使用OpenCV在python中进行形状检测

问题描述

我正在使用OpenCV检测形状及其颜色的项目.

I'm working on a project in which I use OpenCV to detect shapes and their colors.

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).当使用的图像是诸如此使用此代码.请注意，该图像仅用于演示，我的代码中的范围值不适用于这些颜色.

There are 5 colors (red, green, yellow, blue and white) and 4 shapes (rectangle, star, circle and heart). I've been able to reliably discern the colors and I can detect the shapes when the image used is a drawn image like thisusing this code. Note, the image is for demonstration only, the range values in my code are not for these colors.

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

这很大程度上是基于这个问题的答案.

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.我得到的边缘数量千差万别. 这是需要识别其形状的照片的示例.我猜这是由于形状的边缘上的小缺陷造成的，但我无法弄清楚如何用直线近似这些边缘，或者如何可靠地识别圆.为此，我需要更改代码中的什么?密集搜索还没有给我答案，但这可能是因为我没有在搜索中使用正确的术语...

However, when I try to detect the shapes on an actual photo, I cannot reliably use this. The amount of edges I get varies wildly. This is an example of a photo I need to recognize the shapes on.I'm guessing this happens because of small imperfections on the edges of the shapes, but I can't figure out how I would approximate those edges with straight lines, or how I would reliably recognize circles. What would I need to change in the code to do this? Intensive googling hasn't given me an answer yet but that might be because I'm not using the correct terminology in the searches...

此外，如果此问题的格式不正确，请告诉我！

Also, if this question is not formatted correctly, let me know!

推荐答案

这是我继续处理您的图片的代码，该代码可以完成

Here is the code I proceed with your image, the code will do

1. 模糊源
2. Canny Edge检测.
3. 找到轮廓.
4. approxPolyDP用于轮廓.
5. 检查约POlyDP点的总大小.

   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();

这篇关于使用OpenCV在python中进行形状检测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！