image-processing

问题 I have an RGB image that has been converted to a numpy array. I'm trying to calculate the average RGB value of the image using numpy or scipy functions. The RGB values are represented as a floating point from 0.0 - 1.0, where 1.0 = 255. A sample 2x2 pixel image_array: [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]] I have tried: import numpy numpy.mean(image_array, axis=0)` But that outputs: [[0.5 0.5 0.5] [0.5 0.5 0.5]] What I want is just the single RGB average

问题 I have an RGB image that has been converted to a numpy array. I'm trying to calculate the average RGB value of the image using numpy or scipy functions. The RGB values are represented as a floating point from 0.0 - 1.0, where 1.0 = 255. A sample 2x2 pixel image_array: [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]] I have tried: import numpy numpy.mean(image_array, axis=0)` But that outputs: [[0.5 0.5 0.5] [0.5 0.5 0.5]] What I want is just the single RGB average

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I would like to remove the pixels that form a large cluster and keep only the small cluster to analyse (means get pixels number and locations). First I apply a filter to color in white all pixels that has a value lower to 0.66. Then I use the function clump() in R. The model works but I cannot remove only the large cluster. I do not understand how clump function works. Initial image: Results image: plot_r is the image where the pixels with value < 0.66 are changed to 0. plot_rc is the results

问题 I noticed that each time I save a jpg file in PHP, it is saved with sub-sampling. How to remove that? I'm using GD library. 回答1: I believe newer versions of libgd disable chroma-subsampling if you set the quality to 90 or higher. Failing that, you could consider using PHP Imagick and disabling chroma sub-sampling with: $img->setSamplingFactors(array('1x1', '1x1', '1x1')); 来源： https://stackoverflow.com/questions/57350007/how-to-disable-sub-sampling-when-saving-jpg-image-using-php-gd-library

问题 I'm trying convert to white or transparent some rectangles areas within the below image. I'm able to make it with ImageMagick with the following command, that first makes transparent desired colors and finally convert to black the rest with "negate". convert input.png \ -transparent '#4B8DF8' \ -transparent '#27A9E3' \ -transparent '#2295C9' \ -transparent '#E7191B' \ -transparent '#C91112' \ -transparent '#28B779' \ -transparent '#17A769' \ -transparent '#852B99' \ -transparent '#751E88' \

问题 I am trying to understand each layer of Keras while implementing CNN. In Conv2D layer i understand that it creates different convolution layer depending on various feature map values. Now, My question is that Can i see different feature map matrix that are applied on input image to get the convolution layer Can i see the value of matrix that is generated after completion of Conv2D step. Thanks in advance 回答1: You can get the output of a certain convolutional layer in this way: import keras

问题 I trying yolo model in python. To process the data and annotation I'm taking the data in batches. batchsize = 50 #boxList= [] #boxArr = np.empty(shape = (0,26,5)) for i in range(0, len(box_list), batchsize): boxList = box_list[i:i+batchsize] imagesList = image_list[i:i+batchsize] #to convert the annotation from VOC format convertedBox = np.array([np.array(get_boxes_for_id(box_l)) for box_l in boxList]) #pre-process on image and annotaion image_data, boxes = process_input_data(imagesList,max