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79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
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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