1. python 打包图片资源文件和逻辑文件，以及桌面应用图标
pyinstaller -F ae_awb_tool.py -p png_resource.py -i 1.ico -w2. 完整代码如下。其作用是调用MTK 研发的图片信息dump工具，对图片AE 和AWB 进行分析归类到所属的目录中，并统计目录下文件个数，填充到最后一级文件夹名称开头。
3. 将图片文件转换为 py文件的脚本
import base64
import tkinter.messagebox as msgbox
def picture_to_py(pic_name):
convert_pic_past = open("%s" % pic_name, 'rb') # 这里传入要转换的图片
base64_convert_str = base64.b64encode(convert_pic_past.read()) # base64读取(字符串）
convert_pic_past.close() # 关闭读取
convert_write_data = 'img = "%s"' % base64_convert_str.decode() # 处理
a = open('%s.py' % pic_name.replace('.', '_'), 'w+')
a.write(convert_write_data)
a.close()
if __name__ == '__main__':
picture = ["PCBico.png"] # 写入你要转换的文件名，建议把该代码保存到与转换图片相同路径下
for x in picture:
picture_to_py(x)
msgbox.showinfo("tip", "convert successfully.")
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版权声明：本文为CSDN博主「lagesan」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/lagesan/article/details/1284157884. 主逻辑脚本内容如下：
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import os,base64
from tkinter.messagebox import showinfo
import matplotlib.pyplot as plt
import numpy as np
import subprocess
import datetime
import re
from png_resource import img as img_pic
import tkinter as tk
from tkinter import filedialog
import shutil
starttime = datetime.datetime.now()
global debug_Tool
global photopath
def select_tool_dir():
global debug_Tool
debug_entry.set("")
debug_Tool = filedialog.askopenfile(initialdir=os.getcwd()).name
if debug_Tool.endswith("DebugParser.exe"):
debug_entry.set(debug_Tool)
if os.path.exists(os.getcwd()+"\default_path.txt") == False:
with open(os.getcwd() + "\default_path.txt", 'w+') as f_default_path:
f_default_path.close()
with open(os.getcwd()+"\default_path.txt", 'r+') as f_default_path:
lines = f_default_path.readlines()
file_contents = []
for line in lines:
if line.startswith("tool_deault_dir=") == False:
file_contents.append(line)
f_default_path.seek(0)
for content in file_contents:
f_default_path.write(content)
path_str = "tool_deault_dir=" + debug_Tool+'\n'
f_default_path.write(path_str)
f_default_path.close()
def select_picture_path():
global photopath
photo_entry.set("")
photopath = filedialog.askdirectory(initialdir=os.getcwd()) # 使用askdirectory函数选择文件夹
photopath += "/"
photo_entry.set(photopath)
if os.path.exists(os.getcwd()+"\default_path.txt") == False:
with open(os.getcwd() + "\default_path.txt", 'w+') as f_default_path:
f_default_path.close()
with open(os.getcwd() + "\default_path.txt", 'r+') as f_default_path:
lines = f_default_path.readlines()
file_contents = []
for line in lines:
if line.startswith("photo_deault_dir=") == False:
file_contents.append(line)
f_default_path.seek(0)
for content in file_contents:
f_default_path.write(content)
path_str = "photo_deault_dir=" + photopath+'\n'
f_default_path.write(path_str)
f_default_path.close()
def analyse(exifpath):
data = {
"name": str,
"AE_TAG_REALBVX1000": str,
"AE_TAG_HS_EVD": str,
"AE_TAG_FACE_STS_SIZE": str,
"AE_TAG_FLT_DR": str,
"AE_TAG_FLT_FDY": str
}
AE_TAG_REALBVX1000 = []
AE_TAG_HS_EVD = []
AE_TAG_FACE_STS_SIZE = []
AE_TAG_FLT_DR = []
AE_TAG_REALBVX1000_Normal = []
AE_TAG_HS_EVD_Normal = []
# exif 目录
result = {
# 无人脸
"bv -∞~-1500 & evd -∞~1000": [0, []],
"bv -∞~-1500 & evd 1000~4200": [0, []],
"bv -∞~-1500 & evd 4200~6300": [0, []],
"bv -∞~-1500 & evd 6300+": [0, []],
"bv -1500~500 & evd -∞~1000": [0, []],
