)
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json_result = data.to_json(orient='records', lines=True)
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return json_result
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#模型结果输出与真实值合并,返回结果到前端
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def result_merge_fenglifadian(data_path, predictions):
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data = pd.read_csv(data_path)
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data['date'] = pd.to_datetime(data['date'])
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length = data.shape[0]
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if length >= 192+12:
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data = data.loc[-(192+12):]
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true_data = data[['date', 'Power(MW)']]
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pred_time = np.array(true_data['date'].iloc[192:192+12])
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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elif length < 192:
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true_data = data[['date', 'Power(MW)']]
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true_data = data_complement(true_data)
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last_row = data.iloc[-1].copy()
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pred_time = [last_row['date'] + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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else :
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true_data = data[['date', 'Power(MW)']]
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last_row = data.iloc[191].copy()
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pred_time = [last_row['date'] + pd.Timedelta(minutes=15 * (i + 1)) for i in range(96)]
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pred_data = pd.DataFrame({
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'date_time': pred_time,
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'pred_power': predictions
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})
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pred_data = pred_data.to_json(orient='records')
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true_data = true_data.to_json(orient='records')
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return pred_data, true_data
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def show_data_jiawanyuce(col):
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current_directory = os.getcwd()
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train_data_path = os.path.join(current_directory,"tmp","jiawanyuce","jiawan_test.csv")
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data = pd.read_csv(train_data_path)
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# print(data.columns)
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cols = ['date_time']
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cols.append(col)
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# print(cols)
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result = data[cols]
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result = result.iloc[:192]
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json_result = result.to_json(orient='records', lines=True)
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return json_result
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def data_feature(data,x_cols):
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for name in x_cols:
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data[name+"_15_first"] = data[name].shift(1)
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data[name+"_30_first"] = data[name].shift(2)
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data[name+"_45_first"] = data[name].shift(3)
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data[name+"_60_first"] = data[name].shift(4)
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data[name+"_1h_mean"] = data[name].rolling(4).mean()
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data[name+"_1h_max"] = data[name].rolling(4).max()
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data[name+"_1h_min"] = data[name].rolling(4).min()
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data[name+"_1h_median"] = data[name].rolling(4).median()
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data[name+"_1h_std"] = data[name].rolling(4).std()
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data[name+"_1h_var"] = data[name].rolling(4).var()
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data[name+"_1h_skew"] = data[name].rolling(4).skew()
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data[name+"_1h_kurt"] = data[name].rolling(4).kurt()
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data[name+"_1_diff"] = data[name].diff(periods=1)
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data[name+"_2_diff"] = data[name].diff(periods=2)
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data[name+"_2h_mean"] = data[name].rolling(8).mean()
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data[name+"_2h_max"] = data[name].rolling(8).max()
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data[name+"_2h_min"] = data[name].rolling(8).min()
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data[name+"_2h_median"] = data[name].rolling(8).median()
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data[name+"_2h_std"] = data[name].rolling(8).std()
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data[name+"_2h_var"] = data[name].rolling(8).var()
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data[name+"_2h_skew"] = data[name].rolling(8).skew()
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data[name+"_2h_kurt"] = data[name].rolling(8).kurt()
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# 不想要日均的了，太长了
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for name in x_cols:
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data[name+"_d_mean"] = data[name].rolling(4*24).mean()
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data[name+"_d_max"] = data[name].rolling(4*24).max()
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data[name+"_d_min"] = data[name].rolling(4*24).min()
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data[name+"_d_median"] = data[name].rolling(4).median()
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data[name+"_d_std"] = data[name].rolling(4*24).std()
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data[name+"_d_var"] = data[name].rolling(4*24).var()
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data[name+"_d_skew"] = data[name].rolling(4*24).skew()
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data[name+"_d_kurt"] = data[name].rolling(4*24).kurt()
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return data
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def get_pred_data(data,start_index,end_index):
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data['date_time'] = pd.to_datetime(data['date_time'])
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filtered_data = data[(data['date_time'] >= start_index) & (data['date_time'] <= end_index)]
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columns= ['X_ch','X_pr','X_li','X_I','Q','pH','Nm3d-1-ch4','S_gas_ch4']
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data = data_feature(filtered_data,columns)
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# 只要最后一条数据进行预测，即可产生结果
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return data.iloc[-1:,:]
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def float_range(start, end, step):
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while start <= end:
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yield round(start, 10) # 使用 round 来避免浮点数精度问题
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start += step
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def get_params(params):
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# 灰分
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params['A_min'] = params['A_min'] if params['A_min'] is not None else 4
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params['A_max'] = params['A_max'] if params['A_max'] is not None else 48
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params['A_step'] = params['A_step'] if params['A_step'] is not None else 4
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# 挥发分
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params['VM_min'] = params['VM_min'] if params['VM_min'] is not None else 5
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params['VM_max'] = params['VM_max'] if params['VM_max'] is not None else 50
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params['VM_step'] = params['VM_step'] if params['VM_step'] is not None else 5
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# 活化剂比例
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params['KC_min'] = params['KC_min'] if params['KC_min'] is not None else 1
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params['KC_max'] = params['KC_max'] if params['KC_max'] is not None else 4
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params['KC_step'] = params['KC_step'] if params['KC_step'] is not None else 0.5
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# 混合方式
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params['MM_min'] = params['MM_min'] if params['MM_min'] is not None else 0
|
||
params['MM_max'] = params['MM_max'] if params['MM_max'] is not None else 1
|
||
params['MM_step'] = params['MM_step'] if params['MM_step'] is not None else 1
|
||
# 活化温度
|
||
params['AT_min'] = params['AT_min'] if params['AT_min'] is not None else 600
|
||
params['AT_max'] = params['AT_max'] if params['AT_max'] is not None else 900
|
||
params['AT_step'] = params['AT_step'] if params['AT_step'] is not None else 50
|
||
# 活化时间
|
||
params['At_min'] = params['At_min'] if params['At_min'] is not None else 0.5
|
||
params['At_max'] = params['At_max'] if params['At_max'] is not None else 2
|
||
params['At_step'] = params['At_step'] if params['At_step'] is not None else 0.5
|
||
# 升温速率
|
||
params['Rt_min'] = params['Rt_min'] if params['Rt_min'] is not None else 5
|
||
params['Rt_max'] = params['Rt_max'] if params['Rt_max'] is not None else 10
|
||
params['Rt_step'] = params['Rt_step'] if params['Rt_step'] is not None else 5
|
||
|
||
return params
|
||
|
||
|
||
def create_pred_data(params):
|
||
sequence_A = list(float_range(params['A_min'], params['A_max'] , params['A_step']))
|
||
sequence_VM = list(float_range(params['VM_min'], params['VM_max'], params['VM_step']))
|
||
sequence_KC = list(float_range(params['KC_min'], params['KC_max'], params['KC_step']))
|
||
sequence_MM = list(float_range(params['MM_min'], params['MM_max'], params['MM_step']))
|
||
sequence_AT = list(float_range(params['AT_min'], params['AT_max'], params['AT_step']))
|
||
sequence_At = list(float_range(params['At_min'], params['At_max'], params['At_step']))
|
||
sequence_Rt = list(float_range(params['Rt_min'], params['Rt_max'], params['Rt_step']))
|
||
|
||
training_samples = list(itertools.product(sequence_A, sequence_VM, sequence_KC, sequence_MM, sequence_AT, sequence_At, sequence_Rt))
|
||
return training_samples |