51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import cv2
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import numpy as np
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import mediapipe as mp
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import matplotlib.pyplot as plt
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from google.protobuf.json_format import MessageToDict
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# First step is to initialize the Hands class an store it in a variable
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mp_hands = mp.solutions.hands
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# Now second step is to set the hands function which will hold the landmarks points
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hands = mp_hands.Hands(static_image_mode=True, max_num_hands=2, min_detection_confidence=0.3)
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# Last step is to set up the drawing function of hands landmarks on the image
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mp_drawing = mp.solutions.drawing_utils
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# Reading the sample image on which we will perform the detection
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# sample_img = cv2.imread('coba.jpg')
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sample_img = cv2.imread('coba2.png')
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# Here we are specifing the size of the figure i.e. 10 -height; 10- width.
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plt.figure(figsize = [10, 10])
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# Here we will display the sample image as the output.
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# plt.title("Sample Image");plt.axis('off');plt.imshow(sample_img[:,:,::-1]);plt.show()
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results = hands.process(cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB))
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if results.multi_hand_landmarks:
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for hand_no, hand_landmarks in enumerate(results.multi_hand_landmarks):
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print(f'HAND NUMBER: {hand_no+1}')
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print('-----------------------')
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for i in range(2):
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print(f'{mp_hands.HandLandmark(i).name}:')
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print(f'{hand_landmarks.landmark[mp_hands.HandLandmark(i).value]}')
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# Return whether it is Right or Left Hand
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i = results.multi_handedness
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label = MessageToDict(i)['classification'][0]['label']
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if label == 'Left':
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print("ini telapak tangan kiri")
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if label == 'Right':
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print("ini telapak tangan kanan")
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