computer-vision

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 am trying to run a simple license plate image through opencv and pytesseract to get the text but I am having trouble getting anything out of it. Following the tutorial here: https://circuitdigest.com/microcontroller-projects/license-plate-recognition-using-raspberry-pi-and-opencv I'm running on a macbook with everything installed in anaconda and no errors as far as I see, but when I run my code I get the cropped image but no detected number: (computer_vision) mac@x86_64-apple-darwin13 lpr %

问题 when I train the model,I have customized a loss function.The calculation of the loss value in this function requires the function of opencv.See the code,but I get a wrong.I don't know how to solve it,someone can help me?Thanks a lot. #this is my loss function def instance_loss_function(predict,label): best_match_label_image=search_MaxPixelAccuracy_permutation(predict_convert_gray_image(predict),label) predict_image=predict loss_sum=0.0 best_match_label_image_contours_number=len(cv2

问题 I am using a Text Detection called CRAFT (you can check it out in github) which does a good job on major images I have used, but I have noticed that the text detection is very sensitive to lighting conditions. To ilustrate this, see this image: Text detected with CRAFT I am interested in detecting the code part, which is: FBIU0301487. However, it seems that the caracter 'F' cannot be detected even using a threshold equals to zero, i.e. let every bounding box be consired as a valid detection.

问题 I am beginner in OpenCV-Python. I would like to know what could make my undistortion code work for this chessboard picture and not work for this one knowing that the too chessboards have the same dimension. The code i am talking about is as below import cv2 import numpy as np criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) cbrow = 7 cbcol = 9 objp = np.zeros((cbrow * cbcol, 3), np.float32) objp[:, :2] = np.mgrid[0:cbcol, 0:cbrow].T.reshape(-1,2) objpoints = []

问题 I followed this tutorial to create a simple image classification script: https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ train_data = scipy.io.loadmat('extra_32x32.mat') # extract the images and labels from the dictionary object X = train_data['X'] y = train_data['y'] X = X.reshape(X.shape[0]*X.shape[1]*X.shape[2],X.shape[3]).T y = y.reshape(y.shape[0],) X, y = shuffle(X, y, random_state=42) .... clf = RandomForestClassifier() print(clf) start_time = time.time()

问题 I followed this tutorial to create a simple image classification script: https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ train_data = scipy.io.loadmat('extra_32x32.mat') # extract the images and labels from the dictionary object X = train_data['X'] y = train_data['y'] X = X.reshape(X.shape[0]*X.shape[1]*X.shape[2],X.shape[3]).T y = y.reshape(y.shape[0],) X, y = shuffle(X, y, random_state=42) .... clf = RandomForestClassifier() print(clf) start_time = time.time()

问题 I have a CSV file with filename in the first column and a label for the filename in the second column. I also have a third column, which specifies something about the data (whether the data meets a specific condition). It will look something like, +-----------------------------+ | Filepath 1 Label 1 'n' | | | +-----------------------------+ | Filepath 2 Label 2 'n' | | | | | +-----------------------------+ | Filepath 3 Label 3 'n'| | | +-----------------------------+ | Filepath 4 Label 4 'y'|