58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import mediapipe as mp
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import cv2
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class KeypointExtractor:
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def __init__(self):
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self.mp_drawing = mp.solutions.drawing_utils
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# hands extractor
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self.hands = mp.solutions.hands.Hands(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5,
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max_num_hands=2
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)
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# pose extractor
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self.pose = mp.solutions.pose.Pose(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5,
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model_complexity=2
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)
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def extract(self, image, video):
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# load video
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pass
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def extract_from_frame(self, image):
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# Convert the BGR image to RGB and process it with MediaPipe Pose.
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hand_results = self.hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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# Draw the hand annotations on the image.
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draw_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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draw_image.flags.writeable = False
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for hand_landmarks in hand_results.multi_hand_landmarks:
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self.mp_drawing.draw_landmarks(
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draw_image, hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS)
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pose_results = self.pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self.mp_drawing.draw_landmarks(
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draw_image, pose_results.pose_landmarks, mp.solutions.pose.POSE_CONNECTIONS)
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draw_image.flags.writeable = True
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draw_image = cv2.cvtColor(draw_image, cv2.COLOR_RGB2BGR)
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return draw_image
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ke = KeypointExtractor()
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image = cv2.imread('data/test_photo.jpg')
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image = ke.extract_from_frame(image)
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# save image
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cv2.imwrite('test_output.jpg', image) |