יסודות בינה מלאכותית : 08-RB27 – אנימציה , OPENPOSE YOLO
OPENPOSE YOLO
1 קישורים :
https://docs.ultralytics.com/tasks/pose/#what-are-the-available-ultralytics-yolo11-pose-models-and-their-performance-metrics
https://github.com/Alimustoofaa/YoloV8-Pose-Keypoint-Classification
2 .איך ניתן לנתח את את התמונות
3. קישור להורדת וידאו
https://y2meta.tube/convert/?videoId=esGZTeb_AiM
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
from ultralytics import YOLO import cv2 # Define keypoint names as per the model's standard output keypoint_names = [ "Nose", "Left Eye", "Right Eye", "Left Ear", "Right Ear", "Left Shoulder", "Right Shoulder", "Left Elbow", "Right Elbow", "Left Wrist", "Right Wrist", "Left Hip", "Right Hip", "Left Knee", "Right Knee", "Left Ankle", "Right Ankle" ] # Load the YOLOv8 pose model model = YOLO("yolov8n-pose.pt") # Ensure you have the YOLOv8 pose model downloaded # Load the image image_path = r"d:\temp\h1.jpg" image = cv2.imread(image_path) if image is None: print(f"Image not found at {image_path}") else: # Run pose detection results = model.predict(source=image, save=False, conf=0.5) # Extract and print keypoints with names for result in results: print("Detected Keypoints:") for i, (x, y, conf) in enumerate(result.keypoints.data[0]): # Check if the index is within the keypoint names list keypoint_name = keypoint_names[i] if i < len(keypoint_names) else f"Keypoint {i + 1}" print(f"{keypoint_name}: x={x:.2f}, y={y:.2f}, confidence={conf:.2f}") # Display the image with keypoints overlayed annotated_image = results[0].plot() cv2.imshow("Pose Detection", annotated_image) cv2.waitKey(0) cv2.destroyAllWindows() |
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
from ultralytics import YOLO import cv2 # Define keypoint names keypoint_names = [ "Nose", "Left Eye", "Right Eye", "Left Ear", "Right Ear", "Left Shoulder", "Right Shoulder", "Left Elbow", "Right Elbow", "Left Wrist", "Right Wrist", "Left Hip", "Right Hip", "Left Knee", "Right Knee", "Left Ankle", "Right Ankle" ] # Load YOLOv8 pose model model = YOLO("yolov8n-pose.pt") # Ensure the model file is in the same directory or provide the full path # Load the video file video_path = r"d:\temp\d2.mp4" output_path = r"d:\temp\output_pose_scaled.mp4" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Cannot open video file: {video_path}") else: # Get original frame width and height original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(cap.get(cv2.CAP_PROP_FPS)) # Frame rate of the input video # Calculate new dimensions (40% smaller) scaled_width = int(original_width * 0.6) scaled_height = int(original_height * 0.6) # Set up the video writer fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4 output out = cv2.VideoWriter(output_path, fourcc, fps, (scaled_width, scaled_height)) while True: ret, frame = cap.read() if not ret: break # Resize frame (scale down by 40%) scaled_frame = cv2.resize(frame, (scaled_width, scaled_height)) # Run pose detection on the scaled frame results = model.predict(source=scaled_frame, save=False, conf=0.5) # Annotate the frame annotated_frame = results[0].plot() # Write the annotated frame to the output video out.write(annotated_frame) # Display the frame (optional, press 'q' to stop) cv2.imshow("Pose Detection (Scaled 40%)", annotated_frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() out.release() cv2.destroyAllWindows() print(f"Output video saved to {output_path}") |
