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adaptive thresholding is a technique used in image processing to separate the foreground and background of an image by using different threshold values for different regions of the image. opencv is a popular library for computer vision tasks and provides functions to perform adaptive thresholding in python.

in opencv 3.4, the adaptive thresholding function is `cv2.adaptivethreshold()`. this function calculates the threshold value for each pixel based on the intensity of the surrounding pixels. adaptive thresholding is particularly useful when the lighting conditions vary across the image.

here is a step-by-step tutorial on how to perform adaptive thresholding using opencv 3.4 with python 3:

1. import the necessary libraries:


2. load the image:


3. apply adaptive thresholding:

- the `image` is the input grayscale image.
- `255` is the maximum pixel intensity value.
- `cv2.adaptive_thresh_mean_c` specifies that the threshold value is the mean of the neighborhood area.
- `cv2.thresh_binary` specifies that pixels with intensity values above the threshold are set to the maximum value (255 in this case).
- `11` is the size of the neighborhood area (a square window of 11x11 pixels).
- `2` is a constant subtracted from the calculated mean.

4. display the original and thresholded images:


this code snippet demonstrates how to perform adaptive thresholding using opencv 3.4 with python 3. you can experiment with different parameters like the neighborhood size and constant value to see how they affect the thresholding results.

remember to replace `'input_image.jpg'` with the path to your own image file.

i hope this tutorial helps you understand and implement adaptive thresholding in opencv 3.4 with python 3. let me know if you need further assistance or have any questions!

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