Hi all, this post will elaborate on the use of OpenCV-based image analysis for the Animator project. As you may remember from a few weeks back, I managed to get some nice results recognizing LED strips in pictures with OpenCV, but I needed more precision. I finally managed to get some decent results and I'm going to explain what I did and how. First, the results:
Here you can see three LED strips on a minimum brightness setting, utlising the current version of the config pattern. It's pretty simple for now - the first Animator that connects with the Beagle Bone gets colored red, the second gets green and the third goes blue. When the user uploads a photo of the setup, OpenCV finds its orientation (horizontal vs vertical) and relative positions of LED strips, returning a list of three numbers based on the elementary colour it finds in the picture - [0, 1, 2] means that the strips are ordered red, green, blue from left to right (or top to bottom - that information is stored elsewhere) - [2, 1, 0] means it's the opposite order, and so on. This allows the web interface to map user's inputs to their respective LED strips - the user doesn't care if the LED strip they want to configure is the one that connected to the central unit most recently, but they'd be very happy to just say 'hey, make the leftmost strip blue'. The primary goal of this project was to enable them to do exactly this, and if 30 seconds later they rearrange their physical setup, they should be able to just take another photo and work with the web interface as if nothing happened.
Future goals
You probably noticed the attention-catching circles littering the image - I didn't have enough time, nor memory in the Blends, to introduce recognizing direction of the strips, but coupled with some decent maths that'd allow for even nicer features - imagine LED strips emulating water droplets sliding from slopes, with each droplet's speed determined by the angle the LED strip is hanging from. That's a future goal and the circles in the image mark the beginnings, middles and ends of each line detected by OpenCV, for the purpose of colour recognition. The future idea is to make the configuration mode patters exhibit three colours, so that the LED strips' individual colours would be recognized together with their direction. This is not implemented now.
The procedure
The idea is to make OpenCV able to recognize the three coloured lines in the image, get their orientation and relative positions, and use that information later to configure the web interface. This goal is achieved by:
- Preprocessing the image,
- Using probabilistic Hough Transform to find lines,
- Counting the lines (there will be dozens, which is fine for now),
- Finding the middle of each line,
- Checking if the line is horizontal and vertical, and incrementing the corresponding variable
- Getting the dominant colour in the area around the middle of each line,
- Counting the number of lines per colour,
- Counting the average of aggregate X and Y coordinates per colour
- Deciding if the strips are horizontal or vertical based on the sums of vertical and horizontal lines found (point #5),
- Sorting the lines based on averages of X or Y coordinates to get them in left-right or top-bottom order.
Python implementation
The practical implementation of the above looks like this:
#Preprocessing the image: resize, grayscale, threshold, close gaps
myfile = ('uploads/'+os.listdir('uploads')[0])
print('reading image: ', myfile)
src = cv2.imread(myfile)
small = cv2.resize(src, (500, 700))
src = small
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
img = gray
kernel = np.ones((4,4),np.uint8)
ret,img = cv2.threshold(img,120,255,0)
close = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
#Get lines via probabilistic Hough Transform
minLineLength = 500
maxLineGap = 10
lines = cv2.HoughLinesP(close,1,np.pi/180,100,minLineLength,maxLineGap)
radius = 15
nlines = 100
redx = 0
greenx = 0
bluex = 0
