Implementation of the advanced specification code using Mediaapipe, OpenCV and Matsplots
The Balance of Human Poses is a computerized computer technology that convertible visible data to active options regarding one's movements. By using the high-quality machine learning models such as Mysapipe's Blazephose and powerful libraries such as OpenCV, enhancements can track body points with unprecedented points. In this lesson, we evaluate the stimulent integration of this, indicating that Python compositions empower the pose detection of the pose.
First, we include important libraries:
!pip install mediapipe opencv-python-headless matplotlibAfter that, we import the important libraries needed to introduce:
import cv2
import mediapipe as mp
import matplotlib.pyplot as plt
import numpy as npWe start the Mediopical Pose model in the static image mode with enabled SIGMENTATION and a minimum of less than 0.5 self-esteem. It also imported drawing resources of the world and using drawing styles.
mp_pose = mp.solutions.pose
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
pose = mp_pose.Pose(
static_image_mode=True,
model_complexity=1,
enable_segmentation=True,
min_detection_confidence=0.5
)
Here, explaining a_a picture function, which processes the Picture Symbols using Medioppe using Mediaapipe, and is a descriptive image and visual symptoms and observations. If the world's symptoms are available, they are drawn using the default style.
def detect_pose(image_path):
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = pose.process(image_rgb)
annotated_image = image_rgb.copy()
if results.pose_landmarks:
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style()
)
return annotated_image, results.pose_landmarksIt describes the function of the Extract_keyPoints transformers who have been converted to international symbols into the names of named X, Y, Z links.
def visualize_pose(original_image, annotated_image):
plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
plt.title('Original Image')
plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.subplot(1, 2, 2)
plt.title('Pose Estimation')
plt.imshow(annotated_image)
plt.axis('off')
plt.tight_layout()
plt.show()
def extract_keypoints(landmarks):
if landmarks:
keypoints = {}
for idx, landmark in enumerate(landmarks.landmark):
keypoints[mp_pose.PoseLandmark(idx).name] = {
'x': landmark.x,
'y': landmark.y,
'z': landmark.z,
'visibility': landmark.visibility
}
return keypoints
return NoneFinally, we upload a picture from the prescribed way, find and imagine the Mediomarks using mediaapipe, and then printed the keypeint links found.
image_path="/content/Screenshot 2025-03-26 at 12.56.05 AM.png"
original_image = cv2.imread(image_path)
annotated_image, landmarks = detect_pose(image_path)
visualize_pose(original_image, annotated_image)
keypoints = extract_keypoints(landmarks)
if keypoints:
print("Detected Keypoints:")
for name, details in keypoints.items():
print(f"{name}: {details}")In this study, we examined human estate for man using Mediaapipe and OpenCv, showing a wide range of keykoint. We have used the solid pipe that converts pictures into detailed bone maps, cover important steps including the installation of the library, to create a function, seeing techniques, and keypheic release, and keypheic release. We use the high-quality machine models, show how the developers can change the green data into a reasonable development of the understanding of different backgrounds such as sports sites and health care.
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