We Keep Coding

create a 20x20 matrix using numpy broadcast

I'm looking how to create a matrix of 20x20 using Numpy broadcasting, result should look like:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 0, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91, 98, 105, 112, 119, 126, 133, 140, 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 0, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 99, 108, 117, 126, 135, 144, 153, 162, 171, 180, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 0, 11, 22, 33, 44, 55, 66, 77, 88, 99, 110, 121, 132, 143, 154, 165, 176, 187, 198, 209, 220, 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240, 0, 13, 26, 39, 52, 65, 78, 91, 104, 117, 130, 143, 156, 169, 182, 195, 208, 221, 234, 247, 260, 0, 14, 28, 42, 56, 70, 84, 98, 112, 126, 140, 154, 168, 182, 196, 210, 224, 238, 252, 266, 280, 0, 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270, 285, 300, 0, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 272, 288, 304, 320, 0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255, 272, 289, 306, 323, 340, 0, 18, 36, 54, 72, 90, 108, 126, 144, 162, 180, 198, 216, 234, 252, 270, 288, 306, 324, 342, 360, 0, 19, 38, 57, 76, 95, 114, 133, 152, 171, 190, 209, 228, 247, 266, 285, 304, 323, 342, 361, 380, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400]
I tried the following:
broadcast = np.arange(0, 21) *(20)
print(broadcast)
Got:
[0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400]
When providing a broadcast of a range to multiply by, such as: (0, 21) * (0, 21) it results in the below error:
ValueError: operands could not be broadcast together with shapes (21,) (2,)
Is it possible to broadcast by a range ?

Let's take your calculations step by step:
First make an array with arange:
In [166]: x = np.arange(5)
In [167]: x
Out[167]: array([0, 1, 2, 3, 4])
We can multiply it by a scalar:
In [168]: x * (5)
Out[168]: array([ 0, 5, 10, 15, 20])
The () add nothing here.
Then you try to multiply by a tuple:
In [169]: x * (0, 5)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-169-b6f23a3901bc> in <module>
----> 1 x * (0, 5)
ValueError: operands could not be broadcast together with shapes (5,) (2,)
I suspect you expected to somehow multiply the (0,5) 'range' by (0,5) 'range'. That's not how it works. (0,5) is a tuple. In the multiplication expression, the array x controls the action, so the tuple is turned into an array, np.array([0,2]). By broadcasting rules, a 5 element array can't multiply with a 2 element one.
Your first print suggests you want to multiply two arrays (n,) shape array to produces a (n,n) array, or (n*n,).
We could reshape x to (5,1):
In [170]: x[:,None]
Out[170]: # elsewhere this might called a column vector
array([[0],
[1],
[2],
[3],
[4]])
Now by broadcasting:
In [171]: x*x[:,None]
Out[171]:
array([[ 0, 0, 0, 0, 0],
[ 0, 1, 2, 3, 4],
[ 0, 2, 4, 6, 8],
[ 0, 3, 6, 9, 12],
[ 0, 4, 8, 12, 16]])
and if you want it as 1d:
In [172]: np.ravel(x*x[:,None])
Out[172]:
array([ 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 0, 2, 4, 6, 8, 0, 3,
6, 9, 12, 0, 4, 8, 12, 16])
The [171] action is: (5,) * (5,1) => (1,5) * (5,1) => (5,5)
The basic broadcasting rules are:
leading dimensions might be added to match
size 1 dimensions might be adjusted to match
This multiplication is like the outer or cartesian product of two arrays:
In [173]: np.outer(x,x)
Out[173]:
array([[ 0, 0, 0, 0, 0],
[ 0, 1, 2, 3, 4],
[ 0, 2, 4, 6, 8],
[ 0, 3, 6, 9, 12],
[ 0, 4, 8, 12, 16]])

I believe you are looking for something like this:
a = np.arange(21)
b = np.arange(21)
a[:, np.newaxis] * b
More can be found here: https://numpy.org/doc/stable/user/basics.broadcasting.html

Is there a way to convert a grayscale image array into a Tensor to feed to a model

I'm a real starter in machine learning. But I'm trying to deploy the MNIST character recognition example with a Flask server. I already set up the model, trained it and set up Flask.
I've created a simple HTML canvas where I can draw numbers from 0-9. These are sent via AJAX to my python backend.
In the Python backend, I receive the Base64 Image and decode it to a 255-grayscale array. So I have this giant array with information about the image:
[138, 102, 160, 120, 54, 173, 105, 214, 173, 106, 41, 154, 129, 239, 233, 158, 6, 218, 177, 238, 184, 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 1, 144, 0, 0, 1, 144, 8, 6, 0, 0, 0, 128, 191, 54, 204, 0, 0, 28, 81, 73, 68, 65, 84, 120, 94, 237, 221, 9, 172, 109, 87, 89, 7, 240, 63, 145, 72, 152, 154, 128, 128, 168, 16, 32, 21, 80, 169, 80, 202, 148, 20, 16, 43, 161, 45, 80, 160, 140, 50, 180, 34, 29, 24, 210, 42, 2, 130, 180, 1, 25, 211, 34, 163, 96, 27, 134, 14, 32, 101, 146, 25, 139, 165, 45, 40, 164, 12, 134, 22, 104, 9, 160, 76, 13, 40, 168, 164, 32, 36, 101, 10, 4, 131, 89, 118, 95, 122, 237, 240, 222, 121, 251, 158, 179, 246, 218, 107, 255, 78, 242, 114, 31, 183, 103, 237, 181, 190, 223, 183, 30, 255, 220, 187, 207, 222, 251, 26, 241, 34, 64, 128, 0, 1, 2, 35, 4, 174, 49, 98, 140, 33, 4, 8, 16, 32, 64, 32, 2, 196, 38, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30]
But I can't find a way to reshape this array so I can feed it into my tensorflow model, which requires the following input:
Tensor("shape=(28, 28, 1), dtype=float32)
Can you help me find a way to transform this array in Python to a tensor of appropriate size?
Thanks already

Here is some code that should work.
import numpy as np
import tensorflow as tf
data = [138, 102, 160, 120, 54, 173, 105, 214, 173, 106, 41, 154, 129, 239, 233, 158, 6, 218, 177, 238, 184, 137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 1, 144, 0, 0, 1, 144, 8, 6, 0, 0, 0, 128, 191, 54, 204, 0, 0, 28, 81, 73, 68, 65, 84, 120, 94, 237, 221, 9, 172, 109, 87, 89, 7, 240, 63, 145, 72, 152, 154, 128, 128, 168, 16, 32, 21, 80, 169, 80, 202, 148, 20, 16, 43, 161, 45, 80, 160, 140, 50, 180, 34, 29, 24, 210, 42, 2, 130, 180, 1, 25, 211, 34, 163, 96, 27, 134, 14, 32, 101, 146, 25, 139, 165, 45, 40, 164, 12, 134, 22, 104, 9, 160, 76, 13, 40, 168, 164, 32, 36, 101, 10, 4, 131, 89, 118, 95, 122, 237, 240, 222, 121, 251, 158, 179, 246, 218, 107, 255, 78, 242, 114, 31, 183, 103, 237, 181, 190, 223, 183, 30, 255, 220, 187, 207, 222, 251, 26, 241, 34, 64, 128, 0, 1, 2, 35, 4, 174, 49, 98, 140, 33, 4, 8, 16, 32, 64, 32, 2, 196, 38, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30, 32, 64, 128, 0, 129, 81, 2, 2, 100, 20, 155, 65, 4, 8, 16, 32, 32, 64, 236, 1, 2, 4, 8, 16, 24, 37, 32, 64, 70, 177, 25, 68, 128, 0, 1, 2, 2, 196, 30]
data = np.array(data).reshape(28,28,1)
At this point you should be able to use a tensorflow model on this, but if for some reason it does not accept the numpy array, you can try
data = tf.convert_to_tensor(data)
This will turn the numpy array into a tensorflow tensor. Hope that helps!

