├── .gitattributes
├── ColorCount.java
├── LICENSE
├── README.md
├── TestVideo.mp4
├── main.py
├── opencv_python-3.2.0+contrib-cp36-cp36m-win_amd64.whl
├── output.txt
├── output_GIF.gif
├── part-r-00000
└── start.sh


/.gitattributes:
--------------------------------------------------------------------------------
1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:ccb6e05d39983581a9db7912eea9be8f11c229072fb61a75a17e42f410e2bddd
3 | size 179
4 | 


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/ColorCount.java:
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 1 | import java.io.IOException;
 2 | import java.util.*;
 3 | 
 4 | import org.apache.hadoop.io.IntWritable;
 5 | import org.apache.hadoop.io.LongWritable;
 6 | import org.apache.hadoop.io.Text;
 7 | 
 8 | import org.apache.hadoop.mapreduce.Reducer;
 9 | 
10 | import org.apache.hadoop.mapreduce.Mapper;
11 | 
12 | 
13 | import org.apache.hadoop.fs.Path;
14 | import org.apache.hadoop.mapreduce.Job;
15 | import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
16 | import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
17 | 
18 | public class ColorCount{
19 | 	
20 | 	
21 | 	//mapper
22 | 	public static class color_mapper extends Mapper<LongWritable,Text,Text,IntWritable>
23 | {
24 | 	//@Override
25 | 	public void map(LongWritable key,Text value,Context context)
26 | 	throws IOException,InterruptedException{
27 | 		
28 | 		StringTokenizer iterator=new StringTokenizer(value.toString(),",");
29 | 		while(iterator.hasMoreTokens())
30 | 		{
31 | 			value.set(iterator.nextToken());
32 | 			context.write(value,new IntWritable(1));
33 | 		}
34 | 
35 | 	}
36 | }
37 | 
38 | //reducer
39 | public static class color_reducer 
40 | 	extends Reducer<Text,IntWritable,IntWritable,Text>{
41 | //@Override
42 | public void reduce(Text key,Iterable<IntWritable> values,Context context)
43 | throws IOException,InterruptedException{
44 | 	int sum=0;
45 | 	for(IntWritable value:values)
46 | 		{
47 | 			sum=sum+value.get();
48 | 		}
49 | 		Text x=new Text();
50 | 		x.set(key);
51 | 		context.write(new IntWritable(sum),x);
52 | 	}
53 | }
54 | 
55 | 
56 | 
57 | //main
58 | 
59 | 	public static void main (String args[])
60 | 	throws Exception{
61 | 		if(args.length!=2)
62 | 		{
63 | 			System.err.println("useage output.txt <input path> <output path>");
64 | 			System.exit(-1);
65 | 		}
66 | 		Job job=new Job();
67 | 		job.setJarByClass(ColorCount.class);
68 | 		job.setJobName("COLOR COUNT");
69 | 
70 | 		FileInputFormat.addInputPath(job,new Path(args[0]));
71 | 		FileOutputFormat.setOutputPath(job,new Path(args[1]));
72 | 
73 | 		job.setMapperClass(color_mapper.class);
74 | 		job.setReducerClass(color_reducer.class);
75 | 
76 | 		job.setMapOutputKeyClass(Text.class);
77 | 		job.setMapOutputValueClass(IntWritable.class);
78 | 
79 | 		job.setOutputKeyClass(IntWritable.class);
80 | 		job.setOutputValueClass(Text.class);
81 | 
82 | 		System.exit(job.waitForCompletion(true)?0:1);
83 | 
84 | 	}
85 | 
86 | 
87 | }
88 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 SKsaqlain
 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 | 


