image-processing

问题 I have scanned images which have tables as shown in this image: I am trying to extract each box separately and perform OCR but when I try to detect horizontal and vertical lines and then detect boxes it's returning the following image: And when I try to perform other transformations to detect text (erode and dilate) some remains of lines are still coming along with text like below: I cannot detect text only to perform OCR and proper bounding boxes aren't being generated like below: I cannot

问题 It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center. Closed 7 years ago . I want to adjust an image like curves tool in photoshop. It changes image color, constrast, etc in each R,G,B channel or all RGB. any idea to do this task in objective C? I found this link http://www.planet

问题 I was looking to convert given RGB values to the actual colour name in Python or atleast the close match of the colour. Given the values, [50.2862498 72.08996663 70.66173433] I want to be able to distinguish these values from [185.89894822 186.47802004 185.94613642] as a shade of green. Reason I need to do that is to remove shades of green from an image. 回答1: Look at webcolors python package here: https://pypi.org/project/webcolors/1.3/. It looks like the rgb_to_name(rgb_triplet, spec='css3')

问题 I would like to train a deep learning framework (TensorFlow) for object detection with a new object category. As source for the ground truthing I have multiple video files which contain the object (only part of the image contains the object). How should I ground truth the video? Should I extract frame by frame and label every frame even when those video frames will be quite similar? Or what would be best practise for such a task? Open source tools are preferred. 回答1: It usually works as you

问题 I have an image like shown here: My ultimate aim is to extract the vein pattern in the finger. So What I am trying to do is the extraction of the finger object alone. For that purpose, I tried an Otsu thresholding step first, then an erosion and dilation. Now using the binary image as a mask I multiplied element wise with original image to obtain the finger alone (not that accurate though). The code is as below: I = imread('3.5.bmp'); [level] = graythresh(I); BW = im2bw(I,level); [BWm] =

问题 Is anyone else having any problems when the kernel size > 255? The error I'm getting looks like the color channels are overloading...but only when kernel size is > 255. The documentation says nothing about the limits of the kernel size. And vImage is not returning an error. BTW, I'm using vImageBoxConvolve_ARGB8888. It happens on any image, and specifically in the white areas. Here's an output: That multicolor mess in the top left is really just a bright background. Seems odd that with all

问题 In order to detect inselbergs from a geographic region, I've downloaded topographic imagery (relief and declivity) from that region. The declivity images seems best suited for the task. After applying a gaussian blur (or a much faster common blur, with ImageMagick), the image seems ready for automatic detection. Now I'm wondering the best/fastest way to detect these white stains on the black background. My first idea is to use a simple function (no external library) that works like the

问题 Given: A pixel, with its color (denoted as PC0) and alpha value (denoted as PA0), which is layered over a background of some color (denoted as BC). Question: How would you change the alpha value of the pixel (PA0) for another value (denoted as PA1) so that the resulting composite color of the pixel and the background does not change (PBC0 == PBC1)? In other words, how would you find such PC1 that makes the original and resulting composite colors (PBC0 and PBC1) look identical? 回答1: PBC0 = PC0

问题 Given: A pixel, with its color (denoted as PC0) and alpha value (denoted as PA0), which is layered over a background of some color (denoted as BC). Question: How would you change the alpha value of the pixel (PA0) for another value (denoted as PA1) so that the resulting composite color of the pixel and the background does not change (PBC0 == PBC1)? In other words, how would you find such PC1 that makes the original and resulting composite colors (PBC0 and PBC1) look identical? 回答1: PBC0 = PC0

问题 I am trying to use zca_whitening with the keras image processing option, but the calculation gets stuck and never ends. My part of the code causing the problem looks like this: train_datagen = ImageDataGenerator(rotation_range=30, zca_whitening=True) def read_pil_image(img_path, height, width): with open(img_path, 'rb') as f: return np.array(Image.open(f).convert('RGB').resize((width, height))) def load_all_images(dataset_path, height, width, img_ext='jpg'): return np.array([read_pil_image