This is an image containing some text, then a text box, then a Signature & after that a bottom line ending the picture.
And the 2nd image is what I want as output using python.
I have several pictures like this, I want the same cropped output for all the images.
Here is what I tried: I used pytesseract to OCR the image 1st to locate the text box determining that as starting point & thresholding to determine the endpoint of the signature area....then tried using OpenCV to crop that area & saving it in a local directory but the approach is not much promising.
Can someone help me to solve the problem?
There are several relatively simple approaches you could try.
You could use cv2.findContours() to locate the two big black titles "Additional Information" and "Signatures and Certifications" and scale off their position to deduce where the signature is. Your intermediate image might be like this:
You could use "Hit and Miss" matching to locate the 4 corners of the box "The IRS doesn't require your consent...".
You could do flood-fills in say, red, starting from white pixels near the centre of the image till you get a red box filled around the right size for the box "The IRS doesn't require your consent...". Then scale from that.
You could do a flood-fill in black, starting 2/3 of the way down the image and half way across, then find the remaining white area which would be the box "The IRS doesn't require...".
You could look for the large, empty white area below the signature (using summation across the rows) and scale off that. I am showing the summation across the rows to the right of the original image. You could invert first and look for black. Do the summation with:
rowTotals = np.sum(img, axis=1)
Related
For pre-processing, I need to crop out any pixels that are not in the dark box.
The intent is to crop out just the text.
Finally, I do additional processing to turn it into a black and white image perfect for Tesseract.
Unfortunately, I've been finding it difficult to find the y-coordinate to crop at.
I've tried:
Adding in contours to find the box (either too many lines or cannot detect the end of the dark box) - contour lines
Using template matching to find the last margin in the image (fails when the background is different) - working example - failing example
Increasing saturation/brightness - doesn't help isolate
Process extraneous text instead of trying to crop image – unfortunately the extra image data often seems to ruin the accuracy of the text that I want
Assumptions:
The image width, font, line-spacing, and margins is always the same.
The number of lines of text will vary.
The last line of the text will vary.
The background will often be different.
Here are some other images that I would want the algorithm to work with: https://imgur.com/a/Lu4pPBL
Thank you in advance!
I am dealing with some images which contain tables and there are 1 or 2 stickers on them. What I am trying to do is getting rid of those stickers. Using color thresholding (in HSV) and contour detection I am able to create a mask for those stickers. Now I want those stickers to "dissolve" out from there (I don't know the correct term for this). While keeping those tables lines intact, so that my line detection works well (which I have to do after this cleaning).
I tried OpenCV's inpaint. But this doesn't work well here, because the sticker size is big enough.
See this example:
Part of the whole image where the sticker is sticking (inside contents are censored by me). It can be over horizontal lines, or vertical lines, or both. Basically, it's sticking somewhere on the table (maybe over some text too, but that can't be recovered anyway). The background won't be necessary whitish, it can be pink/orange/other colors.
This is the thresholded image, creating a mask of the sticker. We can also get the contour of this if required.
This is the result of cv.inpaint() with radius 3.
What I want is to reconstruct those lines.
My solution
Now my approach is to interpolate the colors in between the sticker contour, to fill it up. For each pixel inside the contour, I will do a vertical interpolation and a horizontal interpolation (interpolation of the boundary colors) and then fill that pixel with the average of both. I am hoping that this will preserve my vertical and horizontal lines at least. (Might fail if it's on a corner of the table). This will also keep the background smooth, my background can have some different colors.
Now my problem is how I can implement this. What I have are contours that I find using OpenCV's get_contours(). I don't know how to get the colors on its boundary and how to interpolate the in-between colors.
Any help is appreciated. Thanks in advance.
Due to confidentiality, I cannot share the whole image.
EDIT
I tried the seam-carving method (implementation). Here are the results:
Vertical seaming
Horizontal seaming
It works well once I know which one to use. And I am not sure how well it will do when we have both horizontal and vertical lines.
PS. Don't suggest a solution which needs to find lines and then work. Because there will be many lines in my whole image.
You can make synthetic example images. To better explain your issue.
As I got it you can use Poisson image editing. Just take a piece of clear paper image and paste it using poisson blending and the mask you extracted.
Check this github repo as instance for examples with code.
