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Merge branch 'Interface_Lounes' of gitlab.enst.fr:pact/2022-2023/pact71 into quentin
This commit is contained in:
Quentin Roussel
98
code/Interface_Lounes/compteurDoigt.py
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98
code/Interface_Lounes/compteurDoigt.py
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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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88
code/Interface_Lounes/reconnaissancePouce.py
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88
code/Interface_Lounes/reconnaissancePouce.py
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@@ -0,0 +1,88 @@
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import cv2
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import numpy as np
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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]
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def reconnaissancePouce(handLandmarks):
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etatDuPouce=["neutre","baissé","levé"]
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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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cap = cv2.VideoCapture(0)
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with 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) as hands:
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while 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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continue
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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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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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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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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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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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# Set variable to keep landmarks positions (x and y)
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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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cv2.putText(image, reconnaissancePouce(handLandmarks), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
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# Flip the image horizontally for a selfie-view display.
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cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cap.release()
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""" etatDuPouce=["neutre","baissé","levé"]
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i=0
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if results.multi_hand_landmarks:
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if(results.multi_hand_landmarks.gestures.categories[0].categoryName==Thumb_Up):
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cv2.putText(image, str(results.multi_hand_landmarks.gestures.categories[0].categoryName), (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
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else:
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cv2.putText(image, "raté", (50, 450), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 10)
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"""
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