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/README.md:
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 1 | # Vehicle-Detection-And-Color-Classification
 2 | This is a simple Project that Detects moving objects in this case vehicles and classifies its color using k-means algorithm and finds the frequency of each color detected using Hadoop Map-Reduce algorithm.<br/>
 3 | ## Working
 4 | The moving object is detected by subtracting the current frame from the previous frame thus any static objects compare to the previous frame is eliminated thus bringing out only the moving object, once the object is detected Morphological operations are done so that noises are eliminated and then Dilation are done to merge distorted blobs, then Contours are constructed to locate exact position of each object so that the corresponding dimensions can be used to extract the actual vehicle image from the original frame to get the RGB value and also to eliminate vehicles that are coming from far distance, once the dimensions are obtained normalization of RGB values are done within the area so that each object has as a single RGB associated to the entire area.<br/>
 5 | Once the RGB values were obtained it was used to get the centroid of the cluster to which it actually belonged to using a k-means algorithm which was trained using predefined clusters centroids and values, but the model worked well but its accuracy can further be improved using different ML models.<br/>
 6 | Once the corresponding colors were obtained the output was written to a file output.txt the final output file is then used to detect the frequency of each color  that was seen in the current traffic and predict which color vehicle was most frequently purchased, using Hadoop Map-Reduce Algorithm.<br/>
 7 | The sample output is shown below.
 8 | ![](output_GIF.gif)
 9 | 
10 | ## Requirements
11 | <ul type=1>
12 |    <li>Python 3.6.5</li>
13 |   <li>OpenCV 3</li>
14 |   <li>java version 1.8.0_181</li>
15 |   <li>Hadoop 2.6.4 on Ubuntu 16.04</li>
16 | </ul>
17 | 
18 | ## Usage
19 | Clone this Repository from the Desktop
20 | ```
21 | $git clone https://github.com/SKsaqlain/Vehicle-Detection-And-Color-Classification ColorCount 
22 | ```
23 | To detect moving Vehicles/Objects run the below command
24 | ```
25 | $cd ColorCount
26 | $python3 main.py
27 | ```
28 | The main.py Script produces output that is the number of different color vehicles detected in the current frame of the video.<br/>
29 | Thus to find the frequency of each color the output.txt file has to be loaded into HDFS, run the below command to upload into HDFS after starting Hadoop.
30 | ```
31 | hadoop fs -put <full path of the fileon local machine> <full path of the Storage on Hadoop>
32 | $hadoop fs -put /home/hduser/Desktop/ColorCount/output.txt /Input
33 | ```
34 | To compile the java Map-Reduce ColorCount program and run the jar file execute the below command
35 | ```
36 | $chmod 777 start.sh
37 | $./start.sh ColorCount
38 | ```
39 | To see the output<br/>
40 | Navigate to "http://localhost:50070" on your web browser<br/>
41 | Click on the Utilities tab (top right)=>Browse the file system<br/>
42 | Click on user=>hduser=>Output=>part-r-00000=> Download<br/>
43 | The downloaded file contains the required output.
44 | 
45 | 
46 | ## License
47 | This project is licensed under the [MIT License](LICENSE.md)
48 | 


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/TestVideo.mp4:
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https://raw.githubusercontent.com/SKsaqlain/Vehicle-Detection-And-Color-Classification/2733e86a0fe6cabd48b5dbc392b51c826e561086/TestVideo.mp4


