python 读写

python udp聊天示例源码

python显示获得google地图文件及路径规划(python show google maps obtained documents and path planning)

基于PYTHON的的坦克大战 支持坦克升级，购买道具( Tankedaizhan based on PYTHON Support tank upgrade, buy items)

基于LDA的人脸降维，精度比二维LDA的要高，有一定的运用价值。(orl tensorflow LDA PCA)

python动画（基于turtle+ math）

说明：  CN对图像进行像素级的分类，从而解决了语义级别的图像分割（semantic segmentation）问题。与经典的CNN在卷积层之后使用全连接层得到固定长度的特征向量进行分类（全联接层＋softmax输出）不同，FCN可以接受任意尺寸的输入图像，采用反卷积层对最后一个卷积层的feature map进行上采样, 使它恢复到输入图像相同的尺寸，从而可以对每个像素都产生了一个预测, 同时保留了原始输入图像中的空间信息, 最后在上采样的特征图上进行逐像素分类。(CN classifies images at the pixel level, thus resolving the problem of semantic segmentation at the semantic level. Unlike classical CNN, which uses full-connection layer to get fixed-length feature vectors after convolution layer for classification (full-connection layer + soft Max output), FCN can accept any size of input image, and uses deconvolution layer to sample feature map of the last convolution layer to restore it to the same size of input image, so that each pixel can be generated. At the same time, the spatial information of the original input image is retained. Finally, the pixel-by-pixel classification is carried out on the feature map sampled above.)

IRC Bot written in Python

利用python的pygame模块编写了一个外星人大战的游戏

import cv2 import numpy as np import math import matplotlib.pyplot as plt class Hog_descriptor(): def __init__(self, img, cell_size=16, bin_size=8): self.img = img self.img = np.sqrt(img / np.max(img)) self.img = img * 255 self.cell_size = cell_size self.bin_size = bin_size self.angle_unit = 360 / self.bin_size # assert type(self.bin_size) == int, "bin_size should be integer," # assert type(self.cell_size) == int, "cell_size should be integer," # assert type(self.angle_unit) == int, "bin_size should be divisible by 360" def extract(self): height, width = self.img.shape gradient_magnitude, gradient_angle = self.global_gradient() gradient_magnitude = abs(gradient_magnitude) cell_gradient_vector = np.zeros((int(height / self.cell_size), int(width / self.cell_size), self.bin_size)) for i in rang

说明：  音频分类算法 思路为：频谱分析->数据增强->卷积分类 之江大赛初赛22名方案(Audio classification algorithm The idea is: spectrum analysis data enhancement convolution classification Scheme of 22 winners in the preliminary competition of Zhijiang competition)