├── src
├── __init__.py
├── test.data
├── color_recognition_api
│ ├── __init__.py
│ ├── knn_classifier.py
│ └── color_histogram_feature_extraction.py
├── black_cat.jpg
├── training_dataset
│ ├── blue
│ │ ├── blue.jpg
│ │ ├── blue1.jpg
│ │ ├── blue2.jpg
│ │ ├── blue3.png
│ │ ├── blue4.jpg
│ │ └── blue5.JPG
│ ├── red
│ │ ├── red1.jpg
│ │ ├── red10.jpg
│ │ ├── red2.jpg
│ │ ├── red3.jpg
│ │ ├── red4.png
│ │ ├── red5.jpg
│ │ ├── red6.jpg
│ │ ├── red7.png
│ │ ├── red8.jpg
│ │ └── red9.png
│ ├── black
│ │ ├── black1.png
│ │ ├── black2.jpg
│ │ ├── black3.png
│ │ ├── black4.jpg
│ │ ├── black5.jpg
│ │ ├── black6.png
│ │ ├── black7.jpg
│ │ ├── black8.jpg
│ │ ├── black9.jpg
│ │ ├── black10.png
│ │ └── black10.png.jpg
│ ├── green
│ │ ├── green1.jpg
│ │ ├── green1.png
│ │ ├── green2.png
│ │ ├── green3.jpg
│ │ ├── green4.jpg
│ │ ├── green5.jpg
│ │ ├── green6.png
│ │ ├── green7.png
│ │ ├── green8.png
│ │ ├── green9.png
│ │ └── green10.png
│ ├── white
│ │ ├── white1.png
│ │ ├── white2.png
│ │ ├── white3.png
│ │ ├── white4.png
│ │ ├── white5.jpg
│ │ ├── white6.jpg
│ │ ├── white7.jpg
│ │ ├── white8.png
│ │ ├── white9.jpg
│ │ └── white10.jpg
│ ├── orange
│ │ ├── orange1.png
│ │ ├── orange10.png
│ │ ├── orange2.png
│ │ ├── orange3.png
│ │ ├── orange5.png
│ │ ├── orange6.png
│ │ ├── orange7.png
│ │ ├── orange8.jpg
│ │ └── orange9.jpg
│ ├── violet
│ │ ├── violet1.png
│ │ ├── violet10.png
│ │ ├── violet2.jpg
│ │ ├── violet3.jpg
│ │ ├── violet4.jpg
│ │ ├── violet5.png
│ │ ├── violet6.png
│ │ ├── violet7.jpg
│ │ ├── violet8.png
│ │ └── violet9.png
│ └── yellow
│ │ ├── yellow1.jpg
│ │ ├── yellow10.png
│ │ ├── yellow2.png
│ │ ├── yellow3.jpg
│ │ ├── yellow4.png
│ │ ├── yellow5.png
│ │ ├── yellow6.png
│ │ ├── yellow7.jpg
│ │ ├── yellow8.jpg
│ │ └── yellow9.jpg
├── training.data
├── color_classification_image.py
└── color_classification_webcam.py
├── LICENSE
├── .gitignore
└── README.md
/src/__init__.py:
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1 |
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/src/test.data:
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/src/color_recognition_api/__init__.py:
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/src/black_cat.jpg:
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/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2018 Ozlu
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/src/training.data:
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1 | 139,0,0,red
2 | 204,22,0,red
3 | 206,0,25,red
4 | 220,0,3,red
5 | 254,0,0,red
6 | 61,13,3,red
7 | 128,24,24,red
8 | 174,32,26,red
9 | 254,0,2,red
10 | 209,23,23,red
11 | 255,255,0,yellow
12 | 255,242,39,yellow
13 | 249,217,94,yellow
14 | 252,234,4,yellow
15 | 255,183,9,yellow
16 | 247,224,23,yellow
17 | 254,242,0,yellow
18 | 246,191,39,yellow
19 | 255,215,12,yellow
20 | 255,166,0,yellow
21 | 159,217,140,green
22 | 125,194,75,green
23 | 37,202,38,green
24 | 0,166,82,green
25 | 64,189,85,green
26 | 0,128,1,green
27 | 35,67,17,green
28 | 123,252,1,green
29 | 0,255,0,green
30 | 33,83,54,green
31 | 125,232,88,green
32 | 252,79,19,orange
33 | 255,102,0,orange
34 | 255,127,0,orange
35 | 255,128,0,orange
36 | 255,103,0,orange
37 | 255,122,1,orange
38 | 254,101,33,orange
39 | 255,153,0,orange
40 | 255,103,0,orange
41 | 249,255,241,white
42 | 253,251,251,white
43 | 242,233,228,white
44 | 242,233,228,white
45 | 250,240,230,white
46 | 243,239,227,white
47 | 255,237,231,white
48 | 229,224,221,white
49 | 248,249,254,white
50 | 238,228,220,white
51 | 9,0,0,black
52 | 28,29,33,black
53 | 27,32,35,black
54 | 36,29,33,black
55 | 0,0,0,black
56 | 49,54,57,black
57 | 47,47,47,black
58 | 40,39,45,black
59 | 37,37,37,black
