def get_data(json_content): imgpath = json_content.get('imagePath') data.append(imgpath) flags = json_content.get('flags') values = list(flags. values()) label = 0 for i in range(len( values)): if values [i]: label= i＋1 data = [] data.append(imgpath) data.append(label) return data temp_json=read_json(r'D:\data_1\flowers\1185.json') print(get_data(temp_json))

代码解释：if name == "main": postfix = 'jpg' imgpath = './VOCdevkit/JPEGImages' xmlpath = './VOCdevkit/Annotations' txtpath = './VOCdevkit/txt'

在这段代码中，将变量postfix赋值为'jpg'，imgpath赋值为'./VOCdevkit/JPEGImages'，xmlpath赋值为'./VOCdevkit/Annotations'，txtpath赋值为'./VOCdevkit/txt'。根据命名规则，这个代码段可能是用于VOC数据集的处理...

import requests import re import os from bs4 import BeautifulSoup from scrapy import Spider from PIL import Image import io def GetBasicInfo(url): res = requests.get(url, headers=headers) res.encoding = 'utf-8' soup = BeautifulSoup(res.text, 'lxml') tmp = soup.find(attrs={'class': 'mhlistbody'}) chapters = tmp.ul.contents chapters.reverse() return chapters def GetRealUrls(mh_info): imgs = [] comic_size = re.findall(r'comic_size:"(.?)"', mh_info)[0] base_url = 'https://mhpic.jumanhua.com/comic/{}.jpg%s.webp' % comic_size num_img = int(re.findall(r'totalimg:(\d+)', mh_info)[0]) pageid = int(re.findall(r'pageid:(\d+)', mh_info)[0]) imgpath = re.findall(r'imgpath:"(.?)"', mh_info)[0] start = 0 while True: idx = imgpath.find('\\', start) if idx == -1: break imgpath = imgpath[:idx] + imgpath[idx+1:] start = idx + 1 for i in range(num_img): realpath = str() for s in imgpath: realpath += chr(ord(s) - pageid % 10) url = base_url.format(realpath + str(i+1)) imgs.append([url, str(i+1)+'.jpg']) return imgs def DownloadChapter(savepath, url): if not os.path.exists(savepath): os.mkdir(savepath) res = requests.get(url, headers=headers) res.encoding = 'utf-8' mh_info = re.findall(r'mh_info={(.*?)}', res.text)[0] img_urls = GetRealUrls(mh_info) for img_url in img_urls: img_content = requests.get(img_url[0]).content filename = os.path.join(savepath, img_url[1]) img = Image.open(io.BytesIO(img_content)) img.save(filename) if name == 'main': url = 'https://www.manhuatai.com/yaoshenji/' headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36'} savepath = url.split('/')[-2] Spider(url,savepath)

这段代码是一个简单的Python脚本，用于下载指定漫画网站的漫画图片。... 具体来说，脚本中的GetBasicInfo函数用于获取漫画的章节信息，GetRealUrls函数用于获取漫画章节中每页图片的真实URL，DownloadChapter...

def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, }这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集，不使用使用 TensorFlow

images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels def get_MNIST_data(num_training=50000, num_validation=10000, num_test=10000): ...

dataset = gdal.Open(imgPath, gdal.GA_ReadOnly)

This line of code opens the image file located at imgPath in read-only mode using the GDAL library. The variable dataset is assigned the dataset object returned by the gdal.Open() function, ...

import os import shutil import PIL.Image as Image import cv2 import numpy as np import pandas as pd def selet_pic(imgpath,labpath): pic_path = os.listdir(labpath) list = [] for alldir in pic_path: child = os.path.join(labpath,alldir) img = Image.open(child) # img.show() pic_arrays = np.array(img) #将图片转化成数组 if np.mean(pic_arrays) == 0: # print("图片为黑色",alldir) list.append(alldir) # print(list) return list def del_pic(list): for i in list: img_path = os.path.join(imgpath,i) lab_path = os.path.join(labpath,i) print(lab_path) os.remove(img_path) #直接删除 os.remove(lab_path) # shutil.move(img_path, img_del_path) # shutil.move(lab_path,lab_del_path) #移到指定位置 if name== "main": imgpath = r'D:\shiyan\img\waterbody\water\img_256' labpath = r'D:\shiyan\img\waterbody\water\label_256' list = selet_pic(imgpath,labpath) # del_pic(list,img_del_path,lab_del_path) del_pic(list)

1. selet_pic函数：该函数接受两个参数imgpath和labpath，分别表示图片文件和标签文件所在的目录。在该函数中，首先通过os.listdir获取标签文件目录下的所有文件名，并将它们存储在pic_path列表中。然后，...