Identify blocks/groups of words in a document based on a key word and positional data?

Considering the we have following input data table.
import pandas as pd
#Pandas settings to see all the data when printing
pd.set_option('display.max_columns', None) # or 1000
pd.set_option('display.max_rows', None) # or 1000
pd.set_option('display.width', 500)
#Load the data
data_array = [[576, 60, 279, 28, 2, 'LzR', 0, 0], [578, 17, 318, 23, 3, 'U', 0, 0], [371, 21, 279, 24, 2, 'K', 0, 0], [373, 134, 317, 25, 3, 'mq77MJc', 0, 0], [537, 32, 317, 25, 3, '53', 0, 0], [373, 201, 355, 25, 4, '7Q7NZzkAzN', 0, 0], [538, 118, 393, 24, 5, 'oNNbgA', 0, 0], [680, 39, 392, 26, 5, 'J9', 0, 0], [1509, 155, 260, 154, 2, 'd', 0, 0], [1731, 98, 268, 123, 2, 'z8', 0, 0], [1876, 385, 271, 120, 2, 'rUqNDY', 0, 0], [1640, 197, 590, 21, 7, 't5gNVHDXQVJ', 0, 0], [1989, 270, 589, 22, 7, 't3I81fBOE9caUfb', 0, 0], [352, 80, 645, 25, 8, 'i5f3', 0, 1], [454, 245, 645, 25, 8, 'KrqcRA7Se7X7', 1, 1], [719, 60, 645, 27, 8, 'bpN', 0, 1], [1640, 161, 642, 22, 8, 'skAzt6Np4', 0, 0], [1822, 51, 643, 21, 8, 'K59', 0, 0], [2082, 177, 642, 22, 8, 'cwyN7wsMhE', 0, 0], [353, 220, 683, 25, 9, 'O8coFUwMUbE', 0, 1], [597, 17, 683, 25, 9, 'L', 0, 1], [1640, 234, 695, 22, 9, 'oVWEKowWnbT2y', 0, 0], [2080, 179, 695, 22, 9, 'FvjigCiC7h', 0, 0], [351, 79, 721, 24, 10, 'OQN3', 0, 1], [476, 202, 720, 25, 10, 'S2gcfJIDze', 0, 1], [2062, 69, 775, 22, 11, 'n9lN', 0, 0], [2155, 8, 775, 21, 11, 'G', 0, 0], [2188, 35, 775, 21, 11, '9X', 0, 0], [2246, 8, 775, 21, 11, 'v', 0, 0], [353, 81, 1003, 21, 13, 'c7ox8', 0, 0], [461, 325, 1003, 22, 13, 'o9GmMYAW4RrpPBY64p', 0, 0], [351, 101, 1037, 22, 14, '9NF7ii', 0, 0], [477, 146, 1037, 21, 14, 'MwTlIkU9', 0, 0], [350, 70, 1071, 22, 15, 'J5XF', 0, 0], [443, 87, 1071, 22, 15, '3m4tM', 0, 0], [553, 32, 1071, 22, 15, 'Ck', 0, 0], [609, 10, 1071, 22, 15, '5', 0, 0], [643, 53, 1071, 22, 15, 'X7Y', 0, 0], [1568, 135, 1092, 20, 16, 'P4', 0, 0], [352, 142, 1105, 22, 16, 'Pjs1GYSG', 0, 0], [516, 45, 1105, 22, 16, 'o9V', 0, 0], [588, 106, 1105, 22, 16, 'WRI8oY', 0, 0], [1563, 132, 1117, 20, 16, '3cZY', 0, 0], [350, 69, 1140, 21, 17, 'GW3y', 0, 0], [441, 35, 1139, 22, 17, 'EO', 0, 0], [497, 51, 1139, 28, 17, 'toN', 0, 0], [570, 49, 1140, 21, 17, 'k11', 0, 0], [643, 51, 1139, 22, 17, 'pod', 0, 0], [715, 89, 1140, 21, 17, '6SQfv', 0, 0], [825, 83, 1139, 22, 17, 'CzC2M', 0, 0], [934, 102, 1140, 21, 17, 'aowjQC', 0, 0], [1062, 51, 1140, 21, 17, 'BtC', 0, 0], [1558, 136, 1142, 20, 17, 'XhJ', 0, 0], [1722, 336, 1115, 25, 16, 'OgtXP2nxOwP7Gb3I', 0, 0], [352, 125, 1174, 21, 18, 'zYmvutc', 0, 0], [498, 45, 1174, 21, 18, 'JvN', 0, 0], [570, 124, 1174, 21, 18, 'TyZdJG4', 0, 0], [352, 64, 1207, 22, 19, 'Lvam', 0, 0], [443, 45, 1208, 21, 19, 'Onk', 0, 0], [516, 123, 1208, 21, 19, 'bPgi7tF', 0, 0], [1946, 12, 1231, 11, 20, 'I', 0, 0], [351, 106, 1241, 23, 20, 'xbAa7n', 0, 0], [479, 306, 1242, 22, 20, 'NEn7uifO17vkyzVVp', 0, 0], [1300, 142, 1242, 27, 20, 'dZukserV', 0, 0], [352, 178, 1275, 34, 21, 'qrxWKyJjjn', 0, 0], [557, 60, 1275, 28, 21, '2Ri5', 0, 0], [1354, 88, 1276, 27, 21, 'ZCp3F', 0, 0], [1558, 197, 1231, 63, 20, 'YgoGs', 0, 0], [1787, 96, 1247, 63, 20, 'Um', 0, 0], [1913, 268, 1231, 63, 20, 'YL7fkaV', 0, 0], [351, 70, 1309, 23, 22, 'kcGD', 0, 0], [443, 142, 1309, 23, 22, 'lGAx6Ljx', 0, 0], [605, 35, 1310, 21, 22, 'Hm', 0, 0], [661, 142, 1310, 27, 22, 'S8gZ5tPE', 0, 0], [1302, 135, 1310, 27, 22, 'gjgVPImz', 0, 0], [1743, 12, 1329, 11, 23, 'Z', 0, 0], [2055, 16, 1324, 17, 23, 'i', 0, 0], [353, 11, 1344, 21, 24, 'L', 0, 0], [386, 53, 1344, 21, 24, 'Q5J', 0, 0], [1300, 142, 1344, 27, 24, '9L9ScEj2', 0, 0], [1558, 400, 1345, 63, 24, 'S8YyUDnXd', 0, 0], [1993, 91, 1345, 62, 24, '4P', 0, 0], [1555, 102, 1605, 35, 25, 'kbGP', 0, 2], [1674, 371, 1605, 44, 25, 'DO1tvoEyiX9AVz6Q', 0, 2], [2062, 147, 1605, 44, 25, 'DtQAa3', 2, 2], [1554, 53, 1669, 35, 26, 'pg', 