60 | 10,18,13,black
61 | 28,28,28,black
62 | 3,91,188,blue
63 | 0,0,254,blue
64 | 0,0,255,blue
65 | 1,119,193,blue
66 | 0,48,143,blue
67 | 0,0,154,blue
68 |
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/src/color_classification_image.py:
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1 | #!/usr/bin/python
2 | # -*- coding: utf-8 -*-
3 | # -------------------------------------------------------------------------
4 | # --- Author : Ahmet Ozlu
5 | # --- Mail : ahmetozlu93@gmail.com
6 | # --- Date : 8th July 2018 - before Google inside look 2018 :)
7 | # -------------------------------------------------------------------------
8 |
9 | import cv2
10 | from color_recognition_api import color_histogram_feature_extraction
11 | from color_recognition_api import knn_classifier
12 | import os
13 | import os.path
14 | import sys
15 |
16 | # read the test image
17 | try:
18 | source_image = cv2.imread(sys.argv[1])
19 | except:
20 | source_image = cv2.imread('black_cat.jpg')
21 | prediction = 'n.a.'
22 |
23 | # checking whether the training data is ready
24 | PATH = './training.data'
25 |
26 | if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
27 | print ('training data is ready, classifier is loading...')
28 | else:
29 | print ('training data is being created...')
30 | open('training.data', 'w')
31 | color_histogram_feature_extraction.training()
32 | print ('training data is ready, classifier is loading...')
33 |
34 | # get the prediction
35 | color_histogram_feature_extraction.color_histogram_of_test_image(source_image)
36 | prediction = knn_classifier.main('training.data', 'test.data')
37 | print('Detected color is:', prediction)
38 | cv2.putText(
39 | source_image,
40 | 'Prediction: ' + prediction,
41 | (15, 45),
42 | cv2.FONT_HERSHEY_PLAIN,
43 | 3,
44 | 200,
45 | )
46 |
47 | # Display the resulting frame
48 | cv2.imshow('color classifier', source_image)
49 | cv2.waitKey(0)
50 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | env/
12 | build/
13 | develop-eggs/
14 | dist/
15 | downloads/
16 | eggs/
17 | .eggs/
18 | lib/
19 | lib64/
20 | parts/
21 | sdist/
22 | var/
23 | wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 |
49 | # Translations
50 | *.mo
51 | *.pot
52 |
53 | # Django stuff:
54 | *.log
55 | local_settings.py
56 |
57 | # Flask stuff:
58 | instance/
59 | .webassets-cache
60 |
61 | # Scrapy stuff:
62 | .scrapy
63 |
64 | # Sphinx documentation
65 | docs/_build/
66 |
67 | # PyBuilder
68 | target/
69 |
70 | # Jupyter Notebook
71 | .ipynb_checkpoints
72 |
73 | # pyenv
74 | .python-version
75 |
76 | # celery beat schedule file
77 | celerybeat-schedule
78 |
79 | # SageMath parsed files
80 | *.sage.py
81 |
82 | # dotenv
83 | .env
84 |
85 | # virtualenv
86 | .venv
87 | venv/
88 | ENV/
89 |
90 | # Spyder project settings
91 | .spyderproject
92 | .spyproject
93 |
94 | # Rope project settings
95 | .ropeproject
96 |
97 | # mkdocs documentation
98 | /site
99 |
100 | # mypy
101 | .mypy_cache/
102 |
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/src/color_classification_webcam.py:
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1 | #!/usr/bin/python
2 | # -*- coding: utf-8 -*-
3 | # ----------------------------------------------
4 | # --- Author : Ahmet Ozlu
5 | # --- Mail : ahmetozlu93@gmail.com
6 | # --- Date : 31st December 2017 - new year eve :)
7 | # ----------------------------------------------
8 |
9 | import cv2
10 | from color_recognition_api import color_histogram_feature_extraction
11 | from color_recognition_api import knn_classifier
12 | import os
13 | import os.path
14 |
15 | cap = cv2.VideoCapture(1)
16 | (ret, frame) = cap.read()
17 | prediction = 'n.a.'
