import os
from pathlib import Path
from typing import List
from flask import Flask, abort, request
from linebot import LineBotApi, WebhookHandler
from linebot.exceptions import InvalidSignatureError
from linebot.models import (ImageMessage, ImageSendMessage, MessageEvent, TextMessage, TextSendMessage)

from detect_and_lineup import detect_and_lineup
from mosaic import mosaic

app = Flask(__name__,static_url_path="/static")

YOUR_CHANNEL_ACCESS_TOKEN = os.environ["YOUR_CHANNEL_ACCESS_TOKEN"]
YOUR_CHANNEL_SECRET = os.environ["YOUR_CHANNEL_SECRET"]

line_bot_api = LineBotApi(YOUR_CHANNEL_ACCESS_TOKEN)
handler = WebhookHandler(YOUR_CHANNEL_SECRET)

SRC_IMAGE_PATH = "static/images/{}.jpg "
MAIN_IMAGE_PATH = "static/images/{}_main.jpg "
PREVIEW_IMAGE_PATH = "static/images/{}_preview.jpg "
FACE_COORDINATES_PATH = "{}.txt"

@app.route("/")
def hello_world():
    return "hello world!"


@app.route("/callback", methods=["POST"])
def callback():
    # get X-Line-Signature header value
    signature = request.headers["X-Line-Signature"]

    # get request body as text
    body = request.get_data(as_text=True)
    app.logger.info("Request body: " + body)

    # handle webhook body
    try:
        handler.handle(body, signature)
    except InvalidSignatureError:
        abort(400)
    return "OK"


@handler.add(MessageEvent, message=TextMessage)
def handle_message(event):
    profile = line_bot_api.get_profile(event.source.user_id)
    user_id = profile.user_id
    if event.message.text == 'Review':
        line_bot_api.reply_message(
        event.reply_token, messages=[TextSendMessage(text="<Review site URL>")]
        )
    else:    
        src_image_path = Path(SRC_IMAGE_PATH.format(user_id)).absolute()
        main_image_path = MAIN_IMAGE_PATH.format(user_id*2)
        preview_image_path = PREVIEW_IMAGE_PATH.format(user_id*2)
        face_coordinates_path = FACE_COORDINATES_PATH.format(user_id)

        numberslist = list(map(int,str(event.message.text).split()))

        with open(face_coordinates_path) as f:
            face_list = [list(map(int,s.strip().split())) for s in f.readlines()]

        mosaic(src=src_image_path, desc=Path(main_image_path).absolute(),numberslist=numberslist,face_list=face_list)
        mosaic(src=src_image_path, desc=Path(preview_image_path).absolute(),numberslist=numberslist,face_list=face_list)
        image_message = ImageSendMessage(
            original_content_url=f"https://<My application name>.herokuapp.com/{main_image_path}",
            preview_image_url=f"https://<My application name>.herokuapp.com/{preview_image_path}",
        )
        app.logger.info(f"https://<My application name>.herokuapp.com/{main_image_path}")
        line_bot_api.reply_message(
            event.reply_token, messages=[image_message,TextSendMessage(text="I'm sorry if you don't like it")]
        )
        src_image_path.unlink()
    

@handler.add(MessageEvent, message=ImageMessage)
def handle_image(event):
    message_id = event.message.id
    profile = line_bot_api.get_profile(event.source.user_id)
    user_id = profile.user_id

    src_image_path = Path(SRC_IMAGE_PATH.format(user_id)).absolute()
    main_image_path = MAIN_IMAGE_PATH.format(user_id)
    preview_image_path = PREVIEW_IMAGE_PATH.format(user_id)
    face_coordinates_path = FACE_COORDINATES_PATH.format(user_id)

    #Save image
    save_image(message_id, src_image_path)

    try:
        face_list = detect_and_lineup(src=src_image_path, desc=Path(main_image_path).absolute())
        detect_and_lineup(src=src_image_path, desc=Path(preview_image_path).absolute())
        #Send image
        image_message = ImageSendMessage(
        original_content_url=f"https://<My application name>.herokuapp.com/{main_image_path}",
        preview_image_url=f"https://<My application name>![S__54403102.jpg](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/832523/9269110c-f218-c528-858d-e3223283b588.jpeg)
.herokuapp.com/{preview_image_path}",
        )
        app.logger.info(f"https://alvinface2.herokuapp.com/{main_image_path}")
        line_bot_api.reply_message(event.reply_token, messages=[image_message, TextSendMessage(text="Can you enter the number of the face you want to mosaic, separated by half-width spaces?\n Example) I want to apply a mosaic to the 1st and 3rd faces\n ☞ Enter "1 3"\n\n'-1'You can also specify the number of the face you want to keep by prefixing it with.\n Example) I want to apply a mosaic to faces other than No. 0 and No. 2.\n☞「-Enter 1 0 2 "")])

