原创 Opencv基礎自學九(複製區域)

import cv2 as cv import numpy as np src = cv.imread("D:/2018-07-31 101509.jpg") cv.namedWindow("input image", cv.WIN

原创 Opencv基礎自學十五(圖像對比)

#巴氏距離 相關性 #直方圖,顏色有255,然後分別統計有多少個 import cv2 as cv import numpy as np from matplotlib import pyplot as plt def creat

原创 機器學習基礎自學五(集成算法)

from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_sc

原创 機器學習基礎自學五(審覈分類算法)

from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_sc

原创 機器學習基礎自學三(評估算法)

from pandas import read_csv from sklearn.model_selection import train_test_split#通用模塊 from sklearn.linear_model import

原创 機器學習基礎自學七(算法調參range形式)

這是另外一種寫法,感覺比較好,用range的形式。 from pandas import read_csv from sklearn.linear_model import Ridge from sklearn.model_select

原创 機器學習基礎自學四(混沌矩陣)

from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_sc

原创 Opencv基礎自學二十五(閉操作)

import cv2 as cv import numpy as np def close_demo(image): print(image.shape) gray = cv.cvtColor(image, cv.

原创 機器學習基礎自學二(數據預處理)

主要是用到了統計學的方法去預處理數據 # 調整數據尺度(0..) from pandas import read_csv from numpy import set_printoptions from sklearn.preproces

原创 Opencv基礎自學二十四(開操作)

import cv2 as cv import numpy as np def open_demo(image): print(image.shape) gray = cv.cvtColor(image, cv.CO

原创 機器學習基礎自學六(算法調參)

from pandas import read_csv from sklearn.linear_model import Ridge from sklearn.model_selection import GridSearchCV #

原创 機器學習基礎自學一(算法審查)

機器學習中導入的庫比較多,需要一個一個確定是否導入成功 # 導入類庫 from pandas import read_csv from pandas.plotting import scatter_matrix from matplot

原创 機器學習基礎自學三(數據特徵)

from pandas import read_csv from sklearn.decomposition import PCA#主要成分分析 from sklearn.feature_selection import RFE#遞歸特

原创 Opencv基礎自學十八(拉普拉斯)

import cv2 as cv import numpy as np def lapalian_demo(image):#拉普拉斯 dst = cv.Laplacian(image, cv.CV_32F)#原生拉普拉斯 lpl

原创 Keras基礎自學十八(簡單圖像處理)

from keras.datasets import mnist import numpy as np from keras.models import Sequential from keras.layers import Dense