First we create an empty (or unititialized) cv::Mat for our data to be read into directly. This can be done once at startup, but on the other hand cv::Mat::create doesn’t really cost much when the image already has matching size and type. The type depends on your needs, usually it’s something like CV_8UC3 for a … Read more
I’m going to assume you’re using C++, but the answer should be the same if you’re using C or Python. The function minAreaRect seems to give angles ranging from -90 to 0 degrees, not including zero, so an interval of [-90, 0). The function gives -90 degrees if the rectangle it outputs isn’t rotated, i.e. … Read more
Step-by-step answer similar to the one you refer to, using the new cv2 Python bindings: 1. Read a grayscale image import cv2 im_gray = cv2.imread(‘grayscale_image.png’, cv2.IMREAD_GRAYSCALE) 2. Convert grayscale image to binary (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) which determines the threshold automatically from the image using Otsu’s method, or if you … Read more
So you have a problem unrelated to your question. However, you can achieve your goal using only OpenCV highgui facilites: #include <opencv2\opencv.hpp> #include <iostream> using namespace std; using namespace cv; vector<Rect> rects; bool bDraw; Rect r; Point base; Mat3b img; Mat3b layer; Mat3b working; void CallBackFunc(int event, int x, int y, int flags, void* userdata) … Read more
“error: (-215)” means that an assertion failed. In this case, cv::imshow asserts that the given image is non-empty: https://github.com/opencv/opencv/blob/b0209ad7f742ecc22de2944cd12c2c9fed036f2f/modules/highgui/src/window.cpp#L281 As noted in the Getting Started with Images OpenCV Python tutorial, if the file does not exist, then cv2.imread() will return None; it does not raise an exception. Thus, the following code also results in the … Read more
I had the same warning. Just modify the line camera = cv.VideoCapture(camera_port) to camera = cv.VideoCapture(camera_port, cv.CAP_DSHOW)
I know this question is a bit old, but I thought I might post the complete solution that worked for me (takes care of the overflow situation by saturating at 255): def increase_brightness(img, value=30): hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) lim = 255 – value v[v > lim] = 255 v[v <= … Read more
I have achieved this by using the following snippet. import cv2 file_name = “grab.png” src = cv2.imread(file_name, 1) tmp = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) _,alpha = cv2.threshold(tmp,0,255,cv2.THRESH_BINARY) b, g, r = cv2.split(src) rgba = [b,g,r, alpha] dst = cv2.merge(rgba,4) cv2.imwrite(“test.png”, dst)
There are many ways to quantize colors. Here I describe four. Uniform quantization Here we are using a color map with uniformly distributed colors, whether they exist in the image or not. In MATLAB-speak you would write qimg = round(img*(N/255))*(255/N); to quantize each channel into N levels (assuming the input is in the range [0,255]. … Read more
There is a standard problem in vision called text detection in images. it is quite different to OCR. OCR concerms itself with what it says, while text detection is about determining if there is text in the image. Adi Shavit’s third link is a method to address this problem. You can look on google scholar … Read more