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/main.py:
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  1 | 
  2 | from scipy.spatial import distance as dist
  3 | from collections import OrderedDict	
  4 | import numpy as np
  5 | from scipy.stats import itemfreq
  6 | import cv2
  7 | import math
  8 | import warnings
  9 | warnings.filterwarnings("ignore")
 10 | 
 11 | #Function to get the centroid of the Object.
 12 | def get_centroid(x, y, w, h):
 13 |     x1 = int(w / 2)
 14 |     y1 = int(h / 2)
 15 | 
 16 |     cx = x + x1
 17 |     cy = y + y1
 18 | 
 19 |     return (cx, cy)
 20 | 
 21 | 
 22 | #function to detect vehical/moving object 
 23 | def detect_vehicles(fg_mask, min_contour_width=35, min_contour_height=35):
 24 | 
 25 |     matches = []
 26 |     frame_copy=fg_mask
 27 |     # finding external contours
 28 |     im, contours, hierarchy = cv2.findContours(
 29 |         fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1)
 30 | 
 31 |     
 32 |     for (i, contour) in enumerate(contours):
 33 |         (x, y, w, h) = cv2.boundingRect(contour)
 34 |         contour_valid = (w >= min_contour_width) and (
 35 |             h >= min_contour_height)
 36 | 
 37 |         if not contour_valid:
 38 |             continue
 39 |         
 40 |         # getting center of the bounding box
 41 |         centroid = get_centroid(x, y, w, h)
 42 | 
 43 |         matches.append(((x, y, w, h), centroid))
 44 | 
 45 |     return matches
 46 | 
 47 | 
 48 | #function to normalize the image so that the entire blob has the same rgb value
 49 | def normalized(down):
 50 | 		s=down.shape
 51 | 		x=s[1]
 52 | 		y=s[0]
 53 | 		norm=np.zeros((y,x,3),np.float32)
 54 | 		norm_rgb=np.zeros((y,x,3),np.uint8)
 55 | 
 56 | 		b=down[:,:,0]
 57 | 		g=down[:,:,1]
 58 | 		r=down[:,:,2]
 59 | 
 60 | 		sum=b+g+r
 61 | 
 62 | 		norm[:,:,0]=b/sum*255.0
 63 | 		norm[:,:,1]=g/sum*255.0
 64 | 		norm[:,:,2]=r/sum*255.0
 65 | 
 66 | 		norm_rgb=cv2.convertScaleAbs(norm)
 67 | 		return norm_rgb	
 68 | 	
 69 | 
 70 | 	
 71 | 
 72 | 
 73 | 
 74 | 
 75 | # initializing color class
 76 | colors = OrderedDict({"red": (255, 0, 0),"green": (0, 255, 0),"blue": (0,0, 255),"white":(255,255,255),"black":(100,100,100)})
 77 | lab = np.zeros((len(colors), 1, 3), dtype="uint8")
 78 | colorNames = []
 79 | 
 80 | 
 81 | f=open("output.txt","w")
 82 | 
 83 | incre=1
 84 | '''
 85 | if(len(x)==0):
 86 | 	#no image name present in the file
 87 | 	incre=1
 88 | else:
 89 | 	#reding the image number 
 90 | 	incre=int(x[-1].split(",")[0].split("_")[-1].split(".")[0])
 91 | f.close()
 92 | '''
 93 | #converting the rbg color to lab colors
 94 | for (i, (name, rgb)) in enumerate(colors.items()):
 95 | 			# update the L*a*b* array and the color names list
 96 | 			lab[i] = rgb
 97 | 			colorNames.append(name)
 98 | lab = cv2.cvtColor(lab, cv2.COLOR_RGB2LAB)
 99 | 
100 | 
101 | #function to label car lab color to a perticular color class
102 | def label(image,lab,colorNames):
103 | 
104 | 		# initialize the minimum distance found thus far
105 | 		minDist = (np.inf, None)
106 |  
107 | 		# loop over the known L*a*b* color values
108 | 		for (i, row) in enumerate(lab):
109 | 			# compute the distance between the current L*a*b*
110 | 			# color value and the mean of the image
111 | 			
112 | 			d = dist.euclidean(row[0],image)
113 |  
114 | 			# if the distance is smaller than the current distance,
115 | 			# then update the bookkeeping variable
116 | 			if d < minDist[0]:
117 | 				minDist = (d, i)
118 |  
119 | 		# return the name of the color with the smallest distance
120 | 		return colorNames[minDist[1]]
121 | 
122 | 
123 | 
124 | #initialising background object used for background elemination 
125 | background=cv2.createBackgroundSubtractorMOG2()
