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Data Purchasing Challenge 2022

Exploration, Body Paint Color Dominance, Image Transforms

Data Exploration, Extracting Body Paint by Color Dominance from image, and Image Transformation

leocd

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")

Nice!

If you want to consider the amount of body paint color as you buy the extra 3000 label, you can use this method.

Below code to save it to a list.

In [ ]:
rgblist = []
for i in range(len(labelmet)):
    if i%100==0:
        print('get dominant color :',i,'of',len(labelmet))
    fname = './training/images/' + labelmet.iloc[i,0]
    im = Image.open(fname)
    cv_image = np.array(im)
    dominant_color = im_glarefilt_domcol(cv_image)
    rgblist.append(dominant_color)

get dominant color : 0 of 5000
get dominant color : 100 of 5000
get dominant color : 200 of 5000
get dominant color : 300 of 5000
get dominant color : 400 of 5000
get dominant color : 500 of 5000
get dominant color : 600 of 5000
get dominant color : 700 of 5000
get dominant color : 800 of 5000
get dominant color : 900 of 5000
get dominant color : 1000 of 5000
get dominant color : 1100 of 5000
get dominant color : 1200 of 5000
get dominant color : 1300 of 5000
get dominant color : 1400 of 5000
get dominant color : 1500 of 5000
get dominant color : 1600 of 5000
get dominant color : 1700 of 5000
get dominant color : 1800 of 5000
get dominant color : 1900 of 5000
get dominant color : 2000 of 5000
get dominant color : 2100 of 5000
get dominant color : 2200 of 5000
get dominant color : 2300 of 5000
get dominant color : 2400 of 5000
get dominant color : 2500 of 5000
get dominant color : 2600 of 5000
get dominant color : 2700 of 5000
get dominant color : 2800 of 5000
get dominant color : 2900 of 5000
get dominant color : 3000 of 5000
get dominant color : 3100 of 5000
get dominant color : 3200 of 5000
get dominant color : 3300 of 5000
get dominant color : 3400 of 5000
get dominant color : 3500 of 5000
get dominant color : 3600 of 5000
get dominant color : 3700 of 5000
get dominant color : 3800 of 5000
get dominant color : 3900 of 5000
get dominant color : 4000 of 5000
get dominant color : 4100 of 5000
get dominant color : 4200 of 5000
get dominant color : 4300 of 5000
get dominant color : 4400 of 5000
get dominant color : 4500 of 5000
get dominant color : 4600 of 5000
get dominant color : 4700 of 5000
get dominant color : 4800 of 5000
get dominant color : 4900 of 5000

Image Transform

Let's check some methods to transform the image into something that shows the mark feature.

As a representation, let's choose the image which got all kind of marks.

