How to get the coordinates of the bounding box in YOLO object detection?

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I need to get the bounding box coordinates generated in the above image using YOLO object detection.

A quick solution is to modify the image.c file to print out the bounding box information:

...
if(bot > im.h-1) bot = im.h-1;

// Print bounding box values 
printf("Bounding Box: Left=%d, Top=%d, Right=%d, Bottom=%d\n", left, top, right, bot); 
draw_box_width(im, left, top, right, bot, width, red, green, blue);
...

Convert YoloV3 output to coordinates of bounding box, label and , Pelase find the below code with comments where ever required, to extract the probability) of # the current object detection scores = detection[5:] YOLO actually # returns the center (x, y)-coordinates of the bounding # box� Each cell in the grid is responsible for predicting the bounding box parameters, the confidence that an object is present and the class probabilities. The resulting bounding box prediction consists of the x and y coordinates of the box’s centre, sqrt(width), sqrt(height) and an object probability score.

There is a nice little python (2 - but with small modifications 3. [ just change the print and strings to binary strings in the main ] ) program you can use in the main repo https://github.com/pjreddie/darknet/blob/master/python/darknet.py

NOTE! The given coordinates are midpoint and width and height.

Bounding box coordinates � Issue #183 � pjreddie/darknet � GitHub, darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg I get a new picture -to-get- the-coordinates-of-the-bounding-box-in-yolo-object-detection/� If you're training models for object detection, you can interactively visualize bounding boxes in Weights & Biases. This short demo focuses on driving scenes, testing a YoloV3 net pretrained on MSCOCO on images from the Berkeley Deep Drive 100K dataset. The API for logging bounding boxes is flexible and intuitive. Below, I explain the

for python user in windows:

first..., do several setting jobs:

  1. setting python path of your darknet folder in environtment path:

    PYTHONPATH = 'YOUR DARKNET FOLDER'

  2. add PYTHONPATH to Path value by add:

    %PYTHONPATH%

  3. edit file coco.data in cfg folder, by change the names folder variable to your coco.names folder, in my case:

    names = D:/core/darknetAB/data/coco.names

with this setting, you can call darknet.py (from alexeyAB\darknet repository) as your python module from any folder.

start scripting:

from darknet import performDetect as scan #calling 'performDetect' function from darknet.py

def detect(str):
    ''' this script if you want only want get the coord '''
    picpath = str
    cfg='D:/core/darknetAB/cfg/yolov3.cfg' #change this if you want use different config
    coco='D:/core/darknetAB/cfg/coco.data' #you can change this too
    data='D:/core/darknetAB/yolov3.weights' #and this, can be change by you
    test = scan(imagePath=picpath, thresh=0.25, configPath=cfg, weightPath=data, metaPath=coco, showImage=False, makeImageOnly=False, initOnly=False) #default format, i prefer only call the result not to produce image to get more performance

    #until here you will get some data in default mode from alexeyAB, as explain in module.
    #try to: help(scan), explain about the result format of process is: [(item_name, convidence_rate (x_center_image, y_center_image, width_size_box, height_size_of_box))], 
    #to change it with generally used form, like PIL/opencv, do like this below (still in detect function that we create):

    newdata = []
    if len(test) >=2:
        for x in test:
            item, confidence_rate, imagedata = x
            x1, y1, w_size, h_size = imagedata
            x_start = round(x1 - (w_size/2))
            y_start = round(y1 - (h_size/2))
            x_end = round(x_start + w_size)
            y_end = round(y_start + h_size)
            data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
            newdata.append(data)

    elif len(test) == 1:
        item, confidence_rate, imagedata = test[0]
        x1, y1, w_size, h_size = imagedata
        x_start = round(x1 - (w_size/2))
        y_start = round(y1 - (h_size/2))
        x_end = round(x_start + w_size)
        y_end = round(y_start + h_size)
        data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
        newdata.append(data)

    else:
        newdata = False

    return newdata

How to use it:

table = 'D:/test/image/test1.jpg'
checking = detect(table)'

to get the coordinate:

if only 1 result:

x1, y1, x2, y2 = checking[2]

if many result:

for x in checking:
    item = x[0]
    x1, y1, x2, y2 = x[2]
    print(item)
    print(x1, y1, x2, y2)

Yolo Framework, I need to get the bounding box coordinates generated in the above image using YOLO object detection. A quick solution is to modify the image.c file to print out� To get the coordinates of the green and purple dots, we need to: green dot = boxXY - boxWH / 2 purple dot = boxXY + boxWH /2 (Please note that the top left corner has smaller cordinates in images) After this, we multiply the coordinates with (32,32) so that the bounding boxes are now in image scale.

