image-processing

问题 This question already has answers here : OpenCV giving wrong color to colored images on loading (2 answers) Closed 11 days ago . I am generating thumbnails for mp4 videos using the following code: import cv2 as cv from PIL import Image vidcap = cv.VideoCapture(videoPath) vidcap.set(cv.CAP_PROP_POS_MSEC, millisecond) #Turn video frame into numpy ndarray success, image = vidcap.read() cv.imwrite('fromImage.jpg', image) #line to be replaced The thumbnail generated from a high budget,

问题 I am using Firebase ML Kit for Face Detection, and in the documentation it says: If necessary, rotate the image so that its imageOrientation property is .up. Create a VisionImage object using the correctly-rotated UIImage. Do not specify any rotation metadata—the default value, .topLeft, must be used. I am running into the problem where photos I upload from the internet tend to work properly but when I take photos from my camera there seems to be issues. I have a feeling it is due to the way

问题 I'm working with a command-line program that does image-processing, and I need to run the same command on an entire folder of images. I have heard that I can run loops in the command prompt, but I have seen all sorts of different examples online and can't figure out the syntax. The images in the folder are labled "single0.pgm, single1.pgm, single2.pgm,..." all the way to single39.pgm. The command I need to run is: DebayerGPU.exe -demosaic DFPD_R -CPU -pattern GRBG -i single0.pgm -o single0

问题 I'm working with a command-line program that does image-processing, and I need to run the same command on an entire folder of images. I have heard that I can run loops in the command prompt, but I have seen all sorts of different examples online and can't figure out the syntax. The images in the folder are labled "single0.pgm, single1.pgm, single2.pgm,..." all the way to single39.pgm. The command I need to run is: DebayerGPU.exe -demosaic DFPD_R -CPU -pattern GRBG -i single0.pgm -o single0

问题 I am reading a DNG image having size 3120 x 4208 by RawPy . dng = rawpy.imread("TestImages/IMG_20200108_161323.dng") When debug, I saw dng has a field named color_matrix - a numpy array with shape 3x4, it looks like that: [[ 0.24399559 0.57969594 0.1763085 0. ] [-0.00469256 0.96858126 0.03611127 0. ] [-0.00366105 -0.06751718 1.0711782 0. ]] . According to RawPy document: Color matrix, read from file for some cameras, calculated for others. Return type: ndarray of shape (3,4) After searching I

问题 I'm working with license plates, what I do is apply a series of filters to it, such as: Grayscale Blur Threshhold Binary The problem is when I doing this, there are some contour like this image at borders, how can I clear them? or make it just black color (masked)? I used this code but sometimes it falls. # invert image and detect contours inverted = cv2.bitwise_not(image_binary_and_dilated) contours, hierarchy = cv2.findContours(inverted,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # get the

问题 I'm working with license plates, what I do is apply a series of filters to it, such as: Grayscale Blur Threshhold Binary The problem is when I doing this, there are some contour like this image at borders, how can I clear them? or make it just black color (masked)? I used this code but sometimes it falls. # invert image and detect contours inverted = cv2.bitwise_not(image_binary_and_dilated) contours, hierarchy = cv2.findContours(inverted,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # get the

问题 I'm working with license plates, what I do is apply a series of filters to it, such as: Grayscale Blur Threshhold Binary The problem is when I doing this, there are some contour like this image at borders, how can I clear them? or make it just black color (masked)? I used this code but sometimes it falls. # invert image and detect contours inverted = cv2.bitwise_not(image_binary_and_dilated) contours, hierarchy = cv2.findContours(inverted,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # get the

问题 I have found a code for the Earth Mover Loss in Keras/Tensrflow. I want to compute the loss for the scores given to images but I can not do it until I get to know the working of the Earth Mover Loss given below. Can someone please describe that what is happening in the code. The last layer of the model or output layer is like: out = Dense(10,activation='softmax')(x) What should be the input types for this method.I have my y_labels in the form of 1.2,4.9 etc etc. I want to use it with Keras

问题 I'm working in semantic segmentation been inspired from the article of Alex Kendall Segnet I found the Implementation here link, with : Input (rgb) shape=(batch_size, width, height, 3) Output (one-hot) shape=(batch_size, width, height, n_classes) the number of output classes is 12. I tried to use The weighted categorical crossentropy loss function since I have my optimal weights vector as below : def weighted_categorical_crossentropy(weights): # weights = [0.9,0.05,0.04,0.01] def wcce(y_true,