18 |
19 | # checking whether the training data is ready
20 | PATH = './training.data'
21 |
22 | if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
23 | print ('training data is ready, classifier is loading...')
24 | else:
25 | print ('training data is being created...')
26 | open('training.data', 'w')
27 | color_histogram_feature_extraction.training()
28 | print ('training data is ready, classifier is loading...')
29 |
30 | while True:
31 |
32 | # Capture frame-by-frame
33 | (ret, frame) = cap.read()
34 |
35 | cv2.putText(
36 | frame,
37 | 'Prediction: ' + prediction,
38 | (15, 45),
39 | cv2.FONT_HERSHEY_PLAIN,
40 | 3,
41 | 200,
42 | )
43 |
44 | # Display the resulting frame
45 | cv2.imshow('color classifier', frame)
46 |
47 | color_histogram_feature_extraction.color_histogram_of_test_image(frame)
48 |
49 | prediction = knn_classifier.main('training.data', 'test.data')
50 | if cv2.waitKey(1) & 0xFF == ord('q'):
51 | break
52 |
53 | # When everything done, release the capture
54 | cap.release()
55 | cv2.destroyAllWindows()
56 |
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/src/color_recognition_api/knn_classifier.py:
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1 | #!/usr/bin/python
2 | # -*- coding: utf-8 -*-
3 | # ----------------------------------------------
4 | # --- Author : Ahmet Ozlu
5 | # --- Mail : ahmetozlu93@gmail.com
6 | # --- Date : 31st December 2017 - new year eve :)
7 | # ----------------------------------------------
8 |
9 | import csv
10 | import random
11 | import math
12 | import operator
13 | import cv2
14 |
15 |
16 | # calculation of euclidead distance
17 | def calculateEuclideanDistance(variable1, variable2, length):
18 | distance = 0
19 | for x in range(length):
20 | distance += pow(variable1[x] - variable2[x], 2)
21 | return math.sqrt(distance)
22 |
23 |
24 | # get k nearest neigbors
25 | def kNearestNeighbors(training_feature_vector, testInstance, k):
26 | distances = []
27 | length = len(testInstance)
28 | for x in range(len(training_feature_vector)):
29 | dist = calculateEuclideanDistance(testInstance,
30 | training_feature_vector[x], length)
31 | distances.append((training_feature_vector[x], dist))
32 | distances.sort(key=operator.itemgetter(1))
33 | neighbors = []
34 | for x in range(k):
35 | neighbors.append(distances[x][0])
36 | return neighbors
37 |
38 |
39 | # votes of neighbors
40 | def responseOfNeighbors(neighbors):
41 | all_possible_neighbors = {}
42 | for x in range(len(neighbors)):
43 | response = neighbors[x][-1]
44 | if response in all_possible_neighbors:
45 | all_possible_neighbors[response] += 1
46 | else:
47 | all_possible_neighbors[response] = 1
48 | sortedVotes = sorted(all_possible_neighbors.items(),
49 | key=operator.itemgetter(1), reverse=True)
50 | return sortedVotes[0][0]
51 |
52 |
53 | # Load image feature data to training feature vectors and test feature vector
54 | def loadDataset(
55 | filename,
56 | filename2,
57 | training_feature_vector=[],
58 | test_feature_vector=[],
59 | ):
60 | with open(filename) as csvfile:
61 | lines = csv.reader(csvfile)
62 | dataset = list(lines)
63 | for x in range(len(dataset)):
64 | for y in range(3):
65 | dataset[x][y] = float(dataset[x][y])
66 | training_feature_vector.append(dataset[x])
67 |
68 | with open(filename2) as csvfile:
69 | lines = csv.reader(csvfile)
70 | dataset = list(lines)
71 | for x in range(len(dataset)):
72 | for y in range(3):
73 | dataset[x][y] = float(dataset[x][y])
74 | test_feature_vector.append(dataset[x])
75 |
76 |
77 | def main(training_data, test_data):
78 | training_feature_vector = [] # training feature vector
79 | test_feature_vector = [] # test feature vector
80 | loadDataset(training_data, test_data, training_feature_vector, test_feature_vector)
81 | classifier_prediction = [] # predictions
82 | k = 3 # K value of k nearest neighbor
