[Bug]: simple_object_detector throws error for aspect ratio mismatch even when all bounding boxes are of the same dimension #2824
Description
What Operating System(s) are you seeing this problem on?
Windows
dlib version
19.24.99
Python version
3.9.13
Compiler
GCC 6.3.0
Expected Behavior
I should not get aspect ratio error while running the dlib simple_object_detector on images with exactly same bounding box dimensions. A sample of the training.xml file is given below.
<dataset> <name>imglab dataset</name> <comment>Created by imglab tool.</comment> <images> <image file="patches\0.jpg" width="4962" height="3508"> <box top="1992" left="932" width="771" height="61" /> <box top="500" left="1166" width="771" height="61" /> <box top="3299" left="2296" width="771" height="61" /> <box top="2514" left="926" width="771" height="61" /> <box top="2752" left="926" width="771" height="61" /> <box top="652" left="514" width="771" height="61" /> <box top="1858" left="1440" width="771" height="61" /> </image> <image file="patches\10.jpg" width="4962" height="3509"> <box top="1281" left="2728" width="771" height="61" /> <box top="2380" left="717" width="771" height="61" /> <box top="2177" left="2456" width="771" height="61" /> </image>......
Current Behavior
Even though all the dimensions are equal I still get the error thrown for aspect ratio mismatch in marked bounding boxes. The error based on the code below is showing this.
Training with C: 1
Training with epsilon: 0.01
Training using 4 threads.
Training with sliding window 302 pixels wide by 21 pixels tall.
Upsample images...
Upsample images...
#################### Error in labelling ####################
Adjusting bounding boxes to have same dimensions i.e. same aspect ratio
Auxilliary training file created: C:\Users\Pratyay Mukherjee\Desktop\IIT Bombay - MTech\ToyoProject+Seminar\PhaseA\demo_line_number_extraction\ARAMCO_TRAIN\aux_training.xml
Training with C: 1
Training with epsilon: 0.01
Training using 4 threads.
Training with sliding window 284 pixels wide by 23 pixels tall.
Upsample images...
Upsample images...
Traceback (most recent call last):
File "C:\Users\Pratyay Mukherjee\Desktop\IIT Bombay - MTech\ToyoProject+Seminar\PhaseA\svm_object_detector_train.py", line 64, in main
dlib.train_simple_object_detector(training_file, model_path, options)
RuntimeError:
Error! An impossible set of object boxes was given for training. All the boxes
need to have a similar aspect ratio and also not be smaller than about 400
pixels in area. The following images contain invalid boxes:
patches\0.jpg
patches\11.jpg
patches\4.jpg
patches\5.jpg
patches\9.jpg
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Pratyay Mukherjee\Desktop\IIT Bombay - MTech\ToyoProject+Seminar\PhaseA\svm_object_detector_train.py", line 84, in
main(args)
File "C:\Users\Pratyay Mukherjee\Desktop\IIT Bombay - MTech\ToyoProject+Seminar\PhaseA\svm_object_detector_train.py", line 69, in main
dlib.train_simple_object_detector(aux_training_file, model_path, options)
RuntimeError:
Error! An impossible set of object boxes was given for training. All the boxes
need to have a similar aspect ratio and also not be smaller than about 400
pixels in area. The following images contain invalid boxes:
patches\0.jpg
patches\10.jpg
patches\11.jpg
patches\4.jpg
patches\5.jpg
Steps to Reproduce
The options are as follows
options = dlib.simple_object_detector_training_options() options.add_left_right_image_flips = bool(flip) #False options.C = svmC #1 options.num_threads = 4 options.be_verbose = True
The up-sample limit is by default 2. The value of the variable assignments to the options is given in the comments.
Anything else?
I am attaching the code here for reference.
import dlib
import os
import argparse
import xml.etree.ElementTree as ET
#replace the curret bounding boxes with the maximum dimensions in all the dataset
#parses the tree and sets the dimensions required
#returns the path of the new XML file saved
def adjust_bounding_boxes(xml_file):
tree = ET.parse(xml_file)
root = tree.getroot()
#compute max width and height
images = root.find('images')
img_lst = images.findall('image')
width_lst, height_lst = [], []
for img in img_lst:
box_lst = img.findall('box')
for box in box_lst:
width_lst.append(int(box.get('width')))
height_lst.append(int(box.get('height')))
maxWidth, maxHeight = max(width_lst), max(height_lst)
#set the new dimensions
for img in img_lst:
box_lst = img.findall('box')
for box in box_lst:
box.set('width', str(maxWidth))
box.set('height', str(maxHeight))
#write the new training file
save_file_path = os.path.join(os.path.split(xml_file)[0], 'aux_training.xml')
print(f"Auxilliary training file created: {save_file_path}")
tree.write(save_file_path)
return save_file_path
def main(args):
#training and testing xml files
training_file = args.train
model_path = args.model
svmC = float(args.svmC)
flip = int(args.flip)
#check on xml file paths
assert os.path.exists(training_file) and os.path.isfile(training_file), "training.xml path does not exist or is not a file"
#setup training configuration
options = dlib.simple_object_detector_training_options()
options.add_left_right_image_flips = bool(flip)
options.C = svmC
options.num_threads = 4
options.be_verbose = True
#training
try:
dlib.train_simple_object_detector(training_file, model_path, options)
except Exception as e:
print(f"#################### Error in labelling ####################")
print(" Adjusting bounding boxes to have same dimensions i.e. same aspect ratio")
aux_training_file = adjust_bounding_boxes(training_file)
dlib.train_simple_object_detector(aux_training_file, model_path, options)
#training and testing accuracy scores
print("Training accuracy: {}".format(dlib.test_simple_object_detector(training_file, model_path)))