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single_image.py
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import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
# For static images:
IMAGE_FILES = ['C:/Users/JUEUN MUN/Capstone design/mediapipe/images/image1.jpg','C:/Users/JUEUN MUN/Capstone design/mediapipe/images/image2.jpg','C:/Users/JUEUN MUN/Capstone design/mediapipe/images/image3.jpg']
with mp_hands.Hands(
static_image_mode=True,
max_num_hands=2,
min_detection_confidence=0.5) as hands:
for idx, file in enumerate(IMAGE_FILES):
# Read an image, flip it around y-axis for correct handedness output (see
# above).
image = cv2.flip(cv2.imread(file), 1)
# Convert the BGR image to RGB before processing.
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# Print handedness and draw hand landmarks on the image.
print('Handedness:', results.multi_handedness)
if not results.multi_hand_landmarks:
continue
image_height, image_width, _ = image.shape
annotated_image = image.copy()
for hand_landmarks in results.multi_hand_landmarks:
print('hand_landmarks:', hand_landmarks.landmark[0])
print(
f'Index finger tip coordinates: (',
f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
)
mp_drawing.draw_landmarks(
annotated_image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
cv2.imwrite(
'/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
# Draw hand world landmarks.
if not results.multi_hand_world_landmarks:
continue
for hand_world_landmarks in results.multi_hand_world_landmarks:
mp_drawing.plot_landmarks(
hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)