"bv -1500~500 & evd 1000~4200": [0, []],
"bv -1500~500 & evd 4200~6300": [0, []],
"bv -1500~500 & evd 6300+": [0, []],
"bv 500~2500 & evd -∞~1000": [0, []],
"bv 500~2500 & evd 1000~4200": [0, []],
"bv 500~2500 & evd 4200~6300": [0, []],
"bv 500~2500 & evd 6300+": [0, []],
"bv 2500~5000 & evd -∞~1000": [0, []],
"bv 2500~5000 & evd 1000~4200": [0, []],
"bv 2500~5000 & evd 4200~6300": [0, []],
"bv 2500~5000 & evd 6300+": [0, []],
"bv 5000~8000 & evd -∞~1000": [0, []],
"bv 5000~8000 & evd 1000~4200": [0, []],
"bv 5000~8000 & evd 4200~6300": [0, []],
"bv 5000~8000 & evd 6300+": [0, []],
"bv 8000~10000 & evd -∞~1000": [0, []],
"bv 8000~10000 & evd 1000~4200": [0, []],
"bv 8000~10000 & evd 4200~6300": [0, []],
"bv 8000~10000 & evd 6300+": [0, []],
"bv 10000+ & evd -∞~1000": [0, []],
"bv 10000+ & evd 1000~4200": [0, []],
"bv 10000+ & evd 4200~6300": [0, []],
"bv 10000+ & evd 6300+": [0, []],
# 人脸
"bv -∞~-2000 & fdr -∞~0": [0, []],
"bv -∞~-2000 & fdr 0~1800": [0, []],
"bv -∞~-2000 & fdr 1800~2800": [0, []],
"bv -∞~-2000 & fdr 2800~4000": [0, []],
"bv -∞~-2000 & fdr 4000+": [0, []],
"bv -2000~0 & fdr -∞~0": [0, []],
"bv -2000~0 & fdr 0~1800": [0, []],
"bv -2000~0 & fdr 1800~2800": [0, []],
"bv -2000~0 & fdr 2800~4000": [0, []],
"bv -2000~0 & fdr 4000+": [0, []],
"bv 0~3000 & fdr -∞~0": [0, []],
"bv 0~3000 & fdr 0~1800": [0, []],
"bv 0~3000 & fdr 1800~2800": [0, []],
"bv 0~3000 & fdr 2800~4000": [0, []],
"bv 0~3000 & fdr 4000+": [0, []],
"bv 3000~6000 & fdr -∞~0": [0, []],
"bv 3000~6000 & fdr 0~1800": [0, []],
"bv 3000~6000 & fdr 1800~2800": [0, []],
"bv 3000~6000 & fdr 2800~4000": [0, []],
"bv 3000~6000 & fdr 4000+": [0, []],
"bv 6000~8500 & fdr -∞~0": [0, []],
"bv 6000~8500 & fdr 0~1800": [0, []],
"bv 6000~8500 & fdr 1800~2800": [0, []],
"bv 6000~8500 & fdr 2800~4000": [0, []],
"bv 6000~8500 & fdr 4000+": [0, []],
"bv 8500+ & fdr -∞~0": [0, []],
"bv 8500+ & fdr 0~1800": [0, []],
"bv 8500+ & fdr 1800~2800": [0, []],
"bv 8500+ & fdr 2800~4000": [0, []],
"bv 8500+ & fdr 4000+": [0, []],
}
with open(exifpath + "SearchResult.exif", 'r') as ef:
lines = ef.readlines()
count = 0
# 拿到每一个exif中需要的信息 放在data中
for line in lines:
count += 1
# 取每一张图片的信息 放在data中
if count % 8 != 0:
if ".jpg" in line:
data["name"] = re.findall(r"[\[](.*?)[\]]", line)[0]
name = photopath.split("/")[-2]
elif line != "\n":
dataname = line.split(":")[0].replace(" ", "")
value = line.split(":")[1].replace(" ", "").replace("\n", "")
data[dataname] = value
else:
# 判断每个exif的区域信息
if int(data["AE_TAG_FACE_STS_SIZE"]) > 13 and int(data["AE_TAG_FLT_FDY"]) != 0:
if 0 > int(data['AE_TAG_FLT_DR']) and -2000 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-2000 & fdr -∞~0'][0] += 1
result['bv -∞~-2000 & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and -2000 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-2000 & fdr 0~1800'][0] += 1
result['bv -∞~-2000 & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and -2000 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-2000 & fdr 1800~2800'][0] += 1
result['bv -∞~-2000 & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and -2000 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-2000 & fdr 2800~4000'][0] += 1
result['bv -∞~-2000 & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and -2000 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-2000 & fdr 4000+'][0] += 1
result['bv -∞~-2000 & fdr 4000+'][1].append(data["name"])
