Python+OpenCV 6種類のリアルタイム画像処理を詳しく解説

mkvirtualenv -p /sur/bin/python3.5 ml3

pip3 install Scikit-learn

import sklearn

pip3 install jieba

import jieba
jieba.cut("My name is Gan Fawn")

import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.metrics import silhouette_score

# Read the data from the four tables
prior = pd.read_csv("/Users/mac/instacart/order_products__prior.csv")
products = pd.read_csv("/Users/mac/instacart/products.csv")
orders = pd.read_csv("/Users/mac/instacart/orders.csv")
aisles = pd.read_csv("/Users/mac/instacart/aisles.csv")
# Merge four tables into one (user-item categories)
_mg = pd.merge(priority, products, on=['product_id', 'product_id']) # both tables should have the key 
_mg = pd.merge(_mg, orders, on=['order_id', 'order_id'])
mt = pd.merge(_mg, aisles, on=['aisle_id', 'aisle_id'])
mt.head(10)

# cross table (special grouping tool)
cross = pd.crosstab(mt['user_id'], mt['aisle']) # specify two parameters, row and column
cross.head(10)

# Perform a principal component analysis
pca = PCA(n_components=0.9)
data = pca.fit_transform(cross) # Inputs must be row and column type so cross-tabulation is used before
data

if values['enhance']:
    enh_val = values['enhancement_slider'] / 40
    clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8))
    lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
    lab[:, :, 0] = clahe.apply(lab[:, :, 0])
    frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)

if event == 'Exit' or event is None:
    break