83 | for x in range(len(test_feature_vector)):
84 | neighbors = kNearestNeighbors(training_feature_vector, test_feature_vector[x], k)
85 | result = responseOfNeighbors(neighbors)
86 | classifier_prediction.append(result)
87 | return classifier_prediction[0]
88 |
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/src/color_recognition_api/color_histogram_feature_extraction.py:
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1 | #!/usr/bin/python
2 | # -*- coding: utf-8 -*-
3 | # ----------------------------------------------
4 | # --- Author : Ahmet Ozlu
5 | # --- Mail : ahmetozlu93@gmail.com
6 | # --- Date : 31st December 2017 - new year eve :)
7 | # ----------------------------------------------
8 |
9 | from PIL import Image
10 | import os
11 | import cv2
12 | import numpy as np
13 | import matplotlib.pyplot as plt
14 | from scipy.stats import itemfreq
15 | from color_recognition_api import knn_classifier as knn_classifier
16 |
17 |
18 | def color_histogram_of_test_image(test_src_image):
19 |
20 | # load the image
21 | image = test_src_image
22 |
23 | chans = cv2.split(image)
24 | colors = ('b', 'g', 'r')
25 | features = []
26 | feature_data = ''
27 | counter = 0
28 | for (chan, color) in zip(chans, colors):
29 | counter = counter + 1
30 |
31 | hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
32 | features.extend(hist)
33 |
34 | # find the peak pixel values for R, G, and B
35 | elem = np.argmax(hist)
36 |
37 | if counter == 1:
38 | blue = str(elem)
39 | elif counter == 2:
40 | green = str(elem)
41 | elif counter == 3:
42 | red = str(elem)
43 | feature_data = red + ',' + green + ',' + blue
44 | # print(feature_data)
45 |
46 | with open('test.data', 'w') as myfile:
47 | myfile.write(feature_data)
48 |
49 |
50 | def color_histogram_of_training_image(img_name):
51 |
52 | # detect image color by using image file name to label training data
53 | if 'red' in img_name:
54 | data_source = 'red'
55 | elif 'yellow' in img_name:
56 | data_source = 'yellow'
57 | elif 'green' in img_name:
58 | data_source = 'green'
59 | elif 'orange' in img_name:
60 | data_source = 'orange'
61 | elif 'white' in img_name:
62 | data_source = 'white'
63 | elif 'black' in img_name:
64 | data_source = 'black'
65 | elif 'blue' in img_name:
66 | data_source = 'blue'
67 | elif 'violet' in img_name:
68 | data_source = 'violet'
69 |
70 | # load the image
71 | image = cv2.imread(img_name)
72 |
73 | chans = cv2.split(image)
74 | colors = ('b', 'g', 'r')
75 | features = []
76 | feature_data = ''
77 | counter = 0
78 | for (chan, color) in zip(chans, colors):
79 | counter = counter + 1
80 |
81 | hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
82 | features.extend(hist)
83 |
84 | # find the peak pixel values for R, G, and B
85 | elem = np.argmax(hist)
86 |
87 | if counter == 1:
88 | blue = str(elem)
89 | elif counter == 2:
90 | green = str(elem)
91 | elif counter == 3:
92 | red = str(elem)
93 | feature_data = red + ',' + green + ',' + blue
94 |
95 | with open('training.data', 'a') as myfile:
96 | myfile.write(feature_data + ',' + data_source + '\n')
97 |
98 |
99 | def training():
100 |
101 | # red color training images
102 | for f in os.listdir('./training_dataset/red'):
103 | color_histogram_of_training_image('./training_dataset/red/' + f)
104 |
105 | # yellow color training images
106 | for f in os.listdir('./training_dataset/yellow'):
107 | color_histogram_of_training_image('./training_dataset/yellow/' + f)
108 |
109 | # green color training images
110 | for f in os.listdir('./training_dataset/green'):
111 | color_histogram_of_training_image('./training_dataset/green/' + f)
112 |
113 | # orange color training images
114 | for f in os.listdir('./training_dataset/orange'):
115 | color_histogram_of_training_image('./training_dataset/orange/' + f)
116 |
117 | # white color training images
