PYTHON DASTURLASH TILI

Kutib oling sun'iy intellekt Uzbek Plov nomli qo'shiq aytdi. https://youtu.be/Jtv06BQ-CBg

Natija nima chiqishini kod yozmasdan taxmin qiling.

😂😂😂

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Umumiy natija

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

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)

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yield Pythonchilar bilasizmi bu nima?

💻 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.

Bu rasmlarni nima bog'lab turadi?

emot nima ish qilishini topganga bitta besh😄

Tez orada python 3.13 chiqadi... https://pythoninsider.blogspot.com/2024/01/python-3130-alpha-3-is-now-available.html