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from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Densedef generate_vgg16(): input_shape = (224, 224, 3) # 传入宽高为244，通道为3的图 # 定义模型 model = Sequential([ # 定义卷积层，通道书为64， 卷积核为(3 x 3), 激活函数使用rel.

import matplotlib.pyplot as plt# 导入VGG16模型from keras.applications.vgg16 import VGG16from keras.preprocessing import imagefrom keras.applications.vgg16 import preprocess_input, decode_predictionsimport numpy as npmodel = VGG16(weights="imagenet", in.

import numpy as npimport pandas as pdfrom keras.models import Sequentialfrom keras.layers import *from keras.wrappers.scikit_learn import *from keras.utils import np_utilsfrom sklearn.model_selection import cross_val_score, KFoldfrom sklearn.prepro.

import numpy as npimport math# 逻辑回归算法class LogisticRegression: def __init__(self): """初始化loigistic regression模型""" self.coef_ = None # 维度 self.intercept_ = None # 截距 self._theta = None # sigmoid函数 def _.

import numpy as npclass kNN: def __init__(self): pass def fit(self, x_train, y_train): self.xtr = x_train self.ytr = y_train def predict(self, k, dis, x_test): assert dis == 'E' or dis == 'M' num_te.

1. Install RequiredPackages 1.sudo apt-get install build-essential 2.sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev 3.sudo apt-get install python-dev python-numpy libtbb2 li...

import mathdef distance(pointx, pointy, x1,y1,x2,y2): a = y2 - y1 b = x1 - x2 x = x2 * y1 - x * y2 dis = (math.fabs(a * pointx + b * pointy + c )) / (math.pow(a * a + b * b, 0.5) return dis

1. 读取图片img = cv2.imread(img_path)2.检测颜色范围(本案例以红色为主)color = [([5, 0, 180], [160, 120, 255])] # 数值按照[b, g, r]排布for (lower, upper) in color: # 创建numpy数组 lower = np.array(lower, dtype='uint8') upper = np.array(upper, dtype='uint8') #...

Tesseract-OCR字符识别简介  OCR(Optical Character Recognition)：光学字符识别，是指对图片文件中的文字进行分析识别，获取的过程。  Tesseract：开源的OCR识别引擎，初期Tesseract引擎由HP实验室研发，后来贡献给了开源软件业，后经由Google进行改进，消除bug，优化，重新发布。1.tesseract-OCR下载安装   https://digi.bib.uni-mannheim.de/tesseract/2.安装完成后查看安装版本

1 背景利用opencv识别数码管，采用传统的穿线法，前提是利用深度学习目标检测的方法，在机柜中将数码管区域切割出来，然后再对切割出来的区域进行识别，判断数字是多少。切割出来的形状如图所示：数码管各段表示为2 识别思路先对数码管进行灰度化和二值化，将数字变为255，背景变为0，然后利用穿线法...