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MyVersion_mpHolisticChangeDrawingColor.py
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MyVersion_mpHolisticChangeDrawingColor.py
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import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic
webcamID = 1
landmark_drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1, color=(255, 255, 0))
connection_drawing_spec = mp_drawing.DrawingSpec(thickness=2, color=(255, 255, 255))
# For webcam input:
holistic = mp_holistic.Holistic(
min_detection_confidence=0.5, min_tracking_confidence=0.5)
cap = cv2.VideoCapture(webcamID)
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# Flip the image horizontally for a later selfie-view display, and convert
# the BGR image to RGB.
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
results = holistic.process(image)
# Draw landmark annotation on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
mp_drawing.draw_landmarks(
image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, landmark_drawing_spec, connection_drawing_spec)
mp_drawing.draw_landmarks(
image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(
image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(
image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec, connection_drawing_spec)
cv2.imshow('MediaPipe Holistic', image)
if cv2.waitKey(5) & 0xFF == 27:
break
holistic.close()
cap.release()