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https://git.roussel.pro/telecom-paris/pact.git
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88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
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].x-handLandmarks[(4*cpt)+5].x,handLandmarks[(4*cpt)+6].y-handLandmarks[(4*cpt)+5].y]
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V2=[handLandmarks[(4*cpt)+8].x-handLandmarks[(4*cpt)+6].x,handLandmarks[(4*cpt)+8].y-handLandmarks[(4*cpt)+6].y]
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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].x-handLandmarks[1].x,handLandmarks[4].y-handLandmarks[1].y]
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V2=[handLandmarks[2].x-handLandmarks[1].x,handLandmarks[2].y-handLandmarks[1].y]
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if((np.dot(V1,V2))>0 and (handLandmarks[4].y>handLandmarks[2].y)):
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i=1
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elif(np.dot(V1,V2)>0 and handLandmarks[4].y<handLandmarks[2].y):
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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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""" |