from ultralytics import YOLO

model = YOLO('yolov8s-pose.pt')

predict = model.predict(source='/home/azmiddin/Projects/watchlist/bus.jpg', show = True, conf = 0.6)
for ans in predict:
    for keys in ans.keypoints:
        print(keys.xy)

tensor([[[340.5744, 148.3254],
         [350.6854, 140.2402],
         [332.6736, 136.6814],
         [  0.0000,   0.0000],
         [314.9926, 136.2933],
         [370.0140, 198.1728],
         [284.8586, 192.8096],
         [386.5782, 262.8971],
         [270.7247, 264.4329],
         [370.6186, 270.7802],
         [319.8119, 262.1946],
         [358.5667, 326.0563],
         [303.6407, 325.0616],
         [  0.0000,   0.0000],
         [  0.0000,   0.0000],
         [  0.0000,   0.0000],
         [  0.0000,   0.0000]]])
tensor([[[446.1829, 116.5201],
         [454.5997, 104.8722],
         [441.8440, 107.2018],
         [489.7914, 101.1642],
         [  0.0000,   0.0000],
         [536.9339, 157.8492],
         [443.6078, 158.5508],
         [538.3223, 228.8252],
         [430.7207, 235.1595],
         [473.5414, 197.9666],
         [430.5384, 262.4939],
         [512.8117, 335.2951],
         [445.9020, 331.1200],
         [  0.0000,   0.0000],
...

import cv2
from ultralytics import YOLO

# Model yuklash
model = YOLO('yolov8n.pt')
#opencv yordamida freymlarni uqiymiz
video_path = "test.mp4"
cap = cv2.VideoCapture(video_path)

while cap.isOpened():
    success, frame = cap.read()
    frame = cv2.resize(frame, (416, 416))
    if success:
        results = model.track(frame, persist=True, conf = 0.5, iou = 0.5)
        annotated_frame = results[0].plot()
        cv2.imshow("Tracking", annotated_frame)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        break

cap.release()
cv2.destroyAllWindows()

pip install mediapipe opencv-python

import cv2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic

holistic = mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5)

cap = cv2.VideoCapture(0)

cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

frame_count = 0

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    frame_count += 1
    if frame_count % 3 != 0: 
        continue

    image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = holistic.process(image_rgb)
    mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
    cv2.imshow('MediaPipe', frame)

    if cv2.waitKey(1) & 0xFF == 27:
        break

cap.release()
cv2.destroyAllWindows()

from ultralytics import YOLO
from ultralytics.solutions import speed_estimation
import cv2

model = YOLO("yolov8n.pt")
names = model.model.names

cap = cv2.VideoCapture("..../test_speed.mp4")
assert cap.isOpened(), "Uqishda xatolik"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

# Video writer
video_writer = cv2.VideoWriter("speed_estimation.avi",
                                cv2.VideoWriter_fourcc(*'mp4v'),
                                fps,
                                (w, h))

line_pts = [(0, 360), (1280, 360)]

# tayor sinfni yuklash
speed_obj = speed_estimation.SpeedEstimator()
speed_obj.set_args(reg_pts=line_pts,
                    names=names,
                    view_img=True)

while cap.isOpened():

    success, im0 = cap.read()
    if not success:
        print("Qayta ishlanmoqda....")
        break

    tracks = model.track(im0, persist=True, show=False)

    im0 = speed_obj.estimate_speed(im0, tracks)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Abu Hurayra roziyallohu anhu aytadi:

«Rasululloh sollallohu alayhi vasallam dedilar: «U (Ramazon)ning oxirgi kechasida ro‘zadorlarning gunohlari kechiriladi», dedilar. Ular: «Ey Allohning  Rasuli, u Qadr kechasimi?», deb so‘rashdi.

Rasululloh sollallohu alayhi vasallam: «Yo‘q, lekin ishchi ishini ado etganidan so‘ng ajri – haqi to‘la qilib beriladi», dedilar.

Imom Ahmad rivoyat qilgan.