CODEWITHSUNDEEP
pythonopencvraspberry-piobject-detection
user3553000
user3553000

Reputation: 111

Detecting a can or bottle in opencv

I am new to OpenCV and have a few questions. I need to detect a bottle or a can based on their shape. For this I am using a raspberry pi board and pi camera. The background is always black and does not change. I have tried many possible solutions to this problem but could not get satisfactory results. The things I have tried include edge detection, morphological transformations, matchShapes(), matchTemplate(). Please let me know if I can do this task efficiently and with maximum accuracy.

A sample image:

enter image description here

Upvotes: 7

Views: 10559

Answers (1)

Hadi
Hadi

Reputation: 5608

I came up with an approach that may help! If you know more things about the can, i.e the width to height ratio it can be more robust by adjusting the rectangle size!

Approach

  • Convert image to HSV color space. Increase V by a factor of 2 in order to have more visible things.
  • Find Sobel derivatives in x and y direction. Compute magnitude with equal weight for both direction.
  • Threshold your image using Otsu method.
  • Apply Closing to you image.
  • Apply Canny edge detector.
  • Find Hough Line Transform.
  • Find Bounding Rectangle of your line image.
  • Superimpose it onto your image.(Finally done :P)

Code

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], hsv_image[:,:,2]
V = V * 2

hsv_image = cv2.merge([H,S,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,cv2.CV_8UC1,0,1)
M = cv2.addWeighted(Dx, 1, Dy,1,0)

# plt.subplot(1,3,1), plt.imshow(Dx, 'gray'), plt.title('Dx')
# plt.subplot(1,3,2), plt.imshow(Dy, 'gray'), plt.title('Dy')
# plt.subplot(1,3,3), plt.imshow(M, 'gray'), 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,1,3.14/180,50,20,10)[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]),(255,255,255),thickness=2)
original = cv2.cvtColor(original,cv2.COLOR_BGR2RGB)
plt.figure(), plt.imshow(original,'gray')


plt.show()

NOTE: you can uncomment the lines for showing the result of each step! I just comment them for the sake of readability.

Result

Result Image

NOTE: If you know the aspect ratio of your can you can fix it better!

I hope that will help. Good Luck :)

Upvotes: 4

Related Questions