How to convert Yolo format bounding box coordinates into OpenCV format

Question

I have Yolo format bounding box annotations of objects saved in a .txt files. Now I want to load those coordinates and draw it on the image using OpenCV, but I don’t know how to convert those float values into OpenCV format coordinates values

I tried this post but it didn’t help, below is a sample example of what I am trying to do

Code and output

import matplotlib.pyplot as plt
import cv2

img = cv2.imread(<image_path>)
dh, dw, _ = img.shape
        
fl = open(<label_path>, 'r')
data = fl.readlines()
fl.close()
        
for dt in data:
            
    _, x, y, w, h = dt.split(' ')
            
    nx = int(float(x)*dw)
    ny = int(float(y)*dh)
    nw = int(float(w)*dw)
    nh = int(float(h)*dh)
            
    cv2.rectangle(img, (nx,ny), (nx+nw,ny+nh), (0,0,255), 1)
            
plt.imshow(img)

Actual Annotations and Image

0 0.286972 0.647157 0.404930 0.371237 
0 0.681338 0.366221 0.454225 0.418060

Answer 1

There's another Q&A on this topic, and there's this¹ interesting comment below the accepted answer. The bottom line is, that the YOLO coordinates have a different centering w.r.t. to the image. Unfortunately, the commentator didn't provide the Python port, so I did that here:

import cv2
import matplotlib.pyplot as plt

img = cv2.imread(<image_path>)
dh, dw, _ = img.shape

fl = open(<label_path>, 'r')
data = fl.readlines()
fl.close()

for dt in data:

    # Split string to float
    _, x, y, w, h = map(float, dt.split(' '))

    # Taken from https://github.com/pjreddie/darknet/blob/810d7f797bdb2f021dbe65d2524c2ff6b8ab5c8b/src/image.c#L283-L291
    # via https://stackoverflow.com/questions/44544471/how-to-get-the-coordinates-of-the-bounding-box-in-yolo-object-detection#comment102178409_44592380
    l = int((x - w / 2) * dw)
    r = int((x + w / 2) * dw)
    t = int((y - h / 2) * dh)
    b = int((y + h / 2) * dh)
    
    if l < 0:
        l = 0
    if r > dw - 1:
        r = dw - 1
    if t < 0:
        t = 0
    if b > dh - 1:
        b = dh - 1

    cv2.rectangle(img, (l, t), (r, b), (0, 0, 255), 1)

plt.imshow(img)
plt.show()

So, for some Lenna image, that'd be the output, which I think shows the correct coordinates w.r.t. your image:

----------------------------------------
System information
----------------------------------------
Platform:     Windows-10-10.0.16299-SP0
Python:       3.8.5
Matplotlib:   3.3.2
OpenCV:       4.4.0
----------------------------------------

---

¹Please upvote the linked answers and comments.

Answer 2

There is a more straight-forward way to do those stuff with pybboxes. Install with,

pip install pybboxes

In your case,

import pybboxes as pbx

yolo_bbox1 = (0.286972, 0.647157, 0.404930, 0.371237)
yolo_bbox2 = (0.681338, 0.366221, 0.454225, 0.418060)
W, H = 300, 300  # WxH of the image
pbx.convert_bbox(yolo_bbox1, from_type="yolo", to_type="voc", image_size=(W, H))
>>> (25, 138, 147, 250)
pbx.convert_bbox(yolo_bbox2, from_type="yolo", to_type="voc", image_size=(W, H))
>>> (136, 47, 273, 173)

Note that, converting to YOLO format requires the image width and height for scaling.

Answer 3

import os
import pybboxes as pbx
import cv2

DATA_PATH = "<data_path>"
                                                  
for i in sorted(os.listdir(DATA_PATH)):
    print(i)
    if i[-1]=="g":
        img = cv2.imread(os.path.join(DATA_PATH, i))
        print(os.path.join(DATA_PATH, i))

        fl = open(os.path.join(DATA_PATH, f"{i[:-3]}txt"), 'r')
        data = fl.readlines()
        fl.close()

        H, W = img.shape[:2]

        for dt in data:
            _, x, y, w, h = map(float, dt.split(' '))
            box_voc = pbx.convert_bbox((x,y,w,h), from_type="yolo", to_type="voc", image_size=(W,H))

            cv2.rectangle(img, (box_voc[0], box_voc[1]), (box_voc[2], box_voc[3]), (0, 0, 255), 3)
        cv2.imshow(i, img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

Answer 4

I've faced this issue recently and here is the solution I've used for multiple YOLO type annotation to CV2 format.

In my scenario, I've got a bunch of label files with multiple-single annotations in it. For saving YOLO format annotations too, I created a new directory and saved converted labels to it.

import os

image_width = 1280
image_height = 720

def yolo_to_voc_convertion(input_file, output_file):
    with open(input_file, 'r') as f:
        lines = f.readlines()

    new_lines = list()
    for line in lines:
        data = line.strip().split(' ')

        class_id = int(data[0])
        x_center = float(data[1])
        y_center = float(data[2])
        width = float(data[3])
        height = float(data[4])

        x_min = int((x_center - (width / 2)) * image_width)
        y_min = int((y_center - (height / 2)) * image_height)
        x_max = int((x_center + (width / 2)) * image_width)
        y_max = int((y_center + (height / 2)) * image_height)

        new_data = f'{class_id} {x_min} {y_min} {x_max} {y_max}\n'
        new_lines.append(new_data)

    with open(output_file, 'w') as f:
        f.writelines(new_lines)

input_folder = '..' # folder that includes .txt files
output_folder = '..' # output folder that will be included new format ann files

for filename in os.listdir(input_folder):
    if filename.endswith('.txt'):
        input_file = os.path.join(input_folder, filename)
        output_file = os.path.join(output_folder, filename)

        yolo_to_voc_convertion(input_file, output_file)

Answer 5

## pip install pybboxes 
import pybboxes as pbx

yolo_normalized = (0.048765432089567184, 0.6583333611488342, 0.09753086417913437, 0.29814815521240234) 

H, W = img.shape[:2]

box_voc = pbx.convert_bbox(yolo_normalized, from_type="yolo", to_type="voc", image_size=(W,H))

print(box_voc)

# [Out]: (0, 153, 29, 242)

## for plotting:

cv2.rectangle(img, (box_voc[0], box_voc[1]), (box_voc[2], box_voc[3]), (0, 0, 255), 1)

Works for me properly :)