lbaihao的专栏

（1）def ctc_lambda_func(args): y_pred, y_true, input_x_width, input_y_width = args # the 2 is critical here since the first couple outputs of the RNN # tend to be garbage: y_pred = y_pred[:, :, :] return keras.backend.ctc_batch_cost(y

import osimport numpy as npimport mathimport cv2 as cv# path = '/media/D/code/OCR/text-detection-ctpn/data/mlt_english+chinese/image'# gt_path = '/media/D/code/OCR/text-detection-ctpn/data/mlt_english+chinese/label'path = '/home/cwq/data/ICDAR13/C.

#coding=utf-8import osimport sysimport xml.etree.ElementTree as ETimport globdef xml_to_txt(indir,outdir): os.chdir(indir) annotations = os.listdir('.') annotations = glob.glob(str(annotations)+'*.xml') for i, file in enumerate(ann.

# -*- coding:UTF-8 -*-import os, sysimport globfrom PIL import Image# 图像存储位置src_img_dir = r"E:\dataset\CTPNSample\JPEGImages"# 图像的 ground truth 的 txt 文件存放位置src_txt_dir = r"E:\dataset\CTPNSample\label"# 生成xml文件存放位置src_xml_dir = r"E:\dataset\CTPNS.

from xml.dom.minidom import Documentimport cv2import osimport globimport shutilimport numpy as npglobal_outdir = '/tmp/TEXTVOC_ICDAR_2015/VOC2007'def generate_xml(name, lines, img_size, class_sets, doncateothers=True): doc = Document() .

from __future__ import divisionimport osimport numpy as npimport mathimport cv2 as cv#download the dataset from here: # http://rrc.cvc.uab.es/?ch=4&com=downloadsg_image_path = '/tmp/ICDAR_2015/training_images'g_gt_path = '/tmp/ICDAR_2015/gt.

from PIL import Imageim = Image.open("1.jpg")img_size = im.sizeprint("w,h{}".format(img_size))x = (600 - 448)/2y = (967 - 448)/2w = 448h = 448im = im.crop((x, y, x+w, y+h))x = (448 - 224)/2y = (448 - 224)/2w = 224h = 224im = im.crop((x, y,.

import numpy as npimport randomimport cv2def non_max_suppress(predicts_dict, threshold): for object_name, bbox in predicts_dict.items(): # 对每一个类别分别进行NMS；一次读取一对键值（即某个类别的所有框） bbox_array = np.array(bbox, dtype=np.float) # 下面分别获取框的...

https://blog.youkuaiyun.com/jclian91/article/details/83038861

https://github.com/Feeyao/License-plate-recognitionhttps://guoqiang.blog.youkuaiyun.com/article/details/104040582?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-18.control&depth_1-utm_source=distribute.pc_rele

以下是我看过最好的全连接层的解释！原文链接：卷积神经网络（CNN）入门讲解关注专栏以上图为例，我们仔细看上图全连接层的结构，全连接层中的每一层是由许多神经元组成的（1 × 4096）的平铺结构，上图不明显，我们看下图：注：上图和我们要做的下面运算无联系并且不考虑激活函数和bias当我第一次看到这个全连接层，我的第一个问题是：它是怎么样把3x3x5的输出，转换成1x4096的形式？很简单，可以理解为在中间做了一个卷积。从上图我们可以看出，我们用一个 3×3×5 的 filter 去卷积

https://blog.youkuaiyun.com/m0_37407756/article/details/80904580