随笔分类 - 编程~python
主要是记录python中常用的一些指令
摘要：1、安装 输入： pip install labelme 2、使用 安装完成以后在终端输入： labelme 再执行一次 pip install labelme 看labelme的安装路径： 进入到其安装路径： 用dir查看当前路径下的文件： 打开labelme如下所示： 3、标注目标检测任务 打开
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摘要：# -*- coding:utf-8 -*- import os import torch import torch.nn as nn from Resnet import resnet18 from torch.autograd import Variable from torchvision i
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摘要：import os import cv2 import numpy as np path = "./test_img/" imglist = os.listdir(path) for fi in imglist: img_folder = path+fi print(img_folder) imag
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摘要：file_out = open('./txt/'+out_txt_name, 'w') size=output_pred_mbbox.shape for i in range(0,size[0]): every_img = output_pred_mbbox[i,:,:,:] every_img_B
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摘要：# -*- coding: utf-8 -*- from PIL import Image from numpy import * for i in range(1,360): pil_im = Image.open('./13.jpeg') pil_im = pil_im.rotate(i) pr
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摘要：def resize(image, size_num): delm=max( image.shape[1], image.shape[0]) if delm>size_num: multiple = delm/size_num ss=1 hh_big=image.shape[1] ww_big=im
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摘要：def find_similar_img(det_im,im_dataset_dir): im_dataset=os.listdir(im_dataset_dir) need_im_name=[] need_im_det_instance=[] print ('now doing get featu
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摘要：def calEuclideanDistance(vec1,vec2): dist = np.sqrt(np.sum(np.square(vec1 - vec2))) return dist
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摘要：def HOG_features(im): #start_time1=time.time() hog = cv2.HOGDescriptor() winStride = (8, 8) padding = (8, 8) hist = hog.compute(im, winStride, padding
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摘要：import time from options.train_options import TrainOptions from data import create_dataset from models import create_model from util.visualizer import
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摘要：import os import cv2 import numpy as np Adir = './trainA/' Bdir = './trainB/' ABdir = './datasets/train/' num=0 for fi in os.listdir(Adir): im_A = cv2
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摘要：f_label_img = cv2.imread('1.jpg') f_label_img = cv2.cvtColor(f_label_img, cv2.COLOR_BGR2GRAY) contours, hierarchy = cv2.findContours(f_label_img, cv2.
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摘要：def load_images(IMG_SIZE = 256): avatars = [] filenames = [] images_list = sorted(glob.glob(f'{opt.avatars}/*')) for i, f in enumerate(images_list): i
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摘要：f='1.jpg' f.endswith('.jpg') or f.endswith('.jpeg') or f.endswith('.png')
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摘要：w_size = 512 h_size = 256 sub_input_label = torch.zeros([input_label.shape[0],input_label.shape[1],h_size,w_size], dtype=torch.float32,device=input_la
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摘要：glass_img = cv2.imread(glass_dir + glass_img_name) test_img = cv2.imread(test_dir + test_img_name) all_w = max(test_img.shape[1], glass_img.shape[1])*
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摘要：import cv2 import os for fi in os.listdir('./CCimage/'): I=cv2.imread('./CCimage/'+fi) cv2.imwrite('./CCimage/'+fi.split('.')[0]+'.png',I) os.remove('
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