If you are going to implement this in python, there is this small python wrapper that I have created in here. Follow the ReadMe file and install it. It will be very easy to install.

After that follow this example code to know how to detect objects. If your detection is det

top_left_x = det.bbox.x
top_left_y = det.bbox.y
width = det.bbox.w
height = det.bbox.h

If you need, you can get the midpoint by:

mid_x, mid_y = det.bbox.get_point(pyyolo.BBox.Location.MID)

Hope this helps..

YOLO object detection with OpenCV, Now that we have grasp on why YOLO is such a useful framework, let's Values 2-5 will be the bounding box coordinates for that object, and� After downloading YOLO and running it by typing ./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg I get a new picture which contains a bounding box. So how can I know the coordinates of that

Inspired from @Wahyu answer above. There are few changes, modification and bug fixes and tested with single object detection and multiple object detection.

# calling 'performDetect' function from darknet.py
from darknet import performDetect as scan
import math


def detect(img_path):
    ''' this script if you want only want get the coord '''
    picpath = img_path
    # change this if you want use different config
    cfg = '/home/saggi/Documents/saggi/prabin/darknet/cfg/yolo-obj.cfg'
    coco = '/home/saggi/Documents/saggi/prabin/darknet/obj.data'  # you can change this too
    # and this, can be change by you
    data = '/home/saggi/Documents/saggi/prabin/darknet/backup/yolo-obj_last.weights'
    test = scan(imagePath=picpath, thresh=0.25, configPath=cfg, weightPath=data, metaPath=coco, showImage=False, makeImageOnly=False,
                initOnly=False)  # default format, i prefer only call the result not to produce image to get more performance

    # until here you will get some data in default mode from alexeyAB, as explain in module.
    # try to: help(scan), explain about the result format of process is: [(item_name, convidence_rate (x_center_image, y_center_image, width_size_box, height_size_of_box))],
    # to change it with generally used form, like PIL/opencv, do like this below (still in detect function that we create):

    newdata = []

    # For multiple Detection
    if len(test) >= 2:
        for x in test:
            item, confidence_rate, imagedata = x
            x1, y1, w_size, h_size = imagedata
            x_start = round(x1 - (w_size/2))
            y_start = round(y1 - (h_size/2))
            x_end = round(x_start + w_size)
            y_end = round(y_start + h_size)
            data = (item, confidence_rate,
                    (x_start, y_start, x_end, y_end), (w_size, h_size))
            newdata.append(data)

    # For Single Detection
    elif len(test) == 1:
        item, confidence_rate, imagedata = test[0]
        x1, y1, w_size, h_size = imagedata
        x_start = round(x1 - (w_size/2))
        y_start = round(y1 - (h_size/2))
        x_end = round(x_start + w_size)
        y_end = round(y_start + h_size)
        data = (item, confidence_rate,
                (x_start, y_start, x_end, y_end), (w_size, h_size))
        newdata.append(data)

    else:
        newdata = False

    return newdata


if __name__ == "__main__":
    # Multiple detection image test
    # table = '/home/saggi/Documents/saggi/prabin/darknet/data/26.jpg'
    # Single detection image test
    table = '/home/saggi/Documents/saggi/prabin/darknet/data/1.jpg'
    detections = detect(table)

    # Multiple detection
    if len(detections) > 1:
        for detection in detections:
            print(' ')
            print('========================================================')
            print(' ')
            print('All Parameter of Detection: ', detection)

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected label: ', detection[0])

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected object Confidence: ', detection[1])

            x1, y1, x2, y2 = detection[2]
            print(' ')
            print('========================================================')
            print(' ')
            print(
                'Detected object top left and bottom right cordinates (x1,y1,x2,y2):  x1, y1, x2, y2')
            print('x1: ', x1)
            print('y1: ', y1)
            print('x2: ', x2)
            print('y2: ', y2)

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected object width and height: ', detection[3])
            b_width, b_height = detection[3]
            print('Weidth of bounding box: ', math.ceil(b_width))
            print('Height of bounding box: ', math.ceil(b_height))
            print(' ')
            print('========================================================')

