pip install python-opencv

import cv2 as cv
import numpy as np
import argparse

parser = argparse.ArgumentParser(
        description='This script is used to run style transfer models from '
                    'https://github.com/jcjohnson/fast-neural-style using OpenCV')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--model', help='Path to .t7 model')
parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
args = parser.parse_args()

net = cv.dnn.readNetFromTorch(args.model)

if args.input:
    cap = cv.VideoCapture(args.input)
else:
    cap = cv.VideoCapture(0)

cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
while cv.waitKey(1) < 0:
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break

    inWidth = args.width if args.width != -1 else frame.shape[1]
    inHeight = args.height if args.height != -1 else frame.shape[0]
    inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
                              (103.939, 116.779, 123.68), swapRB=False, crop=False)

    net.setInput(inp)
    out = net.forward()

    out = out.reshape(3, out.shape[2], out.shape[3])
    out[0] += 103.939
    out[1] += 116.779
    out[2] += 123.68
    out /= 255
    out = out.transpose(1, 2, 0)

    t, _ = net.getPerfProfile()
    freq = cv.getTickFrequency() / 1000
    print(t / freq, 'ms')

    if args.median_filter:
        out = cv.medianBlur(out, args.median_filter)

    cv.imshow('Styled image', out) 

python 1.py --input 1.jpg --model udnie.t7

pip install pystan

conda install -c conda-forge fbprophet
pip install fbprophet

import json

f = open('./data.json', 'r', encoding='utf-8')
json_data = json.load(f)
f.close()

print(json_data)

import pandas as pd
# 将日期和其对应的总粉丝数提取出来为一个pandas的dataframe
list_number = json_data['category_list'][0]['list']
df = pd.DataFrame(list_number)
print(df)

from fbprophet import Prophet

# prophet内部需要将日期列设为ds，预测的值设为y
df.rename(columns={'date':'ds', 'cumulate_user':'y'}, inplace=True)

print(df)

# 调用"先知"生成对象
m = Prophet()

# 使用"先知对象"进行预测
m.fit(df)

# 获得未来30天的数据
future = m.make_future_dataframe(periods=30)

forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())