发送邮件
from email.mime.text import MIMEText
from email.header import Header
from smtplib import SMTP_SSL
#qq邮箱smtp服务器
host_server = 'smtp.qq.com'
#sender_qq为发件人的qq号码
sender_qq = ''
#pwd为qq邮箱的授权码
pwd = ''
#发件人的邮箱
sender_qq_mail = ''
#收件人邮箱
receiver = ''
#邮件的正文内容
mail_content = '你好，我是来自知乎的 ，现在在进行一项用python登录qq邮箱发邮件的测试'
#邮件标题
mail_title = '邮件'
#ssl登录
smtp = SMTP_SSL(host_server)
#set_debuglevel()是用来调试的。参数值为1表示开启调试模式，参数值为0关闭调试模式
smtp.set_debuglevel(1)
smtp.ehlo(host_server)
smtp.login(sender_qq, pwd)
msg = MIMEText(mail_content, "plain", 'utf-8')
msg["Subject"] = Header(mail_title, 'utf-8')
msg["From"] = sender_qq_mail
msg["To"] = receiver
smtp.sendmail(sender_qq_mail, receiver, msg.as_string())
smtp.quit()
coco数据提取yolo
#COCO 格式的数据集转化为 YOLO 格式的数据集
#--json_path 输入的json文件路径
#--save_path 保存的文件夹名字，默认为当前目录下的labels。
import os
import json
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser()
#这里根据自己的json文件位置，换成自己的就行
parser.add_argument('--json_path', default='wildlife_instance_test2017.json',type=str, help="input: coco format(json)")
#这里设置.txt文件保存位置
parser.add_argument('--save_path', default='Lable/test', type=str, help="specify where to save the output dir of labels")
arg = parser.parse_args()
def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = box[0] + box[2] / 2.0
y = box[1] + box[3] / 2.0
w = box[2]
h = box[3]
#round函数确定(xmin, ymin, xmax, ymax)的小数位数
x = round(x * dw, 6)
w = round(w * dw, 6)
y = round(y * dh, 6)
h = round(h * dh, 6)
return (x, y, w, h)
# coding=utf-8
def check_charset(file_path):
import chardet
with open(file_path, "rb") as f:
data = f.read(4)
charset = chardet.detect(data)['encoding']
return charset
if __name__ == '__main__':
json_file = arg.json_path # COCO Object Instance 类型的标注
ana_txt_save_path = arg.save_path # 保存的路径
data = json.load(open(file=json_file,encoding='ISO-8859-1', mode='r'))
if not os.path.exists(ana_txt_save_path):
os.makedirs(ana_txt_save_path)
id_map = {} # coco数据集的id不连续！重新映射一下再输出！
with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f:
# 写入classes.txt
for i, category in enumerate(data['categories']):
f.write(f"{category['name']}\n")
id_map[category['id']] = i
# print(id_map)
#这里需要根据自己的需要，更改写入图像相对路径的文件位置。
list_file = open(os.path.join(ana_txt_save_path, 'test2017.txt'), 'w')
for img in tqdm(data['images']):
filename = img["file_name"]
img_width = img["width"]
img_height = img["height"]
img_id = img["id"]
head, tail = os.path.splitext(filename)
ana_txt_name = head + ".txt" # 对应的txt名字，与jpg一致
f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
for ann in data['annotations']:
if ann['image_id'] == img_id:
box = convert((img_width, img_height), ann["bbox"])
f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
f_txt.close()
#将图片的相对路径写入train2017或val2017的路径
list_file.write('./images/test2017/%s.jpg\n' %(head))
list_file.close()
检测简单物体并裁剪
import cv2
import numpy as np
image = cv2.imread("1 (1).JPG")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)
# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
# blur and threshold the image
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations=4)
closed = cv2.dilate(closed, None, iterations=4)
(cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
# draw a bounding box arounded the detected barcode and display the image
# cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
# cv2.imshow("Image", image)
# cv2.imwrite("contoursImage2.jpg", image)
# cv2.waitKey(0)
Xs = [i[0] for i in box]
Ys = [i[1] for i in box]
x1 = min(Xs)
x2 = max(Xs)
y1 = min(Ys)
y2 = max(Ys)
hight = y2 - y1
width = x2 - x1
cropImg = image[y1-10:y1+hight+10, x1-10:x1+width+10]
cv2.imwrite("contoursImage3.jpg", cropImg)
获取文件夹下文件
def get_file_name(path,filetype):
