From b5b9b3a279dad006344ae068ce5c8fc1d73745bc Mon Sep 17 00:00:00 2001 From: "Kimberly N. McGuire" Date: Sun, 18 Aug 2024 15:43:25 +0200 Subject: [PATCH] Remove notice on using deprecated itemset --- .../py_core/py_basic_ops/py_basic_ops.markdown | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown b/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown index e4ee61ebd2..de9eaaf8c6 100644 --- a/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown +++ b/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown @@ -51,23 +51,6 @@ You can modify the pixel values the same way. Numpy is an optimized library for fast array calculations. So simply accessing each and every pixel value and modifying it will be very slow and it is discouraged. -@note The above method is normally used for selecting a region of an array, say the first 5 rows -and last 3 columns. For individual pixel access, the Numpy array methods, array.item() and -array.itemset() are considered better. They always return a scalar, however, so if you want to access -all the B,G,R values, you will need to call array.item() separately for each value. - -Better pixel accessing and editing method : -@code{.py} -# accessing RED value ->>> img.item(10,10,2) -59 - -# modifying RED value ->>> img.itemset((10,10,2),100) ->>> img.item(10,10,2) -100 -@endcode - Accessing Image Properties --------------------------