Tez orada python 3.13 chiqadi...

https://pythoninsider.blogspot.com/2024/01/python-3130-alpha-3-is-now-available.html

emot nima ish qilishini topganga bitta besh😄

💻 Inson mehnati AI dan ko'ra arzon (hozircha).

MIT universitetining tadqiqotlariga qaraganda. Inson sun'iy intellekt robotlariga qaraganda 2 baravar arzon ishchi kuchi hisoblanadi. Buning ustiga insonlar o'z vazifasidan tashqari ishlarni ham bajarishadi...
Ammo tadqiqotlar bu uzoq davom etmasligi va yaqin yillarda robotlar arzon ishchi kuchiga aylanishini ko'rsatmoqda.

yield

Pythonchilar bilasizmi bu nima?

🏆 Diqqat Musobaqa 🏆

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🤖 RoboContest Round #87 div3 va 🤖 RoboContest Round #88 div2

📊 Div3 - reytingi [1000,1500] oralig'idagi ishtirokchilar uchun rated
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Agar reyting mos kelmasa unrated qatnashish imkoni mavjud, ya'ni reyting berilmaydi/

📆Contest boshlanish vaqti: 05.03.2024 19:30
📆Contest tugash vaqti: 05.03.2024 21:30
⏰Contest davomiyligi: 2 soat
👨‍🏫 Muallif: Sardor Salimov

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(Savollar o'zbekcha va ruscha tillarda bo'ladi)

Rasm yoki freymdan malum predmet obekt ni joylashuvni olamiz.
Bu ham juda oson 5-6 qatorda bajariladi.

from ultralytics import YOLO

model = YOLO("yolov8s.pt")
ans = model.predict(source= ".../person.jpeg", show = True, imgsz = 320, conf = 0.7)
#yolov8s bir nechta turdagi obektlarni detection qila oladi
for obj in ans:
    box = obj.boxes
    print(box)

Agar natija olib kursak

cls: tensor([0., 0., 0., 0., 0., 0., 0.])
conf: tensor([0.8909, 0.8682, 0.8674, 0.8622, 0.8439, 0.8392, 0.7159])
data: tensor([[1.6254e+02, 2.2389e+01, 2.5266e+02, 1.6701e+02, 8.9091e-01, 0.0000e+00],
        [2.3503e+02, 3.1486e+01, 2.9971e+02, 1.6686e+02, 8.6820e-01, 0.0000e+00],
        [2.1997e+01, 5.3749e+01, 7.4538e+01, 1.6752e+02, 8.6741e-01, 0.0000e+00],
        [1.1284e+02, 3.2849e+01, 1.6784e+02, 1.6768e+02, 8.6221e-01, 0.0000e+00],
        [6.3885e+01, 4.3812e+01, 1.1679e+02, 1.6726e+02, 8.4389e-01, 0.0000e+00],
        [3.2759e-02, 5.2869e+00, 4.6813e+01, 1.6724e+02, 8.3916e-01, 0.0000e+00],
        [1.5617e+02, 1.0563e+01, 1.9749e+02, 9.0878e+01, 7.1594e-01, 0.0000e+00]])
id: None
is_track: False
orig_shape: (168, 300)
shape: torch.Size([7, 6])
xywh: tensor([[207.5979,  94.6973,  90.1146, 144.6161],
        [267.3671,  99.1744,  64.6808, 135.3771],
        [ 48.2676, 110.6329,  52.5402, 113.7673],
        [140.3382, 100.2657,  55.0010, 134.8336],
        [ 90.3380, 105.5342,  52.9057, 123.4454],
        [ 23.4231,  86.2625,  46.7806, 161.9512],
        [176.8280,  50.7202,  41.3200,  80.3153]])
xywhn: tensor([[0.6920, 0.5637, 0.3004, 0.8608],
        [0.8912, 0.5903, 0.2156, 0.8058],
        [0.1609, 0.6585, 0.1751, 0.6772],
        [0.4678, 0.5968, 0.1833, 0.8026],
        [0.3011, 0.6282, 0.1764, 0.7348],
        [0.0781, 0.5135, 0.1559, 0.9640],
        [0.5894, 0.3019, 0.1377, 0.4781]])
xyxy: tensor([[1.6254e+02, 2.2389e+01, 2.5266e+02, 1.6701e+02],
        [2.3503e+02, 3.1486e+01, 2.9971e+02, 1.6686e+02],
        [2.1997e+01, 5.3749e+01, 7.4538e+01, 1.6752e+02],
        [1.1284e+02, 3.2849e+01, 1.6784e+02, 1.6768e+02],
        [6.3885e+01, 4.3812e+01, 1.1679e+02, 1.6726e+02],
        [3.2759e-02, 5.2869e+00, 4.6813e+01, 1.6724e+02],
        [1.5617e+02, 1.0563e+01, 1.9749e+02, 9.0878e+01]])
xyxyn: tensor([[5.4180e-01, 1.3327e-01, 8.4218e-01, 9.9408e-01],
        [7.8342e-01, 1.8742e-01, 9.9902e-01, 9.9323e-01],
        [7.3325e-02, 3.1994e-01, 2.4846e-01, 9.9712e-01],
        [3.7613e-01, 1.9553e-01, 5.5946e-01, 9.9811e-01],
        [2.1295e-01, 2.6078e-01, 3.8930e-01, 9.9558e-01],
        [1.0920e-04, 3.1470e-02, 1.5604e-01, 9.9546e-01],
        [5.2056e-01, 6.2872e-02, 6.5829e-01, 5.4094e-01]])

Shunga uxshagan natija beradi.
Natija siz ishlatayotgan freymworkga bogliq (Pytorch, ....)
Demak birinchi listda sinf (0-bu odam umumiy sinflar

names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
)

keyin esa conf yane har bitta obektni aniqlash aniqligi ruyxati (rasmda bir nechta obekt buladi)
keyin esa bizga obekt joylashuvi xywh yane yuqori chap burchag koordinatasi va w-uzunlik h-balandlik beriladi.
bundan tashqari biz turtburchak asosida malumotni olishimiz mumkin yuqori chao va pastgi ong xyxy

Umumiy natija