Panorama Stitching

Code by Rwik Rana, IITGn.

View the project on https://github.com/Rwik2000/Panorama-Stitching for better viewing

This is a Code to stitch multiple images to create panoramas. This is robust for stitching 4 images. This is the tree for the entire repo:\

Panorama Stitching
        |
        |___Dataset
        |       |--I1
        |       |--I2 ....
        |
        |___Outputs
        |
        |___blend.py
        |___Homography.py
        |___Main.py
        |___Warp.py

In this code, I have used concepts of inverse homography, warping, RANSAC and Laplacian Blending to solve the problem statement.

To use the code,

Libraries needed

opencv
numpy
imutils

Procedure:

In the main.py,

Add your dataset in the Dataset Directory.
In line 147, add your datasets in the Datasets array.

Datasets = ["I5"] #ADD YOUR DATASET HERE
for Dataset in Datasets:
    print("Stitching Dataset : ", Dataset)
    Path = "Dataset/"+Dataset
    images=[]
    for filename in os.listdir(Path):
        if filename.endswith(".JPG") or filename.endswith(".PNG"):
            img_dir = Path+'/'+str(filename)
            images.append(cv2.imread(img_dir))

    for i in range(len(images)):
        images[i] = imutils.resize(images[i], width=500)

    images = images[1:5]
    stitcher = panaroma_stitching()
    result, left, right = stitcher.MultiStitch(images)
    print("========>Done! Final Image Saved in Outputs Dir!")
    if os.path.exists("Outputs/"+Dataset):
        shutil.rmtree("Outputs/"+Dataset)
    os.makedirs("Outputs/"+Dataset, )
    cv2.imwrite("Outputs/"+Dataset+"/"+Dataset+".JPG", result)
    cv2.imwrite("Outputs/"+Dataset+"/"+Dataset+"_left.JPG", left)
    cv2.imwrite("Outputs/"+Dataset + "/"+Dataset+"_right.JPG", right)

The output of the same would be save in the Output directory.

Flow of the Code:

SIFT to detect features.
Matching features using KNN
Finding the the homography matrix i.e. the mapping from one image plane to another.
warping the images using the inverse of homography matrix to ensure no distortion/blank spaces remain in the transformed image.
Stitching all the warped images together

Implementation of Ransac:

Refer to the homographyRansac() in Homography.py. Access the object getHomography() of the class to get the desired homography matrix.

        # Set the threshold and number of iterations neede in RANSAC.
        self.ransacThresh = ransacThresh
        self.ransaciter = ransacIter

For Warping, use the InvWarpPerspective() from Warp.py.

Note : Avoid using the warpPersepctive(), because it gives distorted and incomplete projection of the images.

def InvWarpPerspective(self, im, invA, H,output_shape):

        x1 = [0,0,1]
        x2 = [im.shape[1], im.shape[0],1]
        x1_trnsf = H.dot(np.array(x1).T)
        x1_trnsf = list(x1_trnsf/x1_trnsf[2])
        x2_trnsf = H.dot(np.array(x2).T)
        x2_trnsf = list(x2_trnsf/x2_trnsf[2])

        .
        .
        .
        .       
                
        return warpImage

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