# Coordinates of Bounding Box

How can I get the coordinates of a bounding box. This is the code I have been using

``````from __future__ import division
import time
import torch
import torch.nn as nn
import numpy as np
import cv2
from util import *
from darknet import Darknet
from preprocess import prep_image, inp_to_image
import pandas as pd
import random
import argparse
import pickle as pkl

def get_test_input(input_dim, CUDA):
img = cv2.resize(img, (input_dim, input_dim))
img_ =  img[:,:,::-1].transpose((2,0,1))
img_ = img_[np.newaxis,:,:,:]/255.0
img_ = torch.from_numpy(img_).float()
img_ = Variable(img_)

if CUDA:
img_ = img_.cuda()

return img_

def prep_image(img, inp_dim):
"""
Prepare image for inputting to the neural network.

Returns a Variable
"""

orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = cv2.resize(orig_im, (inp_dim, inp_dim))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim

c1 = 0.0
c2 = 0.0
def write(x, img):
global c1
global c2
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2,color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
return img

def arg_parse():
"""
Parse arguements to the detect module

"""

parser = argparse.ArgumentParser(description='YOLO v3 Cam Demo')
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.25)
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "160", type = str)
return parser.parse_args()

if __name__ == '__main__':
cfgfile = "cfg/yolov3.cfg"
weightsfile = "yolov3.weights"
num_classes = 80

args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
CUDA = torch.cuda.is_available()

num_classes = 80
bbox_attrs = 5 + num_classes

model = Darknet(cfgfile)

model.net_info["height"] = args.reso
inp_dim = int(model.net_info["height"])

assert inp_dim % 32 == 0
assert inp_dim > 32

if CUDA:
model.cuda()

model.eval()

videofile = 'video.avi'

cap = cv2.VideoCapture(0)

assert cap.isOpened(), 'Cannot capture source'

frames = 0
start = time.time()
while cap.isOpened():

if ret:

img, orig_im, dim = prep_image(frame, inp_dim)

im_dim = torch.FloatTensor(dim).repeat(1,2)

if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()

output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)

if type(output) == int:
frames += 1

print(c1, c2)
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue

output[:,1:5] = torch.clamp(output[:,1:5], 0.0, float(inp_dim))/inp_dim

#            im_dim = im_dim.repeat(output.size(0), 1)
output[:,[1,3]] *= frame.shape[1]
output[:,[2,4]] *= frame.shape[0]

list(map(lambda x: write(x, orig_im), output))

cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
frames += 1

print(c1,c2)

else:
break

``````

which is basically the same as the demo but prints c1 and c2 at the end.

from this I get
(tensor(27, device=‘cuda:0’, dtype=torch.int32), tensor(51, device=‘cuda:0’, dtype=torch.int32)) (tensor(88, device=‘cuda:0’, dtype=torch.int32), tensor(65, device=‘cuda:0’, dtype=torch.int32))

Are these coordinates and if so how can I use these?