118 | for f in os.listdir('./training_dataset/white'):
119 | color_histogram_of_training_image('./training_dataset/white/' + f)
120 |
121 | # black color training images
122 | for f in os.listdir('./training_dataset/black'):
123 | color_histogram_of_training_image('./training_dataset/black/' + f)
124 |
125 | # blue color training images
126 | for f in os.listdir('./training_dataset/blue'):
127 | color_histogram_of_training_image('./training_dataset/blue/' + f)
128 |
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/README.md:
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1 | # COLOR RECOGNITION
2 |
3 | This project focuses on color classifying by K-Nearest Neighbors Machine Learning Classifier which is trained by R, G, B Color Histogram. It can classify White, Black, Red, Green, Blue, Orange, Yellow and Violet. If you want to classify more color or improve the accuracy you should work on the [training data](https://github.com/ahmetozlu/color_classifier/tree/master/src/training_dataset) or consider about other color features such as [Color Moments](https://en.wikipedia.org/wiki/Color_moments) or [Color Correlogram](http://www.cs.cornell.edu/rdz/Papers/ecdl2/spatial.htm).
4 |
5 | You can use [color_recognition_api](https://github.com/ahmetozlu/color_recognition/tree/master/src/color_recognition_api) to perform real-time color recognition in your projects. You can find a sample usage of [color_recognition_api](https://github.com/ahmetozlu/color_recognition/tree/master/src/color_recognition_api) in this [**repo**](https://github.com/ahmetozlu/vehicle_counting_tensorflow). ***Please contact if you need professional color recognition project with the super high accuracy!***
6 |
7 | ## Quick Demo
8 |
9 | ***Run [color_classification_webcam.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_classification_webcam.py) to perform real-time color recognition on a webcam stream.***
10 |
11 |
12 | 
13 |
14 |
15 | ***Run [color_classification_image.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_classification_image.py) to perform color recognition on a single image.***
16 |
17 |
18 | 
19 |
20 |
21 | ---
22 | **What does this program do?**
23 | 1. **Feature Extraction:** Perform feature extraction for getting the R, G, B Color Histogram values of [training images](https://github.com/ahmetozlu/color_classifier/tree/master/src/training_dataset)
24 | 2. **Training K-Nearest Neighbors Classifier:** Train KNN classifier by R, G, B Color Histogram values
25 | 3. **Classifying by Trained KNN:** Read Web Cam frame by frame, perform feature extraction on each frame and then classify the mean color of it by trained KNN classifier.
26 | ---
27 |
28 | **TODOs:**
29 |
30 | - "Add New Color" utility will be added.
31 | - New feature extractors will be added.
32 | - New classifiers will be added.
33 |
34 |
35 | ## Theory
36 |
37 | In this study, colors are classified by using K-Neares Neşghbor Machine Learning classifier algorithm. This classifier is trained by image R, G, B Color Histogram values. The general work flow is given at the below.
38 |
39 |
40 | 
41 |
42 |
43 | You should know 2 main pheomena to understand basic Object Detection/Recognition Systems of Computer Vision and Machine Learning.
44 |
45 | **1.) Feature Extraction**
46 |
47 | How to represent the interesting points we found to compare them with other interesting points (features) in the image.
48 |
49 | **2.) Classification**
50 |
51 | An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.
52 |
53 | For this project;
54 |
55 | **1.) Feature Extraction** = Color Histogram
56 |
57 | Color Histogram is a representation of the distribution of colors in an image. For digital images, a color histogram represents the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space, the set of all possible colors.
