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restructuration du backend
préparation de l'intégration des modules suivants
This commit is contained in:
Quentin Roussel
@@ -1,98 +0,0 @@
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import cv2
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import mediapipe as mp
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_hands = mp.solutions.hands
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def prodScalaire(V1,V2):
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return (V1[0]*V2[0]+V1[1]*V2[1])/((V1[0]**2+V1[1]**2)**(1/2)*(V2[0]**2+V2[1]**2)**(1/2)) #produit scalaire normalisé
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# initialisation de la caméra
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cap = cv2.VideoCapture(0)
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# initialisation de Mediapipe Hands
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with mp_hands.Hands( static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5) as hands:
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while cap.isOpened():
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# lecture de la vidéo
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ret, frame = cap.read()
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# conversion de l'image en RGB
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image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# détection des mains
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results = hands.process(image)
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# Draw the hand annotations on the image.
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image.flags.writeable = True
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if results.multi_hand_landmarks:
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for hand_landmarks in results.multi_hand_landmarks:
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mp_drawing.draw_landmarks(
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image,
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hand_landmarks,
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mp_hands.HAND_CONNECTIONS,
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mp_drawing_styles.get_default_hand_landmarks_style(),
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mp_drawing_styles.get_default_hand_connections_style())
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# détection du nombre de doigts levés
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hand_landmarks = [0, 0]
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finger_count = 0
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if len(results.multi_hand_landmarks) >0 :
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hand_landmarks[0] = results.multi_hand_landmarks[0]
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V0= [hand_landmarks[0].landmark[2].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[2].y - hand_landmarks[0].landmark[0].y]
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V1= [hand_landmarks[0].landmark[4].x - hand_landmarks[0].landmark[2].x, hand_landmarks[0].landmark[4].y - hand_landmarks[0].landmark[2].y]
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if prodScalaire(V0,V1) > 0.905135675:
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finger_count += 1
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V0= [hand_landmarks[0].landmark[8].x - hand_landmarks[0].landmark[6].x, hand_landmarks[0].landmark[8].y - hand_landmarks[0].landmark[6].y]
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V1= [hand_landmarks[0].landmark[6].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[6].y - hand_landmarks[0].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[0].landmark[12].x - hand_landmarks[0].landmark[10].x, hand_landmarks[0].landmark[12].y - hand_landmarks[0].landmark[10].y]
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V1= [hand_landmarks[0].landmark[10].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[10].y - hand_landmarks[0].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[0].landmark[20].x - hand_landmarks[0].landmark[18].x, hand_landmarks[0].landmark[20].y - hand_landmarks[0].landmark[18].y]
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V1= [hand_landmarks[0].landmark[18].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[18].y - hand_landmarks[0].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[0].landmark[16].x - hand_landmarks[0].landmark[14].x, hand_landmarks[0].landmark[16].y - hand_landmarks[0].landmark[14].y]
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V1= [hand_landmarks[0].landmark[14].x - hand_landmarks[0].landmark[0].x, hand_landmarks[0].landmark[14].y - hand_landmarks[0].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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if len(results.multi_hand_landmarks) >1 :
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hand_landmarks[1] = results.multi_hand_landmarks[1]
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V0= [hand_landmarks[1].landmark[2].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[2].y - hand_landmarks[1].landmark[0].y]
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V1= [hand_landmarks[1].landmark[4].x - hand_landmarks[1].landmark[2].x, hand_landmarks[1].landmark[4].y - hand_landmarks[1].landmark[2].y]
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if prodScalaire(V0,V1) > 0.905135675:
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finger_count += 1
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V0= [hand_landmarks[1].landmark[8].x - hand_landmarks[1].landmark[6].x, hand_landmarks[1].landmark[8].y - hand_landmarks[1].landmark[6].y]
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V1= [hand_landmarks[1].landmark[6].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[6].y - hand_landmarks[1].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[1].landmark[12].x - hand_landmarks[1].landmark[10].x, hand_landmarks[1].landmark[12].y - hand_landmarks[1].landmark[10].y]
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V1= [hand_landmarks[1].landmark[10].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[10].y - hand_landmarks[1].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[1].landmark[20].x - hand_landmarks[1].landmark[18].x, hand_landmarks[1].landmark[20].y - hand_landmarks[1].landmark[18].y]