    # Single detection
    else:
        print(' ')
        print('========================================================')
        print(' ')
        print('All Parameter of Detection: ', detections)

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected label: ', detections[0][0])

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected object Confidence: ', detections[0][1])

        x1, y1, x2, y2 = detections[0][2]
        print(' ')
        print('========================================================')
        print(' ')
        print(
            'Detected object top left and bottom right cordinates (x1,y1,x2,y2):  x1, y1, x2, y2')
        print('x1: ', x1)
        print('y1: ', y1)
        print('x2: ', x2)
        print('y2: ', y2)

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected object width and height: ', detections[0][3])
        b_width, b_height = detections[0][3]
        print('Weidth of bounding box: ', math.ceil(b_width))
        print('Height of bounding box: ', math.ceil(b_height))
        print(' ')
        print('========================================================')

# Single detections output:
# test value  [('movie_name', 0.9223029017448425, (206.79859924316406, 245.4672393798828, 384.83673095703125, 72.8630142211914))]

# Multiple detections output:
# test value  [('movie_name', 0.9225175976753235, (92.47076416015625, 224.9121551513672, 147.2491912841797, 42.063255310058594)),
#  ('movie_name', 0.4900225102901459, (90.5261459350586, 12.4061279296875, 182.5990447998047, 21.261077880859375))]

Guide to Car Detection using YOLO, Scale bounding box coordinates so we can display them properly on our original image (Line 81). Extract coordinates and dimensions of the� Fig. 2. Understanding Ground truth and predicted bounding boxes in object detection. The Methodology The basic steps that are followed in object detection using YOLO according to the paper presented by Joseph Redmon et. al [1], are as follows: An S x S grid is obtained by dividing the image. B bounding boxes are predicted by each grid cell.

Understanding Object Detection Using YOLO, In-depth concept of the YOLO algorithm — bounding boxes, non-max If you have 80 classes that you want the object detector to recognize, you can which converts the YOLO box coordinates (x, y, w, h) to box corners'� It uses a single stage object detection network which is faster than other two-stage deep learning object detectors, such as regions with convolutional neural networks (Faster R-CNNs). The YOLOv2 model runs a deep learning CNN on an input image to produce network predictions. The object detector decodes the predictions and generates bounding boxes.

How to get bounding box coordinates tensorflow, Learn more about object detection by using YOLO. with classifying their labels and finding the bounding box (or polygons) of that object. reference of the ground truth bounding bounding box coordinates. If the annotated coordinates are relative to the image size (as used in YOLO), set it to rel . If the coordinates are absolute values, not depending to the image size, set it to abs

How to Perform Object Detection With YOLOv3 in Keras, Code for How to Perform YOLO Object Detection using OpenCV and PyTorch in Python - Python Code Jun 10, 2019 � Extract the bounding box coordinates,� Code for How to Perform YOLO Object Detection using OpenCV and PyTorch in Python Tutorial View on Github. yolo_opencv.py. import cv2 import numpy as np import time import sys import os CONFIDENCE = 0.5 SCORE_THRESHOLD = 0.5 IOU_THRESHOLD = 0.5 # the neural network configuration config_path = "cfg/yolov3.cfg" # the YOLO net weights file weights_path = "weights/yolov3.weights" # loading all the

Comments
  • Seriously, thank you so much for suggesting image.c. It helped me solve a totally different problem: When running YOLO in Python (via OpenCV-DNN), the detections are given in a float format. And literally every article I've ever seen has the WRONG MATH for turning the YOLO floats (center X/Y, and width/height) into pixel coordinates. But the official image.c has the math! Right here! github.com/pjreddie/darknet/blob/… - I just had to port that to python. :-)
  • @Brian O'Donnell How can I modify the "image.c" to only get four numbers for the coordinates of bounding boxes (without any additional description)?
  • Do you just want the numbers? If so you would want: printf("%d,%d,%d,%d\n", left, top, right, bot);
  • The code is untested there is typo in weight_size and height_size. And you should use test[0] to extract item, confidence_rate, imagedata in the single detection. I have commented below with working code. Anyway lots of thanks for your code that helped me kick start.
  • yeahh..., sorry for the typo...just try to help and inspirate... btw, already fix the typo....should be work now... Noted: The Newest OpenCV (4.1.1 above) already have Darknet RNN model, so, we can implement darknet, straight in opencv. OpenCV like All in One machine now...
  • Howcome you didn't need to use the anchors ?