58 |
59 |
60 | 
61 |
62 |
63 | **2.) Classification** = K-Nearest Neighbors Algorithm
64 |
65 | K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique.
66 |
67 |
68 | 
69 |
70 |
71 | ## Implementation
72 |
73 | [OpenCV](https://pypi.python.org/pypi/opencv-python) was used for color histogram calculations and knn classifier. [NumPy](https://stackoverflow.com/questions/29499815/how-to-install-numpy-on-windows-using-pip-install) was used for matrix/n-dimensional array calculations. The program was developed on Python at Linux environment.
74 |
75 | In the “[src](https://github.com/ahmetozlu/color_recognition/tree/master/src)” folder, there are 2 Python classes which are:
76 |
77 | - **[color_classification_webcam.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_classification_webcam.py):** test class to perform real-time color recognition form webcam stream.
78 |
79 | - **[color_classification_image.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_classification_image.py):** test class to perform color recognition on a single image.
80 |
81 | In the “[color_recognition_api](https://github.com/ahmetozlu/color_recognition/tree/master/src/color_recognition_api)” folder, there are 2 Python classes which are:
82 |
83 | - **[feature_extraction.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/color_histogram_feature_extraction.py):** feature extraction operation class
84 |
85 | - **[knn_classifier.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/knn_classifier.py):** knn classification class
86 |
87 | **1.) Explanation of “[feature_extraction.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/color_histogram_feature_extraction.py)"**
88 |
89 | I can get the RGB color histogram of images by this Python class. For example, plot of RGB color histogram for one of the red images is given at the below.
90 |
91 |
92 | 
93 |
94 |
95 | I decided to use bin number of histogram which has the peak value of pixel count for R, G and B as feature so I can get the dominant R, G and B values to create feature vectors for training. For example, the dominant R, G and B values of the red image which is given at above is [254, 0, 2].
96 |
97 | I get the dominant R, G, B values by using Color Histogram for each training image then I labelled them because KNN classifier is a supervised learner and I deploy these feature vectors in the csv file. Thus, I create my training feature vector dataset. It can be found in the file which name’s is [training.data](https://github.com/ahmetozlu/color_recognition/blob/master/src/training.data) under src folder.
98 |
99 | **2.) Explanation of “[knn_classifier.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/knn_classifier.py)”**
100 |
101 | This class provides these main calculations;
102 |
103 | 1. Fetching training data
104 | 2. Fetching test image features
105 | 3. Calculating euclidean distance
106 | 4. Getting k nearest neighbors
107 | 5. Prediction of color
108 | 6. Returning the prediction is true or false
109 |
110 | **“[color_classification_webcam.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_classification_webcam.py)”** is the main class of my program, it provides;
111 |
112 | 1. Calling [feature_extraction.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/color_histogram_feature_extraction.py) to create training data by feature extraction
113 | 2. Calling [knn_classifier.py](https://github.com/ahmetozlu/color_recognition/blob/master/src/color_recognition_api/knn_classifier.py) for classification
114 |
115 | You can find training data in [here](https://github.com/ahmetozlu/color_classifier/tree/master/src/training_dataset).
116 |
117 | You can find features are got from training data in [here](https://raw.githubusercontent.com/ahmetozlu/color_classifier/master/src/training.data).
118 |
119 | ## Conclusion
120 |
121 | I think, the training data has a huge important in classification accuracy. I created my training data carefully but maybe the accuracy can be higher with more suitable training data.
122 |
123 | Another important thing is lightning and shadows. In my test images, the images which were taken under bad lighting conditions and with shadows are classified wrong (false positives), maybe some filtering algorithm should/can be implemented before the test images send to KNN classifier Thus, accuracy can be improved.
124 |
125 | ## Citation
126 | If you use this code for your publications, please cite it as:
127 |
128 | @ONLINE{cr,
129 | author = "Ahmet Özlü",
130 | title = "Color Recognition",
131 | year = "2018",
132 | url = "https://github.com/ahmetozlu/color_recognition"
133 | }
134 |
135 | ## Author
136 | Ahmet Özlü
137 |
138 | ## License
139 | This system is available under the MIT license. See the LICENSE file for more info.
140 |
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