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From the comments, it sounds like all you want to do is detect the images you have. That makes this an easy problem. If a given image is the same as an image you have, return the information (such as a category) for that image. If the given image does not match an image you have, do not return such information. In Python, that would be something like this. ...


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Assuming the rectangle is as below (easily adjusted if otherwise) Then: The tangent of the angle of rotation is given by $tan(a)=\frac{y_3-y_2}{x_3-x_2}$ Depending on quadrant of interest one can take the opposite coordinates. Getting the inverse tan function gives the angle in radians. The width is given by $w=\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}$ Similar ...


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A region of interest is a patch of the image which is sent to a classifier; it may not match the ground truth object bounding box. The bounding box prediction is computed from the features in the region of interest (e.g., via a linear regression or neural network regression) and should more closely match the ground truth bounding box.


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