126 | 
127 | 
128 | cap=cv2.VideoCapture('TestVideo.mp4')
129 | #initialising frame counter
130 | count_frame=0
131 | while(cap.isOpened()):
132 | 	_,frame=cap.read()
133 | 	#resizing the frame 
134 | 	try:
135 | 		frame=cv2.resize(frame,(640,480))
136 | 	except:
137 | 		break
138 | 	#creating a copy of the frame
139 | 	frame_copy=frame
140 | 	frame_copy_copy=copy =frame[:,:]
141 | 	
142 | 	#applying background elemination
143 | 	bg=background.apply(frame)
144 | 	
145 | 	#additional image processing
146 | 	
147 | 	kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
148 | 	bg= cv2.erode(bg,kernel,iterations = 1)
149 | 	
150 | 	# Fill any small holes
151 | 	closing=cv2.morphologyEx(bg,cv2.MORPH_CLOSE,kernel)
152 | 	cv2.imshow("closing",closing)
153 | 	
154 | 	# Remove noise
155 | 	opening=cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
156 | 	cv2.imshow("removing_noise",opening)
157 | 	
158 | 	# Dilate to merge adjacent blobs
159 | 	dilation=cv2.dilate(opening, kernel, iterations=2)
160 | 
161 | 	# threshold to remove furthur noise 
162 | 	dilation[dilation < 240] = 0
163 | 	bg=dilation
164 | 	
165 | 	#initialising output color list
166 | 	output_color=[]
167 | 	
168 | 	#detecting contour and calculating the co-ordinates of the contours
169 | 	contour_list=detect_vehicles(bg)
170 | 	
171 | 	#traversing through each detected contour 
172 | 	for ele in contour_list:
173 | 		x1=ele[0][0]
174 | 		y1=ele[0][1]
175 | 		x2=x1+ele[0][2]
176 | 		y2=y1+ele[0][3]
177 | 		#extracting the regions that contains car features
178 | 		
179 | 		slice_bg=frame_copy[y1:y2,x1:x2]
180 | 		
181 | 		#normalising the image so that there is uniform color throughout
182 | 		slice_bg=normalized(slice_bg)
183 | 		
184 | 		arr=np.float32(slice_bg)
185 | 		#reshaping the image to a linear form with 3-channels
186 | 		pixels=arr.reshape((-1,3))
187 | 		
188 | 		#number of clusters
189 | 		n_colors=2
190 | 		
191 | 		#number of iterations
192 | 		criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
193 | 		
194 | 		#initialising centroid
195 | 		flags = cv2.KMEANS_RANDOM_CENTERS
196 | 		
197 | 		#applying k-means to detect prominant color in the image
198 | 		_, labels, centroids = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags)
199 | 		
200 | 		
201 | 		palette = np.uint8(centroids)
202 | 		quantized = palette[labels.flatten()]
203 | 		
204 | 		#detecting the centroid with densest cluster  
205 | 		dominant_color = palette[np.argmax(itemfreq(labels)[:, -1])]
206 | 		
207 | 
208 | 
209 | 		r=int(dominant_color[0])
210 | 		g=int(dominant_color[1])
211 | 		b=int(dominant_color[2])
212 | 
213 | 		
214 | 		rgb=np.zeros((1,1,3),dtype='uint8')
215 | 		rgb[0]=(r,g,b)
216 | 		
217 | 		
218 | 		
219 | 		#getting the  label of the car color
220 | 		color=label(rgb,lab,colorNames)
221 | 		
222 | 		
223 | 		output_color.append(color)
224 | 		
225 | 		#drawing rectangle over the detected car 
226 | 		frame_copy= cv2.rectangle(frame_copy,(x1,y1),(x2,y2),(r,g,b),3)
227 | 		font = cv2.FONT_HERSHEY_SIMPLEX
228 | 		#labeling each rectangle with the detected color of the car
229 | 		cv2.putText(frame_copy,color,(x1,y1), font, 2,(r,g,b),2,cv2.LINE_AA)
230 | 	#openinig file to write the ouput of each frame
231 | 	#f=open("output.txt","w")
232 | 	
233 | 	#writing onto the file for every 10 frames
234 | 	
235 | 	if(count_frame%10==0):
236 | 		if(len(output_color)!=0):
237 | 			c=",".join(output_color)+'\n'
238 | 			
239 | 			#image_name="img_"+str(incre)+".jpg,"+c+'\n'
240 | 			f.write(c)
241 | 			# cv2.imwrite(img,frame)
242 | 			incre=incre+1
243 | 			count_frame=0
244 | 	count_frame+=1
245 | 	cv2.imshow("object",frame_copy)
246 | 	if(cv2.waitKey(30)==27 & 0xff):
247 | 		break
248 | 
249 | cap.release()
250 | cv2.destroyAllWindows()


