This commit is contained in:
telereview
2023-03-27 09:16:56 +02:00
23 changed files with 74 additions and 40 deletions

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@@ -119,7 +119,6 @@ def init_database():
if not os.path.isfile(os.path.join(data_dir, word)):
for file in os.listdir(os.path.join(data_dir,word)):
if os.path.isfile(os.path.join(data_dir, word,file)):
print(word,os.path.join(data_dir, word,file))
words.append(word)
files.append(os.path.join(data_dir, word,file))
return words,files
@@ -130,7 +129,23 @@ def get_word_metadata(word):
return data[word]
#Todo : detecte si pas de note donnée
def get_grade():
def record():
sr = 44100 # fréquence d'échantillonnage
duration = 6 # durée d'enregistrement en secondes
filename = "recording" # nom du fichier à enregistrer
record_audio(filename, duration, sr)
audio_query,sr = librosa.load(f'{filename}.wav', sr=sr)
return audio_query,sr
def analyze(audio_query,sr):
coupe_silence(audio_query)
words, files = init_database()
audio_train_list = [librosa.load(file, sr=sr)[0] for file in files]
recognized_word_index = recognize_speech(audio_query, audio_train_list, sr)
recognized_word = words[recognized_word_index]
return get_word_metadata(recognized_word)
def test():
sr = 44100 # fréquence d'échantillonnage
duration = 6 # durée d'enregistrement en secondes
filename = "recording" # nom du fichier à enregistrer

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@@ -1,13 +1,18 @@
import cv2
import mediapipe as mp
import numpy as np
import os
from dotenv import load_dotenv
load_dotenv()
class HandDetector():
def __init__(self):
self.camera_id = int(os.getenv("CAMERA_ID"))
self.mp_drawing = mp.solutions.drawing_utils
self.mp_drawing_styles = mp.solutions.drawing_styles
self.mp_hands = mp.solutions.hands
self.cap = cv2.VideoCapture(0)
self.cap = cv2.VideoCapture(self.camera_id)
self.hands = self.mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
@@ -51,8 +56,9 @@ class HandDetector():
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = self.hands.process(image)
# print(results)
if results.multi_hand_landmarks:
handsPositions = []
for hand_landmarks in results.multi_hand_landmarks:

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@@ -1,5 +1,5 @@
from hand_detector import HandDetector
from audio_detector import get_grade
from audio_detector import record, analyze, test
from network import ApiClient, WebsocketServer
import time
@@ -57,23 +57,26 @@ class Manager():
state, coords, size, finalDecision = res
self.server.sendMessage({"type": "effects", "effects": [{"type": state, "x":coords[0], "y": coords[1], "width": size, "height": size}]})
self.isLastHandPacketEmpty = False
self.timeLastChange = time.time()
if(finalDecision != False):
self.avis["note"] = 10 if finalDecision == "thumbs_up" else 0
self.state = 2
self.timeLastChange = time.time()
self.server.sendMessage({"type": "state", "state": 2})
elif self.isLastHandPacketEmpty == False:
self.server.sendMessage({"type":"effects","effects":[]})
self.isLastHandPacketEmpty = True
def audio(self):
result = get_grade()
audio_query,sr = record()
self.server.sendMessage({"type":"recording_done"})
result = analyze(audio_query,sr)
# result = test()
if(result != False):
print("mot detecté : " + result["display"] + " avec une note de " + str(result["grade"]))
self.server.sendMessage({"type":"new_grade","word":result["display"]})
self.avis["notes_autres"]["test"] = result["grade"]
time.sleep(3)
self.state = 3
self.timeLastChange = time.time()
self.server.sendMessage({"type": "state", "state": 3})
def thankYou(self):

View File

@@ -4,6 +4,9 @@ import json
import os
import threading
import websockets
from dotenv import load_dotenv
load_dotenv()
class WebsocketServer(threading.Thread):
def __init__(self, onMessage, port=os.getenv("PORT"), host=os.getenv("HOST")):