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V1= [hand_landmarks[1].landmark[18].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[18].y - hand_landmarks[1].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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V0= [hand_landmarks[1].landmark[16].x - hand_landmarks[1].landmark[14].x, hand_landmarks[1].landmark[16].y - hand_landmarks[1].landmark[14].y]
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V1= [hand_landmarks[1].landmark[14].x - hand_landmarks[1].landmark[0].x, hand_landmarks[1].landmark[14].y - hand_landmarks[1].landmark[0].y]
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if prodScalaire(V0,V1) > 0:
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finger_count += 1
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# affichage du nombre de doigts levés
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cv2.putText(image, f"Finger count: {finger_count}", (10, 50),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# affichage de la vidéo
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cv2.imshow('Video', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
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if cv2.waitKey(10) & 0xFF == ord('q'):
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break
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# libération de la caméra et des ressources
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cap.release()
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cv2.destroyAllWindows()
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@@ -1,29 +0,0 @@
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import requests
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#Exemple ajout d'un commentaire depuis la borne (site ou geste)
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avis = {
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"note": 8,
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"source": "borne",
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#Optionel
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"auteur_age": 20,
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"notes_autre": '{"proprete":8,"calme":10}',
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"auteur_sexe": 'f',
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"commentaire": "Commentaire"
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}
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res = requests.post("http://localhost:8080/add_review", data=avis)
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# print(res.text)
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#Exemple ajout d'un commentaire trouvé sur les réseaux sociaux
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avis = {
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"auteur_nom": "michel",
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"source": "instagram",
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"note": 8,
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"date": "2022-12-24",
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#Optionel
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"commentaire": "J'ai beaucoup aimé !",
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"lien": "https://instagram.com/si_insta_avait_des_liens_vers_des_commentaires_faudrait_le_mettre_ici",
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"auteur_lien": "https://instagram.com/michel",
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}
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# res = requests.post("http://localhost:8080/add_social_review", data=avis)
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print(res.text)
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84
code/backend_reconnaissance/hand_detector.py
Normal file
84
code/backend_reconnaissance/hand_detector.py
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@@ -0,0 +1,84 @@
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import cv2
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import mediapipe as mp
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import numpy as np
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class HandDetector():
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def __init__(self):
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self.mp_drawing = mp.solutions.drawing_utils
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self.mp_drawing_styles = mp.solutions.drawing_styles
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self.mp_hands = mp.solutions.hands
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self.cap = cv2.VideoCapture(0)
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self.hands = self.mp_hands.Hands(
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model_complexity=0,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5)
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#Paramètres
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self.BUFFER_LENGTH = 30
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self.DETECTION_THRESHOLD = 3/4
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self.resultBuffer = []
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def reconnaissancePouce(self,handLandmarks):
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etatDuPouce=["neutre","thumbs_down","thumbs_up"]
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i=0
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j=0
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for cpt in range (0,4):
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V1=[handLandmarks[(4*cpt)+6][0]-handLandmarks[(4*cpt)+5][0],handLandmarks[(4*cpt)+6][1]-handLandmarks[(4*cpt)+5][1]]
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V2=[handLandmarks[(4*cpt)+8][0]-handLandmarks[(4*cpt)+6][0],handLandmarks[(4*cpt)+8][1]-handLandmarks[(4*cpt)+6][1]]
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j=np.dot(V1,V2)
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if (j>0):
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return etatDuPouce[0]