0, 2], [1624, 104, 1660, 34, 26, 'ZPsJ', 0, 2], [1746, 221, 1659, 38, 26, '7CBPYAUA', 0, 2], [1987, 50, 1657, 46, 26, 'AL', 0, 2], [1555, 407, 1714, 44, 27, 'LA3ShdHUE3DAoOkfiB', 0, 2], [188, 1826, 2340, 3, 29, '4', 0, 0], [2024, 217, 2309, 34, 28, 'DLpZXhKepjdcyW', 0, 0], [2239, 119, 2310, 33, 28, '28otEfj9', 0, 0], [230, 77, 2349, 23, 29, 'Th1YC4R', 0, 0], [476, 89, 2349, 18, 29, 'uFRt5qEx', 0, 0], [1140, 463, 2388, 35, 30, 'Mxcsoj1MOubuEB33', 0, 0], [1708, 40, 2372, 17, 30, 'OfA', 0, 9], [1758, 81, 2372, 22, 30, 'ZQoO7mwr', 0, 9], [1848, 3, 2372, 17, 30, 'M', 0, 9], [1860, 134, 2372, 22, 30, 'IvtUnQ4Zxc29A', 0, 9], [2002, 20, 2376, 13, 30, '3V', 0, 9], [2029, 32, 2372, 17, 30, '6t8', 0, 9], [2070, 133, 2372, 17, 30, 'PdCWscuWGHR', 0, 9], [1709, 171, 2398, 22, 30, 'RsW4Oj1Lhf1ljQV4G', 0, 9], [1890, 148, 2398, 22, 30, 'VSUJUa3tuYIhiXxP', 9, 9], [2048, 34, 2398, 17, 30, 'aAm', 0, 9], [2089, 21, 2403, 12, 30, 'uY', 0, 9], [2118, 53, 2398, 17, 30, '6DDFv', 0, 9], [2179, 28, 2398, 17, 30, 'DKJ', 0, 9], [2214, 66, 2398, 17, 30, 'NBmY9BD', 0, 9], [2289, 57, 2398, 18, 30, 'sYsrT', 0, 9], [1708, 25, 2425, 17, 31, 'jGk', 0, 9], [1736, 34, 2429, 13, 31, 'oX', 0, 9], [1778, 93, 2425, 17, 31, 'OvpfEyhHso', 0, 9], [120, 131, 2510, 23, 32, 'rZCsYsA6im2b', 0, 0], [260, 25, 2515, 18, 32, 'G6', 0, 0], [295, 107, 2510, 18, 32, 'd6eYwhzZuS', 0, 0], [132, 88, 2582, 22, 34, 'Xc84', 3, 3], [231, 223, 2582, 22, 34, 'MnMcBUHVmhl2', 0, 3], [463, 47, 2582, 22, 34, 'Vto', 0, 3], [132, 194, 2616, 22, 35, 'B4f1f4KpCHC', 0, 3], [338, 14, 2616, 22, 35, 'W', 0, 3], [131, 64, 2650, 22, 36, 'UW6t', 0, 3], [216, 181, 2650, 22, 36, 'hLULWi7xdj', 0, 3], [1044, 175, 2510, 18, 32, 'F9f7jvsfmjnXbK', 0, 0], [1226, 25, 2515, 18, 32, 'Vk', 0, 0], [1261, 177, 2510, 23, 32, 'TBlYLSoItzHKpG', 0, 0], [1054, 132, 2544, 22, 33, 'u4vvPgHd', 0, 0], [1053, 36, 2590, 21, 34, 'lN', 0, 4], [1101, 107, 2589, 23, 34, 'ieee4D', 0, 4], [1218, 47, 2589, 23, 34, 'kD6', 0, 4], [1054, 122, 2623, 23, 35, 'Ngf2xWa', 0, 4], [1189, 132, 2624, 22, 35, 'N27RyHsP', 0, 4], [1054, 204, 2657, 23, 36, 'e97JFxWTXfS', 0, 4], [1262, 43, 2658, 22, 36, 'p', 4, 4], [1054, 65, 2692, 22, 37, 'mle1', 0, 4], [1139, 186, 2691, 23, 37, 'o6tA5wFrK', 0, 4], [1337, 39, 2691, 23, 37, 'W3', 0, 4], [1709, 175, 2510, 18, 32, 'DQm27gIhcjmkdB', 0, 0], [1892, 25, 2515, 18, 32, '4Z', 0, 0], [1927, 176, 2510, 23, 32, 'rAP1PxzMyqkxdY', 0, 0], [1720, 132, 2544, 22, 33, 'JpsQeikW', 0, 0], [1719, 35, 2590, 21, 34, 'hD', 0, 5], [1766, 107, 2589, 23, 34, '3vzIwR', 0, 5], [1884, 47, 2589, 23, 34, 'kHw', 0, 5], [1720, 122, 2623, 23, 35, 'MYOKedL', 0, 5], [1854, 132, 2624, 22, 35, 'K8JXFVII', 5, 5], [1720, 204, 2657, 23, 36, 'bBkPRmgyfVp', 0, 5], [1928, 43, 2658, 22, 36, 'j', 0, 5], [1719, 65, 2692, 22, 37, 'RfU4', 0, 5], [1805, 185, 2691, 23, 37, 'wtK1L23Q4', 0, 5], [2003, 38, 2692, 22, 37, 'yY', 0, 5], [130, 255, 2804, 23, 38, 'jgoGjNh2DoLnb2b4PGonGvU', 0, 0], [1044, 117, 2804, 18, 38, 'qGXS7f7gRHy', 0, 0], [1168, 38, 2804, 18, 38, 'UQI', 0, 0], [1215, 102, 2804, 18, 38, 'P764bscKkx', 0, 0], [1320, 38, 2804, 18, 38, 'OtH', 0, 0], [1368, 58, 2804, 18, 38, 'VhrUJ', 0, 0], [1709, 100, 2804, 23, 38, 'zjQgoufCGU', 0, 0], [131, 55, 2852, 21, 40, 'piH', 0, 0], [198, 41, 2858, 15, 40, 'wU6P', 0, 0], [281, 124, 2852, 21, 40, 'riQCT4RX', 0, 0], [454, 138, 2852, 27, 40, 'jSAJPlWhyRE', 0, 0], [612, 77, 2852, 21, 40, 'nVS97', 0, 0], [131, 227, 2886, 21, 41, 'zExU7Poi4QW', 0, 0], [375, 235, 2886, 21, 41, 'pLTfHVP1qzb7Mh2', 0, 0], [138, 100, 2957, 15, 42, 'fv8', 0, 0], [1404, 4, 2978, 4, 42, 'B', 0, 0], [130, 103, 2975, 34, 42, 'qpg', 0, 0], [253, 252, 2974, 19, 42, 'T9SOmYWl4CUrdt8o', 0, 0], [1078, 3, 2972, 40, 42, 'S5', 0, 0], [1103, 62, 2978, 28, 42, 'L6W', 0, 0], [1181, 56, 2978, 28, 42, 'ep1', 0, 0], [1253, 118, 2978, 28, 42, 'oKhrqlI', 0, 0], [1384, 45, 2985, 21, 42, 'OyP', 0, 0], [1444, 132, 2978, 28, 42, 'mvg8Bw5', 