Vfrdtyky

I am trying to convert the JSON in the bbox (bounding box) column into a simple array of values for a DL project in python in a Jupyter notebook.

The possible labels are the following categories: [glass, cardboard, trash, metal, paper].

[{"left":191,"top":70,"width":183,"height":311,"label":"glass"}]



TO



([191 70 183 311], 0)

I'm looking for help to convert the bbox column from the JSON object for a single CSV that contains all the image names and the related bboxes.

UPDATE

The current column is a series so I keep getting a "TypeError: the JSON object must be str, bytes or bytearray, not 'Series'" any time I try to apply JSON operations on the column. So far I have tried to convert the column into JSON object and then pull out the values from the keys.

BB_CSV

You'll want to use a JSON decoder: https://docs.python.org/3/library/json.html

import json

li = json.loads('''[{"left":191,"top":70,"width":183,"height":311,"label":"glass"}]''')

d = dictionary = li[0]

result = ([d[key] for key in "left top width height".split()], 0)

print(result)

Edit:

If you want map the operation of extracting the values from the dictionary to all element of the list, you can do:

extracted = 

for element in li:

    result = ([element[key] for key in "left top width height".split()], 0)

    extracted.append(result)



# print(extracted)

print(extracted[:10])

# `[:10]` is there to limit the number of item displayed to 10

Similarly, as per my comment, if you do not want commas between the extracted numbers in the list, you can use:

without_comma = 

for element, zero in extracted:

    result_string = "([{}], 0)".format(" ".join([str(value) for value in element]))

    without_comma.append(result_string)

It looks like each row of your bbox column contains a dictionary inside of a list. I've tried to replicate your problem as follows. Edit: Clarifying that the below solution assumes that what you're referring to as a "JSON object" is represented as a list containing a single dictionary, which is what it appears to be per your example and screenshot.

# Create empty sample DataFrame with one row

df = pd.DataFrame([None],columns=['bbox'])



# Assign your sample item to the first row

df['bbox'][0] = [{"left":191,"top":70,"width":183,"height":311,"label":"glass"}]

Now, to simple unpack the row you can do:

df['bbox_unpacked'] = df['bbox'].map(lambda x: x[0].values())

Which will get you a new column with a tuple of 5 items.

If you want to go further and apply your labels, you'll likely want to create a dictionary to contain your labeling logic. Per the example you're given in the comments, I've done:

labels = {

    'cardboard': 1,

    'trash': 2,

    'glass': 3

}

This should get your your desired layout if you want a one-line solution without writing your own function.

df['bbox_unpacked'] = df['bbox'].map(lambda x: (list(x[0].values())[:4],labels.get(list(x[0].values())[-1])))

A more readable solution would be to define your own function using the .apply() method. Edit: Since it looks like your JSON object is being stored as a str inside your DataFrame rows, I added json.loads(row) to process the string first before retrieving the keys. You'll need to import json to run.

import json    



def unpack_bbox(row, labels):



    # load the string into a JSON object (in this

    # case a list of length one containing the dictionary;

    # index the list to its first item [0] and use the .values()

    # dictionary method to access the values only 



    keys = list(json.loads(row)[0].values())



    bbox_values = keys[:4]

    bbox_label = keys[-1]



    label_value = labels.get(bbox_label)



    return bbox_values, label_value



df['bbox_unpacked'] = df['bbox'].apply(unpack_bbox,args=(labels,))