In [28]:
labelmet[labelmet['tot_true']==4]
Out[28]:
filename scratch_small scratch_large dent_small dent_large no_mark tot_true
123 1VLlcKVW6b.png 1 1 1 1 0 4
165 28ZzLmdlCo.png 1 1 1 1 0 4
296 3i3kcBrgy6.png 1 1 1 1 0 4
326 45Zf5yJ4Gi.png 1 1 1 1 0 4
725 9w9WMn5P6M.png 1 1 1 1 0 4
785 AbMTNqvZjL.png 1 1 1 1 0 4
1055 E9OhNLe9jA.png 1 1 1 1 0 4
1317 H9NAvyXNfJ.png 1 1 1 1 0 4
1452 IsLf7liu1S.png 1 1 1 1 0 4
1586 KVPBIApzSL.png 1 1 1 1 0 4
1627 KyBqaqeko3.png 1 1 1 1 0 4
1751 MJZ1S4Q5HP.png 1 1 1 1 0 4
1791 MlSejftEPD.png 1 1 1 1 0 4
1977 OvZrcl7tL5.png 1 1 1 1 0 4
2093 QVrPYnsyv5.png 1 1 1 1 0 4
2111 QijD8aJKyS.png 1 1 1 1 0 4
2141 R6qGOzppau.png 1 1 1 1 0 4
2152 RACXBQ9vuA.png 1 1 1 1 0 4
2230 S4xDJ0Zo2v.png 1 1 1 1 0 4
2240 S94W5hhS7s.png 1 1 1 1 0 4
2282 SgntsTQMKA.png 1 1 1 1 0 4
2390 TxVVgQKpCJ.png 1 1 1 1 0 4
2405 UEH00eXqzz.png 1 1 1 1 0 4
2406 UEchKLp992.png 1 1 1 1 0 4
2426 UTjCUZm7pu.png 1 1 1 1 0 4
2433 UZ7CpUnNNv.png 1 1 1 1 0 4
2477 V35WukkrLI.png 1 1 1 1 0 4
2603 Wax1U2UcSZ.png 1 1 1 1 0 4
2657 XA9oPThMwd.png 1 1 1 1 0 4
2902 aKCvoOQNeo.png 1 1 1 1 0 4
3043 c5S2gikVwj.png 1 1 1 1 0 4
3095 cZei7D5wm2.png 1 1 1 1 0 4
3124 cwlAfKmKa3.png 1 1 1 1 0 4
3167 dcT23CSHU6.png 1 1 1 1 0 4
3453 hJAFcLrvHY.png 1 1 1 1 0 4
3514 i56Q9aKAVK.png 1 1 1 1 0 4
3650 jb8L5mBCqI.png 1 1 1 1 0 4
3655 jgYsaPciCK.png 1 1 1 1 0 4
3666 jmFZJTPUG0.png 1 1 1 1 0 4
3721 kTJWcBS0Rd.png 1 1 1 1 0 4
3751 klK3MMSRBp.png 1 1 1 1 0 4
3792 lOcPrWQDLp.png 1 1 1 1 0 4
3968 njuNs367dr.png 1 1 1 1 0 4
3985 o0ZBnSOocE.png 1 1 1 1 0 4
4005 oCl7366jrm.png 1 1 1 1 0 4
4543 uk9Dsu2R8P.png 1 1 1 1 0 4
4605 vNtd8Ron41.png 1 1 1 1 0 4
4764 xComoNfvbP.png 1 1 1 1 0 4
4818 xrQLs3dbIQ.png 1 1 1 1 0 4
4827 xxQ3JU51MI.png 1 1 1 1 0 4
4934 zCOka7SbNd.png 1 1 1 1 0 4

checking the image :

In [33]:
fname = labelmet[labelmet['tot_true']==4].iloc[0,0]
path = './training/images/' + fname
im = Image.open(path)
cv_image = np.array(im)
plt.imshow(cv_image)
plt.show()

Laplacian Edge Detection

In [106]:
lap_image = cv2.Laplacian(cv2.cvtColor(cv_image, cv2.COLOR_RGB2GRAY),cv2.CV_64F)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(cv_image)
ax1.set_title(fname)
ax2.imshow(lap_image,cmap='gray')
ax1.axis("off")
ax2.axis("off")
plt.show()
In [104]:
lap_image2 = cv2.Laplacian(cv_image,cv2.CV_64F)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(cv_image)
ax1.set_title(fname)
ax2.imshow(lap_image2)
ax1.axis("off")
ax2.axis("off")
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Sobel Edge Detection Color

In [103]:
sobelx_image = cv2.Sobel(cv2.cvtColor(cv_image, cv2.COLOR_RGB2GRAY),cv2.CV_64F,1,0,ksize=5)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(cv_image)
ax1.set_title(fname)
ax2.imshow(sobelx_image,cmap='gray')
ax1.axis("off")
ax2.axis("off")
plt.show()
In [105]:
sobely_image = cv2.Sobel(cv2.cvtColor(cv_image, cv2.COLOR_RGB2GRAY),cv2.CV_64F,0,1,ksize=5)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(cv_image)
ax1.set_title(fname)
ax2.imshow(sobely_image,cmap='gray')
ax1.axis("off")
ax2.axis("off")
plt.show()
In [89]:
sobel21_image = cv2.Sobel(cv_image,cv2.CV_64F,2,1,ksize=5)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(cv_image)
ax1.set_title(fname)
ax2.imshow(sobel21_image)
ax1.axis("off")
ax2.axis("off")
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Sharpen Kernel

In [101]:
kernel = np.array([[0, -1, 0],
                   [-1, 5,-1],
                   [0, -1, 0]])
sharp_img1 = cv2.filter2D(src=cv_image, ddepth=-1, kernel=kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(sharp_img1)
ax1.axis("off")
ax2.axis("off")
plt.show()

Edge Detection Kernel 1 (mild)

In [38]:
kernel = np.array([[1, 0, -1],
                   [0, 0,0],
                   [-1, 0, 1]])
sharp_img2 = cv2.filter2D(src=cv_image, ddepth=-1, kernel=kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(sharp_img2)
ax1.axis("off")
ax2.axis("off")
plt.show()

