Jag vill presentera dig för detta - OpenCV - ett bibliotek med öppen källkod i C efter minimum och maximum i bilden) dubbel minval, maxval; CvPoint minloc, Importera cv2-utgång \u003d image.copy () cv2.rectangle (output, (2600, 800), 

However, my attempts to use minAreaRect as a precursor to rotation and cropping are failing to generate a rectangle that contains the input contour. contours, hierarchy = cv2.findContours(morph.copy() ,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contour = contours[0] draw = cv2.cvtColor(morph, cv2.COLOR_GRAY2BGR) cv2.drawContours(draw, [contour], 0

并且这个边的边长是width，另一条边边长是height。. I'm trying to extract the rotated bounding box of contours robustly. I would like to take an image, find the largest contour, get its rotated bounding box, rotate the image to make the bounding box vertical, and crop to size. 经过经验总结和反复的trials and errors, 终于搞清楚了opencv中的 rotation angle角度问题。具体来讲，rotation angle 范围为[-90, 0), 具体角度测算方法可以分为两个步骤(如下图）： 1. x轴逆时针旋转到最近邻近边，该旋转角即为angle 2.

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It is calculated as, Equivalent Diameter = √ 4 * A / Π where A = Area of contour area = cv2.contourArea(cnt) equi_diameter = np.sqrt(4*area/np.pi) 7 - Orientation : Orientation is the angle at which object is directed. (x,y),(MA,ma),angle = cv2 Finding Area Using Contour Finding the contours of a square is a simple task; irregular, skewed, and rotated shapes bring the best out of the cv2.findContours utility function of OpenCV. Let’s take a look at the following image: # Let cnt be the contour and img be the input rect = cv2.minAreaRect(cnt) box = cv2.boxPoints(rect) box = np.int0(box) W = rect[1][0] H = rect[1][1] Xs = [i[0] for i in box] Ys = [i[1] for i in box] x1 = min(Xs) x2 = max(Xs) y1 = min(Ys) y2 = max(Ys) angle = rect[2] if angle < -45: angle += 90 # Center of rectangle in source image center = ((x1+x2)/2,(y1+y2)/2) # Size of the upright rectangle Se hela listan på qiita.com 一个旋转的矩形怎么可以从线画出，围绕中心点旋转还是给出的第一个点？ template = cv2.imread('numbers.png') template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) template = cv2.threshold(template, 10, 255, cv2.THRESH_BINARY_INV) The output of this is as shown below. Getting the contours for the image 画像を傾き補正したいと思います。私は（確かに助けたくさんの）プログラムを書いたことを行うには： は（などTHRESH、拡張を、）を計算しやすいように画像を変換すると、すべてのオブジェクトの周りの輪郭 を計算を描きますテキストの輪郭の周りの4つの極端なポイントが が アイデアはcv2 rect = cv2.boundingRect(cnt) (x, y, w, h) = rect 返回值 rect, 数据结构是tuple, 分别为矩形左上角坐标(x, y), 面积 Area.

Use cv2.boundingRect to get the bounding rectangle (in green), cv2.minAreaRect to get the minimum area rectangle (in red), and cv2.minEnclosingCircle to get minimum enclosing circle (in blue).

area = cv2.contourArea(cnt) x,y,w,h = cv2.boundingRect(cnt) rect_area = w*h extent = float(area)/rect_area 3. """ import cv2 image_size = image.shape x, y, w, h = cv2.boundingRect(contour) area = image_size[0] * image_size[1] if w > h: ratio = float(w) / h else: ratio = float(h) / w # compares contour area to bounding rectangle area fill_ratio = cv2.contourArea(contour) / (w * h) # filter very long narrow objects and small objects is_right_shape For every found contour we now apply approximation to polygons with accuracy +-3 and stating that the curve must be closed. After that we find a bounding rect for every polygon and save it to boundRect. At last we find a minimum enclosing circle for every polygon and save it to center and radius vectors.

computes four extreme points around the text contours (ignoring anything with a margin) draws a rectangle around that area using cv2.minAreaRect. The idea was that cv2.minAreaRect returns the angle as well, which I could use to deskew the image. However, in my case it's –90°. You can see a sample input image here.

rect[0] = pts[np.argmin(s)] Min kod nedan producerar följande utdata, där de röda linjerna markerar de coding: utf-8 -*- import numpy as np import cv2 width = 800 height = 600 img rect[3] : return False return True def draw_triangeles(rect, points, img) : subdiv = cv2. cv::Mat out = cv::Mat::zeros(frame.size(), frame.type()); frame(cv::Rect(0,10, Jag tror att min lösning med affinetransformation är mer ren och bättre prestanda. min enhet är synlig i fastboot-enheter men när jag försöker blinka en anpassad för ACTION_PHONE_STATE_CHANGED men jag behöver inte min app för att  OpenCV provides a function cv2.minAreaRect() for finding the minimum area rotated rectangle. This takes as input a 2D point set and returns a Box2D structure which contains the following details – (center(x, y), (width, height), angle of rotation).

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def get_contour_region_in_min_area_rect(img, cnt): # find the min area rect of contour rect = cv2.minAreaRect(cnt) angle = rect[-1] box = cv2.cv.BoxPoints(rect) box Cv2. MinAreaRect Method (IEnumerable < Point2f >) Finds the minimum area rotated rectangle enclosing a 2D point set.

经过经验总结和反复的trials and errors, 终于搞清楚了opencv中的 rotation angle角度问题。具体来讲，rotation angle 范围为[-90, 0), 具体角度测算方法可以分为两个步骤(如下图）： 1. x轴逆时针旋转到最近邻近边，该旋转角即为angle 2. # get the min area rect: rect = cv2.minAreaRect(c) box = cv2.boxPoints(rect) # convert all coordinates floating point values to int: box = np.int0(box) 1.
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In the above code, we first find the rectangle enclosing the text area based on the four points we provide using the cv2.minAreaRect () method. Then in function crop_rect (), we calculate a rotation matrix and rotate the original image around the rectangle center to straighten the rotated rectangle.

Check out the wikipedia page on Image Moments.