HaarCascade training: merge .lst files

I am working on an own object detector using HaarCascade. BTW, I am stucked at some point, which I'll explain below.

The situation it's this: - I collected 100 images of my object (let's say apples) - I collected about 1500 negatives images - I created negatives.txt, a file which contains paths to the negatives images - I created positives.txt, a file which contains paths, number of objects, coordinates and dimensions of my positive images

Now, this is the problem.

Using opencv_createsamples.exe to augment my data/images, I saw that if I execute it (with same parameters) for each object image, the output will be more or less a 1000 positive images (negatives background + object) together with a .lst file containing path and object coordinates inside the negative image. These coordinates will not change (because I set them when I executed opencv_createsamples.exe).

The question is: it's a good idea to change the parameters createsamples requires for each of my object image and then merge them all ?

Example of what I am doing now:

• opencv_createsample.exe -p1 0.5 -p2 0.6 -p3 0.7
• for one of my object image for each of my negatives
• .lst file with info's (the same if execute for each positive ojbect image)

Example of what I am willing to do:

• for each of my object image for each of my negatives
• opencv_createsample.exe -p1 0.5 -p2 0.6 -p3 0.7 - opencv_createsample.exe -p1 0.6 -p2 0.7 -p3 0.8, and so on (with random values of parameters)
• multiple .lst files with different info's for each object image
• merge of all .lst

I really hope I explained all.

My doubt it's about efficiency of doing this: I will have a better accuracy by training using different objects (of the same class) in different position or it's the same by using only one object ?

Glossary:

• object = what I want to detect (an apple)
• negative image = background image not containing the object
• positive image = processed image (createsamples output) with negative + object

Thanks all

UPDATE

Here after watching Sentdex video on HaarCascade: Training Haar cascade object detection - OpenCV with Python for Image and Video Analysis 20

from HaarCascade training: merge .lst files