Data Purchasing Challenge 2022

Exploration, Body Paint Color Dominance, Image Transforms

Simple data exploration, extracting body paint by color dominance from an image, and image transformation to show dent & scratch features.

Please comment and leave some ❤️ for this notebook if this interesting, helps you, or anything positive!

Any feedback is welcome on the comment.

DATA EXPLORATION, BODY PAINT COLOR DOMINANCE, IMAGE TRANSFORM¶

By : Leo C. D.

Preparation¶

In [93]:

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import cv2
from PIL import Image
import logging

In [4]:

!wget -O "training.tar.gz" "https://s3.us-west-002.backblazeb2.com/aicrowd-private-datasets/zew-data-purchasing-challenge/v0.1/public/training.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=002ae2491b744be0000000002%2F20220208%2Fus-west-002%2Fs3%2Faws4_request&X-Amz-Date=20220208T120105Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=0c022cd224e11e87b71f94ab3a43a151273656f1b83e331538ece80620552f82"

The name is too long, 289 chars total.
Trying to shorten...
New name is training.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=002ae2491b744be0000000002%2F20220208%2Fus-west-002%2Fs3%2Faws4_request&X-Amz-Date=20220208T120105Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=0c022cd224e.
--2022-02-08 12:01:43--  https://s3.us-west-002.backblazeb2.com/aicrowd-private-datasets/zew-data-purchasing-challenge/v0.1/public/training.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=002ae2491b744be0000000002%2F20220208%2Fus-west-002%2Fs3%2Faws4_request&X-Amz-Date=20220208T120105Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=0c022cd224e11e87b71f94ab3a43a151273656f1b83e331538ece80620552f82
Resolving s3.us-west-002.backblazeb2.com (s3.us-west-002.backblazeb2.com)... 206.190.215.254
Connecting to s3.us-west-002.backblazeb2.com (s3.us-west-002.backblazeb2.com)|206.190.215.254|:443... connected.
HTTP request sent, awaiting response... 200 
Length: 319257705 (304M) [application/gzip]
Saving to: ‘training.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=002ae2491b744be0000000002%2F20220208%2Fus-west-002%2Fs3%2Faws4_request&X-Amz-Date=20220208T120105Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=0c022cd224e’

training.tar.gz?X-A 100%[===================>] 304.47M  37.7MB/s    in 8.4s    

2022-02-08 12:01:51 (36.3 MB/s) - ‘training.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=002ae2491b744be0000000002%2F20220208%2Fus-west-002%2Fs3%2Faws4_request&X-Amz-Date=20220208T120105Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=0c022cd224e’ saved [319257705/319257705]

In [5]:

!tar -xzvf "/content/training.tar.gz" -C "/content/"
print("")

Streaming output truncated to the last 5000 lines.
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Data Exploration¶

In [6]:

labelmet = pd.read_csv('./training/labels.csv')
labelmet

Out[6]:

	filename	scratch_small	scratch_large	dent_small	dent_large
0	002YxUqF3Q.png	0	0	0	0
1	00Fo8XYcvC.png	0	0	0	0
2	02s1G8Wwg8.png	0	0	0	0
3	035EI0mrFh.png	0	0	0	0
4	0385gp8ksf.png	1	0	0	0
...	...	...	...	...	...
4995	zyRjR0D5Yn.png	0	0	0	0
4996	zyie8t5n7g.png	0	0	1	0
4997	zzWOcUMi3v.png	1	0	0	0
4998	zzuXJSMmRM.png	1	1	0	0
4999	zzur7bTyHG.png	0	0	0	0

5000 rows × 5 columns

Adding label which show picture with no mark

In [7]:

labelmet['no_mark'] =(labelmet.iloc[:,1:] == 0).all(1).astype(int)

In [8]:

labelmet.describe()

Out[8]:

	scratch_small	scratch_large	dent_small	dent_large	no_mark
count	5000.00000	5000.000000	5000.000000	5000.000000	5000.000000
mean	0.25460	0.171000	0.245200	0.117800	0.476000
std	0.43568	0.376547	0.430249	0.322403	0.499474
min	0.00000	0.000000	0.000000	0.000000	0.000000
25%	0.00000	0.000000	0.000000	0.000000	0.000000
50%	0.00000	0.000000	0.000000	0.000000	0.000000
75%	1.00000	0.000000	0.000000	0.000000	1.000000
max	1.00000	1.000000	1.000000	1.000000	1.000000

