opencv+python 提取国徽轮廓

简单的说，就是最近须要作图像处理相关的项目，以前没关注过这个领域，忽然接触，仍是不少细节思路不明白，中间想了不少临时方案，解决过程，最后探索的结果是用不上，不过也有部分东西能够留下思路做为借鉴。

图像提取轮廓，庆幸找到用 Python 和 OpenCV 检测图片上的条形码这篇文章，学了一些处理的思路和方法。这个也是整个处理过程的基础。这里简单说一个探索过程，就是提取轮廓，目标国徽。

opencv版本不同，用到的api仍是有区别的，这里用到的版本是2.4.13

截取一张国徽照片，参考上面的文章，先作灰度，模糊，二值化，再作闭运算，以后4次腐蚀，4次膨胀，以后查找轮廓，在原图画一个轮廓，看起来不是很理想。

def get_guohui2():
    #获取国徽轮廓
    img = cv2.imread("pic/guohui0.jpg")
    
    #灰度
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #模糊
    blurred = cv2.blur(gray, (9, 9))
    #二值化
    (_, thresh) = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY)
    cv2.imshow("image", thresh)
    cv2.waitKey(0)
    
    #闭运算
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 20))
    close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

    # perform a series of erosions and dilations
    #腐蚀、膨胀
    close = cv2.erode(close, None, iterations = 4)
    close = cv2.dilate(close, None, iterations = 4)
    
    cv2.imshow("image", close)
    cv2.waitKey(0)
    
    #查找轮廓
    (contours, _) = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
    cv2.drawContours(img, contours, -1, (0, 255, 0), 3)
    
    cv2.imshow("image", img)
    cv2.waitKey(0)

后面改了先开运算再闭运算，再调整一些参数，看起来效果好多了

def get_guohui2():
    #获取国徽轮廓
    img = cv2.imread("pic/guohui0.jpg")
    
    #灰度
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #模糊
    blurred = cv2.blur(gray, (3, 3))
    #二值化
    (_, thresh) = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY)
    cv2.imshow("image", thresh)
    cv2.waitKey(0)
    
    #开运算
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    open = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

    # perform a series of erosions and dilations
    #腐蚀、膨胀
    open = cv2.erode(open, None, iterations = 4)
    open = cv2.dilate(open, None, iterations = 4)
    
    cv2.imshow("image", open)
    cv2.waitKey(0)
    
    #闭运算
    close = cv2.morphologyEx(open, cv2.MORPH_CLOSE, kernel)
    cv2.imshow("image", close)
    cv2.waitKey(0)
    
    #查找轮廓
    (contours, _) = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
    cv2.drawContours(img, contours, -1, (0, 255, 0), 3)
    
    cv2.imshow("image", img)
    cv2.waitKey(0)

可是还有一些缺陷，不够完整，后面想到，多是边缘不够明显，因而找了怎么锐化边缘，找到OpenCV图像处理 空间域图像加强（图像锐化 1 基于拉普拉斯算子）。里面都是C++的代码，参考了一下，用了矩阵，小改就完成了。锐化后没作模糊操做。

def get_guohui2():
    #获取国徽轮廓
    img = cv2.imread("pic/guohui0.jpg")
    
    #锐化操做
    kernel = np.matrix('0 -1 0; -1 5 -1; 0 -1 0')
    dst = cv2.filter2D(img,-1,kernel)

    cv2.imshow("Result", dst)
    cv2.waitKey(0)
    
    #灰度
    gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
    
    #二值化
    (_, thresh) = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
    cv2.imshow("image", thresh)
    cv2.waitKey(0)
    
    #开运算
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
    open = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

    # perform a series of erosions and dilations
    #腐蚀、膨胀
    open = cv2.erode(open, None, iterations = 4)
    open = cv2.dilate(open, None, iterations = 4)
    
    cv2.imshow("image", open)
    cv2.waitKey(0)
    
    #闭运算
    close = cv2.morphologyEx(open, cv2.MORPH_CLOSE, kernel)
    cv2.imshow("image", close)
    cv2.waitKey(0)
    
    #查找轮廓
    (contours, _) = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    
    area = cv2.contourArea(contours[0])
    print area
    
    cv2.drawContours(img, [contours[1]], -1, (0, 255, 0), 3)
    
    cv2.imshow("image", img)
    cv2.waitKey(0)

完。