from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser('create image pairs') parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A', type=str, default='./dataset/blurred') parser.add_argument('--fold_B', dest='fold_B', help='input directory for image B', type=str, default='./dataset/sharp') parser.add_argument('--fold_AB', dest='fold_AB', help='output directory', type=str, default='../dataset/out') parser.add_argument('--num_imgs', dest='num_imgs', help='number of images',type=int, default=1000000) parser.add_argument('--use_AB', dest='use_AB', help='if true: (0001_A, 0001_B) to (0001_AB)',action='store_true') args = parser.parse_args() for arg in vars(args): print('[%s] = ' % arg, getattr(args, arg)) splits = os.listdir(args.fold_A) for sp in splits: img_fold_A = os.path.join(args.fold_A, sp) img_fold_B = os.path.join(args.fold_B, sp) img_list = os.listdir(img_fold_A) if args.use_AB: img_list = [img_path for img_path in img_list if '_A.' in img_path] num_imgs = min(args.num_imgs, len(img_list)) print('split = %s, use %d/%d images' % (sp, num_imgs, len(img_list))) img_fold_AB = os.path.join(args.fold_AB, sp) if not os.path.isdir(img_fold_AB): os.makedirs(img_fold_AB) print('split = %s, number of images = %d' % (sp, num_imgs)) for n in range(num_imgs): name_A = img_list[n] path_A = os.path.join(img_fold_A, name_A) if args.use_AB: name_B = name_A.replace('_A.', '_B.') else: name_B = name_A path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) im_B = cv2.imread(path_B, cv2.IMREAD_COLOR) im_AB = np.concatenate([im_A, im_B], 1) cv2.imwrite(path_AB, im_AB),运行上述代码，提示错误：NotADirectoryError: [WinError 267] 目录名称无效。: 'D:\Users\Administrator\PycharmProjects\pythonProject\DeblurGAN-master\datasets\blurred\1.jpg'

def restore(self, save_path, model=None): if model is None: model = self.alg.model checkpoint = torch.load(save_path,map_location=torch.device('cpu')) # import pdb # pdb.set_trace() # a = torch.load_state_dict(checkpoint) model.load_state_dict(checkpoint)

这段代码是用来从指定路径 save_path 中加载模型参数到 model 中的。如果没有指定 model，则默认使用 self.alg.model。这个方法使用了 PyTorch 的 load() 方法来加载保存的模型参数。...

from Bio.PDB import PDBParser ModuleNotFoundError: No module named 'Bio'

而根据引用提供的修改逻辑，可以尝试替换该模块导入的方式为from Bio.Align import substitution_matrices。这样可以避免直接导入Bio.SubsMat.MatrixInfo这个模块而导致的错误。但是，从你提供的引用中并没有...

# 实验一：前向和反向传播算法的代码 # Forward + Backward + Optimize optimizer.zero_grad() # zero the gradient buffer #梯度清零，以免影响其他batch # 前向传播 # import pdb # pdb.set_trace() # loss # 后向传播，计算梯度，请完善以上代码

好的，接下来，我来完善这段代码。 # 实验一：前向和反向传播算法的代码 # Forward + Backward + Optimize # 梯度清零，以免影响其他batch optimizer.zero_grad() # 前向传播 outputs = model(inputs) ...

import lightgbm as lgb from lightgbm import LGBMClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV # 定义xgboost模型 lgb_model = lgb.LGBMClassifier() # 设置参数空间 params = { 'boosting_type': 'gbdt', #'boosting_type': 'dart', 'objective': 'multiclass', 'metric': 'multi_logloss', 'min_child_weight': 1.5, 'num_leaves': 25, 'lambda_l2': 10, 'subsample': 0.7, 'colsample_bytree': 0.7, 'learning_rate': 0.03, 'seed': 2017, "num_class": 2, 'silent': True, } # 输出最优参数 print('Best Parameter:', params) # 使用最优参数构建新的xgboost模型 lgb_model = lgb.LGBMClassifier(params) # 使用训练集对新模型进行拟合 # X_train = np.array(X_train) # pdb.set_trace() lgb_model.fit(X_train, y_train) # 使用新模型进行预测 y_pred = lgb_model.predict(X_test)改为svm模型

from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV # 定义svm模型 svm_model = ...