if 0 > int(data['AE_TAG_FLT_DR']) and 0 > int(data['AE_TAG_REALBVX1000']) >= -2000:
result['bv -2000~0 & fdr -∞~0'][0] += 1
result['bv -2000~0 & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and 0 > int(data['AE_TAG_REALBVX1000']) >= -2000:
result['bv -2000~0 & fdr 0~1800'][0] += 1
result['bv -2000~0 & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and 0 > int(data['AE_TAG_REALBVX1000']) >= -2000:
result['bv -2000~0 & fdr 1800~2800'][0] += 1
result['bv -2000~0 & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and 0 > int(data['AE_TAG_REALBVX1000']) >= -2000:
result['bv -2000~0 & fdr 2800~4000'][0] += 1
result['bv -2000~0 & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and 0 > int(data['AE_TAG_REALBVX1000']) >= -2000:
result['bv -2000~0 & fdr 4000+'][0] += 1
result['bv -2000~0 & fdr 4000+'][1].append(data["name"])
if 0 > int(data['AE_TAG_FLT_DR']) and 3000 > int(data['AE_TAG_REALBVX1000']) >= 0:
result['bv 0~3000 & fdr -∞~0'][0] += 1
result['bv 0~3000 & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and 3000 > int(data['AE_TAG_REALBVX1000']) >= 0:
result['bv 0~3000 & fdr 0~1800'][0] += 1
result['bv 0~3000 & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and 3000 > int(data['AE_TAG_REALBVX1000']) >= 0:
result['bv 0~3000 & fdr 1800~2800'][0] += 1
result['bv 0~3000 & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and 3000 > int(data['AE_TAG_REALBVX1000']) >= 0:
result['bv 0~3000 & fdr 2800~4000'][0] += 1
result['bv 0~3000 & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and 3000 > int(data['AE_TAG_REALBVX1000']) >= 0:
result['bv 0~3000 & fdr 4000+'][0] += 1
result['bv 0~3000 & fdr 4000+'][1].append(data["name"])
if 0 > int(data['AE_TAG_FLT_DR']) and 6000 > int(data['AE_TAG_REALBVX1000']) >= 3000:
result['bv 3000~6000 & fdr -∞~0'][0] += 1
result['bv 3000~6000 & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and 6000 > int(data['AE_TAG_REALBVX1000']) >= 3000:
result['bv 3000~6000 & fdr 0~1800'][0] += 1
result['bv 3000~6000 & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and 6000 > int(data['AE_TAG_REALBVX1000']) >= 3000:
result['bv 3000~6000 & fdr 1800~2800'][0] += 1
result['bv 3000~6000 & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and 6000 > int(data['AE_TAG_REALBVX1000']) >= 3000:
result['bv 3000~6000 & fdr 2800~4000'][0] += 1
result['bv 3000~6000 & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and 6000 > int(data['AE_TAG_REALBVX1000']) >= 3000:
result['bv 3000~6000 & fdr 4000+'][0] += 1
result['bv 3000~6000 & fdr 4000+'][1].append(data["name"])
if 0 > int(data['AE_TAG_FLT_DR']) and 8500 > int(data['AE_TAG_REALBVX1000']) >= 6000:
result['bv 6000~8500 & fdr -∞~0'][0] += 1
result['bv 6000~8500 & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and 8500 > int(data['AE_TAG_REALBVX1000']) >= 6000:
result['bv 6000~8500 & fdr 0~1800'][0] += 1
result['bv 6000~8500 & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and 8500 > int(data['AE_TAG_REALBVX1000']) >= 6000:
result['bv 6000~8500 & fdr 1800~2800'][0] += 1
result['bv 6000~8500 & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and 8500 > int(data['AE_TAG_REALBVX1000']) >= 6000:
result['bv 6000~8500 & fdr 2800~4000'][0] += 1
result['bv 6000~8500 & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and 8500 > int(data['AE_TAG_REALBVX1000']) >= 6000:
result['bv 6000~8500 & fdr 4000+'][0] += 1
