使用OpenCV在python中进行形状检测

时间:2014-02-25 19:13:50

标签: python opencv shapes

我正在开发一个项目,我使用OpenCV来检测形状和颜色。

有5种颜色(红色,绿色,黄色,蓝色和白色)和4种形状(矩形,星形,圆形和心形)。我已经能够可靠地辨别颜色,当我使用的图像是this这样的绘制图像时,我可以检测到形状 使用此代码。请注意,图像仅用于演示,我的代码中的范围值不适用于这些颜色。

import cv2
import numpy as np
class Shape():

    def __init__(self, color, shape, x, y, approx):
        self.color = color
        self.shape = shape
        self.x = x
        self.y = y
        self.approx = approx
def closing(mask):
kernel = np.ones((7,7),np.uint8) 
closing = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
return closing

def opening(mask):
    kernel = np.ones((6,6),np.uint8)
    opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    return opening

#Define Red
lower_red = np.array([0, 90, 60], dtype=np.uint8)
upper_red = np.array([10, 255, 255], dtype=np.uint8)
red = [lower_red, upper_red, 'red']

#Define Green
lower_green = np.array([60, 55, 0], dtype=np.uint8)
upper_green = np.array([100, 255, 120], dtype=np.uint8)
green = [lower_green, upper_green, 'green']

#Define Blue
lower_blue = np.array([90, 20, 60], dtype=np.uint8)
upper_blue = np.array([130, 255, 180], dtype=np.uint8)
blue = [lower_blue, upper_blue, 'blue']

#Define Yellow
lower_yellow = np.array([5, 110, 200], dtype=np.uint8)
upper_yellow = np.array([50, 255, 255], dtype=np.uint8)
yellow = [lower_yellow, upper_yellow, 'yellow']

#Define White
lower_white = np.array([0, 90, 60], dtype=np.uint8)
upper_white = np.array([10, 255, 255], dtype=np.uint8)
white = [lower_white, upper_white ,'white']

colors = [red, green, blue, yellow, white]

def detect_shapes(image_location):
    #Open image
    img = cv2.imread(image_location)

    #Convert to hsv
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    #Shape list
    shapes = []

    #Lay over masks and detect shapes
    for color in colors:
        mask = cv2.inRange(hsv, color[0], color[1])
        mask = closing(mask)
        mask = opening(mask)
        contours, h = cv2.findContours(mask, 1, cv2.CHAIN_APPROX_SIMPLE)
        contours.sort(key = len)
        for contour in contours[-3:]:
            #Amount of edges
            approx = cv2.approxPolyDP(contour, 0.01*cv2.arcLength(contour, True), True)
            #Center locations
            M = cv2.moments(contour)
            if M['m00'] == 0.0:
                continue
            centroid_x = int(M['m10']/M['m00'])
            centroid_y = int(M['m01']/M['m00'])

            if len(approx) == 4:
                shape_name = 'rectangle'
            elif len(approx) == 10:
                shape_name = 'star'
            elif len(approx) >= 11:
                shape_name = 'oval'
            else:
                shape_name ='undefined'

            shape = Shape(color[2], shape_name, centroid_x, centroid_y, len(approx))
            shapes.append(shape)

    return shapes

这很大程度上取决于this question的答案。

但是,当我尝试检测实际照片上的形状时,我无法可靠地使用它。我得到的边缘量变化很大。 This是我需要识别形状的照片示例。 我猜这是因为形状边缘上的小瑕疵,但我无法弄清楚如何用直线逼近这些边缘,或者我如何可靠地识别圆圈。我需要在代码中进行哪些更改才能执行此操作?密集的谷歌搜索还没有给我答案,但可能是因为我没有在搜索中使用正确的术语......

此外,如果此问题格式不正确,请与我们联系!

1 个答案:

答案 0 :(得分:7)

以下是我继续处理您的图片的代码,代码将执行

  1. 模糊来源
  2. Canny Edge检测。
  3. 查找轮廓。
  4. 轮廓的近似值。
  5. 检查aboutPolyDP点的总大小。
  6.   

    代码:

       Mat src=imread("src.jpg",1);
       Mat thr,gray;
       blur(src,src,Size(3,3));
       cvtColor(src,gray,CV_BGR2GRAY);
       Canny(gray,thr,50, 190, 3, false );
       vector<vector<Point> > contours;
       vector<Vec4i> hierarchy;
       findContours( thr.clone(),contours,hierarchy,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE,Point(0,0));
    
       vector<vector<Point> > contours_poly(contours.size());
       vector<Rect> boundRect( contours.size() );
       vector<Point2f>center( contours.size() );
       vector<float>radius( contours.size() );
       vector<vector<Point> >hull( contours.size() );
       for( int i = 0; i < contours.size(); i++ )
        {
        approxPolyDP( Mat(contours[i]), contours_poly[i], 10, true );
        boundRect[i] = boundingRect( Mat(contours_poly[i]) );
        minEnclosingCircle( (Mat)contours_poly[i], center[i], radius[i] );
        convexHull( Mat(contours[i]), hull[i], false );
    
        if( contours_poly[i].size()>15) // Check for corner
           drawContours( src, contours_poly, i, Scalar(0,255,0), 2, 8, vector<Vec4i>(), 0, Point() ); // True object
        else
           drawContours( src, contours_poly, i, Scalar(0,0,255), 2, 8, vector<Vec4i>(), 0, Point() ); // false object
          //drawContours( src, hull, i, Scalar(0,0,255), 2, 8, vector<Vec4i>(), 0, Point() );
          // rectangle( src, boundRect[i].tl(), boundRect[i].br(), Scalar(0,255,0), 2, 8, 0 );
           //circle( src, center[i], (int)radius[i], Scalar(0,0,255), 2, 8, 0 );
        }
       imshow("src",src);
       imshow("Canny",thr);
       waitKey();
    

    enter image description here enter image description here