0, 0], [1593, 55, 2972, 76, 42, 'eG', 0, 0], [218, 5, 3074, 18, 44, 'z', 0, 0], [231, 72, 3058, 18, 44, 'x1Pat7', 0, 0], [605, 5, 3074, 18, 44, 'P', 0, 0], [617, 39, 3058, 18, 44, 'dNT', 0, 0], [1053, 146, 3058, 23, 44, 'q7CLeOJhnI1oa', 0, 0], [1802, 5, 3074, 18, 44, '6', 0, 0], [1815, 72, 3058, 18, 44, 'acKa9h', 0, 0], [2119, 50, 3057, 35, 44, 'uGH', 0, 0], [461, 129, 3125, 29, 45, 'p6L5U', 0, 0], [623, 44, 3125, 29, 45, 'dC', 0, 0], [1046, 266, 3125, 29, 45, '9HBoqUyRbg', 0, 0], [1975, 129, 3125, 29, 45, 'qH1ph', 0, 0], [2136, 45, 3125, 29, 45, 'gG', 0, 0], [218, 5, 3183, 20, 46, 'j', 0, 0], [605, 5, 3183, 20, 46, 'o', 0, 0], [119, 24, 3213, 18, 47, 'QDN', 0, 8], [153, 94, 3213, 18, 47, 'EleVpvP4', 0, 8], [256, 105, 3213, 23, 47, 'dq9L2xQO7', 0, 8], [370, 7, 3223, 2, 47, 'n', 0, 8], [386, 69, 3212, 24, 47, 'L9EKl', 0, 8], [464, 83, 3213, 23, 47, 'AnF2rBIN', 0, 8], [555, 19, 3214, 17, 47, 'k6', 0, 8], [582, 62, 3213, 18, 47, 'y3M3kx', 8, 8], [654, 2, 3213, 18, 47, '1', 0, 8], [666, 139, 3212, 19, 47, 'SkmavPFrrrSv', 0, 8], [808, 52, 3213, 18, 47, 'bJ5S', 0, 8], [200, 100, 3316, 29, 50, 'NmNa', 0, 7], [336, 675, 3316, 29, 50, 'vB759g8XWkL7XXe5tCHZs7tAF', 7, 7], [1046, 42, 3203, 23, 47, 'v4T', 0, 0], [1095, 150, 3202, 19, 47, 'NH7vM6', 0, 0], [1251, 24, 3199, 22, 47, '47', 0, 0], [1802, 5, 3183, 20, 46, 'B', 0, 0], [2119, 5, 3183, 20, 46, 'b', 0, 0], [1714, 254, 3213, 23, 47, '2Za9eGyQyKp4S2rVYahzJNM', 0, 0], [1715, 55, 3261, 21, 48, 'djv', 0, 6], [1781, 41, 3267, 15, 48, '3WHD', 0, 6], [1864, 124, 3261, 21, 48, '8ucAV2oj', 0, 6], [2037, 139, 3261, 27, 48, 'baUoLawp6rY', 0, 6], [2196, 76, 3261, 21, 48, 'sRheu', 6, 6], [1715, 226, 3295, 21, 49, 'hAfhkKsI7Jx', 0, 6], [1959, 234, 3295, 21, 49, 'quecbSW4gEdjSGG', 0, 6], [1715, 176, 3329, 27, 50, 'ciaZR8NxiuEXr1', 0, 6], [1910, 140, 3329, 21, 50, 'vicUyHPNcN', 0, 6]]
data_pd = pd.DataFrame(data_array,columns=["left", "width", "top", "height", "lineNr", "randomWord", "keyWord", "keyWordGroup"])
print(data_pd)
The table contains a main column randomWord and a few other columns with positional coordinates of each word within the document.
To help visualize the data. I wrote this code which makes an image out of the table for a better visualization and understanding of the problem
from PIL import Image, ImageFont, ImageDraw # pip install Pillow
import random
#Create a empty image object
new_im = Image.new('RGB', ((data_pd["left"]+data_pd["width"]).max() + data_pd["left"].min(), (data_pd["top"]+data_pd["height"]).max() + data_pd["top"].min() ), (255,255,255))
draw_new_im = ImageDraw.Draw(new_im)
#Create a dictioanry with random colors to assign for each uniq keyWordGroup
uniqGroups = data_pd["keyWordGroup"].unique()
colors = {}
for g in uniqGroups:
if(g == 0):
colors[str(g)] = "black" # assign black color for non groups
else:
colors[str(g)] = "#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) #generate random color
#Write text to the image
for i, row in data_pd.iterrows():
draw_new_im.text((int(row["left"]), int(row["top"])), str(row["randomWord"]) , fill=colors[str(row["keyWordGroup"])],font=ImageFont.truetype("arial.ttf", int(row["height"])))
#Save the image
new_im.save("TestImage.jpg")
As you can see we have the keyWord column. This column contains the IDs of some key words that we need to find the closest the block/group of text they belongs to.
The question of this post is the following: How can we identify the group/block of text closest to the key word in the keyWord column ? As you see in the image generated, for each keyWord ID we find all the words which are in the proximity and forming a block of text.
The output that i am looking for is in column keyWordGroup which is an example of which words were assign to the key words.
Is there any method we could use to find these block of text based on the keywords and the rest of the positional data given?