更改下面的代码，使img_src_list中的每一个src元素以h_name_list中的每一个hName元素的名称储存 for src in img_src_list: #因为miya自带https无需加前缀 img_data = requests.get(url=src,headers=headers).content for hName in h_name_list: imgPath = './miya/' + hName+'.jpg' with open(imgPath,'wb') as fp: fp.write(img_data) print(hName, '提取完成')

代码如下： img_dict = {} for index, src in enumerate(img_src_list): h_name = h_name_list[index] img_dict[h_name] = src 这段代码遍历 img_src_list 列表，并使用索引从 h_name_list 中取出...

import pickle import numpy as np import os # from scipy.misc import imread def load_CIFAR_batch(filename): with open(filename, 'rb') as f: datadict = pickle.load(f, encoding='bytes') X = datadict[b'data'] Y = datadict[b'labels'] X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float") Y = np.array(Y) return X, Y def load_CIFAR10(ROOT): xs = [] ys = [] for b in range(1, 2): f = os.path.join(ROOT, 'data_batch_%d' % (b,)) X, Y = load_CIFAR_batch(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) return Xtr, Ytr, Xte, Yte def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, } def load_models(models_dir): models = {} for model_file in os.listdir(models_dir): with open(os.path.join(models_dir, model_file), 'rb') as f: try: models[model_file] = pickle.load(f)['model'] except pickle.UnpicklingError: continue return models这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集，不使用TensorFlow

def get_mnist_data(num_training=5000, num_validation=500, num_test=500): mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录 X_train, y_train = load_mnist(mnist_dir, kind='train') X_test, y...

if name == "main": parser = argparse.ArgumentParser() parser.add_argument('--imgpath', type=str, default='images/crowdpose_100024.jpg', help="image path") parser.add_argument('--modelpath', type=str, default='weights/kapao_s_crowdpose.onnx') args = parser.parse_args()

根据你提供的代码，可以看出这个脚本接受两个参数：imgpath 和 modelpath。imgpath 是一个字符串类型的参数，默认值为 'images/crowdpose_100024.jpg'，用于指定图像路径。modelpath 也是一个字符串类型的...

def call(self): # 图像转换 data_transorform = torchvision.transforms.Compose([ torchvision.transforms.Resize((224, 224)), torchvision.transforms.CenterCrop((224, 224)), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) img_list = glob.glob(self.img_dir + os.sep + "*.png") for imgpath in img_list: img = cv2.imread(imgpath) new_img = self.expend_img(img) # 补边 img = Image.fromarray(new_img) img = data_transorform(img) # 转换 img = torch.reshape(img, (-1, 3, self.imgsz, self.imgsz)).to(device) # 维度转换[B,C,H,W] pred = self.model(img) _, pred = torch.max(pred, 1) outputs = self.class_name[pred] print("Image path:", imgpath, " pred:", outputs) # 补边为正方形 def expend_img(self, img, fill_pix=122): ''' :param img: 图片数据 :param fill_pix: 填充像素，默认为灰色，自行更改 :return: ''' h, w = img.shape[:2] # 获取图像的宽高 if h >= w: # 左右填充 padd_width = int(h - w) // 2 padd_h, padd_b, padd_l, padd_r = 0, 0, padd_width, padd_width # 获取上下左右四个方向需要填充的像素 elif h < w: # 上下填充 padd_high = int(w - h) // 2 padd_h, padd_b, padd_l, padd_r = padd_high, padd_high, 0, 0 new_img = cv2.copyMakeBorder(img, padd_h, padd_b, padd_l, padd_r, borderType=cv2.BORDER_CONSTANT, value=[fill_pix, fill_pix, fill_pix]) return new_img if name == 'main': opt = parser_opt() test_img = Test_model(opt) test_img()

这段代码是一个使用PyTorch实现的图像分类模型的测试程序。主要实现了以下功能： 1.读取指定目录下的所有png格式图像文件。 2.对读取到的图像进行补边操作，使其变为正方形。 3.将图像进行预处理，包括图像尺寸的...