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@@ -85,22 +85,22 @@ services:
# #Backend de la borne : scripts pythons de reconnaissances video et audio
# #Envoient les infos a l'interface de la borne par websocket pour mettre a jour l'interface rapidement
# #Met a jour les avis en faisant des requêtes a l'API
backend_reconnaissance:
build: ./backend_reconnaissance
container_name: backend_reconnaissance
restart: always
devices:
- /dev/video3:/dev/video0
- /dev/snd:/dev/snd
environment:
- PORT=5000
- HOST=backend_reconnaissance
- API_HOST=reviews_api
- API_PORT=8080
ports:
#Ce container est le serveur websocker dont le client est l'interface de la borne qui tourne dans le navigateur
- 5000:5000
user: root
# backend_reconnaissance:
# build: ./backend_reconnaissance
# container_name: backend_reconnaissance
# restart: always
# devices:
# - /dev/video3:/dev/video0
# - /dev/snd:/dev/snd
# environment:
# - PORT=5000
# - HOST=backend_reconnaissance
# - API_HOST=reviews_api
# - API_PORT=8080
# ports:
# #Ce container est le serveur websocker dont le client est l'interface de la borne qui tourne dans le navigateur
# - 5000:5000
# user: root
video_loopback:
build: ./video_loopback

View File

@@ -7,16 +7,21 @@ class AudioPage {
set enabled(isEnabled) {
this.isEnabled = isEnabled;
this.DOMElement.style.display = isEnabled ? "block" : "none";
document.getElementById("grade").innerHTML = "";
document.getElementById("audio_status").innerHTML = "Enregistrement...";
}
onRecordingDone() {
if(this.isEnabled) {
document.getElementById("audio_status").innerHTML = "Traitement...";
}
}
setGrade(grade) {
if(this.isEnabled) {
document.getElementById("grade").innerHTML = grade.toString();
document.getElementById("audio_status").innerHTML = grade;
}
}
reset() {
document.getElementById("grade").innerHTML = "";
document.getElementById("audio_status").innerHTML = "Enregistrement...";
}
}

View File

@@ -1,5 +1,5 @@
class WebsocketClient {
constructor(onNewEffects, onNewState, onNewGrade, onReset) {
constructor(onNewEffects, onNewState, onNewGrade, onReset, onRecordingDone) {
this.socket = new WebSocket("ws://localhost:5000");
this.socket.addEventListener("open", (event) => {
this.socket.send("connected");
@@ -13,10 +13,13 @@ class WebsocketClient {
}else if(msg.type == "state") {
onNewState(msg.state);
}else if(msg.type == "new_grade") {
onNewGrade(Number(msg.grade));
onNewGrade(msg.word);
}else if(msg.type == "reset") {
onReset();
}
else if(msg.type == "recording_done") {
onRecordingDone();
}
};
}
}

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@@ -21,6 +21,7 @@ class StateManager {
(state) => this.setState(state),
(grade) => this._audioPage.setGrade(grade),
() => this.reset(),
() => this._audioPage.onRecordingDone(),
);
this._sleepingPage.enabled = true;

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@@ -35,17 +35,15 @@
<div class="title">
<h1>Dites-nous en plus</h1>
</div>
<p>Donnez une note sur 10 au critère suivant</p>
<table>
<tr>
<th>Critère</td>
<th>Note / 10</td>
</tr>
<tr>
<td>Calme</td>
<td><span id="grade"></span>/10</td>
</tr>
</table>
<p>Comment avez vous trouvé l'exposition ... ?</p>
<p>Dites un mot parmis la liste suivante</p>
<ul>
<li>J'ai beaucoup aimé</li>
<li>génial</li>
<li>Ennuyant</li>
<li>Nul</li>
</ul>
<p>Mot détécté : <span id="audio_status"></span></p>
</div>
</div>
<div id="thank-you">