        #Save face coordinate text file
        with open(face_coordinates_path, "w", encoding='utf-8') as f:
            for i in range(len(face_list)):
                f.write(" ".join([str(x) for x in face_list[i]]) + "\n")
        
    except Exception:
        line_bot_api.reply_message(
        event.reply_token, TextSendMessage(text='I couldn't find a recognizable face')
        )


def public_attr(obj) -> List[str]:
    return [x for x in obj.__dir__() if not x.startswith("_")]


def save_image(message_id: str, save_path: str) -> None:
    """Save"""
    message_content = line_bot_api.get_message_content(message_id)
    with open(save_path, "wb") as f:
        for chunk in message_content.iter_content():
            f.write(chunk)


if __name__ == "__main__":
    port = int(os.environ.get("PORT", 5000))
    app.run(host="0.0.0.0", port=port)

import numpy as np
from matplotlib import pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import cv2

def detect_and_lineup(src: str, desc: str) -> None:
    """Find and list faces
    :params src:
Image path to load
    :params desc:
Destination path
    """

    #Specifying the path of the cascade file (classifier of feature trained data)
    cascade_file = './cascade/haarcascade_frontalface_alt2.xml'

    #Image loading
    image = cv2.imread(str(src))
    #Convert to grayscale
    image_gs = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

    #Reading the feature amount file for face recognition
    cascade = cv2.CascadeClassifier(cascade_file)
    #Face recognition
    face_list = cascade.detectMultiScale(image_gs,
                                        scaleFactor=1.1,
                                        minNeighbors=1,
                                        minSize=(20,20))  #Ignore ranges below 20x20 pixels. To prevent the background from being mistaken for a face
    
    length = len(face_list)
    #Arrange pm x pm sheets in tiles
    pm = 1
    while pm**2 < length:
        pm += 1
    
    #Display a list of images in tiles
    fig, ax = plt.subplots(pm, pm, figsize=(10, 10))
    fig.subplots_adjust(hspace=0, wspace=0)

    for k in range(pm**2):
        i = k // pm  #Vertical
        j = k % pm  #side
        ax[i, j].xaxis.set_major_locator(plt.NullLocator())
        ax[i, j].yaxis.set_major_locator(plt.NullLocator())
        if k < length:
            x,y,w,h = face_list[k]
            #Cut out a face using array access
            #The image type is an array of Numpy(numpy.ndarray).. Easy to use
            face_img = image[y:y+h,x:x+w]
            face_img = np.asarray(face_img)
            face_img = cv2.resize(face_img, (300, 300), cv2.INTER_LANCZOS4)
            face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
            ax[i, j].text(30, 60, str(pm*i+j), fontsize=30, color='red')
            ax[i, j].imshow(face_img)
        else:
            img = cv2.imread('./white.jpg')
            img = np.asarray(img)
            img = cv2.resize(img, (300, 300), cv2.INTER_LANCZOS4)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            ax[i, j].imshow(img)

    plt.savefig(desc)
    return face_list

import numpy as np
from matplotlib import pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import cv2

def mosaic(src: str, desc: str, numberslist=[], face_list=[]) -> None:
    """
    :params src:
Image path to load
    :params desc:
Destination path
    :numberslist:
List of numbers entered by the user
    :face_list:
        detect_and_List of face coordinates recognized by lineup
    """

    #Image loading
    image = cv2.imread(str(src))

    #When the user specifies the number of the face they want to keep
    new_numberslist = []
    if numberslist[0] == -1:
        for num in range(len(face_list)):
            if num not in numberslist:
                new_numberslist.append(num)
    numberslist = new_numberslist

    for i,f in enumerate(face_list):
        x,y,w,h = f
        if i not in numberslist:
            continue
        #Reduce the cropped image at the specified magnification
        face_img = image[y:y+h,x:x+w]
        face_img = cv2.resize(face_img, (min(10,w//10),min(10,h//10)))
        #Restore the reduced image to its original size
        #Specify how to resize with the argument interpolation (cv).INTER_LINEAR has inconspicuous mosaic corners)
        face_img = cv2.resize(face_img,(w,h),interpolation=cv2.INTER_AREA)
        #Paste to the original image
        image[y:y+h,x:x+w] = face_img   

    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    im = Image.fromarray(image)
    im.save(desc)
    return True