PyTorchとYOLOv5で検出したオブジェクトの バウンディングボックスの座標情報を取得する

#推論を実行
result = model(img)

#バウンディングボックスの情報を取得
obj = result.pandas().xyxy[0]
print(obj)

YOLOv5  2022-10-22 Python-3.9.7 torch-1.12.1+cpu CPU

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
Adding AutoShape... 
         xmin        ymin        xmax        ymax  confidence  class    name
0  219.176910  407.951538  346.656036  873.833313    0.864783      0  person
1   50.105721  395.405731  239.340057  913.149780    0.860630      0  person
2  673.475281  400.604767  810.000000  875.171875    0.849397      0  person
3   14.653307  219.916382  810.000000  810.014282    0.816330      5     bus
4    0.084950  553.378845   64.705879  874.105347    0.644388      0  person

['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

import cv2
import torch

model = torch.hub.load('ultralytics/yolov5', 'yolov5s') 
img = cv2.imread('（YoloV5のフォルダが配置されているパス）\yolov5\\data\\images\\cats.jpg')
result = model(img)

#推論結果を取得
obj = result.pandas().xyxy[0]

#バウンディングボックスの情報を取得
for  i in range(len(obj)):
    name = obj.name[i]
    xmin = obj.xmin[i]
    ymin = obj.ymin[i]
    xmax = obj.xmax[i]
    ymax = obj.ymax[i]

    print("name =", name, "xmin =", xmin, "ymin =", ymin, "xmax =", ymax, "ymin =", ymax)

#バウンディングボックスを描画
result.render()
cv2.imshow('result', result.ims[0])
cv2.waitKey(0)

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
Adding AutoShape... 
name = cat xmin = 444.32781982421875 ymin = 155.30255126953125 xmax = 761.1204833984375 ymin = 761.1204833984375
name = cat xmin = 653.5517578125 ymin = 57.53466796875 xmax = 797.1552124023438 ymin = 797.1552124023438        
name = cat xmin = 93.90316772460938 ymin = 343.8182373046875 xmax = 781.89013671875 ymin = 781.89013671875
name = dog xmin = 94.64581298828125 ymin = 335.69921875 xmax = 779.980712890625 ymin = 779.980712890625