first commit

main.py

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