[Bug]: simple_object_detector throws error for aspect ratio mismatch even when all bounding boxes are of the same dimension

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.

imglab dataset Created by imglab tool. ......` ### 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))) ```

davisking / dlib