ImgAug_yolov7

This approach uses Data Augmentation to generate new samples given a training/validation dataset without the Keras Augmentation.

this is a fork of ZhuJD-China/ImgAug_yolov5

Prerequisites They are the same as YOLOv5, but make sure you have already installed them.

Recall: YOLOv5 requires the dataset to be in the darknet format. Here’s an outline of what it looks like:

• One txt with labels file per image
• One row per object
• Each row is class x_center y_center width height format.
• Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.

Class numbers are zero-indexed (start from 0). Example:

Image properties: width=1156 pix, height=1144 pix.
bounding box properties: xmin=1032, ymin=20, xmax=1122, ymax=54, object_name="Ring".
Let objects_list="bracelet","Earring","Ring","Necklace"
YOLOv5 format: f"{category_idx} {x1 + bbox_width / 2} {y1 + bbox_height / 2} {bbox_width} {bbox_height}\n"

𝑏𝑏𝑜𝑥𝑤𝑖𝑑𝑡ℎ=𝑥𝑚𝑎𝑥/𝑤𝑖𝑑𝑡ℎ−𝑥𝑚𝑖𝑛/𝑤𝑖𝑑𝑡ℎ=(1122−1032)/1156=0.07785467128027679 
𝑏𝑏𝑜𝑥ℎ𝑒𝑖𝑔ℎ𝑡=𝑦𝑚𝑎𝑥/ℎ𝑒𝑖𝑔ℎ𝑡−𝑦𝑚𝑖𝑛/ℎ𝑒𝑖𝑔ℎ𝑡=(54−20)/1144=0.029720279720279717 
𝑥𝑐𝑒𝑛𝑡𝑒𝑟=𝑥𝑚𝑖𝑛/𝑤𝑖𝑑𝑡ℎ+𝑏𝑏𝑜𝑥𝑤𝑖𝑑𝑡ℎ/2=0.9316608996539792 
𝑦𝑐𝑒𝑛𝑡𝑒𝑟=𝑦𝑚𝑖𝑛/ℎ𝑒𝑖𝑔ℎ𝑡+𝑏𝑏𝑜𝑥ℎ𝑒𝑖𝑔ℎ𝑡/2=0.032342657342657344 
category_idx=2
Final result: 2 0.9316608996539792 0.032342657342657344 0.07785467128027679 0.029720279720279717

some functions:

• Data Reading and Storage Functions
• Photometric Transformations
• Geometric Transformations¶
• Random Occlusion

Build virtual environment:

python3 -m venv ./venv
source ./venv/bin/activate
pip install -r requirements.txt

Run preparation

define the directory for the images and the annotations in the file at the appropriate line. in the actual versin the images and the annotations might be in the same directory.

in the AUG.py file:

#define here the location of the images and the annotation. in this version they might be in the same directory
####################################################
image_directory = 'test_aug/images/train'
labels_directory = 'test_aug/labels/train'
####################################################

Run

run only once per dataset! otherwise the augmentation will be applied to the already augmented files again. On a modern computer it takes approx. 1 sec per image to get the enhanced versions for each image of the test set. Be prepared to wait if you have a huge number of images.

python3 AUG.py

to leave environment:

deactivate