result['bv 6000~8500 & fdr 4000+'][1].append(data["name"])
if 0 > int(data['AE_TAG_FLT_DR']) and int(data['AE_TAG_REALBVX1000']) >= 8500:
result['bv 8500+ & fdr -∞~0'][0] += 1
result['bv 8500+ & fdr -∞~0'][1].append(data["name"])
if 1800 > int(data['AE_TAG_FLT_DR']) >= 0 and int(data['AE_TAG_REALBVX1000']) >= 8500:
result['bv 8500+ & fdr 0~1800'][0] += 1
result['bv 8500+ & fdr 0~1800'][1].append(data["name"])
if 2800 > int(data['AE_TAG_FLT_DR']) >= 1800 and int(data['AE_TAG_REALBVX1000']) >= 8500:
result['bv 8500+ & fdr 1800~2800'][0] += 1
result['bv 8500+ & fdr 1800~2800'][1].append(data["name"])
if 4000 > int(data['AE_TAG_FLT_DR']) >= 2800 and int(data['AE_TAG_REALBVX1000']) >= 8500:
result['bv 8500+ & fdr 2800~4000'][0] += 1
result['bv 8500+ & fdr 2800~4000'][1].append(data["name"])
if int(data['AE_TAG_FLT_DR']) >= 4000 and int(data['AE_TAG_REALBVX1000']) >= 8500:
result['bv 8500+ & fdr 4000+'][0] += 1
result['bv 8500+ & fdr 4000+'][1].append(data["name"])
else:
if 1000 > int(data['AE_TAG_HS_EVD']) and -1500 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-1500 & evd -∞~1000'][0] += 1
result['bv -∞~-1500 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and -1500 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-1500 & evd 1000~4200'][0] += 1
result['bv -∞~-1500 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and -1500 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-1500 & evd 4200~6300'][0] += 1
result['bv -∞~-1500 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and -1500 > int(data['AE_TAG_REALBVX1000']):
result['bv -∞~-1500 & evd 6300+'][0] += 1
result['bv -∞~-1500 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and 500 > int(data['AE_TAG_REALBVX1000']) >= -1500:
result['bv -1500~500 & evd -∞~1000'][0] += 1
result['bv -1500~500 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and 500 > int(data['AE_TAG_REALBVX1000']) >= -1500:
result['bv -1500~500 & evd 1000~4200'][0] += 1
result['bv -1500~500 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and 500 > int(data['AE_TAG_REALBVX1000']) >= -1500:
result['bv -1500~500 & evd 4200~6300'][0] += 1
result['bv -1500~500 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and 500 > int(data['AE_TAG_REALBVX1000']) >= -1500:
result['bv -1500~500 & evd 6300+'][0] += 1
result['bv -1500~500 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and 2500 > int(data['AE_TAG_REALBVX1000']) >= 500:
result['bv 500~2500 & evd -∞~1000'][0] += 1
result['bv 500~2500 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and 2500 > int(data['AE_TAG_REALBVX1000']) >= 500:
result['bv 500~2500 & evd 1000~4200'][0] += 1
result['bv 500~2500 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and 2500 > int(data['AE_TAG_REALBVX1000']) >= 500:
result['bv 500~2500 & evd 4200~6300'][0] += 1
result['bv 500~2500 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and 2500 > int(data['AE_TAG_REALBVX1000']) >= 500:
result['bv 500~2500 & evd 6300+'][0] += 1
result['bv 500~2500 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and 5000 > int(data['AE_TAG_REALBVX1000']) >= 2500:
result['bv 2500~5000 & evd -∞~1000'][0] += 1
result['bv 2500~5000 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and 5000 > int(data['AE_TAG_REALBVX1000']) >= 2500:
result['bv 2500~5000 & evd 1000~4200'][0] += 1
result['bv 2500~5000 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and 5000 > int(data['AE_TAG_REALBVX1000']) >= 2500:
