How to make an artificial intelligence LINE bot with Flask + LINE Messaging API
How to make an artificial intelligence LINE bot with Flask + LINE Messaging API
Nakano Hitoshi's book
It was pretty interesting. At the end of the sample exercise is a bot app that hides faces with stamps by face recognition using Google Vision API. There are many apps like this, but it may be easier to use if you use a LINE bot.
Finally, he is given homework to try to make multiple face recognitions correspond. Only one person is supported in the book app. There is no answer. It's easy. Let's do our best on our own, Mr. Nakano.
I made an answer with a little effort, so for reference.
Face detection of the image sent by the user with Google Vison API and reply of the composite photo hidden by cat.png (supports multiple faces)
#app.py
#Face detection of the image sent by the user with Google Vison API and cat.Reply a composite photo hidden in png(Supports multiple faces)
import io
import os
import base64
import json
import requests
from flask import Flask, request, abort
from PIL import Image #Pillow install pip3 install pillow
from linebot import (
LineBotApi, WebhookHandler
)
from linebot.exceptions import (
InvalidSignatureError
)
from linebot.models import (
MessageEvent, TextMessage, TextSendMessage, ImageMessage, ImageSendMessage
)
#LINE access token and application secret
ACCESS_TOKEN = ''
SECRET = ''
#Google Vision API key
API_KEY = ''
app = Flask(__name__)
line_bot_api = LineBotApi(ACCESS_TOKEN)
handler = WebhookHandler(SECRET)
@app.route('/')
def hello_world():
return 'Hello World!'
@app.route('/callback',methods=['POST'])
def callback():
signature = request.headers['X-Line-Signature']
body = request.get_data(as_text=True)
app.logger.info("Request body: " + body)
try:
handler.handle(body, signature)
except InvalidSignatureError:
print("Invalid signature. Please check your channel access token/channel secret.")
abort(400)
return 'OK'
@handler.add(MessageEvent,message=ImageMessage)
def handle_message(event):
try:
message_content = line_bot_api.get_message_content(event.message.id)
# event.message.Image body data can be read by specifying id
# message_content.content #Acquired image file body
image_base64 = base64.b64encode(message_content.content) #Convert image files to base64
#Create request body (json.dumps()Converted to JSON)
req_body = json.dumps({
'requests': [{
'image': {
'content': image_base64.decode('utf-8')
},
'features': [{
'type': 'FACE_DETECTION',
'maxResults': 20,
}]
}]
})
#Vision API endpoint ↓
res = requests.post("https://vision.googleapis.com/v1/images:annotate?key=" + API_KEY, data=req_body)
#print('res content is' + res.text)
result = res.json()
vertices = result["responses"][0]["faceAnnotations"]
#print('The contents of vertices are' + json.dumps(vertices))
##response content is response.See json.
if vertices:
print('I was able to get')
image_base = Image.open(io.BytesIO(message_content.content))
for face in vertices:
corner = face["boundingPoly"]['vertices'][0]
print('corner is' + json.dumps(corner))
print('face["boundingPoly"]["vertices"][1]["x"]Is' + json.dumps(face["boundingPoly"]['vertices'][1]["x"]))
width = face["boundingPoly"]['vertices'][1]["x"] - face["boundingPoly"]['vertices'][0]["x"]
height = face["boundingPoly"]['vertices'][2]["y"] - face["boundingPoly"]['vertices'][1]["y"]
image_cover = Image.open('static/cat.png') # cat.png must be an alpha channel image. ValueError:bad transparency mask error
image_cover = image_cover.resize((width,height))
image_base.paste(image_cover, (corner['x'],corner['y']), image_cover)
# Image.paste(im, box=None, mask=None)
print('for loop end')
image_base.save('static/' + event.message.id + '.jpg')
line_bot_api.reply_message(
event.reply_token,
ImageSendMessage(
original_content_url = "https://hidden-savannah-xxxxx.herokuapp.com/static/" + event.message.id + ".jpg ",
preview_image_url = "https://hidden-savannah-xxxxx.herokuapp.com/static/" + event.message.id + ".jpg "
)
)
except:
line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text="I couldn't recognize my face (animals aren't good, only humans. It's tough to have a profile or too close up")
)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 5000)))
reference:
Just in case, the response content of print ('vertices content is' + json.dumps (vertices)) on line 80
[
{
"boundingPoly": {