The solution comprises two steps:
Group words to closest keyword (I wouldn't call it clustering as the centers of the groups are already given here, as opposed to clustering where you try to find clusters with no a priori known locations)
Remove outliers that don't seem to really belong to this keyword, although this keywords is the closest by distance.
Grouping is straightforward by assigning keyword numbers by distance using vector quantization. The only thing we have to bear in mind here is that the keyword numbers in the original dataframe don't appear in ordered sequence, but in vq groups are numbered in sequence starting with 0. That's why we have to map the new keyword group numbers to the given keyword numbers.
Removing outliers can be done in different ways, and there were no strict requirements in the question on how the keyword groups should be formed. I chose a very simple approach: take the mean and standard deviation of the distances from the keyword to all keyword group members and consider words with distances greater than Mean + x * StdDev as outliers. A choice of x = 1.5 gives good results.
import pandas as pd
#Load the data
data_array = [[576, 60, 279, 28, 2, 'LzR', 0, 0], [578, 17, 318, 23, 3, 'U', 0, 0], [371, 21, 279, 24, 2, 'K', 0, 0], [373, 134, 317, 25, 3, 'mq77MJc', 0, 0], [537, 32, 317, 25, 3, '53', 0, 0], [373, 201, 355, 25, 4, '7Q7NZzkAzN', 0, 0], [538, 118, 393, 24, 5, 'oNNbgA', 0, 0], [680, 39, 392, 26, 5, 'J9', 0, 0], [1509, 155, 260, 154, 2, 'd', 0, 0], [1731, 98, 268, 123, 2, 'z8', 0, 0], [1876, 385, 271, 120, 2, 'rUqNDY', 0, 0], [1640, 197, 590, 21, 7, 't5gNVHDXQVJ', 0, 0], [1989, 270, 589, 22, 7, 't3I81fBOE9caUfb', 0, 0], [352, 80, 645, 25, 8, 'i5f3', 0, 1], [454, 245, 645, 25, 8, 'KrqcRA7Se7X7', 1, 1], [719, 60, 645, 27, 8, 'bpN', 0, 1], [1640, 161, 642, 22, 8, 'skAzt6Np4', 0, 0], [1822, 51, 643, 21, 8, 'K59', 0, 0], [2082, 177, 642, 22, 8, 'cwyN7wsMhE', 0, 0], [353, 220, 683, 25, 9, 'O8coFUwMUbE', 0, 1], [597, 17, 683, 25, 9, 'L', 0, 1], [1640, 234, 695, 22, 9, 'oVWEKowWnbT2y', 0, 0], [2080, 179, 695, 22, 9, 'FvjigCiC7h', 0, 0], [351, 79, 721, 24, 10, 'OQN3', 0, 1], [476, 202, 720, 25, 10, 'S2gcfJIDze', 0, 1], [2062, 69, 775, 22, 11, 'n9lN', 0, 0], [2155, 8, 775, 21, 11, 'G', 0, 0], [2188, 35, 775, 21, 11, '9X', 0, 0], [2246, 8, 775, 21, 11, 'v', 0, 0], [353, 81, 1003, 21, 13, 'c7ox8', 0, 0], [461, 325, 1003, 22, 13, 'o9GmMYAW4RrpPBY64p', 0, 0], [351, 101, 1037, 22, 14, '9NF7ii', 0, 0], [477, 146, 1037, 21, 14, 'MwTlIkU9', 0, 0], [350, 70, 1071, 22, 15, 'J5XF', 0, 0], [443, 87, 1071, 22, 15, '3m4tM', 0, 0], [553, 32, 1071, 22, 15, 'Ck', 0, 0], [609, 10, 1071, 22, 15, '5', 0, 0], [643, 53, 1071, 22, 15, 'X7Y', 0, 0], [1568, 135, 1092, 20, 16, 'P4', 0, 0], [352, 142, 1105, 22, 16, 'Pjs1GYSG', 0, 0], [516, 45, 1105, 22, 16, 'o9V', 0, 0], [588, 106, 1105, 22, 16, 'WRI8oY', 0, 0], [1563, 132, 1117, 20, 16, '3cZY', 0, 0], [350, 69, 1140, 21, 17, 'GW3y', 0, 0], [441, 35, 1139, 22, 17, 'EO', 0, 0], [497, 51, 1139, 28, 17, 'toN', 0, 0], [570, 49, 1140, 21, 17, 'k11', 0, 0], [643, 51, 1139, 22, 17, 'pod', 0, 0], [715, 89, 1140, 21, 17, '6SQfv', 0, 0], [825, 83, 1139, 22, 17, 'CzC2M', 0, 0], [934, 102, 1140, 21, 17, 'aowjQC', 0, 0], [1062, 51, 1140, 21, 17, 'BtC', 0, 0], [1558, 136, 1142, 20, 17, 'XhJ', 0, 0], [1722, 336, 1115, 25, 16, 'OgtXP2nxOwP7Gb3I', 0, 0], [352, 125, 1174, 21, 18, 'zYmvutc', 0, 0], [498, 45, 1174, 21, 18, 'JvN', 0, 0], [570, 124, 1174, 21, 18, 'TyZdJG4', 0, 0], [352, 64, 1207, 22, 19, 'Lvam', 0, 0], [443, 45, 1208, 21, 19, 'Onk', 0, 0], [516, 123, 1208, 21, 19, 'bPgi7tF', 0, 0], [1946, 12, 1231, 11, 20, 'I', 0, 0], [351, 106, 1241, 23, 20, 'xbAa7n', 0, 0], [479, 306, 1242, 22, 20, 'NEn7uifO17vkyzVVp', 0, 0], [1300, 142, 1242, 27, 20, 'dZukserV', 0, 0], [352, 178, 1275, 34, 21, 'qrxWKyJjjn', 0, 0], [557, 60, 1275, 28, 21, '2Ri5', 0, 0], [1354, 88, 1276, 27, 21, 'ZCp3F', 0, 0], [1558, 197, 1231, 63, 20, 'YgoGs', 0, 0], [1787, 96, 1247, 63, 20, 'Um', 0, 0], [1913, 268, 1231, 63, 20, 'YL7fkaV', 0, 0], [351, 70, 1309, 23, 22, 'kcGD', 0, 0], [443, 142, 1309, 23, 22, 'lGAx6Ljx', 0, 0], [605, 35, 1310, 21, 22, 'Hm', 0, 0], [661, 142, 1310, 27, 22, 'S8gZ5tPE', 0, 0], [1302, 135, 1310, 27, 22, 'gjgVPImz', 0, 0], [1743, 12, 1329, 11, 23, 'Z', 0, 0], [2055, 16, 1324, 17, 23, 'i', 0, 0], [353, 11, 1344, 21, 24, 'L', 0, 0], [386, 53, 1344, 21, 24, 'Q5J', 0, 0], [1300, 142, 1344, 27, 24, '9L9ScEj2', 0, 0], [1558, 400, 1345, 63, 24, 'S8YyUDnXd', 0, 0], [1993, 91, 1345, 62, 24, '4P', 0, 0], [1555, 102, 1605, 35, 25, 'kbGP', 0, 2], [1674, 371, 1605, 44, 25, 'DO1tvoEyiX9AVz6Q', 0, 2], [2062, 147, 1605, 44, 25, 'DtQAa3', 2, 2], [1554, 53, 1669, 35, 26, 'pg', 0, 2], [1624, 104, 1660, 34, 26, 'ZPsJ', 0, 2], [1746, 221, 1659, 38, 26, '7CBPYAUA', 0, 2], [1987, 50, 1657, 46, 26, 'AL', 0, 2], [1555, 407, 1714, 44, 27, 'LA3ShdHUE3DAoOkfiB', 0, 2], [188, 1826, 2340, 3, 29, '4', 0, 0], [2024, 217, 2309, 34, 28, 'DLpZXhKepjdcyW', 0, 0], [2239, 119, 2310, 33, 28, '28otEfj9', 0, 0], [230, 77, 2349, 