-
Notifications
You must be signed in to change notification settings - Fork 0
/
blob_detection.py
165 lines (132 loc) · 5.73 KB
/
blob_detection.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
"""
Bennet Outland
Rocker Robotics
National Robotics Challenge | Autonomous Vehicle Challenge
Blob Detection Algorithm
License: MIT
Thank you to the creators of the OpenCV Docs for the great documentation and
example code that was modified to achieve these results.
Input: USB Camera Video, Scaling Factor
Basic Process:
. Scale down the Video
. Create masks of given color ranges (Blue, Yellow, and Red in this case)
. Load SimpleBlobDetector and filter by area
. Calculate blob size and approximate turning angle to blob
Return: Blob Size, Turning Angle to Blob, Bucket Color {'Blue': 0, 'Yellow': 1, 'Red': 2}
"""
import cv2 as cv
import numpy as np
def relative_angle(x, frame_width):
"""
x: x position of the center of the blob
frame_width: width of the frame
Return: the relative/approximate angle based upon the
center of the blob and its position on the screen.
"""
return ((-np.pi / frame_width) * x) + np.pi
def blob_detection(hsv, lower_color, upper_color, color):
"""
hsv: frame converted to HSV color format
lower_color: lowest designated HSV color [numpy array, rank 1, 3 entries]
upper_color: highest designated HSV color [numpy array, rank 1, 3 entries]
color: string identifying the color to be identified
Note: color variable is used to determine whether or not the color to
identify is red, so the circularity can be adjusted.
Return: Keypoints.
"""
# Threshold the HSV image to get only blue colors
mask = cv.inRange(hsv, lower_color, upper_color)
# Bitwise-AND mask and original image
#res = cv.bitwise_and(frame,frame, mask = mask)
inv_mask = cv.bitwise_not(mask)
params = cv.SimpleBlobDetector_Params()
#Thresholds for reporting
params.minThreshold = 50
params.maxThreshold = 1000 #10000
#Area filtering. Make sure that the areas are of a reasonable size
params.filterByArea = True
params.minArea = 50
params.maxArea = 1000
#Color filtering: search for black blobs
params.filterByColor = True
params.blobColor = 0
#Circularity
"""
f = (4 * np.pi * w * h) / (2 * w + 2 * h) ** 2
= 0.78 +- 0.16 (20% tolerance) => [0.62, 0.93] (Blue/Yellow)
= 0.65 +- 0.13 (20% tolerance) => [0.52, 0.78] (Red)
"""
params.filterByCircularity = True
if (color == "red" or color == "Red"):
params.minCircularity = 0.52 #Red: 0.52, Blue/Yellow: 0.62
params.maxCircularity = 0.78 #Red: 0.78, Blue/Yello: 0.93
else:
params.minCircularity = 0.62 #Red: 0.52, Blue/Yellow: 0.62
params.maxCircularity = 0.93 #Red: 0.78, Blue/Yellow: 0.93
#Negate the following filters
params.filterByInertia = False
params.filterByConvexity = False
ver = (cv.__version__).split('.')
if int(ver[0]) < 3:
detector = cv.SimpleBlobDetector(params)
else:
detector = cv.SimpleBlobDetector_create(params)
#Detect blobs
keypoints = detector.detect(inv_mask)
return keypoints
def main(cap, scale):
while (True):
#Read each frame
_, frame = cap.read()
#Scale down the frame and determine the image width
frame = cv.resize(frame,None,fx=scale, fy=scale, interpolation = cv.INTER_CUBIC)
frame_width = frame.shape[1]
#Convert image to HSV
hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
#Define color ranges. Note: Will need to be tweaked for production runs
#Blue:
lower_blue = np.array([115,50,50])
upper_blue = np.array([123,255,255])
#Red:
lower_red = np.array([150, 15, 15])
upper_red = np.array([250, 255, 255])
#Yellow:
lower_yellow = np.array([30,25,25]) #[30, 25, 25]
upper_yellow = np.array([85,255,255])
#Report Blue Buckets
kp_b = blob_detection(hsv, lower_blue, upper_blue, 'blue')
try:
#print(kp_b[0].size, relative_angle(kp_b[0].pt[0], frame_width), 'blue')
return [kp_b[0].size, relative_angle(kp_b[0].pt[0], frame_width), 0]
except IndexError:
pass
#Report Yellow Buckets
kp_y = blob_detection(hsv, lower_yellow, upper_yellow, 'yellow')
try:
#print(kp_y[0].size, relative_angle(kp_y[0].pt[0], frame_width), 'yellow')
return [kp_y[0].size, relative_angle(kp_y[0].pt[0], frame_width), 1]
except IndexError:
pass
#Report Red Buckets
kp_r = blob_detection(hsv, lower_red, upper_red, 'red')
try:
#print(kp_r[0].size, relative_angle(kp_r[0].pt[0], frame_width), 'red')
return [kp_y[0].size, relative_angle(kp_y[0].pt[0], frame_width), 2]
except IndexError:
pass
"""
#Used for video demonstration
frame_with_keypoints_b = cv.drawKeypoints(frame, kp_b, np.array([]), (255, 0, 0), cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_r = cv.drawKeypoints(frame, kp_r, np.array([]), (0, 0, 255), cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_y = cv.drawKeypoints(frame, kp_y, np.array([]), (0, 255, 255), cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_br = cv.bitwise_or(frame_with_keypoints_b, frame_with_keypoints_r)
frame_with_keypoints_bry = cv.bitwise_or(frame_with_keypoints_br, frame_with_keypoints_y)
cv.imshow("Keypoints", frame_with_keypoints_bry)
k = cv.waitKey(5) & 0xFF
if k == 27:
break
cv.destroyAllWindows()
"""
#Define camera input. Can be file or camera index
capture = cv.VideoCapture('test_footage_sim_01_trim.mp4')
main(capture, 0.25)