"vertices": [
{
"x": 917,
"y": 318
},
{
"x": 1174,
"y": 318
},
{
"x": 1174,
"y": 616
},
{
"x": 917,
"y": 616
}
]
},
"fdBoundingPoly": {
"vertices": [
{
"x": 971,
"y": 396
},
{
"x": 1163,
"y": 396
},
{
"x": 1163,
"y": 588
},
{
"x": 971,
"y": 588
}
]
},
"landmarks": [
{
"type": "LEFT_EYE",
"position": {
"x": 1031.1968,
"y": 456.0161,
"z": 0.0003030986
}
},
{
"type": "RIGHT_EYE",
"position": {
"x": 1112.0862,
"y": 460.92987,
"z": 28.232975
}
},
{
"type": "LEFT_OF_LEFT_EYEBROW",
"position": {
"x": 1008.84607,
"y": 436.544,
"z": -1.8571037
}
},
{
"type": "RIGHT_OF_LEFT_EYEBROW",
"position": {
"x": 1060.1007,
"y": 440.86813,
"z": -7.585352
}
},
{
"type": "LEFT_OF_RIGHT_EYEBROW",
"position": {
"x": 1095.2485,
"y": 442.76245,
"z": 5.0468025
}
},
{
"type": "RIGHT_OF_RIGHT_EYEBROW",
"position": {
"x": 1131.141,
"y": 444.30832,
"z": 41.595203
}
},
{
"type": "MIDPOINT_BETWEEN_EYES",
"position": {
"x": 1075.8728,
"y": 455.9283,
"z": -1.5975293
}
},
{
"type": "NOSE_TIP",
"position": {
"x": 1080.8457,
"y": 504.33997,
"z": -20.247692
}
},
{
"type": "UPPER_LIP",
"position": {
"x": 1071.2343,
"y": 531.5437,
"z": -1.6211907
}
},
{
"type": "LOWER_LIP",
"position": {
"x": 1069.6505,
"y": 551.9242,
"z": 4.4038887
}
},
{
"type": "MOUTH_LEFT",
"position": {
"x": 1035.7985,
"y": 538.815,
"z": 8.222528
}
},
{
"type": "MOUTH_RIGHT",
"position": {
"x": 1101.0676,
"y": 541.8905,
"z": 30.981604
}
},
{
"type": "MOUTH_CENTER",
"position": {
"x": 1070.1655,
"y": 541.40643,
"z": 4.1978736
}
},
{
"type": "NOSE_BOTTOM_RIGHT",
"position": {
"x": 1092.8889,
"y": 510.94235,
"z": 16.238985
}
},
{
"type": "NOSE_BOTTOM_LEFT",
"position": {
"x": 1049.6199,
"y": 507.50146,
"z": 0.9902145
}
},
{
"type": "NOSE_BOTTOM_CENTER",
"position": {
"x": 1072.0765,
"y": 515.82806,
"z": -2.7877321
}
},
{
"type": "LEFT_EYE_TOP_BOUNDARY",
"position": {
"x": 1037.2472,
"y": 452.2355,
"z": -4.3320293
}
},
{
"type": "LEFT_EYE_RIGHT_CORNER",
"position": {
"x": 1047.4124,
"y": 459.2465,
"z": 6.317641
}
},
{
"type": "LEFT_EYE_BOTTOM_BOUNDARY",
"position": {
"x": 1030.3141,
"y": 461.9699,
"z": 0.34013578
}
},
{
"type": "LEFT_EYE_LEFT_CORNER",
"position": {
"x": 1018.07513,
"y": 455.93164,
"z": 2.3924496
}
},
{
"type": "LEFT_EYE_PUPIL",
"position": {
"x": 1034.6456,
"y": 457.22366,
"z": -1.4217875
}
},
{
"type": "RIGHT_EYE_TOP_BOUNDARY",
"position": {
"x": 1109.9236,
"y": 456.6617,
"z": 21.767094
}
},
{
"type": "RIGHT_EYE_RIGHT_CORNER",
"position": {
"x": 1119.8134,
"y": 462.12448,
"z": 38.996845
}
},
{
"type": "RIGHT_EYE_BOTTOM_BOUNDARY",
"position": {
"x": 1110.3936,
"y": 466.81308,
"z": 26.98832
}
},
{
"type": "RIGHT_EYE_LEFT_CORNER",
"position": {
"x": 1094.9646,
"y": 462.28857,
"z": 22.470396
}
},
{
"type": "RIGHT_EYE_PUPIL",
"position": {
"x": 1109.2263,
"y": 461.79114,
"z": 25.238665
}
},
{
"type": "LEFT_EYEBROW_UPPER_MIDPOINT",
"position": {
"x": 1037.4519,
"y": 429.95596,
"z": -10.386488
}
},
{
"type": "RIGHT_EYEBROW_UPPER_MIDPOINT",
"position": {
"x": 1116.0272,
"y": 434.71762,
"z": 18.003847
}
},
{
"type": "LEFT_EAR_TRAGION",
"position": {
"x": 954.1669,
"y": 484.3548,
"z": 76.21559
}
},
{
"type": "RIGHT_EAR_TRAGION",
"position": {
"x": 1119.6852,
"y": 494.08078,
"z": 135.9113
}
},
{
"type": "FOREHEAD_GLABELLA",
"position": {
"x": 1078.9543,
"y": 441.30212,
"z": -4.084726
}
},
{
"type": "CHIN_GNATHION",
"position": {
"x": 1062.5234,
"y": 589.9864,
"z": 16.94458
}
},
{
"type": "CHIN_LEFT_GONION",
"position": {
"x": 968.6994,
"y": 536.28186,
"z": 52.295383
}
},
{
"type": "CHIN_RIGHT_GONION",
"position": {
"x": 1117.5015,
"y": 545.4246,
"z": 105.74548
}
}
],
"rollAngle": 4.5907497,
"panAngle": 19.758451,
"tiltAngle": -3.1237326,
"detectionConfidence": 0.91960925,
"landmarkingConfidence": 0.5607769,
"joyLikelihood": "VERY_UNLIKELY",
"sorrowLikelihood": "VERY_UNLIKELY",
"angerLikelihood": "VERY_UNLIKELY",
"surpriseLikelihood": "VERY_UNLIKELY",
"underExposedLikelihood": "VERY_UNLIKELY",
"blurredLikelihood": "VERY_UNLIKELY",
"headwearLikelihood": "LIKELY"
},
・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・
]
It looks like this
Here are some of the books that didn't work: [Deploy Flask app to heroku (bitterly)](https://qiita.com/atomyah/items/91196f5fda95e4b9c7a6)
Reference: [Face detection using Google Cloud Vision in Python](https://vaaaaaanquish.hatenablog.com/?page=1471257396)
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