pathList=[]
for root,dirs,files in os.walk(path):
for file in files:
if file.endswith(filetype):
pathList.append(file)
return pathList
数据获取
import cv2
import numpy as np
import os
def get_file_name(path,filetype):
pathList=[]
for root,dirs,files in os.walk(path):
for file in files:
if file.endswith(filetype):
pathList.append(file)
return pathList
def control_photo(ori_path,save_path):
image = cv2.imread(ori_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)
# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
# blur and threshold the image
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations=4)
closed = cv2.dilate(closed, None, iterations=4)
(cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
# draw a bounding box arounded the detected barcode and display the image
# cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
# cv2.imshow("Image", image)
# cv2.imwrite("contoursImage2.jpg", image)
# cv2.waitKey(0)
Xs = [i[0] for i in box]
Ys = [i[1] for i in box]
x1 = min(Xs)
x2 = max(Xs)
y1 = min(Ys)
y2 = max(Ys)
hight = y2 - y1
width = x2 - x1
cropImg = image[y1-10:y1+hight+10, x1-10:x1+width+10]
cv2.imwrite(save_path, cropImg)
if __name__ == '__main__':
for img_name in get_file_name("2A",".JPG"):
control_photo("2A/"+img_name,"cut_"+img_name)
# COCO 2017 dataset http://cocodataset.org
# download command/URL (optional)
download: bash ./scripts/get_coco.sh
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ./data/lote/images/train # 118287 images
val: ./data/lote/images/val # 5000 images
test: ./data/lote/images/test # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# number of classes
nc: 11
# class names
names: ['Pandas大熊猫','RedPandas小熊猫','MartesFlavigula黄喉鸬','MacacaThibetana藏猕猴','RhinopithecusRoxellana金丝猴','HystrixBrachyura豪猪','SusScrofa野猪','RusaUnicolor水鹿','ElaphodusCephalophus簇绒鹿','CapricornisMilneedwardsii中华鬣羚','PseudoisNayaur青羊']
存储txt信息
import torch, os
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
from evaluation.eval_wrapper import eval_lane
import torch
import os
def get_file_name(path,filetype):
pathList=[]
for root,dirs,files in os.walk(path):
for file in files:
if file.endswith(filetype):
pathList.append(file)
return pathList
def save_txt(save_dir,str_info):
path=save_dir+"/tusimple_eval.txt"
os.makedirs(os.path.dirname(path),exist_ok=True)
with open(path,"a") as f:
f.write(str_info)
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
cfg_test_model_path= "1115_1422_b32_zbt_mas_cssc_b_test_2"
cfg_backbone='18p'
cfg_dataset = 'Tusimple'
cls_num_per_lane = 56
cfg_griding_num=100
cfg_num_lanes=4
cfg_test_work_dir="./tmp"
cfg_data_root="D:\pythoncode\dataset\Tusimple"
distributed = False
if 'WORLD_SIZE' in os.environ:
distributed = int(os.environ['WORLD_SIZE']) > 1
net = parsingNet(pretrained = False, backbone=cfg_backbone,cls_dim = (cfg_griding_num+1,cls_num_per_lane, cfg_num_lanes),
use_aux=False).cuda() # we dont need auxiliary segmentation in testing
model_strlist=get_file_name(cfg_test_model_path,"pth")
for model_str in model_strlist:
cfg_test_model_temp=cfg_test_model_path
cfg_test_model_temp=cfg_test_model_path+"/"+model_str
#这里设置循环
state_dict = torch.load(cfg_test_model_temp, map_location = 'cpu')['model']
compatible_state_dict = {}
for k, v in state_dict.items():
if 'module.' in k:
compatible_state_dict[k[7:]] = v
else:
compatible_state_dict[k] = v
net.load_state_dict(compatible_state_dict, strict = False)
if not os.path.exists(cfg_test_work_dir):
os.mkdir(cfg_test_work_dir)
strlist=eval_lane(net, cfg_dataset, cfg_data_root, cfg_test_work_dir, cfg_griding_num, False, distributed)
model_savestr=model_str+"\n"+strlist
save_txt(cfg_test_model_path,model_savestr)
