- #Add border to text opencv how to#
- #Add border to text opencv install#
- #Add border to text opencv code#
Note: For more information on cv2.minAreaRect, please see this excellent explanation by Adam Goodwin. When zero is reached, the angle is set back to -90 degrees again and the process continues. As the rectangle is rotated clockwise the angle value increases towards zero. The cv2.minAreaRect function returns angle values in the range [-90, 0). We pass these coordinates into cv2.minAreaRect which then computes the minimum rotated rectangle that contains the entire text region. Line 30 finds all (x, y)-coordinates in the thresh image that are part of the foreground. # otherwise, just take the inverse of the angle to make # returned angle trends to 0 - in this special case we # range [-90, 0) as the rectangle rotates clockwise the # the `cv2.minAreaRect` function returns values in the # compute a rotated bounding box that contains allĬoords = np.column_stack(np.where(thresh > 0)) # are greater than zero, then use these coordinates to Given this thresholded image, we can now compute the minimum rotated bounding box that contains the text regions: # grab the (x, y) coordinates of all pixel values that Our text is now white on a black background. When applying computer vision and image processing operations, it’s common for the foreground to be represented as light while the background (the part of the image we are not interested in) is dark.Ī thresholding operation ( Lines 23 and 24) is then applied to binarize the image: Figure 2: Applying a thresholding operation to binarize our image. Our input images contain text that is dark on a light background however, to apply our text skew correction process, we first need to invert the image (i.e., the text is now light on a dark background - we need the inverse). # threshold the image, setting all foreground pixels to Gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # and background to ensure foreground is now "white" and Our next step is to isolate the text in the image: # convert the image to grayscale and flip the foreground The image is then loaded from disk on Line 13. We only need a single argument here, -image, which is the path to our input image. We then parse our command line arguments on Lines 7-10.
#Add border to text opencv install#
We’ll be using OpenCV via our cv2 bindings, so if you don’t already have OpenCV installed on your system, please refer to my list of OpenCV install tutorials to help you get your system setup and configured. Lines 2-4 import our required Python packages. # construct the argument parse and parse the argumentsĪp.add_argument("-i", "-image", required=True, įrom there, insert the following code: # import the necessary packages To get started, open up a new file and name it correct_skew.py. To see how our text skew correction algorithm is implemented with OpenCV and Python, be sure to read the next section. The goal our text skew correction algorithm will be to correctly determine the direction and angle of the rotation, then correct for it. The second component of the filename is the actual number of degrees the image has been rotated by. The first part of the filename specifies whether our image has been rotated counter-clockwise (negative) or clockwise (positive). The filenames of the four files follow: $ ls images/
#Add border to text opencv how to#
The text block itself is from Chapter 11 of my book, Practical Python and OpenCV, where I’m discussing contours and how to utilize them for image processing and computer vision. Similar to Félix’s example, I have prepared a small dataset of four images that have been rotated by a given number of degrees: Figure 1: Our four example images that we’ll be applying text skew correction to with OpenCV and Python.
#Add border to text opencv code#
We’ll then write Python and OpenCV code to automatically detect and correct the text skew angle in our images. We’ll start by creating a simple dataset that we can use to evaluate our text skew corrector. The remainder of this blog post will demonstrate how to deskew text using basic image processing operations with Python and OpenCV. Looking for the source code to this post? Jump Right To The Downloads Section Text skew correction with OpenCV and Python
0 kommentar(er)