Edge Detection Kernel 2 (moderate)

In [39]:
kernel = np.array([[0, -1, 0],
                   [-1, 4, -1],
                   [0, -1, 0]])
sharp_img3 = cv2.filter2D(src=cv_image, ddepth=-1, kernel=kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(sharp_img3)
ax1.axis("off")
ax2.axis("off")
plt.show()

Edge Detection Kernel 3 (high)

In [40]:
kernel = np.array([[-1, -1, -1],
                   [-1, 8, -1],
                   [-1, -1, -1]])
sharp_img4 = cv2.filter2D(src=cv_image, ddepth=-1, kernel=kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(sharp_img4)
ax1.axis("off")
ax2.axis("off")
plt.show()

Dilate

In [41]:
kernel = np.ones((5,5),np.uint8)
dilation_img = cv2.dilate(cv_image,kernel,iterations = 1)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(dilation_img)
ax1.axis("off")
ax2.axis("off")
plt.show()

Closing Morph

In [42]:
closing_img = cv2.morphologyEx(cv_image, cv2.MORPH_CLOSE, kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(closing_img)
ax2.set_title('transformed')
ax1.axis("off")
ax2.axis("off")
plt.show()

Gradient Morph

In [43]:
gradient_img = cv2.morphologyEx(cv_image, cv2.MORPH_GRADIENT, kernel)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(gradient_img)
ax2.set_title('transformed')
ax1.axis("off")
ax2.axis("off")
plt.show()

Canny Edge Detection

In [107]:
edges = cv2.Canny(cv_image,50,2)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(edges,cmap='gray')
ax2.set_title('transformed')
ax1.axis("off")
ax2.axis("off")
plt.show()
In [121]:
edges2 = cv2.Canny(cv_image,1,1)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(im)
ax1.set_title(fname)
ax2.imshow(edges2,cmap='gray')
ax2.set_title('transformed')
ax1.axis("off")
ax2.axis("off")
plt.show()

Compilation

In [94]:
logger = logging.getLogger()
old_level = logger.level
In [127]:
logger.setLevel(100)

fig, ax = plt.subplots(3, 5, figsize=(15,10))
ax[0,0].imshow(cv_image)
ax[0,0].set_title(fname)
ax[0,0].axis("off")
ax[0,1].imshow(lap_image,cmap='gray')
ax[0,1].set_title('laplace_gray')
ax[0,1].axis("off")
ax[0,2].imshow(lap_image2)
ax[0,2].set_title('laplace_color')
ax[0,2].axis("off")
ax[0,3].imshow(sobelx_image,cmap='gray')
ax[0,3].set_title('sobel_x')
ax[0,3].axis("off")
ax[0,4].imshow(sobely_image,cmap='gray')
ax[0,4].set_title('sobel_y')
ax[0,4].axis("off")
ax[1,0].imshow(sobel21_image)
ax[1,0].set_title('sobelx2y1_color')
ax[1,0].axis("off")
ax[1,1].imshow(sharp_img1)
ax[1,1].set_title('sharpen')
ax[1,1].axis("off")
ax[1,2].imshow(sharp_img2)
ax[1,2].set_title('Edge Detection 1')
ax[1,2].axis("off")
ax[1,3].imshow(sharp_img3)
ax[1,3].set_title('Edge Detection 2')
ax[1,3].axis("off")
ax[1,4].imshow(sharp_img4)
ax[1,4].set_title('Edge Detection 3')
ax[1,4].axis("off")
ax[2,0].imshow(dilation_img)
ax[2,0].set_title('dilate')
ax[2,0].axis("off")
ax[2,1].imshow(closing_img)
ax[2,1].set_title('closing')
ax[2,1].axis("off")
ax[2,2].imshow(gradient_img)
ax[2,2].set_title('gradient')
ax[2,2].axis("off")
ax[2,3].imshow(edges,cmap='gray')
ax[2,3].set_title('canny edge')
ax[2,3].axis("off")
ax[2,4].imshow(edges2,cmap='gray')
ax[2,4].set_title('canny edge 2')
ax[2,4].axis("off")
plt.subplots_adjust(wspace=0.1, hspace=0.1)
plt.show()

logger.setLevel(old_level)

Thank You!

Please leave some ❤️ for this notebook!

Any comment or something to revise just hmu!

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