How to make Bounding Boxes in Python – Best function

In this article, I give you my complete function to draw bounding boxes easily in Python with OpenCV, adaptable for COCO dataset.

As a Python developer, it’s not always easy to keep in mind all the existing functions.

And sometimes analysing the debates on StackOverflow to find THE right answer can be a waste of time more than anything else.

That’s why in this article I propose you a function that WORKS to display Bounding Boxes on an image with label AND score.

The function to make Bounding Boxes

The function can be broken down into two parts.

First, we want to be able to easily display a bounding box on an image.

On top of that, we want to display the label of the detected object.

Here is the function that allows you to do this:

def box_label(image, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
  lw = max(round(sum(image.shape) / 2 * 0.003), 2)
  p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
  cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
  if label:
    tf = max(lw - 1, 1)  # font thickness
    w, h = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0]  # text width, height
    outside = p1[1] - h >= 3
    p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
    cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA)  # filled
    cv2.putText(image,
                label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                0,
                lw / 3,
                txt_color,
                thickness=tf,
                lineType=cv2.LINE_AA)

Next, we need a function that displays the image and all the bounding boxes we want to see.

To do this we will loop through the list of BB and call box_label() on each one.

In addition to this, the function supports several cases.

For example, by default, the function displays the label of each object AND the detection score, but you can display only the label if you wish.

If you are on Google Colab, the function detects it and adapts its code to display the image on your screen.

Here are the attributes of the function:

THE PANE METHOD FOR DEEP LEARNING!

Get your 7 DAYS FREE TRAINING to learn how to create your first ARTIFICIAL INTELLIGENCE!

For the next 7 days I will show you how to use Neural Networks.

You will learn what Deep Learning is with concrete examples that will stick in your head.

BEWARE, this email series is not for everyone. If you are the kind of person who likes theoretical and academic courses, you can skip it.

But if you want to learn the PANE method to do Deep Learning, click here :

  • image – your image in array numpy format
  • boxes – your list of Bounding Boxes in the format [x1, y1, x2, y2, score, label] – if the score is not present indicate score=False
  • labels – the labels are by default aligned with those of the COCO dataset, otherwise you can indicate your own
  • colors – the colours corresponding to the labels
  • score – if True, displays the score
  • conf – the minimum confidence threshold (the score) required to display a bounding box
def plot_bboxes(image, boxes, labels=[], colors=[], score=True, conf=None):
  #Define COCO Labels
  if labels == []:
    labels = {0: u'__background__', 1: u'person', 2: u'bicycle',3: u'car', 4: u'motorcycle', 5: u'airplane', 6: u'bus', 7: u'train', 8: u'truck', 9: u'boat', 10: u'traffic light', 11: u'fire hydrant', 12: u'stop sign', 13: u'parking meter', 14: u'bench', 15: u'bird', 16: u'cat', 17: u'dog', 18: u'horse', 19: u'sheep', 20: u'cow', 21: u'elephant', 22: u'bear', 23: u'zebra', 24: u'giraffe', 25: u'backpack', 26: u'umbrella', 27: u'handbag', 28: u'tie', 29: u'suitcase', 30: u'frisbee', 31: u'skis', 32: u'snowboard', 33: u'sports ball', 34: u'kite', 35: u'baseball bat', 36: u'baseball glove', 37: u'skateboard', 38: u'surfboard', 39: u'tennis racket', 40: u'bottle', 41: u'wine glass', 42: u'cup', 43: u'fork', 44: u'knife', 45: u'spoon', 46: u'bowl', 47: u'banana', 48: u'apple', 49: u'sandwich', 50: u'orange', 51: u'broccoli', 52: u'carrot', 53: u'hot dog', 54: u'pizza', 55: u'donut', 56: u'cake', 57: u'chair', 58: u'couch', 59: u'potted plant', 60: u'bed', 61: u'dining table', 62: u'toilet', 63: u'tv', 64: u'laptop', 65: u'mouse', 66: u'remote', 67: u'keyboard', 68: u'cell phone', 69: u'microwave', 70: u'oven', 71: u'toaster', 72: u'sink', 73: u'refrigerator', 74: u'book', 75: u'clock', 76: u'vase', 77: u'scissors', 78: u'teddy bear', 79: u'hair drier', 80: u'toothbrush'}
  #Define colors
  if colors == []:
    #colors = [(6, 112, 83), (253, 246, 160), (40, 132, 70), (205, 97, 162), (149, 196, 30), (106, 19, 161), (127, 175, 225), (115, 133, 176), (83, 156, 8), (182, 29, 77), (180, 11, 251), (31, 12, 123), (23, 6, 115), (167, 34, 31), (176, 216, 69), (110, 229, 222), (72, 183, 159), (90, 168, 209), (195, 4, 209), (135, 236, 21), (62, 209, 199), (87, 1, 70), (75, 40, 168), (121, 90, 126), (11, 86, 86), (40, 218, 53), (234, 76, 20), (129, 174, 192), (13, 18, 254), (45, 183, 149), (77, 234, 120), (182, 83, 207), (172, 138, 252), (201, 7, 159), (147, 240, 17), (134, 19, 233), (202, 61, 206), (177, 253, 26), (10, 139, 17), (130, 148, 106), (174, 197, 128), (106, 59, 168), (124, 180, 83), (78, 169, 4), (26, 79, 176), (185, 149, 150), (165, 253, 206), (220, 87, 0), (72, 22, 226), (64, 174, 4), (245, 131, 96), (35, 217, 142), (89, 86, 32), (80, 56, 196), (222, 136, 159), (145, 6, 219), (143, 132, 162), (175, 97, 221), (72, 3, 79), (196, 184, 237), (18, 210, 116), (8, 185, 81), (99, 181, 254), (9, 127, 123), (140, 94, 215), (39, 229, 121), (230, 51, 96), (84, 225, 33), (218, 202, 139), (129, 223, 182), (167, 46, 157), (15, 252, 5), (128, 103, 203), (197, 223, 199), (19, 238, 181), (64, 142, 167), (12, 203, 242), (69, 21, 41), (177, 184, 2), (35, 97, 56), (241, 22, 161)]
    colors = [(89, 161, 197),(67, 161, 255),(19, 222, 24),(186, 55, 2),(167, 146, 11),(190, 76, 98),(130, 172, 179),(115, 209, 128),(204, 79, 135),(136, 126, 185),(209, 213, 45),(44, 52, 10),(101, 158, 121),(179, 124, 12),(25, 33, 189),(45, 115, 11),(73, 197, 184),(62, 225, 221),(32, 46, 52),(20, 165, 16),(54, 15, 57),(12, 150, 9),(10, 46, 99),(94, 89, 46),(48, 37, 106),(42, 10, 96),(7, 164, 128),(98, 213, 120),(40, 5, 219),(54, 25, 150),(251, 74, 172),(0, 236, 196),(21, 104, 190),(226, 74, 232),(120, 67, 25),(191, 106, 197),(8, 15, 134),(21, 2, 1),(142, 63, 109),(133, 148, 146),(187, 77, 253),(155, 22, 122),(218, 130, 77),(164, 102, 79),(43, 152, 125),(185, 124, 151),(95, 159, 238),(128, 89, 85),(228, 6, 60),(6, 41, 210),(11, 1, 133),(30, 96, 58),(230, 136, 109),(126, 45, 174),(164, 63, 165),(32, 111, 29),(232, 40, 70),(55, 31, 198),(148, 211, 129),(10, 186, 211),(181, 201, 94),(55, 35, 92),(129, 140, 233),(70, 250, 116),(61, 209, 152),(216, 21, 138),(100, 0, 176),(3, 42, 70),(151, 13, 44),(216, 102, 88),(125, 216, 93),(171, 236, 47),(253, 127, 103),(205, 137, 244),(193, 137, 224),(36, 152, 214),(17, 50, 238),(154, 165, 67),(114, 129, 60),(119, 24, 48),(73, 8, 110)]
  
