我们尝试通过以下代码使用Python和YOLOv8获取检测到的对象名称。

import cv2
from ultralytics import YOLO

def main():
    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

    model = YOLO("yolov8n.pt")

    while True:
        ret, frame = cap.read()
        result = model(frame, agnostic_nms=True)[0]

        print(result)

        if cv2.waitKey(30) == 27:
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

日志中显示以下两种类型。

0: 384x640 1 person, 151.2ms
Speed: 0.6ms preprocess, 151.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)

第二个日志是我们使用print显示的日志，从现在开始我们如何获得person？假设我们通过给names 0得到person，但是我们从哪里得到0呢？

ultralytics.yolo.engine.results.Results object with attributes:

boxes: ultralytics.yolo.engine.results.Boxes object
keypoints: None
keys: ['boxes']
masks: None
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'}
orig_img: array([[[51, 58, 64],
        [52, 59, 65],
        [54, 59, 65],
        ...,
        [64, 68, 74],
        [62, 67, 73],
        [62, 67, 73]],

       [[51, 58, 64],
        [53, 59, 65],
        [54, 59, 65],
        ...,
        [63, 68, 74],
        [62, 67, 73],
        [62, 67, 73]],

       [[53, 58, 64],
        [53, 58, 64],
        [53, 58, 64],
        ...,
        [61, 67, 73],
        [61, 67, 73],
        [61, 67, 73]],

       ...,

       [[43, 48, 58],
        [42, 47, 57],
        [41, 46, 56],
        ...,
        [24, 35, 49],
        [23, 34, 48],
        [23, 34, 48]],

       [[44, 48, 59],
        [43, 47, 57],
        [42, 46, 56],
        ...,
        [26, 35, 49],
        [26, 35, 49],
        [24, 33, 48]],

       [[45, 48, 59],
        [43, 45, 56],
        [40, 43, 54],
        ...,
        [26, 35, 49],
        [26, 35, 49],
        [25, 33, 48]]], dtype=uint8)
orig_shape: (720, 1280)
path: 'image0.jpg'
probs: None
speed: {'preprocess': 1.6682147979736328, 'inference': 79.47301864624023, 'postprocess': 1.0020732879638672}

我们想知道这样的解决方案。但是如果不可能，我们可以使用另一种方法，如果它是Python和YOLOv8的组合。我们计划显示边界框和对象名称。

附加信息

我修改了代码如下。

ret, frame = cap.read()
        # result = model(frame, agnostic_nms=True)[0]
        result = model([frame])[0]

        boxes = result.boxes
        masks = result.masks
        probs = result.probs

        print("[boxes]==============================")
        print(boxes)
        print("[masks]==============================")
        print(masks)
        print("[probs]==============================")
        print(probs)

毕竟，下面的person不包括在内。我们应该如何确定？

[boxes]==============================
WARNING ⚠️ 'Boxes.boxes' is deprecated. Use 'Boxes.data' instead.
ultralytics.yolo.engine.results.Boxes object with attributes:

boxes: tensor([[4.7356e+01, 7.2858e+00, 1.1974e+03, 7.1092e+02, 8.6930e-01, 0.0000e+00]])
cls: tensor([0.])
conf: tensor([0.8693])
data: tensor([[4.7356e+01, 7.2858e+00, 1.1974e+03, 7.1092e+02, 8.6930e-01, 0.0000e+00]])
id: None
is_track: False
orig_shape: tensor([ 720, 1280])
shape: torch.Size([1, 6])
xywh: tensor([[ 622.4028,  359.1004, 1150.0942,  703.6293]])
xywhn: tensor([[0.4863, 0.4988, 0.8985, 0.9773]])
xyxy: tensor([[  47.3557,    7.2858, 1197.4500,  710.9150]])
xyxyn: tensor([[0.0370, 0.0101, 0.9355, 0.9874]])
[masks]==============================
None
[probs]==============================
None