redy = 0
greeny = 0
bluey = 0
redi = 0
greeni = 0
bluei = 0
horz = 0
vert = 0
for n in range(nlines):
#keep counting lines until there is at least one per colour
if (redi>0 and greeni>0 and bluei>0):
break
x1,y1,x2,y2 = lines[0][n]
#find centres of lines
x3 = int((x2+x1)/2)
y3 = int((y2+y1)/2)
#find dominant colours in a 30px circle around the line centre
roi_size = radius
roi_values = src[(y3-roi_size):(y3+roi_size), (x2-roi_size):(x2+roi_size)]
mean_blue = int(np.mean(roi_values[:,:,0]))
mean_green = int(np.mean(roi_values[:,:,1]))
mean_red = int(np.mean(roi_values[:,:,2]))
colour = ''
#also, find the line's horizontal and vertical shift
horz += abs(x2-x1)
vert += abs(y2-y1)
#operations needed for getting the average of aggregate positions of lines
if(max(mean_red, mean_green, mean_blue) == mean_red):
colour = 1
linecolour = (0,0,255)
redx+=x3
redy+=y3
redi = redi+1
elif(max(mean_red, mean_green, mean_blue) == mean_green):
colour = 2
linecolour = (0,255,0)
greenx+=x3
greeny+=y3
greeni = greeni+1
else:
colour = 3
linecolour = (255,0,0)
bluex+=x3
bluey+=y3
bluei = bluei+1
#print("{}: {}x{} R: {} G: {} B: {}, {} r {} g {} b {}".format(n, x3, y3, mean_red, mean_green, mean_blue, colour, redi, greeni, bluei) )
#Diagnostics - draw lines and circles if you like
#cv2.line(small,(x1,y1),(x2,y2),linecolour,2)
#cv2.circle(small, (x1,y1), radius, (255,0,0))
#cv2.circle(small, (x2,y2), radius, (0,0,255))
#cv2.circle(small, (x3, y3), radius, (0,255,0))
redx /= redi
greenx /= greeni
bluex /= bluei
redy /= redi
greeny /= greeni
bluey /= bluei
os.remove(myfile)
print("Rx: {} Gx: {} Bx: {} Ry: {} Gy: {} By: {} horz: {} vert: {}".format(redx, greenx, bluex, redy, greeny, bluey, horz, vert))
#decide if strips are horizontal or vertical, then sort them and put them in shared variables for the main process to read
if(horz>vert):
print('horizontal.')
dict = {'1' : redy, '2' : greeny, '3' : bluey}
ids = sorted(dict.items(), key=lambda kv: kv[1])
print(ids)
n1.value=int(ids[0][0])
n2.value=int(ids[1][0])
n3.value=int(ids[2][0])
print(n1.value, n2.value, n3.value)
else:
print('vertical.')
dict = {'0' : redx, '1' : greenx, '2' : bluex}
ids = sorted(dict.items(), key=lambda kv: kv[1])
print(ids)
n1.value=int(ids[0][0])
n2.value=int(ids[1][0])
n3.value=int(ids[2][0])
print(n1.value, n2.value, n3.value)
Step-through
Here's what happens to the image at each step of the process:
1. Opening, resizing, converting to grayscale
2. Thresholding
3. Closing gaps
While we could definitely improve, this is enough for the probabilistic Hough transform to detect a decent amount of lines. Naturally, for the colour recognition, we come back to the full-colour image.
Debugging output
The output of the above code's debugging messages is as follows:
Stage one: Number of analysed line, coordinates on the centre, mean colour content of the 30px-wide area around the centre, dominant colour (1=red etc.), total number of lines found per colour.
0: 206x235 R: 59 G: 17 B: 27, 1 r 1 g 0 b 0
1: 234x443 R: 86 G: 36 B: 45, 1 r 2 g 0 b 0
2: 371x388 R: 13 G: 36 B: 93, 3 r 2 g 0 b 1
3: 372x575 R: 12 G: 32 B: 90, 3 r 2 g 0 b 2
4: 210x277 R: 119 G: 38 B: 42, 1 r 3 g 0 b 2
5: 371x430 R: 15 G: 35 B: 94, 3 r 3 g 0 b 3
6: 208x235 R: 47 G: 20 B: 25, 1 r 4 g 0 b 3
7: 194x143 R: 121 G: 22 B: 29, 1 r 5 g 0 b 3
8: 371x541 R: 22 G: 32 B: 89, 3 r 5 g 0 b 4
9: 372x388 R: 24 G: 44 B: 92, 3 r 5 g 0 b 5
10: 152x388 R: 63 G: 75 B: 65, 2 r 5 g 1 b 5
Stage two: Averages of each colour's X and Y coordinates, factors used to find strips' orientation (sum of X differences between lines' starts and ends, same for Ys)
Rx: 210.4 Gx: 152.0 Bx: 371.4 Ry: 266.6 Gy: 388.0 By: 464.4 horz: 131 vert: 1053
Let's make a decision - is the dimensional shift larger in X than Y on average?
vertical.
Sort the colours in a dictionary based on average Xs (we don't care about Ys anymore) and print the dictionary:
[('g', 152.0), ('r', 210.4), ('b', 371.4)]
Stay tuned for a video demo in my final blog post!