  #plot each boxes
  for box in boxes:
    #add score in label if score=True
    if score :
      label = labels[int(box[-1])+1] + " " + str(round(100 * float(box[-2]),1)) + "%"
    else :
      label = labels[int(box[-1])+1]
    #filter every box under conf threshold if conf threshold setted
    if conf :
      if box[-2] > conf:
        color = colors[int(box[-1])]
        box_label(image, box, label, color)
    else:
      color = colors[int(box[-1])]
      box_label(image, box, label, color)

  #show image
  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

  try:
    import google.colab
    IN_COLAB = True
  except:
    IN_COLAB = False

  if IN_COLAB:
    cv2_imshow(image) #if used in Colab
  else :
    cv2.imshow(image) #if used in Python

Let’s use the function with the YOLOv8 model.

To do this, we install the ultralytics library:

!pip install ultralytics &> /dev/null

Then we load the object detection model:

from ultralytics import YOLO

model = YOLO("yolov8n.pt")

Then we detect the objects and display the bounding boxes with our plot_bboxes() function and a confidence threshold of 0.8:

import numpy as np
from PIL import Image
import requests
from io import BytesIO
import cv2
from google.colab.patches import cv2_imshow

response = requests.get("https://images.unsplash.com/photo-1635341914750-7f38b76856d8?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1973&q=80")
image = Image.open(BytesIO(response.content))
image = np.asarray(image)

results = model.predict(image)

plot_bboxes(image, results[0].boxes.boxes, conf=0.8)

And here is the result.

Function to draw Bounding Boxes – source

Use the function as you wish!

And see you soon on Inside Machine Learning 😉

THE PANE METHOD FOR DEEP LEARNING!

Get your 7 DAYS FREE TRAINING to learn how to create your first ARTIFICIAL INTELLIGENCE!

For the next 7 days I will show you how to use Neural Networks.

You will learn what Deep Learning is with concrete examples that will stick in your head.

BEWARE, this email series is not for everyone. If you are the kind of person who likes theoretical and academic courses, you can skip it.

But if you want to learn the PANE method to do Deep Learning, click here :

Tom Keldenich
Tom Keldenich

Data Engineer & passionate about Artificial Intelligence !

Founder of the website Inside Machine Learning

Leave a Reply

Your email address will not be published. Required fields are marked *

Enter your email to receive for free

The PANE method for Deep Learning

* indicates required

 

You will receive one email per day for 7 days – then you will receive my newsletter.
Your information will never be given to third parties.

You can unsubscribe in 1 click from any of my emails.

Entre ton email pour recevoir gratuitement
la méthode PARÉ pour faire du Deep Learning


Tu recevras un email par jour pendant 7 jours - puis tu recevras ma newsletter.
Tes informations ne seront jamais cédées à des tiers.

Tu peux te désinscrire en 1 clic depuis n'importe lequel de mes emails.