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detectWithWebCamera.py
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detectWithWebCamera.py
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import cv2
import numpy as np
import time
from imutils.perspective import four_point_transform
#from imutils import contours
#import imutils
camera = cv2.VideoCapture(0)
def findTrafficSign():
'''
This function find blobs with blue color on the image.
After blobs were found it detects the largest square blob, that must be the sign.
'''
# define range HSV for blue color of the traffic sign
lower_blue = np.array([85,100,70])
upper_blue = np.array([115,255,255])
while True:
# grab the current frame
(grabbed, frame) = camera.read()
if not grabbed:
print("No input image")
break
frame = imutils.resize(frame, width=500)
frameArea = frame.shape[0]*frame.shape[1]
# convert color image to HSV color scheme
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define kernel for smoothing
kernel = np.ones((3,3),np.uint8)
# extract binary image with active blue regions
mask = cv2.inRange(hsv, lower_blue, upper_blue)
# morphological operations
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# find contours in the mask
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
# defite string variable to hold detected sign description
detectedTrafficSign = None
# define variables to hold values during loop
largestArea = 0
largestRect = None
# only proceed if at least one contour was found
if len(cnts) > 0:
for cnt in cnts:
# Rotated Rectangle. Here, bounding rectangle is drawn with minimum area,
# so it considers the rotation also. The function used is cv2.minAreaRect().
# It returns a Box2D structure which contains following detals -
# ( center (x,y), (width, height), angle of rotation ).
# But to draw this rectangle, we need 4 corners of the rectangle.
# It is obtained by the function cv2.boxPoints()
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
# count euclidian distance for each side of the rectangle
sideOne = np.linalg.norm(box[0]-box[1])
sideTwo = np.linalg.norm(box[0]-box[3])
# count area of the rectangle
area = sideOne*sideTwo
# find the largest rectangle within all contours
if area > largestArea:
largestArea = area
largestRect = box
# draw contour of the found rectangle on the original image
if largestArea > frameArea*0.02:
cv2.drawContours(frame,[largestRect],0,(0,0,255),2)
#if largestRect is not None:
# cut and warp interesting area
warped = four_point_transform(mask, [largestRect][0])
# show an image if rectangle was found
#cv2.imshow("Warped", cv2.bitwise_not(warped))
# use function to detect the sign on the found rectangle
detectedTrafficSign = identifyTrafficSign(warped)
#print(detectedTrafficSign)
# write the description of the sign on the original image
cv2.putText(frame, detectedTrafficSign, tuple(largestRect[0]), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)
# show original image
cv2.imshow("Original", frame)
# if the `q` key was pressed, break from the loop
if cv2.waitKey(1) & 0xFF is ord('q'):
cv2.destroyAllWindows()
print("Stop programm and close all windows")
break
def identifyTrafficSign(image):
'''
In this function we select some ROI in which we expect to have the sign parts. If the ROI has more active pixels than threshold we mark it as 1, else 0
After path through all four regions, we compare the tuple of ones and zeros with keys in dictionary SIGNS_LOOKUP
'''
# define the dictionary of signs segments so we can identify
# each signs on the image
SIGNS_LOOKUP = {
(1, 0, 0, 1): 'Turn Right', # turnRight
(0, 0, 1, 1): 'Turn Left', # turnLeft
(0, 1, 0, 1): 'Move Straight', # moveStraight
(1, 0, 1, 1): 'Turn Back', # turnBack
}
THRESHOLD = 150
image = cv2.bitwise_not(image)
# (roiH, roiW) = roi.shape
#subHeight = thresh.shape[0]/10
#subWidth = thresh.shape[1]/10
(subHeight, subWidth) = np.divide(image.shape, 10)
subHeight = int(subHeight)
subWidth = int(subWidth)
# mark the ROIs borders on the image
cv2.rectangle(image, (subWidth, 4*subHeight), (3*subWidth, 9*subHeight), (0,255,0),2) # left block
cv2.rectangle(image, (4*subWidth, 4*subHeight), (6*subWidth, 9*subHeight), (0,255,0),2) # center block
cv2.rectangle(image, (7*subWidth, 4*subHeight), (9*subWidth, 9*subHeight), (0,255,0),2) # right block
cv2.rectangle(image, (3*subWidth, 2*subHeight), (7*subWidth, 4*subHeight), (0,255,0),2) # top block
# substract 4 ROI of the sign thresh image
leftBlock = image[4*subHeight:9*subHeight, subWidth:3*subWidth]
centerBlock = image[4*subHeight:9*subHeight, 4*subWidth:6*subWidth]
rightBlock = image[4*subHeight:9*subHeight, 7*subWidth:9*subWidth]
topBlock = image[2*subHeight:4*subHeight, 3*subWidth:7*subWidth]
# we now track the fraction of each ROI
leftFraction = np.sum(leftBlock)/(leftBlock.shape[0]*leftBlock.shape[1])
centerFraction = np.sum(centerBlock)/(centerBlock.shape[0]*centerBlock.shape[1])
rightFraction = np.sum(rightBlock)/(rightBlock.shape[0]*rightBlock.shape[1])
topFraction = np.sum(topBlock)/(topBlock.shape[0]*topBlock.shape[1])
segments = (leftFraction, centerFraction, rightFraction, topFraction)
segments = tuple(1 if segment > THRESHOLD else 0 for segment in segments)
cv2.imshow("Warped", image)
if segments in SIGNS_LOOKUP:
return SIGNS_LOOKUP[segments]
else:
return None
def main():
findTrafficSign()
if __name__ == '__main__':
main()