CODEWITHSUNDEEP
pythonexcel
A_K
A_K

Reputation: 912

Fixing error 'slice' error to add list of elements in separate Excel cells

I am trying to get the list of colours in an image to be listed in separate cells in an excel sheet along with the count and percentage

I have managed to transfer data to excel sheet but it was all combined in one cell. I have searched how to do it but now I am getting

TypeError: unhashable type: 'slice'

Here is what I have tried

import pandas as pd
from PIL import Image
from collections import Counter
import prettytable

img = Image.open("Original 2.JPG")
size = w, h = img.size
data = img.load()

colors = []
for x in range(w):
    for y in range(h):
        color = data[x, y]
        hex_color = '#'+''.join([hex(c)[2:].rjust(2, '0') for c in color])
        colors.append(hex_color)

#pt = prettytable.PrettyTable(['Color', 'Count', 'Percentage'])

total = w * h

for color, count in Counter(colors).items():
    percent = int(count/total * 100)
    if percent > 0:
        #         pt.add_row([color, count, percent])

        # print(pt, total)

        final = {'colors': [colors],
                 'count': [count],
                 'percent': [percent]
                 }

        df = pd.DataFrame()
        df['colors'] = final[0::3]   <--------------Error returning from here
        df['count'] = final[1::3]
        df['percent'] = final[2::3]

        df.to_excel(r'C:\Users\Ahmed\Desktop\Project\export_dataframe.xlsx',
                    index=False, header=True)

Upvotes: 1

Views: 74

Answers (2)

GordonAitchJay
GordonAitchJay

Reputation: 4860

The code within your for loop really doesn't make sense.

This if statement is always true, which makes it redundant: if percent > 0: Edit: Ignore this. The call to int can return 0, which would cause this if statement to be false.

Everything below that, including writing to an excel file, is executed for every colour. Presumably this is an indentation error.

df['colors'] = final[0::3]   <--------------Error returning from here

final is a dict. You need to access it using one of its 3 keys. For example: final['colors'], which would return the entire list of pixel colours, including duplicates.

What you want can be achieved with this code:

import pandas as pd
from PIL import Image
from collections import Counter
import prettytable

img = Image.open("Original 2.JPG")
size = w, h = img.size
data = img.load()

colors = []
for x in range(w):
    for y in range(h):
        color = data[x, y]
        hex_color = '#'+''.join([hex(c)[2:].rjust(2, '0') for c in color])
        colors.append(hex_color)

#pt = prettytable.PrettyTable(['Color', 'Count', 'Percentage'])

total = w * h

colors, counts = zip(*Counter(colors).items())
percentages = tuple(count / total for count in counts)

df = pd.DataFrame()
df['colors'] = colors
df['count'] = counts
df['percent'] = percentages

df.to_excel(r'C:\Users\Ahmed\Desktop\Project\export_dataframe.xlsx',
            index=False, header=True)

The 2 key lines are:

colors, counts = zip(*Counter(colors).items())
percentages = tuple(count / total for count in counts)

The first line creates 2 tuples with all the unique colours and their counts. A tuple is basically an immutable list. zip combined with the * unpacking operator is used to transform the key and value pairs from Counter(colors).items() to their own separate tuples.

The second line creates a tuple from a generator expression which gives us the percentages of all the colours.

colors, counts, and percentages are all aligned so the same index refers to the same colour.

Upvotes: 1

Kamil
Kamil

Reputation: 686

I decided to use lists over dictionary. I have not seen any particular advantage. I also removed both removed int() from:

percent = int(count/total * 100)

and

if percent > 0:

Because if you have image with many hues of colours, the condition would never be passed.

The complete code is as follows:

import pandas as pd
from PIL import Image
from collections import Counter

img = Image.open("Original 2.JPG")
size = w, h = img.size
data = img.load()

colors = []
for x in range(w):
    for y in range(h):
        color = data[x, y] 
        hex_color = '#'+''.join([hex(c)[2:].rjust(2, '0') for c in color])
        colors.append(hex_color)

total = w * h

color_hex = []
color_count = []
color_percent =[]

df = pd.DataFrame()
for color, count in Counter(colors).items():
    percent = count/total * 100 # Do not make it int. Majority of colors are < 1%, unless you want >= 1%
    color_hex.append(color)
    color_count.append(count)
    color_percent.append(percent)

df['color'] = color_hex
df['count'] = color_count
df['percent'] = color_percent


df.to_excel(r'C:\Users\Ahmed\Desktop\Project\export_dataframe.xlsx',
                    index=False, header=True)

Upvotes: 2

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