129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
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import cv2
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import json
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import mediapipe as mp
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import numpy as np
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from scipy.spatial import distance
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# Load JSON data
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with open('data_yoga.json') as f:
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data = json.load(f)
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image_name = "Camel"
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image = None
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# Extract landmarks from JSON data
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landmarks_from_json = []
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the_landmarks = None
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# Load the image
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for the_data in data:
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if the_data['name'] == image_name:
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for lm in the_data['landmarks']:
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landmarks_from_json.append([lm['coordinates'][0], lm['coordinates'][1]])
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the_landmarks = the_data['landmarks']
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image = cv2.imread(the_data['image_name'])
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# Initialize MediaPipe pose module
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mp_pose = mp.solutions.pose
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mp_drawing = mp.solutions.drawing_utils
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# Function to calculate similarity between two sets of landmarks
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def calculate_similarity(landmarks1, landmarks2):
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if not landmarks1 or not landmarks2:
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return 0
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# Normalize landmarks
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norm_landmarks1 = np.array(landmarks1) - np.mean(landmarks1, axis=0)
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norm_landmarks2 = np.array(landmarks2) - np.mean(landmarks2, axis=0)
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# Calculate the distance between corresponding landmarks
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dists = [distance.euclidean(lm1, lm2) for lm1, lm2 in zip(norm_landmarks1, norm_landmarks2)]
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# Calculate similarity as the inverse of the average distance
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similarity = 1 / (1 + np.mean(dists))
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return similarity * 100
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# Draw landmarks and connections on the image
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def draw_landmarks(image, landmarks):
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annotated_image = image.copy()
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for landmark in landmarks:
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# Extract landmark coordinates
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landmark_x = int(landmark['coordinates'][0] * annotated_image.shape[1])
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landmark_y = int(landmark['coordinates'][1] * annotated_image.shape[0])
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# Draw a circle at the landmark position
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cv2.circle(annotated_image, (landmark_x, landmark_y), 5, (0, 255, 0), -1)
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# Add text with the body part label
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cv2.putText(annotated_image, landmark['body'], (landmark_x, landmark_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
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(255, 255, 255), 1)
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# Draw connections between landmarks
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connections = mp_pose.POSE_CONNECTIONS
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for connection in connections:
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start_idx = connection[0]
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end_idx = connection[1]
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if 0 <= start_idx < len(landmarks) and 0 <= end_idx < len(landmarks):
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start_landmark = landmarks[start_idx]['coordinates']
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end_landmark = landmarks[end_idx]['coordinates']
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start_x = int(start_landmark[0] * annotated_image.shape[1])
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start_y = int(start_landmark[1] * annotated_image.shape[0])
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end_x = int(end_landmark[0] * annotated_image.shape[1])
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end_y = int(end_landmark[1] * annotated_image.shape[0])
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cv2.line(annotated_image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
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return annotated_image
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# Annotate and display the image
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annotated_image = draw_landmarks(image, the_landmarks)
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cv2.imshow('Image with Landmarks and Connections', annotated_image)
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# Open webcam
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cap = cv2.VideoCapture(0)
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with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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# Convert the BGR image to RGB
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image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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image_rgb.flags.writeable = False
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# Process the image and detect the pose
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results = pose.process(image_rgb)
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# Convert the RGB image back to BGR
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image_rgb.flags.writeable = True
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image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
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# Draw the pose annotation on the image
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if results.pose_landmarks:
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mp_drawing.draw_landmarks(
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image_bgr,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2),
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mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2),
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)
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# Extract landmarks
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landmarks_from_webcam = []
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for lm in results.pose_landmarks.landmark:
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landmarks_from_webcam.append([lm.x, lm.y])
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# Calculate similarity
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similarity = calculate_similarity(landmarks_from_json, landmarks_from_webcam)
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cv2.putText(image_bgr, f'Similarity: {similarity:.2f}%', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# Display the image
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cv2.imshow('Webcam Pose', image_bgr)
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if cv2.waitKey(10) & 0xFF == ord('q'):
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break
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# Release resources
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cap.release()
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cv2.destroyAllWindows()
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cv2.waitKey(1)
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