result['bv 2500~5000 & evd 4200~6300'][0] += 1
result['bv 2500~5000 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and 5000 > int(data['AE_TAG_REALBVX1000']) >= 2500:
result['bv 2500~5000 & evd 6300+'][0] += 1
result['bv 2500~5000 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and 8000 > int(data['AE_TAG_REALBVX1000']) >= 5000:
result['bv 5000~8000 & evd -∞~1000'][0] += 1
result['bv 5000~8000 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and 8000 > int(data['AE_TAG_REALBVX1000']) >= 5000:
result['bv 5000~8000 & evd 1000~4200'][0] += 1
result['bv 5000~8000 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and 8000 > int(data['AE_TAG_REALBVX1000']) >= 5000:
result['bv 5000~8000 & evd 4200~6300'][0] += 1
result['bv 5000~8000 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and 8000 > int(data['AE_TAG_REALBVX1000']) >= 5000:
result['bv 5000~8000 & evd 6300+'][0] += 1
result['bv 5000~8000 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and 10000 > int(data['AE_TAG_REALBVX1000']) >= 8000:
result['bv 8000~10000 & evd -∞~1000'][0] += 1
result['bv 8000~10000 & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and 10000 > int(data['AE_TAG_REALBVX1000']) >= 8000:
result['bv 8000~10000 & evd 1000~4200'][0] += 1
result['bv 8000~10000 & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and 10000 > int(data['AE_TAG_REALBVX1000']) >= 8000:
result['bv 8000~10000 & evd 4200~6300'][0] += 1
result['bv 8000~10000 & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and 10000 > int(data['AE_TAG_REALBVX1000']) >= 8000:
result['bv 8000~10000 & evd 6300+'][0] += 1
result['bv 8000~10000 & evd 6300+'][1].append(data["name"])
if 1000 > int(data['AE_TAG_HS_EVD']) and int(data['AE_TAG_REALBVX1000']) >= 10000:
result['bv 10000+ & evd -∞~1000'][0] += 1
result['bv 10000+ & evd -∞~1000'][1].append(data["name"])
if 4200 > int(data['AE_TAG_HS_EVD']) >= 1000 and int(data['AE_TAG_REALBVX1000']) >= 10000:
result['bv 10000+ & evd 1000~4200'][0] += 1
result['bv 10000+ & evd 1000~4200'][1].append(data["name"])
if 6300 > int(data['AE_TAG_HS_EVD']) >= 4200 and int(data['AE_TAG_REALBVX1000']) >= 10000:
result['bv 10000+ & evd 4200~6300'][0] += 1
result['bv 10000+ & evd 4200~6300'][1].append(data["name"])
if int(data['AE_TAG_HS_EVD']) >= 6300 and int(data['AE_TAG_REALBVX1000']) >= 10000:
result['bv 10000+ & evd 6300+'][0] += 1
result['bv 10000+ & evd 6300+'][1].append(data["name"])
# 把这一个exif放数组中
if int(data['AE_TAG_FACE_STS_SIZE']) > 13 and int(data["AE_TAG_FLT_FDY"]) != 0:
AE_TAG_REALBVX1000.append(data['AE_TAG_REALBVX1000'])
AE_TAG_FLT_DR.append(data['AE_TAG_FLT_DR'])
else:
AE_TAG_REALBVX1000_Normal.append(data['AE_TAG_REALBVX1000'])
AE_TAG_HS_EVD_Normal.append(data['AE_TAG_HS_EVD'])
return result, name, AE_TAG_HS_EVD_Normal, AE_TAG_REALBVX1000_Normal, AE_TAG_REALBVX1000,AE_TAG_FLT_DR,
def run_AE():
global photopath
result_CCT = [ "CCT -∞ ~ 2300", "CCT =2300 ~ 2850", "CCT =2850 ~ 3750",
"CCT =3750 ~ 5100", "CCT =5100 ~ 6500", "CCT =6500 ~ ∞"]
for cct_key in result_CCT:
dir_name = photopath + "\\" + cct_key
if os.path.isdir(dir_name):
shutil.rmtree(dir_name)
result_AE=["Face","Normal"]
for cct_key in result_AE:
dir_name = photopath + "\\" + cct_key
if os.path.isdir(dir_name):
shutil.rmtree(dir_name)
