Saw this on Hackaday and had to try it.
# CarSpeed Version 2.0
# import the necessary packages
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import math
import datetime
import cv2
# place a prompt on the displayed image
def prompt_on_image(txt):
global image
cv2.putText(image, txt, (10, 35),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# calculate speed from pixels and time
def get_speed(pixels, ftperpixel, secs):
if secs > 0.0:
return ((pixels * ftperpixel)/ secs) * 0.681818
else:
return 0.0
# calculate elapsed seconds
def secs_diff(endTime, begTime):
diff = (endTime - begTime).total_seconds()
return diff
# record speed in .csv format
def record_speed(res):
global csvfileout
f = open(csvfileout, 'a')
f.write(res+"\n")
f.close
# mouse callback function for drawing capture area
def draw_rectangle(event,x,y,flags,param):
global ix,iy,fx,fy,drawing,setup_complete,image, org_image, prompt
if event == cv2.EVENT_LBUTTONDOWN:
drawing = True
ix,iy = x,y
elif event == cv2.EVENT_MOUSEMOVE:
if drawing == True:
image = org_image.copy()
prompt_on_image(prompt)
cv2.rectangle(image,(ix,iy),(x,y),(0,255,0),2)
elif event == cv2.EVENT_LBUTTONUP:
drawing = False
fx,fy = x,y
image = org_image.copy()
prompt_on_image(prompt)
cv2.rectangle(image,(ix,iy),(fx,fy),(0,255,0),2)
# define some constants
DISTANCE = 76 #<---- enter your distance-to-road value here
MIN_SPEED = 0 #<---- enter the minimum speed for saving images
SAVE_CSV = False #<---- record the results in .csv format in carspeed_(date).csv
THRESHOLD = 15
MIN_AREA = 175
BLURSIZE = (15,15)
IMAGEWIDTH = 640
IMAGEHEIGHT = 480
RESOLUTION = [IMAGEWIDTH,IMAGEHEIGHT]
FOV = 53.5 #<---- Field of view
FPS = 30
SHOW_BOUNDS = True
SHOW_IMAGE = True
# the following enumerated values are used to make the program more readable
WAITING = 0
TRACKING = 1
SAVING = 2
UNKNOWN = 0
LEFT_TO_RIGHT = 1
RIGHT_TO_LEFT = 2
# calculate the the width of the image at the distance specified
frame_width_ft = 2*(math.tan(math.radians(FOV*0.5))*DISTANCE)
ftperpixel = frame_width_ft / float(IMAGEWIDTH)
print("Image width in feet {} at {} from camera".format("%.0f" % frame_width_ft,"%.0f" % DISTANCE))
# state maintains the state of the speed computation process
# if starts as WAITING
# the first motion detected sets it to TRACKING
# if it is tracking and no motion is found or the x value moves
# out of bounds, state is set to SAVING and the speed of the object
# is calculated
# initial_x holds the x value when motion was first detected
# last_x holds the last x value before tracking was was halted
# depending upon the direction of travel, the front of the
# vehicle is either at x, or at x+w
# (tracking_end_time - tracking_start_time) is the elapsed time
# from these the speed is calculated and displayed
state = WAITING
direction = UNKNOWN
initial_x = 0
last_x = 0
#-- other values used in program
base_image = None
abs_chg = 0
mph = 0
secs = 0.0
ix,iy = -1,-1
fx,fy = -1,-1
drawing = False
setup_complete = False
tracking = False
text_on_image = 'No cars'
prompt = ''
# initialize the camera. Adjust vflip and hflip to reflect your camera's orientation
camera = PiCamera()
camera.resolution = RESOLUTION
camera.framerate = FPS
camera.vflip = True
camera.hflip = True
rawCapture = PiRGBArray(camera, size=camera.resolution)
# allow the camera to warm up
time.sleep(0.9)
# create an image window and place it in the upper left corner of the screen
cv2.namedWindow("Speed Camera")
cv2.moveWindow("Speed Camera", 10, 40)
# call the draw_rectangle routines when the mouse is used
cv2.setMouseCallback('Speed Camera',draw_rectangle)
# grab a reference image to use for drawing the monitored area's boundry
camera.capture(rawCapture, format="bgr", use_video_port=True)
image = rawCapture.array
rawCapture.truncate(0)
