from flask import Flask, request, jsonify
from utils import restful
from utils.deblur import deblur_image
from utils.couplet import coup
from utils.genres.complete import final_model as completeMusicModel
from utils.genres.split import final_model as splitMusicModel
from utils.genres import feature_extract
from flask_cors import *
import base64
import numpy as np
import cv2
import re
import jieba
from io import BytesIO
from PIL import Image
from wordcloud import WordCloud, ImageColorGenerator
import config
import os

app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False
# CORS(app=app, supports_credentials=True)
f=open(config.stopwords,"r")
stopwords={}.fromkeys(f.read().split("\n"))
f.close()
#加载用户自定义词典
jieba.load_userdict(config.user_dict)

@app.route('/')
def hello_world():
    return 'Hello World!'

@app.route("/api/couplet", methods=['GET'])
def couplet():
    up = request.args.get('up')
    print(up)
    return jsonify({
        "up":up,
        "down":coup(up)
    })

@app.route("/api/image/face_detection", methods=["POST"])
def face_detection():
    print("hello")
    base = request.json["base64"]
    base = re.sub('^data:image/.+;base64,', '', base)
    img = base64.b64decode(base)
    nparr = np.fromstring(img,np.uint8)  
    img=cv2.imdecode(nparr,cv2.COLOR_RGB2BGR)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 必须要经过一次转换最后在转到HSV，否则颜色空间出错

    h,s,v = cv2.split(img)
    img_face = np.zeros(shape=(img.shape[0], img.shape[1]))
    h_low, h_up, s_low, s_up, v_low, v_up = 0, 30, 30, 220, 90, 255
    img_face[(v >= v_low) * (v <= v_up) * (h >= h_low) * (h <= h_up) * (s >= s_low) * (s <= s_up)] = 255
    
    image_encode = cv2.imencode('.jpg', img_face) # (True, array([[255],[216], [255],...,[138],
    base64_data = str(base64.b64encode(image_encode[1]))  # b'/9j/4AAQSK...
    data = "data:image/jpg;base64,"+base64_data[2:-1]

    return jsonify({
        "msg": "succss",
        "data": data
    })

@app.route("/api/nlp/genwordcloud", methods=["POST"])
def genwordcloud():
    specifiedWords = request.json['specifiedWords']
    text = request.json['text']
    maskBackgroundImageBase64 = request.json['maskBackgroundImageBase64']
    #强调特殊名词
    for sw in specifiedWords.split(','):
        jieba.suggest_freq((sw), True)

    segs=jieba.cut(text)
    mytext_list=[]
    #文本清洗
    for seg in segs:
        if seg not in stopwords and seg!=" ":
            mytext_list.append(seg.replace(" ",""))
    cloud_text=",".join(mytext_list) 

    base64_data = re.sub('^data:image/.+;base64,', '', maskBackgroundImageBase64)
    byte_data = base64.b64decode(base64_data)
    image_data = BytesIO(byte_data)
    img = np.array(Image.open(image_data))
    wc = WordCloud(background_color="white", mask=img,max_words=2000,font_path="C:\Windows\Fonts\STFANGSO.TTF",min_font_size=1,max_font_size=60, stopwords=None)

    wc.generate(cloud_text)
    wc_img = wc.to_array()
    image_encode = cv2.imencode('.jpg', wc_img) # (True, array([[255],[216], [255],...,[138],
    base64_data = str(base64.b64encode(image_encode[1]))  # b'/9j/4AAQSK...
    data = "data:image/jpg;base64,"+base64_data[2:-1]
    return jsonify({
        'data': data
    })


@app.route("/api/image/deblur", methods=['POST'])
def deblur():
    width = request.json['width']
    height = request.json['height']
    base64str = request.json['base64']
    base64str = re.sub('^data:image/.+;base64,', '', base64str)
    bytestr = base64.b64decode(base64str)
    image = Image.open(BytesIO(bytestr))
    deblured_img = np.array(deblur_image.deblur(image).resize((width, height)))
    image_encode = cv2.imencode('.jpg',deblured_img)
    base64_data = str(base64.b64encode(image_encode[1]))  # b'/9j/4AAQSK...
    data = "data:image/jpg;base64," + base64_data[2:-1]

    return jsonify({
        "data": data
    })


def ToFile(base64data, file=r"E:\python_project\test_restful\music.wav"):
    try:
        ori_image_data = base64.b64decode(base64data)
        fout = open(file, 'wb')
        fout.write(ori_image_data)
        fout.close()
        print("success")
        return True
    except Exception as e:
        print(e)
        return False

@app.route("/api/signal/genres", methods=['POST'])
def genres():
    data = request.json['data']
    # print(data)
    result = ToFile(data)
    if result:
        a = request.json['a']
        a = float(a) if a else 0.08
        b = request.json['b']
        b = float(b) if b else 0.05
        slice = request.json['slice']
        print(a, b, type(a), type(b), slice, type(slice))
        file = os.path.join(config.dir, "music.wav")
        print(file)
        mfcc, logfbank = feature_extract.Processsor(file, a, b, slice).run()
        if not slice:  # 不切片
            model = completeMusicModel.CompleteModel([config.genres_complete_mfcc, config.genres_complete_logfbank]).get_final_model()
            p = model.predict({'mfcc_input': np.expand_dims(mfcc, axis=1),'attention_logbank_input': np.expand_dims(logfbank, axis=1)})[0].astype(float)/2
        else:
            model = splitMusicModel.SplitModel([config.genres_split_mfcc, config.genres_split_logfbank]).get_final_model()
            p = model.predict({'mfcc_input': np.expand_dims(mfcc, axis=1),'attention_logbank_input': np.expand_dims(logfbank, axis=1)}).astype(float)/2
            p = np.around(np.average(p, axis=0), decimals=4)

        probability = jsonify({"result":[
            {"name":"blues", "prob": round(p[0], 4)},
            {"name":"classical", "prob": round(p[1], 4)},
            {"name":"country", "prob": round(p[2], 4)},
            {"name":"disco", "prob": round(p[3], 4)},
            {"name":"hiphop", "prob": round(p[4], 4)},
            {"name":"jazz", "prob": round(p[5], 4)},
            {"name":"metal", "prob": round(p[6], 4)},
            {"name":"pop", "prob": round(p[7], 4)},
            {"name":"reggae", "prob": round(p[8], 4)},
            {"name":"rock", "prob": round(p[9], 4)},
        ]})
        return probability
    return "hello"

if __name__ == '__main__':
    app.run(port=8888)