I am trying to use OpenCV to measure size of filament ( that plastic material used for 3D printing)
What I am trying to do is measuring filament size ( that plastic material used for 3D printing ). The idea is that I use led panel to illuminate filament, then take image with camera, preprocess the image, apply edge detections and calculate it's size. Most filaments are fine made of one colour which is easy to preprocess and get fine results.
The problem comes with transparent filament. I am not able to get useful results. I would like to ask for a little help, or if someone could push me the right directions. I have already tried cropping the image to heigh that is a bit higher than filament, and width just a few pixels and calculating size using number of pixels in those images, but this did not work very well. So now I am here and trying to do it with edge detections
works well for filaments of single colour
not working for transparent filament
Code below is working just fine for common filaments, the problem is when I try to use it for transparent filament. I have tried adjusting tresholds for Canny function. I have tried different colour-spaces. But I am not able to get the results.
Images that may help to understand:
https://imgur.com/gallery/CIv7fxY
image = cv.imread("../images/img_fil_2.PNG") # load image
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) # convert image to grayscale
edges = cv.Canny(gray, 100, 200) # detect edges of image
You can use the assumption that the images are taken under the same conditions.
Your main problem is that the reflections in the transparent filament are detected as edges. But, since the image is relatively simple, without any other edges, you can simply take the upper and the lower edge, and measure the distance between them.
A simple way of doing this is to take 2 vertical lines (e.g. image sides), find the edges that intersect the line (basically traverse a column in the image and find edge pixels), and connect the highest and the lowest points to form the edges of the filament. This also removes the curvature in the filament, which I assume is not needed for your application.
You might want to use 3 or 4 vertical lines, for robustness.
I am working on an application where I need feature like Cam Scanner where document is to be detected in an image. For that I am using Canny Edge detection followed by Hough Transform.
The results look promising but the text in the document is creating issues as explained via images below:
Original Image
After canny edge detection
After hough transform
My issue lies in the third image, the text in original mage near the bottom has forced hough transform to detect the horizontal line(2nd cluster from bottom).
I know I can take the largest quadrilateral and that would work fine in most cases, but still I want to know any other ways where in this processing I can ignore the effect of text on the edges.
Any help would be appreciated.
I solved the issue of text with the help of median filter of size 15(square) in an image of 500x700.
Median filter doesn't affect the boundaries of the paper, but can help eliminate the text completely.
Using that I was able to get much more effective boundaries.
Another approach you could try is to use thresholding to find the paper boundaries. This would create a binary image. You can then examine the blobs of white pixels and see if any are large enough to be the paper and have the right dimensions. If it fits the criteria, you can find the min/max points of this blob to represent the paper.
There are several ways to do the thresholding, including iterative, otsu, and adaptive.
Also, for best results you may have to dilate the binary image to close the black lines in the table as shown in your example.
Okay so i am trying to find homography of a soccer match. What i have till now is
Read images from a folder which is basically many cropped images of a template soccer field. Basically this has images for center circle and penalty lines etc.
Read video stream from a file and crop it into many smaller segments.
Loop inside the images in video stream and inside that another loop for images that i read from folder.
Now in the two images that i get through iteration , i applied a green filter because of my assumption that field is green
Use orb to find points and then find matches.
Now the Problem is that because of players and some noise from croud, i am unable to find proper matches for homography. Also removing them is a problem because that also tends to hide the soccer field lines that i need to calculate the homography on.
Any suggestions on this is greatly appreciated. Also below are some sample code and images that i am using.
"Code being used"
Sample images
Output that i am getting
The image on right of output is a frame from video and that on left is the same sample image that i uploaded after filterGreen function as can be seen from the code.
Finally what i want is for the image to properly map to center circle so i can draw a cube in center, Somewhat similar to "This example" . Thanks in advance for helping me out.
An interesting technique to throw at this problem is RASL. It computes homographies that align stacks of related images. It does not require that you specify corresponding points on the images, but operates directly on the image pixels. It is robust against image occlusions (eg, players moving in the foreground).
I've just released a Python implementation here: https://github.com/welch/rasl
(there are also links there to the original RASL paper, MATLAB implementation, and data).
I am unsure if you'd want to crop the input images to that center circle, or if the entire frames can be aligned. Try both and see.