# child = subprocess.Popen(f"{debug} -dump {photopath} -find \"AE_TAG_HS_EVD|AE_TAG_REALBVX1000|AE_TAG_FACE_STS_SIZE|AE_TAG_FLT_DR\" -save {photopath}/exif/")
child1 = subprocess.Popen(
f"{debug_Tool} -dump {photopath} -find \"AE_TAG_HS_EVD|AE_TAG_REALBVX1000|AE_TAG_FACE_STS_SIZE|AE_TAG_FLT_DR|AE_TAG_FLT_FDY\" -save {photopath}/")
child1.wait()
result,name, AE_TAG_HS_EVD_Normal, AE_TAG_REALBVX1000_Normal, AE_TAG_REALBVX1000,AE_TAG_FLT_DR, = analyse(photopath)
os.mkdir(photopath + "Face")
os.mkdir(photopath + "Normal")
for dirname in result:
if not os.path.exists(str(result[dirname][0]) + " " + dirname.replace(" ", " ")):
os.mkdir(photopath + str(result[dirname][0]) + " " + dirname.replace(" ", " "))
for photoname in os.listdir(photopath):
for dirname in result:
if photoname in result[dirname][1]:
shutil.copyfile(photopath + photoname,
photopath + str(result[dirname][0]) + " " + dirname.replace(" ",
" ") + f"/{photoname}")
# 帮助跳出外层循环
switchflag = True
for dirpath, dirnames, filenames in os.walk(photopath):
if switchflag:
break
for dirname in dirnames:
if dirname == "Face" or dirname == "Normal":
switchflag = False
break
if "evd" in dirname:
shutil.move(photopath + dirname, photopath + "Normal")
if "fdr" in dirname:
shutil.move(photopath + dirname, photopath + "Face")
x = len(AE_TAG_FLT_DR)
y = len(AE_TAG_HS_EVD_Normal)
result1 = '{:.2%}'.format(x / (x + y))
result2 = '{:.2%}'.format(y / (x + y))
##绘制Normal散点图表
x_bv = np.asarray(AE_TAG_REALBVX1000_Normal, dtype=int)
y_evd = np.asarray(AE_TAG_HS_EVD_Normal, dtype=int)
my_x_ticks = [-1500, 500, 2500, 5000, 8000, 10000]
my_y_ticks = [0, 1000, 4200, 6300]
plt.xlim(-8000, 15000)
plt.ylim(-5000, 10000)
plt.xticks(ticks=my_x_ticks)
plt.yticks(ticks=my_y_ticks)
# 设置图表标题并给坐标轴加上标签
plt.title(f'{name} Normal:{y}/{x + y} {result2}', fontsize=24)
plt.xlabel('AE_TAG_REALBVX1000', fontsize=14)
plt.ylabel('AE_TAG_HS_EVD', fontsize=14)
plt.scatter(x_bv, y_evd, s=30,label="pic")
plt.legend()
plt.savefig(f'{photopath}Normal/result_Normal.png', dpi=300)
plt.close()
##绘制Face散点图
x_bv = np.asarray(AE_TAG_REALBVX1000, dtype=int)
y_dr = np.asarray(AE_TAG_FLT_DR, dtype=int)
my_x_ticks = [-2000, 0, 3000, 6000, 8500]
my_y_ticks = [0, 1800, 2800, 4000]
plt.xlim(-8000, 15000)
plt.ylim(-2000, 8000)
plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
# 设置图表标题并给坐标轴加上标签
plt.title(f'{name} Face:{x}/{x + y} {result1}', fontsize=24)
plt.xlabel('AE_TAG_REALBVX1000', fontsize=14)
plt.ylabel('AE_TAG_FLT_DR', fontsize=14)
plt.scatter(x_bv, y_dr, s=20, label="pic")
plt.legend()
plt.savefig(f'{photopath}Face/result_Face.png', dpi=300)
plt.close()
endtime = datetime.datetime.now()
showinfo(title='提示', message='AE分类 操作成功！')
class result_LV_obj:
def __init__(self):
self.result_LV = {
"LV -∞ ~ 0" : 0,
'LV =0 ~ 20' : 0,
'LV =20 ~ 40' : 0,
'LV =40 ~ 60' : 0,
'LV =60 ~ 80' : 0,
'LV =80 ~ 100' : 0,
'LV =100 ~ 120': 0,
'LV =120 ~ 140': 0,
'LV =140 ~ 160': 0,
'LV =160 ~ 180': 0,
}
def analyse_AWB(exifpath,result_CCT):
with open(exifpath + "\SearchResult.exif", 'r', encoding='utf-8') as ef:
lines = ef.readlines()
cur_file_name = ''
cur_file_path = ''
cur_CCT_key = ''
cur_LV_key = ''
for line in lines:
cur_line_is_CCT = 0
cur_line_is_LV = 0
if ".jpg" in line:
cur_file_path = exifpath + '\\'
cur_file_name = re.findall(r"[\[](.*?)[\]]", line)[0]
cur_CCT_key = ''
cur_LV_key = ''
continue
elif line != "\n":
data_name = line.split(":")[0].replace(" ", "")
data_value = line.split(":")[1].replace(" ", "").replace("\n", "")
