scikit-image

问题 Outline: I need to use scikit-image inside some AWS lambda functions, so I'm looking to build a custom AWS lambda layer containing scikit-image . My questions in general should apply to any python module, notably scikit-learn, or any custom layer in general I think. Background: After much googling and reading it seems the best way to do that is to use docker to run the AWS lambda runtime locally, and then inside there install/compile scikit-image (or whichever module you're looking for).

问题 Outline: I need to use scikit-image inside some AWS lambda functions, so I'm looking to build a custom AWS lambda layer containing scikit-image . My questions in general should apply to any python module, notably scikit-learn, or any custom layer in general I think. Background: After much googling and reading it seems the best way to do that is to use docker to run the AWS lambda runtime locally, and then inside there install/compile scikit-image (or whichever module you're looking for).

问题 I am explaining the scenario with a piece of data: Ex. data set. GA_ID PN_ID PC_ID MBP_ID GR_ID AP_ID class 0.033 6.652 6.681 0.194 0.874 3.177 0 0.034 9.039 6.224 0.194 1.137 0 0 0.035 10.936 10.304 1.015 0.911 4.9 1 0.022 10.11 9.603 1.374 0.848 4.566 1 0.035 2.963 17.156 0.599 0.823 9.406 1 0.033 10.872 10.244 1.015 0.574 4.871 1 0.035 21.694 22.389 1.015 0.859 9.259 1 0.035 10.936 10.304 1.015 0.911 4.9 1 0.035 10.936 10.304 1.015 0.911 4.9 1 0.035 10.936 10.304 1.015 0.911 4.9 0 0.036 1

问题 For example, the lbp code of the pixel with coordinate (1, 1) is possible to calculate it with the pixels (0, 0); (0, 1); (0, 2); (1, 2); (2, 2); (2, 1); (2, 0); (1, 0) but the pixels of the extremes do not have those 8 neighborhood pixels, that is, the pixel (0, 0) only has 3 neighbors. This question comes to me because I have obtained the LBP image using sicikit image, the code is as follows: lbp = feature.local_binary_pattern (gray, 8, 1, 'ror') Then I printed the values ​​of the gray

问题 I have been using skim age's thresholding algorithms to get some binary mask. For example, I obtain binary images like this: What I am trying to figure out is how can I fit a circle to this binary mask. The constraint is the circle should cover as much of the white areas as possible and the whole circumference of the circle should lie entirely on the white parts. I have been wrecking my head on how I can do this efficiently but have come up with no solution that works. One approach I thought

问题 I tried this code: import cv2 image = cv2.imread("sample.jpg") pixel = image[200, 550] print pixel But I am getting error as: 'Nonetype' no attributes error getitem This error is getting displayed after executing the third line of code. 回答1: How to fix the error There are two potential causes for this error to happen: The file name is misspelled. The image file is not in the current working directory. To fix this issue you should make sure the filename is correctly spelled (do case sensitive

问题 I have a TIFF image file from a confocal microscope which I can open in ImageJ, but which I would like to get into Python. The format of the TIFF is as follows: There are 30 stacks in the Z dimension. Each Z layer has three channels from different fluorescent markers. Each channel has a depth of 8 bits. The image dimensions are 1024x1024. I can, in principle, read the file with skimage (which I plan to use to further analyse the data) using the tifffile plugin. However, what I get is not

问题 I refered this link to combine two images, and it works if both images are being RGB formated Combining Two Images with OpenCV The question is how to combine a RGB image with a Grayed image, since the RGB image is three dimensions but gray image is two dimensions? 回答1: RGB images are 3-dimensional whereas grayscale images are 2-dimensional. In order for the combination to be possible, you need to add one dimension to the grayscale image. If x is a 2-dimensional array x , the simplest way to

问题 I'm using the skimage transform module's resize method. Not always, but sometimes, I'm getting an error on this line: candidate = resize(np.copy(img[candidate_box[0]:candidate_box[2],candidate_box[1]:candidate_box[3]]), (50,100)) It tells me: ValueError: Buffer not C contiguous How can I fix this? 回答1: Reshaping (and other operations) will sometimes disrupt the contiguity of an array. You can check whether this has happened by looking at the flags : >>> a = np.arange(10).reshape(5, 2).T >>> a

问题 Normally I would preprocess the data before I feed it into my model for classification. This is however not possible and thus am stuck either to enhance the performance of the model further (somehow) or include useful preprocessing steps directly inside the model. How can I do that? The best solution I found thus far, included re-implementing the functionality I want using Keras backend. This is far from a good solution and thus I am hoping someone has an idea, how to salavage the situation.