CODEWITHSUNDEEP
pythonopencvimage-processing
Jon Cage
Jon Cage

Reputation: 37458

How do I programmatically find the pixel locations of specific features in an image?

I'm building an automated electricity / gas meter reader using OpenCV and Python. I've got as far as taking shots with a webcam:

enter image description here

I can then use afine transform to unwarp the image (an adaptation of this example):

def unwarp_image(img):
    rows,cols = img.shape[:2]
    # Source points
    left_top = 12
    left_bottom = left_top+2
    top_left = 24
    top_right = 13
    bottom = 47
    right = 180
    srcTri = np.array([(left_top,top_left),(right,top_right),(left_bottom,bottom)], np.float32)

    # Corresponding Destination Points. Remember, both sets are of float32 type
    dst_height=30
    dstTri = np.array([(0,0),(cols-1,0),(0,dst_height)],np.float32)

    # Affine Transformation
    warp_mat = cv2.getAffineTransform(srcTri,dstTri)   # Generating affine transform matrix of size 2x3
    dst = cv2.warpAffine(img,warp_mat,(cols,dst_height))     # Now transform the image, notice dst_size=(cols,rows), not (rows,cols)

    #cv2.imshow("crop_img", dst)
    #cv2.waitKey(0)

    return dst

..which gives me an image something like this:

enter image description here

I still need to extract the text using some sort of OCR routine but first I'd like to automate the part that identifies what pixel locations to apply the affine transform to. So if someone knocks the webcam it doesn't stop the software working.

Upvotes: 6

Views: 491

Answers (1)

Diana
Diana

Reputation: 1321

Since your image is pretty much planar, you can look into finding the homography between the image you get from the webcam and the desired image (in the upright position).

Edit: This will rotate the image in the upright position. Once you've registered your image (brought it in the upright position), you could do row-wise or column-wise projections (sum all the pixels along the columns to get one vector, sum all the pixels along the rows to get one vector). You can use these vectors to figure out where you have a jump in color, and crop it there.

Alternatively you can use the Hough transform, which gives you lines in an image. You can probably get away with not registering the image if you do this.

Upvotes: 2

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