代码解释：val_size = 0.1 test_size = 0.2 postfix = 'jpg' imgpath = './VOCdevkit/JPEGImages' txtpath = './VOCdevkit/txt'

这段代码可以用来准备数据集，其中val_size和test_size分别表示用于验证集和测试集的数据比例，postfix表示图片文件的后缀名，imgpath表示图片所在文件夹的路径，txtpath表示对应的标签文件所在文件夹的路径。

coding=UTF-8 from flask import Flask, render_template, request, send_from_directory from werkzeug.utils import secure_filename from iconflow.model.colorizer import ReferenceBasedColorizer from skimage.feature import canny as get_canny_feature from torchvision import transforms from PIL import Image import os import datetime import torchvision import cv2 import numpy as np import torch import einops transform_Normalize = torchvision.transforms.Compose([ transforms.Normalize(0.5, 1.0)]) ALLOWED_EXTENSIONS = set([‘png’, ‘jpg’, ‘jpeg’]) app = Flask(name) train_model = ReferenceBasedColorizer() basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 def allowed_file(filename): return ‘.’ in filename and filename.rsplit(‘.’, 1)[1] in ALLOWED_EXTENSIONS def load_model(log_path=‘/mnt/4T/lzq/IconFlowPaper/checkpoints/normal_model.pt’): global train_model state = torch.load(log_path) train_model.load_state_dict(state[‘net’]) @app.route(“/”, methods=[“GET”, “POST”]) def hello(): if request.method == ‘GET’: return render_template(‘upload.html’) @app.route(‘/upload’, methods=[“GET”, “POST”]) def upload_lnk(): if request.method == ‘GET’: return render_template(‘upload.html’) if request.method == ‘POST’: try: file = request.files['uploadimg'] except Exception: return None if file and allowed_file(file.filename): format = "%Y-%m-%dT%H:%M:%S" now = datetime.datetime.utcnow().strftime(format) filename = now + '_' + file.filename filename = secure_filename(filename) basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 # upload_path = os.path.join(basepath,secure_filename(f.filename)) file.save(os.path.join(basepath, filename)) else: filename = None return filename @app.route(‘/download/string:filename’, methods=[‘GET’]) def download(filename): if request.method == “GET”: if os.path.isfile(os.path.join(basepath, filename)): return send_from_directory(basepath, filename, as_attachment=True) pass def get_contour(img): x = np.array(img) canny = 0 for layer in np.rollaxis(x, -1): canny |= get_canny_feature(layer, 0) canny = canny.astype(np.uint8) * 255 kernel = np.array([ [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], ], dtype=np.uint8) canny = cv2.dilate(canny, kernel) # canny = Image.fromarray(canny) return canny @app.route(‘/embedding//’, methods=[“GET”, “POST”]) def icontran(img, reference): global train_model if request.method == ‘POST’: imgPath = os.path.join(basepath, img) referencePath = os.path.join(basepath, reference) img = cv2.imread(imgPath) if img is None or img.size <= 0: return None contour = get_contour(img).astype(np.float32).copy() contour = 255 - contour reference = cv2.imread(referencePath).astype(np.float32) reference = cv2.cvtColor(reference, cv2.COLOR_BGR2RGB) reference = transform_Normalize(torch.from_numpy(reference).permute(2, 0, 1).unsqueeze(0).float()/ 255.0) contour = transform_Normalize(torch.from_numpy(contour).unsqueeze(0).unsqueeze(0).float()/ 255.0) train_model.eval() transfer = train_model(contour, reference) transfer = transfer.squeeze(0) transfer = (transfer + 0.5).clamp(0, 1).mul_(255).permute(1, 2, 0).type(torch.uint8).numpy() transfer = transfer.numpy() cv2.imwrite(imgPath, transfer) return basepath # success if name == “main”: load_model() app.run(host=‘10.21.16.144’, port=9999, debug=True) 用puthon写一个调用这个服务器的gui