23, 29, 'Th1YC4R', 0, 0], [476, 89, 2349, 18, 29, 'uFRt5qEx', 0, 0], [1140, 463, 2388, 35, 30, 'Mxcsoj1MOubuEB33', 0, 0], [1708, 40, 2372, 17, 30, 'OfA', 0, 9], [1758, 81, 2372, 22, 30, 'ZQoO7mwr', 0, 9], [1848, 3, 2372, 17, 30, 'M', 0, 9], [1860, 134, 2372, 22, 30, 'IvtUnQ4Zxc29A', 0, 9], [2002, 20, 2376, 13, 30, '3V', 0, 9], [2029, 32, 2372, 17, 30, '6t8', 0, 9], [2070, 133, 2372, 17, 30, 'PdCWscuWGHR', 0, 9], [1709, 171, 2398, 22, 30, 'RsW4Oj1Lhf1ljQV4G', 0, 9], [1890, 148, 2398, 22, 30, 'VSUJUa3tuYIhiXxP', 9, 9], [2048, 34, 2398, 17, 30, 'aAm', 0, 9], [2089, 21, 2403, 12, 30, 'uY', 0, 9], [2118, 53, 2398, 17, 30, '6DDFv', 0, 9], [2179, 28, 2398, 17, 30, 'DKJ', 0, 9], [2214, 66, 2398, 17, 30, 'NBmY9BD', 0, 9], [2289, 57, 2398, 18, 30, 'sYsrT', 0, 9], [1708, 25, 2425, 17, 31, 'jGk', 0, 9], [1736, 34, 2429, 13, 31, 'oX', 0, 9], [1778, 93, 2425, 17, 31, 'OvpfEyhHso', 0, 9], [120, 131, 2510, 23, 32, 'rZCsYsA6im2b', 0, 0], [260, 25, 2515, 18, 32, 'G6', 0, 0], [295, 107, 2510, 18, 32, 'd6eYwhzZuS', 0, 0], [132, 88, 2582, 22, 34, 'Xc84', 3, 3], [231, 223, 2582, 22, 34, 'MnMcBUHVmhl2', 0, 3], [463, 47, 2582, 22, 34, 'Vto', 0, 3], [132, 194, 2616, 22, 35, 'B4f1f4KpCHC', 0, 3], [338, 14, 2616, 22, 35, 'W', 0, 3], [131, 64, 2650, 22, 36, 'UW6t', 0, 3], [216, 181, 2650, 22, 36, 'hLULWi7xdj', 0, 3], [1044, 175, 2510, 18, 32, 'F9f7jvsfmjnXbK', 0, 0], [1226, 25, 2515, 18, 32, 'Vk', 0, 0], [1261, 177, 2510, 23, 32, 'TBlYLSoItzHKpG', 0, 0], [1054, 132, 2544, 22, 33, 'u4vvPgHd', 0, 0], [1053, 36, 2590, 21, 34, 'lN', 0, 4], [1101, 107, 2589, 23, 34, 'ieee4D', 0, 4], [1218, 47, 2589, 23, 34, 'kD6', 0, 4], [1054, 122, 2623, 23, 35, 'Ngf2xWa', 0, 4], [1189, 132, 2624, 22, 35, 'N27RyHsP', 0, 4], [1054, 204, 2657, 23, 36, 'e97JFxWTXfS', 0, 4], [1262, 43, 2658, 22, 36, 'p', 4, 4], [1054, 65, 2692, 22, 37, 'mle1', 0, 4], [1139, 186, 2691, 23, 37, 'o6tA5wFrK', 0, 4], [1337, 39, 2691, 23, 37, 'W3', 0, 4], [1709, 175, 2510, 18, 32, 'DQm27gIhcjmkdB', 0, 0], [1892, 25, 2515, 18, 32, '4Z', 0, 0], [1927, 176, 2510, 23, 32, 'rAP1PxzMyqkxdY', 0, 0], [1720, 132, 2544, 22, 33, 'JpsQeikW', 0, 0], [1719, 35, 2590, 21, 34, 'hD', 0, 5], [1766, 107, 2589, 23, 34, '3vzIwR', 0, 5], [1884, 47, 2589, 23, 34, 'kHw', 0, 5], [1720, 122, 2623, 23, 35, 'MYOKedL', 0, 5], [1854, 132, 2624, 22, 35, 'K8JXFVII', 5, 5], [1720, 204, 2657, 23, 36, 'bBkPRmgyfVp', 0, 5], [1928, 43, 2658, 22, 36, 'j', 0, 5], [1719, 65, 2692, 22, 37, 'RfU4', 0, 5], [1805, 185, 2691, 23, 37, 'wtK1L23Q4', 0, 5], [2003, 38, 2692, 22, 37, 'yY', 0, 5], [130, 255, 2804, 23, 38, 'jgoGjNh2DoLnb2b4PGonGvU', 0, 0], [1044, 117, 2804, 18, 38, 'qGXS7f7gRHy', 0, 0], [1168, 38, 2804, 18, 38, 'UQI', 0, 0], [1215, 102, 2804, 18, 38, 'P764bscKkx', 0, 0], [1320, 38, 2804, 18, 38, 'OtH', 0, 0], [1368, 58, 2804, 18, 38, 'VhrUJ', 0, 0], [1709, 100, 2804, 23, 38, 'zjQgoufCGU', 0, 0], [131, 55, 2852, 21, 40, 'piH', 0, 0], [198, 41, 2858, 15, 40, 'wU6P', 0, 0], [281, 124, 2852, 21, 40, 'riQCT4RX', 0, 0], [454, 138, 2852, 27, 40, 'jSAJPlWhyRE', 0, 0], [612, 77, 2852, 21, 40, 'nVS97', 0, 0], [131, 227, 2886, 21, 41, 'zExU7Poi4QW', 0, 0], [375, 235, 2886, 21, 41, 'pLTfHVP1qzb7Mh2', 0, 0], [138, 100, 2957, 15, 42, 'fv8', 0, 0], [1404, 4, 2978, 4, 42, 'B', 0, 0], [130, 103, 2975, 34, 42, 'qpg', 0, 0], [253, 252, 2974, 19, 42, 'T9SOmYWl4CUrdt8o', 0, 0], [1078, 3, 2972, 40, 42, 'S5', 0, 0], [1103, 62, 2978, 28, 42, 'L6W', 0, 0], [1181, 56, 2978, 28, 42, 'ep1', 0, 0], [1253, 118, 2978, 28, 42, 'oKhrqlI', 0, 0], [1384, 45, 2985, 21, 42, 'OyP', 0, 0], [1444, 132, 2978, 28, 42, 'mvg8Bw5', 0, 0], [1593, 55, 2972, 76, 42, 'eG', 0, 0], [218, 5, 3074, 18, 44, 'z', 0, 0], [231, 72, 3058, 18, 44, 'x1Pat7', 0, 0], [605, 5, 3074, 18, 44, 'P', 0, 0], [617, 39, 3058, 18, 44, 'dNT', 0, 0], [1053, 146, 3058, 23, 44, 'q7CLeOJhnI1oa', 0, 0], [1802, 5, 3074, 18, 44, '6', 0, 0], [1815, 72, 3058, 18, 44, 'acKa9h', 0, 0], [2119, 50, 3057, 35, 44, 'uGH', 0, 0], [461, 129, 3125, 29, 45, 'p6L5U', 0, 0], [623, 44, 3125, 29, 45, 'dC', 0, 0], [1046, 266, 3125, 29, 45, '9HBoqUyRbg', 0, 0], [1975, 129, 3125, 29, 45, 'qH1ph', 0, 0], [2136, 45, 3125, 29, 45, 'gG', 0, 0], [218, 5, 3183, 20, 46, 'j', 0, 0], [605, 5, 3183, 20, 46, 'o', 0, 0], [119, 24, 3213, 18, 47, 'QDN', 0, 8], [153, 94, 3213, 18, 47, 'EleVpvP4', 0, 8], [256, 105, 3213, 23, 47, 'dq9L2xQO7', 0, 8], [370, 7, 3223, 2, 47, 'n', 0, 8], [386, 69, 3212, 24, 47, 'L9EKl', 0, 8], [464, 83, 3213, 23, 47, 'AnF2rBIN', 0, 8], [555, 19, 3214, 17, 47, 'k6', 0, 8], [582, 62, 3213, 18, 47, 'y3M3kx', 8, 8], [654, 2, 3213, 18, 47, '1', 0, 8], [666, 139, 3212, 19, 47, 'SkmavPFrrrSv', 0, 8], [808, 52, 3213, 18, 47, 'bJ5S', 0, 8], [200, 100, 3316, 29, 50, 'NmNa', 0, 7], [336, 675, 3316, 29, 50, 'vB759g8XWkL7XXe5tCHZs7tAF', 7, 7], [1046, 42, 3203, 23, 47, 'v4T', 0, 0], [1095, 150, 3202, 19, 47, 'NH7vM6', 0, 0], [1251, 24, 3199, 22, 47, '47', 0, 0], [1802, 5, 3183, 20, 46, 'B', 0, 0], [2119, 5, 3183, 20, 46, 'b', 0, 0], [1714, 254, 3213, 23, 47, '2Za9eGyQyKp4S2rVYahzJNM', 0, 0], [1715, 55, 3261, 21, 48, 'djv', 0, 6], [1781, 41, 3267, 15, 48, '3WHD', 0, 6], [1864, 124, 3261, 21, 48, '8ucAV2oj', 0, 6], [2037, 139, 3261, 27, 48, 'baUoLawp6rY', 0, 6], [2196, 76, 3261, 21, 48, 'sRheu', 6, 6], [1715, 226, 3295, 21, 49, 'hAfhkKsI7Jx', 0, 6], [1959, 234, 3295, 21, 49, 'quecbSW4gEdjSGG', 0, 6], [1715, 176, 3329, 27, 50, 'ciaZR8NxiuEXr1', 0, 6], [1910, 140, 3329, 21, 50, 'vicUyHPNcN', 0, 6]]
