erosion | python scripts to simulate erosion

I have this image, and I'm trying to read it with Tesseract:

My code is like that:

I tried implementing morphological erosion on digital images with Python. I uploaded a binary image and defined a structural element - ones(3,3). But when I run the code, the following error occurs:

RuntimeError: sequence argument must have length equal to input rank

Please assit. Below are my codes:

im really trying to figure out how to count "✓" this symbol in a column for a performance monitor. I really cannot figure out the code for this... i tried a lot of possible ways but no luck. In visual basic i managed to do it but in C# ,i just cant figure it out.

Here are some code examples that i tried.

I have been asking several questions for locating and extracting maze from photos on SOF, but none of the answers I get work across different photos, not even across 4 testing photos. Every time when I tweaked the code to make it work for 1 photo, it will fail on the rest of photos due to warped corners/parts or light etc. I feel that I need to find a way which is insensitive to warped image and different intensity of light or the different colors of maze walls(the lines inside a maze).

I have been trying to make it work for 3 weeks without a luck. Before I drop the idea, I would like to ask is it possible to just use Image Processing without AI to locate and extract a maze from a photo? If yes, could you please show me how to do it?

Here are the code and photos:

I have an image like this:

after I applied some processings e.g. cv2.Canny(), it looks like this now:

As you can see that the black lines become hollow. I have tried erosion and dilation, but if I do them many times, the 2 entrances will be closed(meaning become connected line or closed contour).

How could I make those lines solid like the below image while keep the 2 entrances not affected?

Update 1

I have tested the following answers with a few of photos, but the code seems customized to only be able to handle this one particular picture. Due to the restriction of SOF, I cannot upload photos larger than 2MB, so I uploaded them into my Microsoft OneDrive folder for your convenience to test.

https://1drv.ms/u/s!Asflam6BEzhjgbIhgkL4rt1NLSjsZg?e=OXXKBK

Update 2

I picked up @fmw42's post as answer as his answer is the most detailed one. It doesn't answer my question but points out the correct way to process maze which is my ultimate goal. I like his approach of answering questions, firstly tells you what each step should do so that you have a clear idea about how to do the task, then provide the full code example from beginning to end. Very helpful.

Due to the limitation of SOF, I can only pick up one answer. If multiple answers are allowed, I would also pick up Shamshirsaz.Navid's answer. His answer not only points to the correct direction to solve the issue, but also the explanation with visualization really works well for me~! I guess it works equally well for all people who are trying to understand why each line of code is needed. Also he follows up my questions in comments, this makes the SOF a bit interactive :)

The Threshold track bar in Ann Zen's answer is also a very useful tip for people to quickly find out a optimal value.

I am trying to improve this image with the help of morphological operations. The result I get is good but it takes almost 40 seconds to get the resulting image and I was wondering if there is any other method to get a similar or even better result without taking too long.

Below I attach the images and the code that i used to enhance the original image. Thanks

Original spine image Final spine image

I am looking to extract the coordinates of a features perimeter. My intension is to use these coordinates to find the nearest edge-to-edge Euclidean distance for all features of interest from a .tif image. I am relatively new to scikit-image and am wondering if this can be done using options available from https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops ?

I am able to extract the filled area binary boolean matrix from skimage.measure.regionprops.

I already asked this question and I solved one task of my original question. However, I am still struggling with obtaining a decent pre-processing image.

In general, I am trying to approximate different shapes of a weld bead geometry cross section in additive manufacturing with a graph or ideally (but not necessarily) a function. The regions are the outer shape as well as the individual layers. (see following images) Prior to that I must pre-process the image so that I can apply my algorithm which predicts the distance between the layers as well as the width of each layer.

So far, I applied some pre-processing methods to extract the relevant pixels which represent the geometry of a weld bead which are shown as white pixels (see third image) and which are relevant for predicting the height and shape of each weld bead layer. I derived this image with canny edge detection and thresholding methods in different colour spaces as (including greyscale) and multiple morphological operations such as closing erosion and dilation prior to that.

As you can see, my results weren't good, I either loose too much information of the "relevant" transition region or get too much noise between the weld bead layers. The "noisy" areas are the transition areas between individual layers of metal and only show up in this way, so in general there is not a "better" or "sharper" transition in thus less "noise". Pictures 3 and 4 are an example of some of the image pre-processing methods I used.

In my mind a somehow densitiy-based (e. g. for a binary image regarding the white pixels) denoising would maybe be helpful to eliminate the noise between the layers so in the region where it is not desired while preserving the pixels in the relevant transition region.

If anybody who's experienced in image processing or image vision can give me some advice, I would highly appreciate that!

If you need to take a look at my code, just let me know. Thank you! :)

I need to draw a line in an image where no pixel is thicker than 1 pixel in the horizontal dimension.

Despite I use thickness=1 in poly lines,