if data_name == 'AWB_TAG_CCT':
cur_line_is_CCT = 1
elif data_name == 'AWB_TAG_ALGO_SCENE_LV':
cur_line_is_LV = 1
else:
continue
if cur_line_is_CCT == 1:
cur_CCT = int(data_value)
if cur_CCT < 2300:
cur_CCT_key = 'CCT -∞ ~ 2300'
elif cur_CCT < 2850:
cur_CCT_key = 'CCT =2300 ~ 2850'
elif cur_CCT < 3750:
cur_CCT_key = 'CCT =2850 ~ 3750'
elif cur_CCT < 5100:
cur_CCT_key = 'CCT =3750 ~ 5100'
elif cur_CCT < 6500:
cur_CCT_key = 'CCT =5100 ~ 6500'
else:
cur_CCT_key = 'CCT =6500 ~ ∞'
if cur_LV_key != '':
if not os.path.exists(cur_file_path+ cur_CCT_key + "/" + cur_LV_key):
if not os.path.exists(cur_file_path+ cur_CCT_key):
os.mkdir(cur_file_path+ cur_CCT_key )
os.mkdir(cur_file_path + cur_CCT_key + "/" + cur_LV_key)
result_CCT[cur_CCT_key].result_LV[cur_LV_key] += 1
src_path = cur_file_path + cur_file_name
dst_path = cur_file_path + cur_CCT_key + "\\" + cur_LV_key +"\\"+cur_file_name
with open(src_path, 'rb') as f_src:
data = f_src.read()
with open(dst_path, 'wb') as f_dst:
f_dst.write(data)
f_dst.close()
f_src.close()
cur_CCT_key = ''
cur_LV_key = ''
cur_file_name = ''
elif cur_line_is_LV == 1:
cur_LV = int(data_value)
if cur_LV < 0:
cur_LV_key = 'LV -∞ ~ 0'
elif cur_LV < 20:
cur_LV_key = 'LV =0 ~ 20'
elif cur_LV < 40:
cur_LV_key = 'LV =20 ~ 40'
elif cur_LV < 60:
cur_LV_key = 'LV =40 ~ 60'
elif cur_LV < 80:
cur_LV_key = 'LV =60 ~ 80'
elif cur_LV < 100:
cur_LV_key = 'LV =80 ~ 100'
elif cur_LV < 120:
cur_LV_key = 'LV =100 ~ 120'
elif cur_LV < 140:
cur_LV_key = 'LV =120 ~ 140'
elif cur_LV < 160:
cur_LV_key = 'LV =140 ~ 160'
elif cur_LV < 180:
cur_LV_key = 'LV =160 ~ 180'
def run_AWB():
result_LV_0 = result_LV_obj()
result_LV_1 = result_LV_obj()
result_LV_2 = result_LV_obj()
result_LV_3 = result_LV_obj()
result_LV_4 = result_LV_obj()
result_LV_5 = result_LV_obj()
result_CCT = {
"CCT -∞ ~ 2300": result_LV_0,
"CCT =2300 ~ 2850": result_LV_1,
"CCT =2850 ~ 3750": result_LV_2,
"CCT =3750 ~ 5100": result_LV_3,
"CCT =5100 ~ 6500": result_LV_4,
"CCT =6500 ~ ∞": result_LV_5,
}
global photopath
for cct_key in result_CCT:
dir_name = photopath + "\\" + cct_key
if os.path.isdir(dir_name):
shutil.rmtree(dir_name)
result_AE=["Face","Normal"]
for cct_key in result_AE:
dir_name = photopath + "\\" + cct_key
if os.path.isdir(dir_name):
shutil.rmtree(dir_name)
child1_AWB = subprocess.Popen(
f"{debug_Tool} -dump {photopath} -find \"AWB_TAG_CCT|AWB_TAG_ALGO_SCENE_LV\" -save {photopath}/")
child1_AWB.wait()
analyse_AWB(photopath,result_CCT)
for cct_key in result_CCT:
for lv_key in result_CCT[cct_key].result_LV:
lv_count = result_CCT[cct_key].result_LV[lv_key]
if os.path.exists(photopath+'\\'+ cct_key +'\\'+ lv_key) == False:
if os.path.exists(photopath+'\\'+ cct_key) == False:
os.mkdir(photopath + '\\' + cct_key )
os.mkdir(photopath+'\\'+ cct_key +'\\'+ lv_key)
os.rename(photopath+'\\'+ cct_key +'\\'+ lv_key,
photopath+'\\'+ cct_key +'\\'+ '_'+str(lv_count)+'_'+lv_key)
showinfo(title='提示', message='AWB分类 操作成功！')
# 初始化布局
root = tk.Tk()
root.title("AE_AWB图片分类小工具")
img_use_pic = open(r'C:PCBico.png', 'wb') # 创建临时使用图片文件，这里放在了C盘的temp文件夹里
img_use_pic.write(base64.b64decode(img_pic))
img_use_pic.close()
image = tk.PhotoImage(file='C:PCBico.png')
root.iconphoto(False,image)
root.wm_iconphoto(False,image)
max_w, max_h = root.maxsize()
root.geometry(f'500x300+{int((max_w - 500) / 2)}+{int((max_h - 300) / 2)}') # 居中
root.resizable(width=False, height=False)
