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OpenCV3与深度学习实例:Dlib+VGG Face实现两张脸部图像相似度比较

实现思路: 1、使用Dlib识别并提取脸部图像 2、使用VGG Face模型提取脸部特征 3、使用余弦相似度算法比较两张脸部图像的特征 代码如下: import time import numpy as np import sklearn im...

09/29 13:48
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OpenCV3与深度学习实例:使用MobileNet SSD检测物体

#coding:utf-8 import numpy as np import argparse import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--image"...

09/29 10:06
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OpenCV图像处理实例:使用vlfeat的SLIC Superpixel实现图像分割

SLIC Superpixel算法可以参考:https://www.cnblogs.com/supersponge/p/6546082.html #include <iostream> #include <opencv2/opencv.hpp> extern "C" { #include "vlfeat/generic.h" #incl...

09/27 20:58
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OpenCV图像处理实例:libuv+cvui显示摄像头视频

#include <iostream> #include <opencv2/opencv.hpp> #define CVUI_IMPLEMENTATION #include <cvui.h> extern "C"{ #include <uv.h> } using namespace std; #define WINDOW_NAME "libuv cam...

09/26 01:22
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深度学习与图像处理实例:人像背景虚化与背景替换

简单人像背景虚化处理思路如下: 对图像内容分割,提取人像,背景 背景模糊处理 人像与模糊处理后的背景融合 本实例使用DeepLabV3图像分割深度学习模型实现。代码如下: import numpy as np...

09/18 23:47
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深度学习与人脸识别之-身份识别

1.加载已知身份人脸数据 #载入已经人脸数据 def load_known_faces(dirname): for img in fnmatch.filter(os.listdir(dirname), '*.jpg'): print('load image:',img) image = facer.loa......

09/15 11:10
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深度学习与人脸识别之-脸部分割与校正

1.检测脸部 def read_im_and_landmarks(fname): if not osp.exists(fname): raise Exception('Cannot find image file: {}'.format(fname)) im = cv2.imread(fname, cv2.IMREAD_COLOR) ......

09/14 23:16
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OpenCV3与深度学习实例-使用SSD Inception模型进行物体检测

#coding:utf-8 # Object Detection using SSD Inception arquitecture trained on COCO dataset import cv2 import sys FROZEN_GRAPH = "datas/models/tensorflow/ssd_inception_v2_coco.pb"...

OpenCV3与深度学习实例-使用YOLOV3进行物体检测

import cv2 import argparse import numpy as np ap = argparse.ArgumentParser() ap.add_argument('-i', '--image', required=False,default='datas/images/people.jpg', help = 'path t......

09/12 11:49
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OpenCV3与深度学习实例-使用OpenPose进行人体姿态估算

import cv2 import time import numpy as np import matplotlib.pyplot as plt import os # Load a Caffe Model if not os.path.isdir('model'): os.mkdir("model") protoFile = "datas/mo.....

09/12 11:46
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OpenCV3与深度学习实例-使用GoogLeNet模型进行图片分类识别

#coding:utf-8 import cv2 as cv import time import numpy as np def predict(image_path): prototxt = 'datas/models/caffe/bvlc_googlenet.prototxt' caffemodel = 'datas/models/caffe/b...

09/12 11:38
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Numpy图像处理快速入门2

1.导入需要的库 %matplotlib inline # 使用notebook时需要声明 import matplotlib.pyplot as plt import numpy as np import imageio as io import scipy import scipy.misc as sc 2.读取图像...

07/30 16:29
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Python使用Google翻译

1.安装相关库 pip install googletrans 2.翻译 from googletrans import Translator # 设置Google翻译服务地址 translator = Translator(service_urls=[ 'translate.google.cn' ]) # 翻译成中...

07/09 17:44
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Numpy图像处理快速入门

1.导入必要的库 import numpy as np import scipy %matplotlib inline import matplotlib.pyplot as plt 2.加载图片 image = matplotlib.pyplot.imread('../datas/f4.jpg') #读入的是灰度图像...

07/05 22:54
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MXNet动手学深度学习笔记:循环神经网络RNN实现

#coding:utf-8 ''' 循环神经网络 ''' import mxnet as mx from mxnet import ndarray as nd from mxnet.gluon import nn import random import os import sys sys.path.append(os.getcwd()) ...

MXNet动手学深度学习笔记:ResNet实现

#coding:utf-8 ''' ResNet ''' from mxnet.gluon import nn from mxnet import nd import sys import os sys.path.append(os.getcwd()) import utils from mxnet import gluon from mxnet im...

MXNet动手学深度学习笔记:GoogLeNet神经网络实现

#coding:utf-8 from mxnet.gluon import nn from mxnet import nd import sys import os sys.path.append(os.getcwd()) import utils from mxnet import gluon from mxnet import init clas...

MXNet动手学深度学习笔记:VGG神经网络实现

#coding:utf-8 ''' VGG网络 ''' from mxnet.gluon import nn from mxnet import ndarray as nd import mxnet as mx from mxnet import init import os import sys sys.path.append(os.getcw...

MXNet动手学深度学习笔记:卷积神经网络实现

#coding:utf-8 ''' 卷积神经网络 ''' import mxnet as mx from mxnet.gluon import nn from mxnet import ndarray as nd from mxnet import gluon try: ctx = mx.gpu() _ = nd.zer...

MXNet动手学深度学习笔记:卷积计算

#coding:utf-8 ''' 卷积计算 ''' import mxnet as mx from mxnet.gluon import nn from mxnet import ndarray as nd # 卷积层 # 输入输出的数据格式是: batch * channel * height * width...

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