# 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,
)
```
Raw data
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"description": "\r\n# Calculate the simlilarity of 2 or more pictures with OpenCV\r\n\r\n## Tested against Windows 10 / Python 3.11 / Anaconda\r\n\r\n### pip install a-cv2-calculate_simlilarity\r\n\r\n\r\n```python\r\n\r\nfrom a_cv2_calculate_simlilarity import add_similarity_to_cv2\r\nadd_similarity_to_cv2() #monkeypatch\r\n\r\n#if you don't want to use a monkey patch:\r\n#from a_cv2_calculate_simlilarity import calculate_simlilarity\r\n\r\n\r\ncalculate_simlilarity(\r\n im1: Any,\r\n im2: Any,\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n) -> tuple:\r\n r\"\"\"\r\n Calculate structural similarity between two images.\r\n\r\n This function computes the structural similarity index (SSIM) between two images,\r\n which measures the similarity of their structural patterns. The SSIM values range\r\n from -1 to 1, where a higher value indicates greater similarity.\r\n\r\n Parameters:\r\n im1: Any\r\n Image 1, which can be provided as a URL, file path, base64 string, numpy array,\r\n or PIL image.\r\n im2: Any\r\n Image 2, which can be provided as a URL, file path, base64 string, numpy array,\r\n or PIL image.\r\n width: int, optional\r\n Width of the resized images for comparison (default is 100).\r\n height: int, optional\r\n Height of the resized images for comparison (default is 100).\r\n interpolation: int, optional\r\n Interpolation method for resizing (default is cv2.INTER_LINEAR).\r\n with_alpha: bool, optional\r\n Whether to include alpha channel if present (default is False).\r\n\r\n Returns:\r\n tuple\r\n A tuple containing four SSIM values in the order (B, G, R, Alpha).\r\n\r\n Example:\r\n resa = calculate_simlilarity(\r\n r\"https://avatars.githubusercontent.com/u/77182807?v=4\",\r\n r\"https://avatars.githubusercontent.com/u/77182807?v=4\",\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n )\r\n print(resa)\r\n resa2 = calculate_simlilarity(\r\n r\"https://avatars.githubusercontent.com/u/77182807?v=4\",\r\n r\"https://avatars.githubusercontent.com/u/1024025?v=4\",\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n )\r\n print(resa2)\r\n\r\n resa2 = calculate_simlilarity(\r\n r\"C:\\Users\\hansc\\Downloads\\1633513733_526_Roblox-Royale-High.jpg\",\r\n r\"C:\\Users\\hansc\\Downloads\\Roblox-Royale-High-Bobbing-For-Apples (1).jpg\",\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n )\r\n print(resa2)\r\n\r\n resa2 = calculate_simlilarity(\r\n r\"C:\\Users\\hansc\\Documents\\test1.png\",\r\n r\"C:\\Users\\hansc\\Documents\\test2.png\",\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n )\r\n print(resa2)\r\n \r\n \r\ncompare_all_images_with_all_images(\r\n imagelist,\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n delete_cache=True,\r\n):\r\n r\"\"\"\r\n Compare a list of images with each other and return a similarity matrix.\r\n\r\n This function compares a list of images with each other using the `calculate_simlilarity`\r\n function and returns a similarity matrix as a pandas DataFrame. Each element in the matrix\r\n represents the similarity between two images.\r\n\r\n Parameters:\r\n imagelist: list\r\n List of images to compare. Each image can be provided as a URL, file path, base64 string,\r\n numpy array, or PIL image.\r\n width: int, optional\r\n Width of the resized images for comparison (default is 100).\r\n height: int, optional\r\n Height of the resized images for comparison (default is 100).\r\n interpolation: int, optional\r\n Interpolation method for resizing (default is cv2.INTER_LINEAR).\r\n with_alpha: bool, optional\r\n Whether to include alpha channel if present (default is False).\r\n delete_cache: bool, optional\r\n Whether to clear the cache of preprocessed images (default is True).\r\n\r\n Returns:\r\n pandas.DataFrame\r\n A DataFrame representing the similarity matrix between the images.\r\n\r\n Example:\r\n add_similarity_to_cv2()\r\n df = cv2.calculate_simlilarity_of_all_pics(\r\n [\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7191107_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7191107_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7213836_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7213836_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7253843_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7253843_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7274426_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7274426_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7286225_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7286225_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7301136_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7301136_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7312635_0.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7312635_1.png\",\r\n r\"C:\\Users\\hansc\\Downloads\\testcompare\\10462.7325586_0.png\",\r\n ],\r\n width=100,\r\n height=100,\r\n interpolation=cv2.INTER_LINEAR,\r\n with_alpha=False,\r\n )\r\n```\r\n",
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