https://github.com/hansalemaos/a_cv2_calculate_simlilarity
Calculate the simlilarity of 2 pictures with OpenCV
https://github.com/hansalemaos/a_cv2_calculate_simlilarity
batch compare cv2 numpy opencv2 pictures similarity similarity-matrix similarity-score ssim
Last synced: 3 months ago
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Calculate the simlilarity of 2 pictures with OpenCV
- Host: GitHub
- URL: https://github.com/hansalemaos/a_cv2_calculate_simlilarity
- Owner: hansalemaos
- License: mit
- Created: 2022-10-27T04:59:30.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-20T06:00:52.000Z (about 2 years ago)
- Last Synced: 2025-06-08T00:50:28.617Z (4 months ago)
- Topics: batch, compare, cv2, numpy, opencv2, pictures, similarity, similarity-matrix, similarity-score, ssim
- Language: Python
- Homepage: https://pypi.org/project/a-cv2-calculate-simlilarity
- Size: 68.4 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
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README
# Calculate the simlilarity of 2 or more pictures with OpenCV
## Tested against Windows 10 / Python 3.11 / Anaconda
### pip install a-cv2-calculate_simlilarity
```python
from a_cv2_calculate_simlilarity import add_similarity_to_cv2
add_similarity_to_cv2() #monkeypatch
#if you don't want to use a monkey patch:
#from a_cv2_calculate_simlilarity import calculate_simlilarity
calculate_simlilarity(
im1: Any,
im2: Any,
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
) -> tuple:
r"""
Calculate structural similarity between two images.
This function computes the structural similarity index (SSIM) between two images,
which measures the similarity of their structural patterns. The SSIM values range
from -1 to 1, where a higher value indicates greater similarity.
Parameters:
im1: Any
Image 1, which can be provided as a URL, file path, base64 string, numpy array,
or PIL image.
im2: Any
Image 2, which can be provided as a URL, file path, base64 string, numpy array,
or PIL image.
width: int, optional
Width of the resized images for comparison (default is 100).
height: int, optional
Height of the resized images for comparison (default is 100).
interpolation: int, optional
Interpolation method for resizing (default is cv2.INTER_LINEAR).
with_alpha: bool, optional
Whether to include alpha channel if present (default is False).
Returns:
tuple
A tuple containing four SSIM values in the order (B, G, R, Alpha).
Example:
resa = calculate_simlilarity(
r"https://avatars.githubusercontent.com/u/77182807?v=4",
r"https://avatars.githubusercontent.com/u/77182807?v=4",
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
)
print(resa)
resa2 = calculate_simlilarity(
r"https://avatars.githubusercontent.com/u/77182807?v=4",
r"https://avatars.githubusercontent.com/u/1024025?v=4",
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
)
print(resa2)
resa2 = calculate_simlilarity(
r"C:\Users\hansc\Downloads\1633513733_526_Roblox-Royale-High.jpg",
r"C:\Users\hansc\Downloads\Roblox-Royale-High-Bobbing-For-Apples (1).jpg",
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
)
print(resa2)
resa2 = calculate_simlilarity(
r"C:\Users\hansc\Documents\test1.png",
r"C:\Users\hansc\Documents\test2.png",
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
)
print(resa2)
compare_all_images_with_all_images(
imagelist,
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
delete_cache=True,
):
r"""
Compare a list of images with each other and return a similarity matrix.
This function compares a list of images with each other using the `calculate_simlilarity`
function and returns a similarity matrix as a pandas DataFrame. Each element in the matrix
represents the similarity between two images.
Parameters:
imagelist: list
List of images to compare. Each image can be provided as a URL, file path, base64 string,
numpy array, or PIL image.
width: int, optional
Width of the resized images for comparison (default is 100).
height: int, optional
Height of the resized images for comparison (default is 100).
interpolation: int, optional
Interpolation method for resizing (default is cv2.INTER_LINEAR).
with_alpha: bool, optional
Whether to include alpha channel if present (default is False).
delete_cache: bool, optional
Whether to clear the cache of preprocessed images (default is True).
Returns:
pandas.DataFrame
A DataFrame representing the similarity matrix between the images.
Example:
add_similarity_to_cv2()
df = cv2.calculate_simlilarity_of_all_pics(
[
r"C:\Users\hansc\Downloads\testcompare\10462.7191107_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7191107_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7213836_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7213836_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7253843_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7253843_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7274426_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7274426_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7286225_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7286225_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7301136_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7301136_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7312635_0.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7312635_1.png",
r"C:\Users\hansc\Downloads\testcompare\10462.7325586_0.png",
],
width=100,
height=100,
interpolation=cv2.INTER_LINEAR,
with_alpha=False,
)
```