AutoPano - Panorama Stitching

The panorama stitching project uses SIFT feature descriptors for key point matching and RANSAC for outlier rejection. The images are blended with distance transformation, resulting in a seamless and coherent panoramic image.

Requirements

To run the panorama stitching project, ensure you have the following libraries installed:

• numpy
• opencv-python (cv2)
• argparse
• os (standard library)
• math (standard library)
• glob (standard library)

You can install the required libraries using pip:

pip install numpy opencv-python argparse

You can test the given data set using following command or can create custom dataset as follows:

To run stitching:

python3 Wrapper.py --Path $your-path-to-dataset --Scale $scale-factor

Here,

• Path parser will take the path from the current directory to dataset location
• Scale parser can be used if your image size if big, and you want to reduce the size

To create custom dataset:

• create a dataset folder and add all images, name all images in numberical order (be carful of order)

Example of running wrapper on custom dataset:

python3 Wrapper.py --Path ../Data/Set1 --Scale 4

Result

for detailed results check - devsonni.github.io/AutoPano