2. How to do Image Data Collection?

1. Data Collection:

a) Web Scraping:

You can use tools like:

• • BeautifulSoup: It’s a Python library for web scraping purposes to pull the data out of HTML and XML files.

• • Scrapy: An open-source web-crawling framework for Python.

Example: If you want to collect images of cats and dogs from a website:

import requests
from bs4 import BeautifulSoup

URL = 'your_target_website_url'
page = requests.get(URL)
soup = BeautifulSoup(page.content, "html.parser")

img_tags = soup.find_all('img')

urls = [img['src'] for img in img_tags]

for url in urls:
    response = requests.get(url)
    with open('path_to_save_images', 'wb') as file:
        file.write(response.content)

b) Datasets Available Online: There are several publicly available datasets for image segmentation, such as:

• • COCO

• • ADE20K

• • Cityscapes

• • Pascal VOC

c) Create Your Own: Use your smartphone or camera to capture images. This is especially useful if you have a niche requirement that’s not available in public datasets.

2. Annotation for Image Segmentation:

Annotation tools help you label the collected images for segmentation. Some popular and free tools are:

• • Labelbox

• • VGG Image Annotator (VIA)

• • LabelMe

3. Pre-processing:

a) Image Augmentation:

Enhance your dataset’s size and variability using augmentations. These can be:

• • Rotation

• • Shearing

• • Zooming

• • Horizontal/Vertical flipping

• • Cropping

imgaug and Augmentor are good Python libraries for this.

b) Resize:

Resizing all images to a standard size can help in faster and consistent training.

c) Normalization:

Normalize pixel values to be in the range [0,1] or [-1,1]. It helps in faster convergence during training.

normalized_image = image / 255.0

d) Color Spaces:

Sometimes, converting an image to a different color space (like HSV or LAB) can provide better results.

e) Histogram Equalization:

Enhance the contrast of your images. OpenCV’s equalizeHist function can help.

f) Removing Noise:

Denoising images can be useful, especially if they were taken in low-light conditions. OpenCV’s fastNlMeansDenoisingColored can be employed.

4. Example:

Let’s say you’re building a segmentation model for different types of fruits:

• • Collection:
    • • Use web scraping to collect images of fruits.
    • • Manually capture images of fruits.

• • Annotation:
    • • Use tools like Labelbox to manually segment and label parts of the fruits.

• • Pre-processing:
    • • Resize: Standardize all images to 256×256.
    • • Augment: Use random rotations and zooms to artificially increase your dataset size.
    • • Normalize: Ensure pixel values are in [0,1].

By following these steps, you’ll have a dataset ready for training your image segmentation model.