找到detect.py，在大概113行，找到plot_one_box

# Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + ".txt", "a") as f:
                            f.write(("%g " * 5 + "\n") % (cls, *xywh))  # label format

                    if save_img or view_img:  # Add bbox to image
                        label = "%s %.2f" % (names[int(cls)], conf)
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

ctr+鼠标点击，进入general.py，并自动定位到plot_one_box函数，修改函数为

def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    print("左上点的坐标为：(" + str(c1[0]) + "," + str(c1[1]) + ")，右下点的坐标为(" + str(c2[0]) + "," + str(c2[1]) + ")")

即可输出目标坐标信息了

附：python yolov5检测模型返回坐标的方法实例代码

python yolov5检测模型返回坐标的方法 直接搜索以下代码替换下

if save_img or view_img:  # Add bbox to image
                        label = f"{names[int(cls)]} {conf:.2f}"
                        c1, c2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
                        print("左上点的坐标为：(" + str(c1[0]) + "," + str(c1[1]) + ")，右下点的坐标为(" + str(c2[0]) + "," + str(c2[1]) + ")")
                        return [c1,c2]

if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--weights", nargs="+", type=str, default="yolov5s.pt", help="model.pt path(s)")
parser.add_argument("--source", type=str, default="data/images", help="source") # file/folder, 0 for webcam
parser.add_argument("--img-size", type=int, default=640, help="inference size (pixels)")
parser.add_argument("--conf-thres", type=float, default=0.25, help="object confidence threshold")
parser.add_argument("--iou-thres", type=float, default=0.45, help="IOU threshold for NMS")
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
parser.add_argument("--view-img", action="store_true", help="display results")
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --class 0, or --class 0 2 3")
parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
parser.add_argument("--augment", action="store_true", help="augmented inference")
parser.add_argument("--update", action="store_true", help="update all models")
parser.add_argument("--project", default="runs/detect", help="save results to project/name")
parser.add_argument("--name", default="exp", help="save results to project/name")
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
opt = parser.parse_args()

check_requirements(exclude=("pycocotools", "thop"))

opt.source="data/images/1/"
result=detect()
print("最终检测结果:",result);

总结

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