Counting the number of samples each label

In [9]:

label_counts = labelmet.drop(["filename"],axis=1).sum(axis=0).sort_values(ascending=False)
plt.figure(figsize=(5,5))
sns.barplot(y=label_counts.index.values, x=label_counts.values, order=label_counts.index)
plt.xlabel('number samples')
plt.show()

Number of samples with its respective number of label

In [12]:

labelmet["tot_true"] = labelmet.drop(["filename"],axis=1).iloc[:,:4].sum(axis=1)
count_per = np.round(100 * labelmet["tot_true"].value_counts() / labelmet.shape[0], 2)
plt.figure(figsize=(10,5))
sns.barplot(x=count_per.index.values, y=count_per.values, palette="tab10")
plt.xlabel("Number of mark label (+) per image")
plt.ylabel("% data from dataset")
plt.show()

As you can see, the image which consist multiple mark are really few.

Now let's see the correlation of between mark / label

In [15]:

plt.figure(figsize=(8,8))
sns.heatmap(labelmet[labelmet.tot_true>1].drop(["filename", "tot_true", "no_mark"],axis=1).corr(),annot=True, vmin=-1, vmax=1)
plt.show()

As you can see small dent got negative correlation with small scratch, the same with large dent and large scratch

Dominant / Base Color¶

Now let's see the base paint of the image, there are a lot of method like for example below :

In [16]:

#https://stackoverflow.com/questions/3241929/python-find-dominant-most-common-color-in-an-image
def get_dominant_color(pil_img, palette_size=16):
    # Resize image
    img = pil_img.copy()
    img.thumbnail((100, 100))

    # Reduce colors
    paletted = img.convert('P', palette=Image.ADAPTIVE, colors=palette_size)

    # Find the color most often
    palette = paletted.getpalette()
    color_counts = sorted(paletted.getcolors(), reverse=True)
    palette_index = color_counts[0][1]
    dominant_color = palette[palette_index*3:palette_index*3+3]

    return dominant_color

And the result like this :

In [24]:

plt.figure(figsize=(20, 20))
for i in range(18):
    ax = plt.subplot(6, 6, 2*i + 1)
    fname = './training/images/' + labelmet.iloc[i+30,0]
    im = Image.open(fname)
    plt.imshow(im)
    plt.title(labelmet.iloc[i,0])
    plt.axis("off")
    ax = plt.subplot(6, 6, 2*(i+1))
    dominant_color = get_dominant_color(im, palette_size=16)
    domcolordata = np.expand_dims(np.vstack((np.array(dominant_color), np.array(dominant_color))),axis=0)
    plt.imshow(domcolordata)
    plt.title('dominant color')
    plt.axis("off")

But as you can see, there is a problem with light glare and shadow so sometimes the result is not the actual body paint or dominant color.

Let's filter those first :

In [25]:

#modified from https://stackoverflow.com/questions/50899692/most-dominant-color-in-rgb-image-opencv-numpy-python
def im_glarefilt_domcol(a):
    flat = a.reshape(-1,a.shape[-1])
    a2D = flat[np.all(flat < 190, axis = 1)]
    a2D = a2D[np.any(a2D > 80, axis = 1)]
    col_range = (256, 256, 256) 
    a1D = np.ravel_multi_index(a2D.T, col_range)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

In [27]:

plt.figure(figsize=(20, 20))
for i in range(18):
    ax = plt.subplot(6, 6, 2*i + 1)
    fname = './training/images/' + labelmet.iloc[i+30,0]
    im = Image.open(fname)
    plt.imshow(im)
    plt.title(labelmet.iloc[i,0])
    plt.axis("off")
    ax = plt.subplot(6, 6, 2*(i+1))
    cv_image = np.array(im)
    dominant_color = im_glarefilt_domcol(cv_image)
    domcolordata = np.expand_dims(np.vstack((np.array(dominant_color), np.array(dominant_color))),axis=0)
    plt.imshow(domcolordata)
    plt.title('dominant color')
    plt.axis("off")