NEU CS5330 Course Lab Project 1: A computer vision application to identify sky pixels in a set of images using traditional image processing techniques with OpenCV.

https://huggingface.co/spaces/RedBottle13/Sky-Segmentation

• Step 1: Transform BGR images into HSV format.
• Step 2: Determine the HSV range using a dataset of sky images.
• Step 3: Employ the identified HSV range to create an initial mask.
• Step 4: Execute morphological operations for fine-tuning the mask.
• Step 5: Conduct connected component analysis to complete the sky mask.

1. The sky is consistently located in the upper portion of the images.
2. Sky images fall into three categories based on conditions:

• Clear/Sunny: Displaying bright blue color.
• Sunrise/Sunset: Displaying red, orange, or pink colors.
• Overcast: Displaying pale or gray colors.

3. The sky region is a contiguous area.

1. Sky Recognition Dataset

• Purpose: Analyzes the sky during daylight and sunset.
• Content: Consists of 58 ground-based sky images.
• Source: Sky Recognition Dataset

2. Skylines 12 Dataset

• Purpose: Provides a diverse range of high-resolution images.
• Content: Includes 120 images capturing skylines from 12 different cities.
• Source: Skylines 12 Dataset

3. New York City Midtown with Empire State Building at Sunset Stock Photo

• Purpose: Supplements the dataset with a specific urban sunset scene.
• Source: New York City Midtown with Empire State Building at Sunset Stock Photo