pretty new to Python in general, but hopefully my question will make sense.

I have a list that contains lists with irregular lengths, and I am trying to cover the list into a data frame structure so I can save it as a CSV. I want to also make sure i have a way of combining the lists that are within their family lists by adding IDs so i can combine them later.

An overview: i ran easyocr on several images which extract texts from images, so i want the imageID so i can find the text and relate it back to the image it was nested within.

An example of data looks like this:

[[],
 [],
 [([[163, 165], [219, 165], [219, 185], [163, 185]],
   'HXRESLAGET',
   0.6614451489762804)],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [([[185, 47], [257, 47], [257, 63], [185, 63]],
   'Ahabngue',
   0.0021960531212282365),
  ([[330.01941932430907, 26.803883864861817],
    [375.8479983040051, 14.47000105999682],
    [378.98058067569093, 29.196116135138183],
    [334.1520016959949, 40.52999894000318]],
   '',
   0.0),
  ([[281.0, 40.0],
    [331.87056283872016, 26.507942743332098],
    [335.0, 40.0],
    [284.12943716127984, 53.4920572566679]],
   'o',
   0.026024344610394934)],
 [],
 [],
 [([[71.60127388858555, 57.08292994374676],
    [116.99796908286363, 67.93629984577466],
    [113.39872611141445, 81.91707005625324],
    [68.00203091713637, 71.06370015422534]],
   'eiao]',
   0.04165130315364712)],
 []]

The structure that I am seeking as an end goal is this:

I tried:

results_bound = []
for i in results_:
    for j in i:
      results_bound.append(results_[i][j][0])

and received, which i know doesn't work, clearly, as i was just trying to do one list at a time (the bounding box column), but my main goal was to combine them all together:

TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_18959/2194324409.py in <module>
      2 for i in results_:
      3     for j in range(1,20):
----> 4       results_bound.append(results_[i][j][0])

TypeError: list indices must be integers or slices, not list

hopefully the question is ok, let me know if you need anymore details or information.

CodePudding user response：

First you could use print() to see what you have in variables - it helps to see what is needed in code.

Using for-loops (without range()) you get lists, not indexes, so results_[i][j][0] is wrong.

If you have empty list then you should skip them - ie. if i: .... to run code only when i is not empty.

data = [[],
 [],
 [([[163, 165], [219, 165], [219, 185], [163, 185]],
   'HXRESLAGET',
   0.6614451489762804)],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [],
 [([[185, 47], [257, 47], [257, 63], [185, 63]],
   'Ahabngue',
   0.0021960531212282365),
  ([[330.01941932430907, 26.803883864861817],
    [375.8479983040051, 14.47000105999682],
    [378.98058067569093, 29.196116135138183],
    [334.1520016959949, 40.52999894000318]],
   '',
   0.0),
  ([[281.0, 40.0],
    [331.87056283872016, 26.507942743332098],
    [335.0, 40.0],
    [284.12943716127984, 53.4920572566679]],
   'o',
   0.026024344610394934)],
 [],
 [],
 [([[71.60127388858555, 57.08292994374676],
    [116.99796908286363, 67.93629984577466],
    [113.39872611141445, 81.91707005625324],
    [68.00203091713637, 71.06370015422534]],
   'eiao]',
   0.04165130315364712)],
 []]

# ---

number = 0

output_data = []

for row in data:
    #print(row)

    if row: # skip empty list

        for item in row:
            #print(item)

            number  = 1
            boundingbox, letters, confidence = item

            print('image number:', number)
            print('boundingbox:', boundingbox)
            print('letters:', letters)
            print('confidence:', confidence)
            print('----')

            output_data.append( [number, boundingbox, letters, confidence] )
            
# ---

import pandas as pd

df = pd.DataFrame(output_data, columns=['number', 'boundingbox', 'letters', 'confidence'])

print(df.to_string())

Result

image number: 1
boundingbox: [[163, 165], [219, 165], [219, 185], [163, 185]]
letters: HXRESLAGET
confidence: 0.6614451489762804
----
image number: 2
boundingbox: [[185, 47], [257, 47], [257, 63], [185, 63]]
letters: Ahabngue
confidence: 0.0021960531212282365
----
image number: 3
boundingbox: [[330.01941932430907, 26.803883864861817], [375.8479983040051, 14.47000105999682], [378.98058067569093, 29.196116135138183], [334.1520016959949, 40.52999894000318]]
letters: 
confidence: 0.0
----
image number: 4
boundingbox: [[281.0, 40.0], [331.87056283872016, 26.507942743332098], [335.0, 40.0], [284.12943716127984, 53.4920572566679]]
letters: o
confidence: 0.026024344610394934
----
image number: 5
boundingbox: [[71.60127388858555, 57.08292994374676], [116.99796908286363, 67.93629984577466], [113.39872611141445, 81.91707005625324], [68.00203091713637, 71.06370015422534]]
letters: eiao]
confidence: 0.04165130315364712
----



   number                                                                                                                                                           boundingbox     letters  confidence
0       1                                                                                                                      [[163, 165], [219, 165], [219, 185], [163, 185]]  HXRESLAGET    0.661445
1       2                                                                                                                          [[185, 47], [257, 47], [257, 63], [185, 63]]    Ahabngue    0.002196
2       3  [[330.01941932430907, 26.803883864861817], [375.8479983040051, 14.47000105999682], [378.98058067569093, 29.196116135138183], [334.1520016959949, 40.52999894000318]]                0.000000
3       4                                                      [[281.0, 40.0], [331.87056283872016, 26.507942743332098], [335.0, 40.0], [284.12943716127984, 53.4920572566679]]           o    0.026024
4       5    [[71.60127388858555, 57.08292994374676], [116.99796908286363, 67.93629984577466], [113.39872611141445, 81.91707005625324], [68.00203091713637, 71.06370015422534]]       eiao]    0.041651