# 初始化Entry控件
debug_entry = tk.StringVar() # 选择目录信息
photo_entry = tk.StringVar() # 输出路径信息
if os.path.exists(os.getcwd() + "\default_path.txt"):
with open(os.getcwd() + "\default_path.txt", 'r') as f_default_path:
lines = f_default_path.readlines()
print(lines)
for line in lines:
if line.startswith("tool_deault_dir=") :
default_tool_dir = line.split("tool_deault_dir=")[1].replace("\n", "")
print(default_tool_dir)
print(default_tool_dir)
if "DebugParser.exe" in default_tool_dir:
debug_entry.set(default_tool_dir)
debug_Tool = default_tool_dir
if line.startswith("photo_deault_dir=") :
default_photo_dir = line.split("photo_deault_dir=")[1].replace("\n", "")
if os.path.isdir(default_photo_dir):
photo_entry.set(default_photo_dir)
photopath = default_photo_dir
# === 布局控件
# raw路径 label
label = tk.Label(root, text="DP路径", font=("", 15))
label.place(x=50, y=80)
# 输入路径
raw_path = tk.Entry(root, textvariable=debug_entry, font=('', 15), width=20)
raw_path.place(x=130, y=80)
# 选择文件夹 按钮
button_raw = tk.Button(root, text="选择文件", command=select_tool_dir, font=('', 10), width=15)
button_raw.place(x=350, y=80)
# out路径 label
label2 = tk.Label(root, text="Pic路径", font=("", 15))
label2.place(x=50, y=160)
# out信息框
out_path = tk.Entry(root, textvariable=photo_entry, font=('', 15), width=20)
out_path.place(x=130, y=160)
button_raw = tk.Button(root, text="选择文件夹", command=select_picture_path, font=('', 10), width=15)
button_raw.place(x=350, y=160)
button_raw = tk.Button(root, text="运行AE分类", command=run_AE, font=('', 10), width=15)
button_raw.place(x=80, y=220)
button_raw = tk.Button(root, text="运行AWB分类", command=run_AWB, font=('', 10), width=15)
button_raw.place(x=240, y=220)
root.mainloop()
os.remove(r'C:PCBico.png')要点1：Popen 调用windows工具，命令行可执行的命令
subprocess.Popen(
f"{debug_Tool} -dump {photopath} -find \"AWB_TAG_CCT|AWB_TAG_ALGO_SCENE_LV\" -save {photopath}/")要点2：将图片文件转换为 py 文件，从代码中调用，方便打包
from png_resource import img as img_pic
root = tk.Tk()
img_use_pic = open(r'C:PCBico.png', 'wb') # 创建临时使用图片文件，这里放在了C盘的temp文件夹里
img_use_pic.write(base64.b64decode(img_pic))
img_use_pic.close()
image = tk.PhotoImage(file='C:PCBico.png')
root.iconphoto(False,image)
root.wm_iconphoto(False,image)
os.remove(r'C:PCBico.png') #结束时调用要点3：python 用class 模拟C 语音的结构体，字典嵌套实现如下，可以都放入class 内部简化代码
class result_LV_obj:
def __init__(self):
self.result_LV = {
"LV -∞ ~ 0" : 0,
'LV =0 ~ 20' : 0,
'LV =20 ~ 40' : 0,
'LV =40 ~ 60' : 0,
'LV =60 ~ 80' : 0,
'LV =80 ~ 100' : 0,
'LV =100 ~ 120': 0,
'LV =120 ~ 140': 0,
'LV =140 ~ 160': 0,
'LV =160 ~ 180': 0,
}
def run_AWB():
result_LV_0 = result_LV_obj()
result_LV_1 = result_LV_obj()
result_LV_2 = result_LV_obj()
result_LV_3 = result_LV_obj()
result_LV_4 = result_LV_obj()
result_LV_5 = result_LV_obj()
result_CCT = {
"CCT -∞ ~ 2300": result_LV_0,
"CCT =2300 ~ 2850": result_LV_1,
"CCT =2850 ~ 3750": result_LV_2,
"CCT =3750 ~ 5100": result_LV_3,
"CCT =5100 ~ 6500": result_LV_4,
"CCT =6500 ~ ∞": result_LV_5,
}未解决问题：
1. 原本采用 递归 遍历文件夹 的方式，每完成对当前文件的分类时直接修改目标文件夹名称；总是会提示文件夹仍然被占用错误。
2. 后尝试递归函数放入单独的线程中执行，子线程遍历过程中维护2个列表，分别保存需要修改的文件名完整路径 和 重命名后的文件名完整路径，并在这个子线程结束调用后再遍历列表，对文件夹进行重命名，仍然有相同文件仍然被占用错误。主线程中在子线程结束后仍然加2秒延时，还是会有这个错误。。
3. 尝试列表中直接写死直接对文件夹调用重命名，可以成功。。。未知文件被占用的根本原因。文章来源：https://www.toymoban.com/news/detail-610062.html
4. 文件被占用查询占用情况详细信息方法未尝试成功，待继续分析。文章来源地址https://www.toymoban.com/news/detail-610062.html
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