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V1=[handLandmarks[4][0]-handLandmarks[1][0],handLandmarks[4][1]-handLandmarks[1][1]]
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V2=[handLandmarks[2][0]-handLandmarks[1][0],handLandmarks[2][1]-handLandmarks[1][1]]
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if((np.dot(V1,V2))>0 and (handLandmarks[4][1]>handLandmarks[2][1])):
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i=1
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elif(np.dot(V1,V2)>0 and handLandmarks[4][1]<handLandmarks[2][1]):
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i=2
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return etatDuPouce[i]
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def detect(self):
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if self.cap.isOpened():
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success, image = self.cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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return False
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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results = self.hands.process(image)
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# print(results)
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handLandmarks = []
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if results.multi_hand_landmarks:
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for hand_landmarks in results.multi_hand_landmarks:
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# Fill list with x and y positions of each landmark
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for landmarks in hand_landmarks.landmark:
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handLandmarks.append([landmarks.x, landmarks.y])
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thumbState = self.reconnaissancePouce(handLandmarks)
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self.resultBuffer.append(thumbState)
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if(len(self.resultBuffer) > self.BUFFER_LENGTH):
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self.resultBuffer.pop(0)
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thumbsUpCount = sum(map(lambda x : x == "thumbs_up", self.resultBuffer))
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thumbsDownCount = sum(map(lambda x : x == "thumbs_down", self.resultBuffer))
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print(thumbsUpCount,thumbsDownCount)
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if(thumbsUpCount > self.DETECTION_THRESHOLD * self.BUFFER_LENGTH):
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result = "thumbs_up"
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elif(thumbsDownCount > self.DETECTION_THRESHOLD * self.BUFFER_LENGTH):
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result = "thumbs_down"
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else:
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result = False
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if(thumbState != "neutre"):
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return thumbState, handLandmarks[9], np.linalg.norm(np.array(handLandmarks[9]) - np.array(handLandmarks[0])), result
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return False
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@@ -1,78 +0,0 @@
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import cv2
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import mediapipe as mp
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import numpy as np
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_hands = mp.solutions.hands
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cap = cv2.VideoCapture(0)
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hands = mp_hands.Hands(
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model_complexity=0,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5)
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BUFFER_LENGTH = 30
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TH_FRACTION = 3/4
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resultBuffer = []
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def reconnaissancePouce(handLandmarks):
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etatDuPouce=["neutre","thumbs_down","thumbs_up"]
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i=0
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j=0
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for cpt in range (0,4):
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V1=[handLandmarks[(4*cpt)+6][0]-handLandmarks[(4*cpt)+5][0],handLandmarks[(4*cpt)+6][1]-handLandmarks[(4*cpt)+5][1]]
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V2=[handLandmarks[(4*cpt)+8][0]-handLandmarks[(4*cpt)+6][0],handLandmarks[(4*cpt)+8][1]-handLandmarks[(4*cpt)+6][1]]
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j=np.dot(V1,V2)
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if (j>0):
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return etatDuPouce[0]
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V1=[handLandmarks[4][0]-handLandmarks[1][0],handLandmarks[4][1]-handLandmarks[1][1]]
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V2=[handLandmarks[2][0]-handLandmarks[1][0],handLandmarks[2][1]-handLandmarks[1][1]]
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if((np.dot(V1,V2))>0 and (handLandmarks[4][1]>handLandmarks[2][1])):
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i=1
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elif(np.dot(V1,V2)>0 and handLandmarks[4][1]<handLandmarks[2][1]):
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i=2
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return etatDuPouce[i]
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def getThumbState():
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if cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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return False
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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results = hands.process(image)
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# print(results)
