/
smart_traffic_management_system.py
446 lines (375 loc) · 13.4 KB
/
smart_traffic_management_system.py
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import numpy as np
import time
import cv2
import os
import re
import vctrl
import removing_func
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BOARD)
GPIO.setwarnings(False)
#setting pins for lights
r1 = 37
y1 = 35
g1 = 33
r2=31
y2=29
g2=23
r3=21
y3=19
g3=15
r4=13
y4=11
g4=7
#setting pin functions
GPIO.setup(r1, GPIO.OUT)
GPIO.setup(y1, GPIO.OUT)
GPIO.setup(g1, GPIO.OUT)
GPIO.setup(r2, GPIO.OUT)
GPIO.setup(y2, GPIO.OUT)
GPIO.setup(g2, GPIO.OUT)
GPIO.setup(r3, GPIO.OUT)
GPIO.setup(y3, GPIO.OUT)
GPIO.setup(g3, GPIO.OUT)
GPIO.setup(r4, GPIO.OUT)
GPIO.setup(y4, GPIO.OUT)
GPIO.setup(g4, GPIO.OUT)
#initial default state
GPIO.output(r1, True)
GPIO.output(y1, False)
GPIO.output(g1, False)
GPIO.output(r2, True)
GPIO.output(y2, False)
GPIO.output(g2, False)
GPIO.output(r3, True)
GPIO.output(y3, False)
GPIO.output(g3, False)
GPIO.output(r4, False)
GPIO.output(y4, False)
GPIO.output(g4, True)
#default initial green light delay
green=1
#default yellow light delay
yellow=2
#lane to start with
p=1
#for system to work infinite times
while(1):
#for not exceeding 4 lanes
while(p<5):
l=0
i = 0
k = 0
#defining all required parameters for each lane
if p==1:
videoname=2
x=300
w=600
y=0
h=400
folder="images1/"
elif p==2:
videoname=2
x=0
w=300
y=0
h=400
folder="images2/"
elif p==3:
videoname=0
x=300
w=600
y=0
h=400
folder="images3/"
elif p==4:
videoname=0
x=0
w=300
y=0
h=400
folder="images4/"
#emptying the folder before writing in any image
removing_func.remove_img(folder)
#capturing video
cap = cv2.VideoCapture(videoname)
#setting resolution
cap.set(3, 1280)
cap.set(4, 720)
#every second frame of the video is to be captured
frame_no=2
#duration until camera will work, green is the delay of previos lane
capture_duration = green
start_time = time.time()
#for reading
col_images = []
col_frames = os.listdir(folder)
# sort file names
col_frames.sort(key=lambda f: int(re.sub('\D', '', f)))
#incase of zero vehicles
if green==0:
capture_duration=1
elif green-2<0:
capture_duration=1
elif green-2==0:
capture_duration=1
else:
capture_duration=green-2
#capturing video
while ( int(time.time() - start_time) < capture_duration ):
ret, frame = cap.read()
if ret == False:
break
else:
#checking if it is the second frame
l=(i % frame_no == 0)
print("l: "+str(l))
if l==True:
k += 1
frame = cv2.resize(frame, (600, 400))
cv2.imwrite(os.path.join(folder, str(k) + '.jpg'), frame)
print(frame.shape)
# append the frames to the list
frame = cv2.resize(frame, (600, 400))
print(frame.shape)
frame=frame[y:h,x:w]
print(frame.shape)
col_images.append(frame)
print(k)
i += 1
print(k)
#print("--- %s seconds ---" % (time.time() - start_time))
# load the COCO class labels our YOLO model was trained on
labelsPath = "classes.names"
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),dtype="uint8")
# paths to the YOLO weights and model configuration
weightsPath = "yolov4final.weights"
configPath = "yolov4testing.cfg"
# load our YOLO object detector trained on COCO dataset (80 classes)
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
# load our input image and grab its spatial dimensions
image = col_images[k-1]
#image=image[x:w,y:h]
(H, W) = image.shape[:2]
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# initialize our lists of detected bounding boxes, confidences, and
# class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > 0.5:
# scale the bounding box coordinates back relative to the
# size of the image, keeping in mind that YOLO actually
# returns the center (x, y)-coordinates of the bounding
# box followed by the boxes' width and height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top and
# and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates, confidences,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
count=0
