# Image Feature Matching with Julia
This code demonstrates how to perform image feature matching using Julia programming language, specifically utilizing the `ImageFeatures.jl`, `Images.jl`, `Rotations.jl`, `ImageDraw.jl`, and `CoordinateTransformations.jl` packages.
## Usage
1. Install the required Julia packages:
```julia
using Pkg
Pkg.add("ImageFeatures")
Pkg.add("Images")
Pkg.add("Rotations")
Pkg.add("ImageDraw")
Pkg.add("CoordinateTransformations")
-
Load the necessary packages:
using ImageFeatures using Images using Rotations, ImageDraw, CoordinateTransformations
-
Load the image:
img = load("wp2788550-adventure-time-wallpaper-deviantart.jpg")
-
Convert the image to grayscale:
img_gray = Gray.(img)
-
Perform rotation:
img_rotated = rotation(img_gray)
-
Calculate keypoints:
kp1, kp2 = keypoints_calculation(img_gray, img_rotated)
-
Compute FREAK descriptors:
freak_params = FREAK() desc1, ret1_kp = create_descriptor(img_gray, kp1, freak_params) desc2, ret2_kp = create_descriptor(img_rotated, kp2, freak_params)
-
Match keypoints:
matches = match_keypoints(ret1_kp, ret2_kp, desc1, desc2, 0.1)
-
Visualize the results:
grid = hcat(img_gray, img_rotated) offset = CartesianIndex(0, size(img_gray, 2)) draw!(grid, [LineSegment(m[1], m[2] + offset) for m in matches])
This code performs the following steps:
- Loads an image and converts it to grayscale.
- Rotates the grayscale image by a specified angle.
- Calculates keypoints using the FAST corner detection algorithm.
- Computes FREAK descriptors for the keypoints.
- Matches keypoints between the original and rotated images.
- Visualizes the matched keypoints on a grid image.
- Julia programming language
ImageFeatures.jl
Images.jl
Rotations.jl
ImageDraw.jl
CoordinateTransformations.jl
This code is without any guarantees. Feel free to modify and use it according to your requirements.