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yolodarknet

How to crop the detected object after training using YOLO?

I am using YOLO for model training. I want to crop the detected object. For Darknet repository am using is: https://github.com/AlexeyAB/darknet/

For Detection and storing output coordinates in a text file am using this: !./darknet detector test data_for_colab/obj.data data_for_colab/yolov3-tiny-obj.cfg yolov3-tiny-obj_10000.weights -dont_show -ext_output < TEST.txt > result.txt Result.jpg

Solution

  • Considering in the TEST.txt file you have details as the sample image. You can use re module of python for text pattern detection, ie your "class_name".

    Parsing the .txt file

    import re
path='/content/darknet/result.txt'
myfile=open(path,'r')
lines=myfile.readlines()
pattern= "class_name"

for line in lines:
  if re.search(pattern,line):
    Cord_Raw=line
Cord=Cord_Raw.split("(")[1].split(")")[0].split("  ")

    Now we will get the coordinates in a list.

    Coordinate calculation

    x_min=int(Cord[1])
x_max=x_min + int(Cord[5])
y_min=int(Cord[3])
y_max=y_min+ int(Cord[7])

    Cropping from the actual image

    import cv2
img = cv2.imread("Image.jpg")
crop_img = img[y_min:y_max, x_min:x_max]
cv2.imwrite("Object.jpg",crop_img)