t=0
print('Types of objects detected in image:')
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
t+=1
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the image
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
print(str(t)+') '+LABELS[classIDs[i]])
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,0.5, color, 2)
if LABELS[classIDs[i]]=='car':
count=count+1
#print("Number of vehicles= "+ str(count))
# show the output image
#print("--- %s seconds ---" % (time.time() - start_time))
cv2.imshow("Image", image)
cv2.waitKey(0)
green=vctrl.delays(count)
#lights
if green==0:
if p==1:
##yellow
GPIO.output(r1, False)
GPIO.output(y1, True)
GPIO.output(g1, False)
GPIO.output(r4, False)
GPIO.output(y4, True)
GPIO.output(g4, False)
time.sleep(yellow)
GPIO.output(r4, True)
GPIO.output(y4, False)
GPIO.output(g4, False)
elif p==2:
##yellow
GPIO.output(r2, False)
GPIO.output(y2, True)
GPIO.output(g2, False)
GPIO.output(r1, False)
GPIO.output(y1, True)
GPIO.output(g1, False)
time.sleep(yellow)
GPIO.output(r1, True)
GPIO.output(y1, False)
GPIO.output(g1, False)
elif p==3:
##yellow
GPIO.output(r2, False)
GPIO.output(y2, True)
GPIO.output(g2, False)
GPIO.output(r3, False)
GPIO.output(y3, True)
GPIO.output(g3, False)
time.sleep(yellow)
GPIO.output(r2, True)
GPIO.output(y2, False)
GPIO.output(g2, False)
elif p==4:
##yellow
GPIO.output(r4, False)
GPIO.output(y4, True)
GPIO.output(g4, False)
GPIO.output(r3, False)
GPIO.output(y3, True)
GPIO.output(g3, False)
time.sleep(yellow)
GPIO.output(r3, True)
GPIO.output(y3, False)
GPIO.output(g3, False)
#setting lights according to the lane
else:
if p==1:
##yellow
GPIO.output(r1, False)
GPIO.output(y1, True)
GPIO.output(g1, False)
GPIO.output(r4, False)
GPIO.output(y4, True)
GPIO.output(g4, False)
time.sleep(yellow)
##green
GPIO.output(r1, False)
GPIO.output(y1, False)
GPIO.output(g1, True)
GPIO.output(r2, True)
GPIO.output(y2, False)
GPIO.output(g2, False)
GPIO.output(r3, True)
GPIO.output(y3, False)
GPIO.output(g3, False)
GPIO.output(r4, True)
GPIO.output(y4, False)
GPIO.output(g4, False)
print(p)
time.sleep(green)
elif p==2:
##yellow
GPIO.output(r2, False)
GPIO.output(y2, True)
GPIO.output(g2, False)
GPIO.output(r1, False)
GPIO.output(y1, True)
GPIO.output(g1, False)
time.sleep(yellow)
##green
GPIO.output(r2, False)
GPIO.output(y2, False)
GPIO.output(g2, True)
GPIO.output(r1, True)
GPIO.output(y1, False)
GPIO.output(g1, False)
GPIO.output(r3, True)
GPIO.output(y3, False)
GPIO.output(g3, False)
GPIO.output(r4, True)
GPIO.output(y4, False)
GPIO.output(g4, False)
print(p)
time.sleep(green)
elif p==3:
##yellow
GPIO.output(r2, False)
GPIO.output(y2, True)
GPIO.output(g2, False)
GPIO.output(r3, False)
GPIO.output(y3, True)
GPIO.output(g3, False)
time.sleep(yellow)
##green
GPIO.output(r3, False)
GPIO.output(y3, False)
GPIO.output(g3, True)
GPIO.output(r1, True)
GPIO.output(y1, False)
GPIO.output(g1, False)
GPIO.output(r2, True)
GPIO.output(y2, False)
GPIO.output(g2, False)
GPIO.output(r4, True)
GPIO.output(y4, False)
GPIO.output(g4, False)
print(p)
time.sleep(green)
elif p==4:
##yellow
GPIO.output(r4, False)
GPIO.output(y4, True)
GPIO.output(g4, False)
GPIO.output(r3, False)
GPIO.output(y3, True)
GPIO.output(g3, False)
time.sleep(yellow)
##green
GPIO.output(r4, False)
GPIO.output(y4, False)
GPIO.output(g4, True)
GPIO.output(r1, True)
GPIO.output(y1, False)
GPIO.output(g1, False)
GPIO.output(r2, True)
GPIO.output(y2, False)
GPIO.output(g2, False)
GPIO.output(r3, True)
GPIO.output(y3, False)
GPIO.output(g3, False)
time.sleep(green)
print("P before changing"+str(p))
p+=1
print("P after changing"+str(p))
removing_func.remove_img(folder)
cap.release()
if p==5:
p=1