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handLandmarks = []
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if results.multi_hand_landmarks:
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for hand_landmarks in results.multi_hand_landmarks:
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# Fill list with x and y positions of each landmark
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for landmarks in hand_landmarks.landmark:
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handLandmarks.append([landmarks.x, landmarks.y])
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thumbState = reconnaissancePouce(handLandmarks)
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resultBuffer.append(thumbState)
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if(len(resultBuffer) > BUFFER_LENGTH):
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resultBuffer.pop(0)
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thumbsUpCount = sum(map(lambda x : x == "thumbs_up", resultBuffer))
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thumbsDownCount = sum(map(lambda x : x == "thumbs_down", resultBuffer))
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print(thumbsUpCount,thumbsDownCount)
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if(thumbsUpCount > TH_FRACTION * BUFFER_LENGTH):
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result = "thumbs_up"
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elif(thumbsDownCount > TH_FRACTION * BUFFER_LENGTH):
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result = "thumbs_down"
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else:
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result = False
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if(thumbState != "neutre"):
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return thumbState, handLandmarks[9], np.linalg.norm(np.array(handLandmarks[9]) - np.array(handLandmarks[0])), result
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return False
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@@ -1,45 +1,5 @@
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import asyncio
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import json
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import math
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import websockets
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import random
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import os
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import time
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from hands import getThumbState
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class WebsocketServer:
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def __init__(self,getEffects,port=os.getenv("PORT"),host=os.getenv("HOST")) -> None:
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self.thumbResult = None
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self.state = 0
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self.host = host
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self.port = port
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self.getEffects = getEffects
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async def run(self):
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async with websockets.serve(self.handler, self.host, self.port):
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await asyncio.Future()
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async def handler(self,websocket):
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while True:
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if(self.state == 0):
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messages, result = self.getEffects()
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if(messages != False):
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if(result == False):
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await websocket.send(json.dumps(messages))
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else:
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self.thumbResult = result
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self.state = 1
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await websocket.send('{"type":"state","state":2}')
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def getEffects():
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res = getThumbState()
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if(res != False):
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state, coords, size, result = res
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return {"type": "effects", "effects": [{"type": state, "x":coords[0], "y": coords[1], "width": size, "height": size}]}, result
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else:
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return False,False
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server = WebsocketServer(getEffects)
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asyncio.run(server.run())
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from manager import Manager
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if __name__ == "__main__":
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print("backend started")
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m = Manager()
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m.loop()
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44
code/backend_reconnaissance/manager.py
Normal file
44
code/backend_reconnaissance/manager.py
Normal file
@@ -0,0 +1,44 @@
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from hand_detector import HandDetector
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from network import WebsocketServer
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import time
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class Manager():
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def __init__(self):
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self.state = 0
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self.avis = {
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"note": None,
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"commentaire": None,