data_pd = pd.DataFrame(data_array,columns=["left", "width", "top", "height", "lineNr", "randomWord", "keyWord", "keyWordGroup"])
### group words to keywords
from scipy.cluster.vq import vq
keywords = pd.concat([data_pd[data_pd.keyWord!=0].left + data_pd[data_pd.keyWord!=0].width/2, data_pd[data_pd.keyWord!=0].top + data_pd[data_pd.keyWord!=0].height/2], axis=1)
words = pd.concat([data_pd[data_pd.keyWord==0].left + data_pd[data_pd.keyWord==0].width/2, data_pd[data_pd.keyWord==0].top + data_pd[data_pd.keyWord==0].height/2], axis=1)
res = vq(words.to_numpy(),keywords.to_numpy())
### remove outliers
import numpy as np
factor = 1.5
limits = []
# calculate limit as limit = mean + factor * stddev for each keyWord
for i in range(len(keywords)):
limits.append(np.mean(res[1][res[0]==i]) + factor * np.std(res[1][res[0]==i]))
# mark words with distance > limit as outliers
for i in range(len(res[0])):
if res[1][i] > limits[res[0][i]]:
res[0][i] = -1
### assign results to dataframe
words['keyWordGroupNew'] = res[0] + 1
keywords['keyWordGroupNew'] = range(1, len(keywords) + 1)
data_pd = pd.concat([data_pd, pd.concat([words['keyWordGroupNew'],keywords['keyWordGroupNew']])], axis=1, join='outer')
# renumber keyWordGroup according to keyWord numbering
dic = dict(zip(range(1, len(keywords) + 1), data_pd[data_pd.keyWord!=0]['keyWord']))
dic[0] = 0
data_pd.keyWordGroupNew = data_pd.keyWordGroupNew.map(dic)
from PIL import Image, ImageFont, ImageDraw # pip install Pillow
import random
#Create a empty image object
new_im = Image.new('RGB', ((data_pd["left"]+data_pd["width"]).max() + data_pd["left"].min(), (data_pd["top"]+data_pd["height"]).max() + data_pd["top"].min() ), (255,255,255))
draw_new_im = ImageDraw.Draw(new_im)
#Create a dictioanry with random colors to assign for each uniq keyWordGroupNew
uniqGroups = data_pd["keyWordGroupNew"].unique()
colors = {}
i = 0
for g in uniqGroups:
if(g == 0):
colors[str(g)] = "black" # assign black color for non groups
else:
colors[str(g)] = "hsl(" + str(70 + i * 290 / (len(uniqGroups) - 2)) + ",100%,50%)"
i += 1
#Write text to the image
for i, row in data_pd.iterrows():
draw_new_im.text((int(row["left"]), int(row["top"])), str(row["randomWord"]) , fill=colors[str(row["keyWordGroupNew"])],font=ImageFont.truetype("arial.ttf", int(row["height"])))
if row["keyWord"] > 0:
draw_new_im.rectangle([row["left"] ,row["top"], row["left"]+row["width"], row["top"]+row["height"]], outline=colors[str(row["keyWordGroupNew"])])
#Save the image
new_im.save("out-std.jpg")
As you see in the code, I also made two small improvements to the image generation: uniformly distribute the colors over the hue range from yellow to red and draw frames around the keywords.
Another approach to outlier detection is called local outlier factor. This technique marks isolated words thar are not surrounded by other group members as outliers.
### group words to keywords
from scipy.cluster.vq import vq
keywords = pd.concat([data_pd[data_pd.keyWord!=0].left + data_pd[data_pd.keyWord!=0].width/2, data_pd[data_pd.keyWord!=0].top + data_pd[data_pd.keyWord!=0].height/2], axis=1)
words = pd.concat([data_pd[data_pd.keyWord==0].left + data_pd[data_pd.keyWord==0].width/2, data_pd[data_pd.keyWord==0].top + data_pd[data_pd.keyWord==0].height/2], axis=1)
res = vq(words.to_numpy(),keywords.to_numpy())
# assign results
words['keyWordGroupNew'] = res[0] + 1
keywords['keyWordGroupNew'] = range(1, len(keywords) + 1)
### remove outliers
from sklearn.neighbors import LocalOutlierFactor
clf = LocalOutlierFactor(n_neighbors=10, contamination=0.1)
for i in range(1, len(keywords)+1):
y_pred = clf.fit_predict(words[words.keyWordGroupNew==i].iloc[:,0:2].to_numpy())
words.loc[words[words.keyWordGroupNew==i][y_pred==-1].index,'keyWordGroupNew'] = 0 # mark as outlier
### save results to dataframe
data_pd = pd.concat([data_pd, pd.concat([words['keyWordGroupNew'],keywords['keyWordGroupNew']])], axis=1, join='outer')
# renumber keyWordGroup according to keyWord numbering
dic = dict(zip(range(1, len(keywords) + 1), data_pd[data_pd.keyWord!=0]['keyWord']))
dic[0] = 0
data_pd.keyWordGroupNew = data_pd.keyWordGroupNew.map(dic)
# image generation as in previous example ...
This doesn't work so well for relatively small groups and the results are visually not so good as with the other method when the keyword is located outside the center of the group.
The result for contamination = 0.1, which is a commonly used value, is here. For details see the original paper and the sklearn docs.
Conclusion: both approached give satisfactory results that can be tuned by adjusting the factor x or contamination respectively.