def pre_img(image): myimage = image.convert('L') # 转换成灰度图 myimage = np.array(myimage) # print(myimage.shape) ret, img1 = cv.threshold(myimage, 100, 255, cv.THRESH_BINARY_INV) # cv.namedWindow('img1',0) # cv.resizeWindow('img1',600,600) # cv.imshow('img1',img1) # print(type(img1)) # print(img1.shape) # print(img1.size) # cv.waitKey(2) kernel1 = np.ones((10, 10), np.uint8) # 做一次膨胀 img2 = cv.dilate(img1, kernel1) # cv.namedWindow('img2', 0) # cv.resizeWindow('img2', 600, 600) # cv.imshow('img2', img2) '剔除小连通域' contours, hierarchy = cv.findContours(img2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) # print(len(contours),hierarchy) for i in range(len(contours)): area = cv.contourArea(contours[i]) if area < 150: # '设定连通域最小阈值，小于该值被清理' cv.drawContours(img2, [contours[i]], 0, 0, -1) # cv2.imwrite('img2.jpg', img2) # import pdb;pdb.set_trace() img5 = cv.resize(img2, (28, 28)) # cv.namedWindow('img5', 0) # cv.resizeWindow('img5', 600, 600) # cv.imshow('img5', img5) # cv2.imwrite('img5.jpg', img5) # import pdb;pdb.set_trace() return img5 img_pre = pre_img(img) # # cv.imshow('img_pre', img_pre) # 将数据类型由uint8转为float32 img = img_pre.astype(np.float32) # 图片数据归一化 img = img / 255 改成单个代码可以运行

import numpy as np def pre_img(image): myimage = image.convert('L') # 转换成灰度图 myimage = np.array(myimage) ret, img1 = cv.threshold(myimage, 100, 255, cv.THRESH_BINARY_INV) kernel1 = np.ones...

pdb.set_trance()不能加到函数里吗

可以将pdb.set_trace()添加到函数中，以便在函数执行时进入调试模式。例如： python import pdb def my_function(): a = 1 b = 2 pdb.set_trace() # 在这里添加调试器 c = a + b return c my_function...

myimage = cv.cvtColor(img, cv.COLOR_BGR2GRAY) ret, img1 = cv.threshold(myimage, 100, 255, cv.THRESH_BINARY_INV) # cv.namedWindow('img1',0) # cv.resizeWindow('img1',600,600) # cv.imshow('img1',img1) # print(type(img1)) # print(img1.shape) # print(img1.size) # cv.waitKey(2) kernel1 = np.ones((10, 10), np.uint8) # 做一次膨胀 img2 = cv.dilate(img1, kernel1) # cv.namedWindow('img2', 0) # cv.resizeWindow('img2', 600, 600) # cv.imshow('img2', img2) contours, hierarchy = cv.findContours(img2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) # print(len(contours),hierarchy) for i in range(len(contours)): area = cv.contourArea(contours[i]) if area < 150: # '设定连通域最小阈值，小于该值被清理' cv.drawContours(img2, [contours[i]], 0, 0, -1) # import pdb;pdb.set_trace() # cv.imwrite('yuchuli.jpg', img2) ###########预处理 # import pdb;pdb.set_trace() not_row = img2[[not np.all(img2[i] == 0) for i in range(img2.shape[0])], :] bot_col = not_row[:, [not np.all(not_row[:, i] == 0) for i in range(not_row.shape[1])]] # import pdb;pdb.set_trace() # print(bot_col.shape) if bot_col.shape[0] > bot_col.shape[1]: if bot_col.shape[1] % 2 == 0: img_new = np.concatenate((np.zeros([bot_col.shape[0], int((bot_col.shape[0] - bot_col.shape[1]) / 2)]), bot_col, np.zeros([bot_col.shape[0], int((bot_col.shape[0] - bot_col.shape[1]) / 2)])), 1) if bot_col.shape[1] % 2 == 1: img_new = np.concatenate((np.zeros( [bot_col.shape[0], int((bot_col.shape[0] - bot_col.shape[1] - 1) / 2)]), bot_col, np.zeros( [bot_col.shape[0], int((bot_col.shape[0] - bot_col.shape[1] + 1) / 2)])), 1) cv.imwrite('fenge.jpg', img_new) ###########分割 file_path = 'fenge.jpg' return file_path这个具体以何种方法进行分割的

not_row = img2[[not np.all(img2[i] == 0) for i in range(img2.shape[0])], :] bot_col = not_row[:, [not np.all(not_row[:, i] == 0) for i in range(not_row.shape[1])]] if bot_col.shape[0] > bot_col.shape...

cv2pdb.exe

根据exe 生成对应的pdb 文件

msvcr120d.i386_it_notbrk_PDB_msvcr120d.i386_

msvcr120d.i386.pdb is too hard to download it

MD5.rar_md5.pdb_主板序列号

VB2005实现MD5加密，取硬盘序列号，主板序列号，并加密，适合使用做软件密码保护。

import pdb;pdb.set_trace()

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import os.path import pdb import time

def restore(self, save_path, model=None): if model is None: model = self.alg.model checkpoint = torch.load(save_path,map_location=torch.device('cpu')) # import pdb # pdb.set_trace() # a = torch.load_state_dict(checkpoint) model.load_state_dict(checkpoint)

from Bio.PDB import PDBParser ModuleNotFoundError: No module named 'Bio'

# 实验一：前向和反向传播算法的代码 # Forward + Backward + Optimize optimizer.zero_grad() # zero the gradient buffer #梯度清零，以免影响其他batch # 前向传播 # import pdb # pdb.set_trace() # loss # 后向传播，计算梯度，请完善以上代码

pdb.set_trance()不能加到函数里吗

cv2pdb.exe

msvcr120d.i386_it_notbrk_PDB_msvcr120d.i386_

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