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"notes_autres": {}
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}
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self.server = WebsocketServer(None)
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self.server.start()
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self.handDetector = HandDetector()
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print("Backend ready")
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def loop(self):
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while(True):
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if(self.state == 0):
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self.sleep()
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if(self.state == 1):
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self.camera()
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time.sleep(0.01)
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def sleep(self):
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res = self.handDetector.detect()
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if(res != False):
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self.state = 1
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self.server.sendMessage({"type": "state", "state": 1})
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def camera(self):
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res = self.handDetector.detect()
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if(res != False):
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state, coords, size, finalDecision = res
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self.server.sendMessage({"type": "effects", "effects": [{"type": state, "x":coords[0], "y": coords[1], "width": size, "height": size}]})
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if(finalDecision != False):
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self.avis["note"] = 10 if finalDecision == "thumbs_up" else 0
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self.state = 2
|
||||
self.server.sendMessage({"type": "state", "state": 2})
|
||||
|
||||
|
||||
|
||||
31
code/backend_reconnaissance/network.py
Normal file
31
code/backend_reconnaissance/network.py
Normal file
@@ -0,0 +1,31 @@
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import websockets
|
||||
|
||||
class WebsocketServer(threading.Thread):
|
||||
def __init__(self, onMessage, port=os.getenv("PORT"), host=os.getenv("HOST")):
|
||||
threading.Thread.__init__(self)
|
||||
self.host = host
|
||||
self.port = port
|
||||
self.messageQueue = []
|
||||
self.onMessage = onMessage
|
||||
|
||||
def run(self):
|
||||
print("server thread started")
|
||||
asyncio.run(self.runServer())
|
||||
|
||||
async def runServer(self):
|
||||
async with websockets.serve(self.handler, self.host, self.port):
|
||||
await asyncio.Future()
|
||||
|
||||
async def handler(self,websocket):
|
||||
while True:
|
||||
for msg in self.messageQueue:
|
||||
await websocket.send(json.dumps(msg))
|
||||
self.messageQueue.pop(0)
|
||||
await asyncio.sleep(0.01)
|
||||
|
||||
def sendMessage(self,message):
|
||||
self.messageQueue.append(message)
|
||||
@@ -1,88 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_drawing_styles = mp.solutions.drawing_styles
|
||||
mp_hands = mp.solutions.hands
|
||||
|
||||
def prodScalaire(V1,V2):
|
||||
return V1[0]*V2[0]+V1[1]*V2[1]
|
||||
|
||||
def reconnaissancePouce(handLandmarks):
|
||||
etatDuPouce=["neutre","baissé","levé"]
|
||||
i=0
|
||||
j=0
|
||||
for cpt in range (0,4):
|
||||
V1=[handLandmarks[(4*cpt)+6][0]-handLandmarks[(4*cpt)+5][0],handLandmarks[(4*cpt)+6][1]-handLandmarks[(4*cpt)+5][1]]
|
||||
V2=[handLandmarks[(4*cpt)+8][0]-handLandmarks[(4*cpt)+6][0],handLandmarks[(4*cpt)+8][1]-handLandmarks[(4*cpt)+6][1]]
|
||||
j=np.dot(V1,V2)
|
||||
if (j>0):
|
||||
return etatDuPouce[0]
|
||||
V1=[handLandmarks[4][0]-handLandmarks[1][0],handLandmarks[4][1]-handLandmarks[1][1]]
|
||||
V2=[handLandmarks[2][0]-handLandmarks[1][0],handLandmarks[2][1]-handLandmarks[1][1]]
|
||||
if((np.dot(V1,V2))>0 and (handLandmarks[4][1]>handLandmarks[2][1])):
|
||||
i=1
|
||||
elif(np.dot(V1,V2)>0 and handLandmarks[4][1]<handLandmarks[2][1]):
|
||||
i=2
|
||||
return etatDuPouce[i]
|
||||
|
||||
|
||||
cap = cv2.VideoCapture(0)
|
||||
with mp_hands.Hands(
|
||||
model_complexity=0,
|
||||
min_detection_confidence=0.5,
|
||||
min_tracking_confidence=0.5) as hands:
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
print("Ignoring empty camera frame.")
|
||||
# If loading a video, use 'break' instead of 'continue'.
|
||||
continue
|
||||
|
||||
# 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 = hands.process(image)
|
||||
|
||||
# Draw the hand annotations on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
if results.multi_hand_landmarks:
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
mp_drawing.draw_landmarks(
|
||||
image,
|
||||
hand_landmarks,
|
||||
mp_hands.HAND_CONNECTIONS,
|
||||
mp_drawing_styles.get_default_hand_landmarks_style(),
|
||||
mp_drawing_styles.get_default_hand_connections_style())
|
||||
|
||||
# Set variable to keep landmarks positions (x and y)
|
||||
handLandmarks = []
|
||||
if results.multi_hand_landmarks:
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
# Fill list with x and y positions of each landmark
|
||||
for landmarks in hand_landmarks.landmark:
|
||||
handLandmarks.append([landmarks.x, landmarks.y])
|
||||
|
||||
cv2.putText(image, reconnaissancePouce(handLandmarks), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
|
||||
|
||||
# Flip the image horizontally for a selfie-view display.
|
||||
cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
cap.release()
|
||||
|
||||
|
||||
|
||||
|
||||
""" etatDuPouce=["neutre","baissé","levé"]
|
||||
i=0
|
||||
|
||||
if results.multi_hand_landmarks:
|
||||
|
||||
if(results.multi_hand_landmarks.gestures.categories[0].categoryName==Thumb_Up):
|
||||
cv2.putText(image, str(results.multi_hand_landmarks.gestures.categories[0].categoryName), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
|
||||
else:
|
||||
cv2.putText(image, "raté", (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
|
||||
"""
|
||||
Reference in New Issue
Block a user