Encode numpy array using uncompressed RLE for COCO dataset

To create a COCO dataset of annotated images, you need to convert binary masks into either polygons or uncompressed run length encoding representations depending on the type of object.
The pycocotools library has functions to encode and decode into and from compressed RLE, but nothing for polygons and uncompressed RLE.
I can use skimage's measure library to generate polygons of masks, but I'm not sure how to create uncompressed RLEs.
I can use this RLE encoder to create a representation of RLE from an image, but I'm not sure what format COCO expects. COCO just mentions that they use a "custom Run Length Encoding (RLE) scheme"
for example,
ground_truth_binary_mask = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)
rle(fortran_ground_truth_binary_mask)
outputs:
(array([26, 36, 46, 56, 61]), array([3, 3, 3, 3, 1]))
this is what a coco RLE looks like:
{
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}
}

Information on the format is available here: https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
RLE is a simple yet efficient format for storing binary masks. RLE
first divides a vector (or vectorized image) into a series of
piecewise constant regions and then for each piece simply stores the
length of that piece. For example, given M=[0 0 1 1 1 0 1] the RLE
counts would be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would
be [0 6 1] (note that the odd counts are always the numbers of zeros).
import numpy as np
from itertools import groupby
def binary_mask_to_rle(binary_mask):
rle = {'counts': [], 'size': list(binary_mask.shape)}
counts = rle.get('counts')
for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
if i == 0 and value == 1:
counts.append(0)
counts.append(len(list(elements)))
return rle
test_list_1 = np.array([0, 0, 1, 1, 1, 0, 1])
test_list_2 = np.array([1, 1, 1, 1, 1, 1, 0])
print(binary_mask_to_rle(test_list_1))
print(binary_mask_to_rle(test_list_2))
output:
{'counts': [2, 3, 1, 1], 'size': [7]}
{'counts': [0, 6, 1], 'size': [7]}
You can use mask.frPyObjects(rle, size_x, size_y) to encode the RLE, and then do all the usual mask operations.
import json
import numpy as np
from pycocotools import mask
from skimage import measure
ground_truth_binary_mask = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)
encode the mask to RLE:
rle = binary_mask_to_rle(fortran_ground_truth_binary_mask)
print(rle)
output:
{'counts': [6, 1, 40, 4, 5, 4, 5, 4, 21], 'size': [9, 10]}
compress the RLE, and then decode:
compressed_rle = mask.frPyObjects(rle, rle.get('size')[0], rle.get('size')[1])
mask.decode(compressed_rle)
output:
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

As an improvement of #waspinator 's answer. This is 35% faster.
def binary_mask_to_rle(binary_mask):
rle = {'counts': [], 'size': list(binary_mask.shape)}
counts = rle.get('counts')
last_elem = 0
running_length = 0
for i, elem in enumerate(binary_mask.ravel(order='F')):
if elem == last_elem:
pass
else:
counts.append(running_length)
running_length = 0
last_elem = elem
running_length += 1
counts.append(running_length)
return rle

In order to decode the binary masks encoded in the COCO annotations, you need to first use the COCO's API to get the RLE and then use opencv to get the contours like this:
# Import libraries
import numpy as np
import cv2
import json
import mask
# Read the annotations
file_path = "coco/annotations/stuff_annotations_trainval2017/stuff_train2017.json"
with open(file_path, 'r') as f:
data = json.load(f)
updated_data = []
# For each annotation
for annotation in data['annotations']:
# Initialize variables
obj = {}
segmentation = []
segmentation_polygons = []
# Decode the binary mask
mask_list = mask.decode(annotation['segmentation'])
mask_list = np.ascontiguousarray(mask_list, dtype=np.uint8)
mask_new, contours, hierarchy = cv2.findContours((mask_list).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Get the contours
for contour in contours:
contour = contour.flatten().tolist()
segmentation.append(contour)
if len(contour) > 4:
segmentation.append(contour)
if len(segmentation) == 0:
continue
# Get the polygons as (x, y) coordinates
for i, segment in enumerate(segmentation):
poligon = []
poligons = []
for j in range(len(segment)):
poligon.append(segment[j])
if (j+1)%2 == 0:
poligons.append(poligon)
poligon = []
segmentation_polygons.append(poligons)
# Save the segmentation and polygons for the current annotation
obj['segmentation'] = segmentation
obj['segmentation_polygons'] = segmentation_polygons
updated_data.append(obj)
Note: Only the COCO stuff 2017 annotations are using binary masks, the COCO person 2017 annotations aren't, so you don't need to decode the latter and find their contours.
Inspired by this solution.

Do it like this:
import numpy as np
from pycocotools import mask as m
# Create bool array
n_a = np.random.normal(size=(10, 10))
b_a = np.array(n_a > 0.5, dtype=np.bool, order='F')
# Encode bool array
e_a = m.encode(b_a)
d_a = m.decode(e_a)
# Decode byte string rle encoded mask
sz = pred['mask']['size']
c = pred['mask']['counts'][2:-1]
es = str.encode(c)
t = {'size': [450, 800], 'counts': es}
dm = m.decode(t)

difference between all values in csv columns - python

At first, I'm sorry for my so poor level in python,
so I have the next problem:
1) I red a lot of answers at this resource, but nothing work for me
(np.abs(a.values[:,np.newaxis]-a2.values) and simple np.diff() and a lot of another ways)
2!) I have csv file the following form:
A 12 43 51 10 74
B 14 32 31 27 23
C 13 62 13 33 82
D 18 31 73 70 42
and I need receive residual between all columns in raws, so
A:12-43 12-51 12-10 12-74... 43-12 43-51 43-10 43-74...
B:12-43 12-51 12-10 12-74... 43-12 43-51 43-10 43-74...
after that I need power 2 in 12-43 12-51 12-10 12-74... 43-12 43-51 43-10 43-74...
I understand, that pandas good works with tables, but how I can to make this?
And if you can, please in what way I need go, that to cut-off 10% of extreme results? Thank you very much for you attention and feature help.

pandas doesn't easily accept arrays as elements, so numpy is a good help here.
First make all the differences, by line (axis=1) :
data="""
A 12 43 51 10 74
B 14 32 31 27 23
C 13 62 13 33 82
D 18 31 73 70 42
"""
pd.read_table(io.StringIO(data),header=None,index_col=0,sep=' ')
all_differences=np.apply_along_axis(lambda x:np.subtract.outer(x,x).ravel(),axis=1,arr=df)
Then sort for cut-off :
all_differences.sort(axis=1)
and select the good values, discarding the 0 resulting from L[i]-L[i].
n=df.shape[1]
cutoff =[i for i in range(n*n) if n*n*5//100<=i<n*(n-1)//2 or n*(n+1)//2<=i<n*n*95//100]
res=2.**all_differences[:,cutoff]

I suggest using numpy. For computing the difference you can do
>>> a = numpy.array([[12, 43, 51, 10, 74],
... [14, 32, 31, 27, 23],
... [13, 62, 13, 33, 82],
... [18, 31, 73, 70, 42]])
>>> difference_matrix = numpy.repeat(a, a.shape[-1], axis=-1) - numpy.tile(a, a.shape[-1])
>>> difference_matrix
array([[ 0, -31, -39, 2, -62, 31, 0, -8, 33, -31, 39, 8, 0,
41, -23, -2, -33, -41, 0, -64, 62, 31, 23, 64, 0],
[ 0, -18, -17, -13, -9, 18, 0, 1, 5, 9, 17, -1, 0,
4, 8, 13, -5, -4, 0, 4, 9, -9, -8, -4, 0],
[ 0, -49, 0, -20, -69, 49, 0, 49, 29, -20, 0, -49, 0,
-20, -69, 20, -29, 20, 0, -49, 69, 20, 69, 49, 0],
[ 0, -13, -55, -52, -24, 13, 0, -42, -39, -11, 55, 42, 0,
3, 31, 52, 39, -3, 0, 28, 24, 11, -31, -28, 0]])
If you want to square the result you can simply apply it to the matrix and each element will be squared:
>>> difference_matrix ** 2
array([[ 0, 961, 1521, 4, 3844, 961, 0, 64, 1089, 961, 1521,
64, 0, 1681, 529, 4, 1089, 1681, 0, 4096, 3844, 961,
529, 4096, 0],
[ 0, 324, 289, 169, 81, 324, 0, 1, 25, 81, 289,
1, 0, 16, 64, 169, 25, 16, 0, 16, 81, 81,
64, 16, 0],
[ 0, 2401, 0, 400, 4761, 2401, 0, 2401, 841, 400, 0,
2401, 0, 400, 4761, 400, 841, 400, 0, 2401, 4761, 400,
4761, 2401, 0],
[ 0, 169, 3025, 2704, 576, 169, 0, 1764, 1521, 121, 3025,
1764, 0, 9, 961, 2704, 1521, 9, 0, 784, 576, 121,
961, 784, 0]])