这是一段 Python 代码，它使用 Flask 框架来创建一个网站应用。它还导入了一些需要使用的库，如 werkzeug、numpy、torch 等。该代码可以对图片进行一些处理，如调整亮度、色彩等。其中包括一个可以将黑白图片变成...

img = cv2.imread(imgpath, cv2.IMREAD_COLOR)什么意思

这是用 OpenCV 库的函数...其中 imgpath 是图像文件的路径。cv2.IMREAD_COLOR 表示以彩色模式读取图像，即读取的图像数组是一个 (height, width, 3) 的数组，最后一个维度分别代表蓝色通道、绿色通道和红色通道。

怎么解决无法从sklearn.datasets中导入fetch_mldata。

images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels X_train, y_train = load_mnist('path/to/mnist', kind='train') X_test, y_test = ...

clear clc pathname = uigetdir; name_list=dir(pathname); for i=3:22 name_list(i).引用了不存在的字段 'num'。出错 LoadData (line 33)num=zeros(20,1); name_list(i).num(i-2)=1; end %pathname = uigetdir; img_name1=importdata('E:\\train.txt'); img_train_num=size(img_name1,1); num=0; for i=1:img_train_num img_name_char=cell2mat(img_name1(i)); label_1(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_1(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); train_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata1(:,num)=temp; end for i=1:img_train_num for j=1:20 if strcmp(name_list(j+2).name,label_1(i).name) label_1(i).num=name_list(j+2).num; end end train_y(:,i)=uint8(label_1(i).num); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %pathname = uigetdir; img_name1=importdata('E\\test.txt'); img_test_num=size(img_name1,1); num=0; for i=1:img_test_num img_name_char=cell2mat(img_name1(i)); label_2(i).name=extractBefore(cell2mat(img_name1(i)),'_'); imgPath = [pathname,'\',label_2(i).name,'\',cell2mat(img_name1(i))]; temp = imread(imgPath); test_x(:,:,i)=temp; % temp = double(temp(:)); % num=num+1; % imagedata2(:,num)=temp; end for i=1:img_test_num for j=1:20 if strcmp(name_list(j+2).name,label_2(i).name) label_2(i).num=name_list(j+2).num; end end test_y(:,i)=uint8(label_2(i).num); end save('E\\imgdata_uint8.mat', 'train_x','train_y','test_x','test_y');

2. img_name1=importdata('E:\\train.txt'); 和 img_name1=importdata('E\\test.txt'); 这两行代码中的路径分隔符不一致，应该都使用反斜杠\。 3. save('E\\imgdata_uint8.mat', 'train_x','train_y','test_...

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代码解释：if __name__ == "__main__": postfix = 'jpg' imgpath = './VOCdevkit/JPEGImages' xmlpath = './VOCdevkit/Annotations' txtpath = './VOCdevkit/txt'

dataset = gdal.Open(imgPath, gdal.GA_ReadOnly)

if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--imgpath', type=str, default='images/crowdpose_100024.jpg', help="image path") parser.add_argument('--modelpath', type=str, default='weights/kapao_s_crowdpose.onnx') args = parser.parse_args()

代码解释：val_size = 0.1 test_size = 0.2 postfix = 'jpg' imgpath = './VOCdevkit/JPEGImages' txtpath = './VOCdevkit/txt'

img = cv2.imread(imgpath, cv2.IMREAD_COLOR)什么意思

怎么解决无法从sklearn.datasets中导入fetch_mldata。

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代码解释：if name == "main": postfix = 'jpg' imgpath = './VOCdevkit/JPEGImages' xmlpath = './VOCdevkit/Annotations' txtpath = './VOCdevkit/txt'

if name == "main": parser = argparse.ArgumentParser() parser.add_argument('--imgpath', type=str, default='images/crowdpose_100024.jpg', help="image path") parser.add_argument('--modelpath', type=str, default='weights/kapao_s_crowdpose.onnx') args = parser.parse_args()