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/opencv_python-3.2.0+contrib-cp36-cp36m-win_amd64.whl:
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1 | version https://git-lfs.github.com/spec/v1
2 | oid sha256:96b1ec878d7adb203051539dcdb44d58937362217de24fc19a05a5f5774542e4
3 | size 51590654
4 | 


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/output.txt:
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 1 | white
 2 | white
 3 | white
 4 | white
 5 | white
 6 | white
 7 | white
 8 | white,black,white
 9 | black,black
10 | black
11 | black
12 | black
13 | black,red
14 | red,green
15 | red,red
16 | black
17 | black,green
18 | black,green
19 | black,green
20 | black
21 | black
22 | black
23 | black
24 | black
25 | black
26 | black
27 | black
28 | black
29 | black
30 | black
31 | black
32 | black
33 | black
34 | red
35 | white
36 | black,black
37 | black,black
38 | black,black
39 | black,black
40 | black,black
41 | black,black,white
42 | black,black
43 | black
44 | black,white
45 | red,black
46 | black,red,black,white
47 | black,black,black
48 | black
49 | black
50 | black,black
51 | white
52 | black
53 | black,white
54 | black,black,white
55 | black
56 | black,black,white
57 | black
58 | black,white
59 | black
60 | black
61 | black
62 | black
63 | white
64 | white
65 | 


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/output_GIF.gif:
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https://raw.githubusercontent.com/SKsaqlain/Vehicle-Detection-And-Color-Classification/2733e86a0fe6cabd48b5dbc392b51c826e561086/output_GIF.gif


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/part-r-00000:
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1 | 64	black
2 | 4	green
3 | 7	red
4 | 20	white
5 | 


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/start.sh:
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 1 | #	!!! MAKE APPROPRIATE CHANGES TO  THE PATH !!! 
 2 | 
 3 | 
 4 | cd /home/hduser/Desktop/ColorCount
 5 | echo " COMPILATION IN PROGRESS !!!"
 6 | javac -classpath /usr/local/hadoop/share/hadoop/common/hadoop-common-2.6.4.jar:/usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.4.jar:/usr/local/hadoop/share/hadoop/common/lib/commons-cli-1.2.jar -d /home/hduser/Desktop/ColorCount $1.java
 7 | echo " "
 8 | echo " CREATING DIRECTORY "
 9 | echo " "
10 | rm -rf c || mkdir c
11 | mkdir c || echo "directory  c exists"
12 | echo " "
13 | echo "MOVING .Class FILES TO THE CREATED DIRECTORY"
14 | mv *.class c
15 | echo " "
16 | 
17 | cd /home/hduser/Desktop/ColorCount
18 | echo " "
19 | echo "CREATING JAR FILE"
20 | echo " "
21 | jar -cvf $1.jar -C /home/hduser/Desktop/ColorCount/c .
22 | echo " "
23 | echo " "
24 | echo " "
25 | echo "DELETING EXISTING OUTPUT FILES"
26 | # hadoop fs -rm -r -skipTrash /user/hduser/Output || echo " unable to delete Output folder"
27 | 
28 | hadoop fs -rm -r -skipTrash /Output || echo "unable to delete /Output folder"
29 | echo " "
30 | echo " "
31 | echo " "
32 | echo "STARTING TO EXECUTE !!"
33 | cd /usr/local/hadoop
34 | echo " "
35 | bin/hadoop jar /home/hduser/Desktop/ColorCount/$1.jar $1 /Input /Output 
36 | echo " "
37 | echo " "
38 | 


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