python – 在opencv中检测罐子或瓶子

途径

>将图像转换为HSV色彩空间.将V增加2倍,以获得更多可见的东西.
>在x和y方向找到Sobel衍生物.计算两个方向的重量相等的量值.
>使用Otsu方法阈值图像.
>将Closing应用于您的图像.
>应用Canny边缘检测器.
>找到Hough Line Transform.
>查找线图像的边界矩形.
>将其叠加到您的图像上.(最后完成：P)

码

image = cv2.imread('image3.jpg',cv2.IMREAD_COLOR)
original = np.copy(image)
if image is None:
    print 'Can not read/find the image.'
    exit(-1)

hsv_image = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)
H,S,V = hsv_image[:,:,0],hsv_image[:,1],2]
V = V * 2

hsv_image = cv2.merge([H,V])
image = cv2.cvtColor(hsv_image,cv2.COLOR_HSV2RGB)
image = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
# plt.figure(),plt.imshow(image)

Dx = cv2.Sobel(image,cv2.CV_8UC1,1,0)
Dy = cv2.Sobel(image,1)
M = cv2.addWeighted(Dx,Dy,0)

# plt.subplot(1,3,1),plt.imshow(Dx,'gray'),plt.title('Dx')
# plt.subplot(1,2),plt.imshow(Dy,plt.title('Dy')
# plt.subplot(1,3),plt.imshow(M,plt.title('Magnitude')

ret,binary = cv2.threshold(M,10,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# plt.figure(),plt.imshow(binary,'gray')

binary = binary.astype(np.uint8)
binary = cv2.morphologyEx(binary,cv2.MORPH_CLOSE,cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(20,20)))
edges = cv2.Canny(binary,50,100)
# plt.figure(),plt.imshow(edges,'gray')

lines = cv2.HoughLinesP(edges,3.14/180,20,10)[0]
output = np.zeros_like(M,dtype=np.uint8)
for line in lines:
    cv2.line(output,(line[0],line[1]),(line[2],line[3]),(100,200,50),thickness=2)
# plt.figure(),plt.imshow(output,'gray')

points = np.array([np.transpose(np.where(output != 0))],dtype=np.float32)
rect = cv2.boundingRect(points)
cv2.rectangle(original,(rect[1],rect[0]),(rect[1]+rect[3],rect[0]+rect[2]),(255,255),thickness=2)
original = cv2.cvtColor(original,cv2.COLOR_BGR2RGB)
plt.figure(),plt.imshow(original,'gray')


plt.show()

注意：您可以取消注释行以显示每个步骤的结果！我只是为了便于阅读而评论它们.

结果

注意：如果您知道罐头的纵横比,可以更好地修复它！

我希望这会有所帮助.祝好运 ：)