org_image = image.copy()
if SAVE_CSV:
csvfileout = "carspeed_{}.cvs".format(datetime.datetime.now().strftime("%Y%m%d_%H%M"))
record_speed('Date,Day,Time,Speed,Image')
else:
csvfileout = ''
prompt = "Define the monitored area - press 'c' to continue"
prompt_on_image(prompt)
# wait while the user draws the monitored area's boundry
while not setup_complete:
cv2.imshow("Speed Camera",image)
#wait for for c to be pressed
key = cv2.waitKey(1) & 0xFF
# if the `c` key is pressed, break from the loop
if key == ord("c"):
break
# the monitored area is defined, time to move on
prompt = "Press 'q' to quit"
# since the monitored area's bounding box could be drawn starting
# from any corner, normalize the coordinates
if fx > ix:
upper_left_x = ix
lower_right_x = fx
else:
upper_left_x = fx
lower_right_x = ix
if fy > iy:
upper_left_y = iy
lower_right_y = fy
else:
upper_left_y = fy
lower_right_y = iy
monitored_width = lower_right_x - upper_left_x
monitored_height = lower_right_y - upper_left_y
print("Monitored area:")
print(" upper_left_x {}".format(upper_left_x))
print(" upper_left_y {}".format(upper_left_y))
print(" lower_right_x {}".format(lower_right_x))
print(" lower_right_y {}".format(lower_right_y))
print(" monitored_width {}".format(monitored_width))
print(" monitored_height {}".format(monitored_height))
print(" monitored_area {}".format(monitored_width * monitored_height))
# capture frames from the camera (using capture_continuous.
# This keeps the picamera in capture mode - it doesn't need
# to prep for each frame's capture.
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
#initialize the timestamp
timestamp = datetime.datetime.now()
# grab the raw NumPy array representing the image
image = frame.array
# crop area defined by [y1:y2,x1:x2]
gray = image[upper_left_y:lower_right_y,upper_left_x:lower_right_x]
# convert the fram to grayscale, and blur it
gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, BLURSIZE, 0)
# if the base image has not been defined, initialize it
if base_image is None:
base_image = gray.copy().astype("float")
lastTime = timestamp
rawCapture.truncate(0)
cv2.imshow("Speed Camera", image)
# compute the absolute difference between the current image and
# base image and then turn eveything lighter gray than THRESHOLD into
# white
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(base_image))
thresh = cv2.threshold(frameDelta, THRESHOLD, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in any holes, then find contours
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
(_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# look for motion
motion_found = False
biggest_area = 0
# examine the contours, looking for the largest one
for c in cnts:
(x1, y1, w1, h1) = cv2.boundingRect(c)
# get an approximate area of the contour
found_area = w1*h1
# find the largest bounding rectangle
if (found_area > MIN_AREA) and (found_area > biggest_area):
biggest_area = found_area
motion_found = True
x = x1
y = y1
h = h1
w = w1
if motion_found:
if state == WAITING:
# intialize tracking
state = TRACKING
initial_x = x
last_x = x
initial_time = timestamp
last_mph = 0
text_on_image = 'Tracking'
print(text_on_image)
print("x-chg Secs MPH x-pos width")
else:
# compute the lapsed time
secs = secs_diff(timestamp,initial_time)
if secs >= 15:
state = WAITING
direction = UNKNOWN
text_on_image = 'No Car Detected'
motion_found = False
biggest_area = 0
rawCapture.truncate(0)
base_image = None
print('Resetting')
continue
if state == TRACKING:
if x >= last_x:
direction = LEFT_TO_RIGHT
abs_chg = x + w - initial_x
else:
direction = RIGHT_TO_LEFT
abs_chg = initial_x - x
mph = get_speed(abs_chg,ftperpixel,secs)
print("{0:4d} {1:7.2f} {2:7.0f} {3:4d} {4:4d}".format(abs_chg,secs,mph,x,w))
real_y = upper_left_y + y
real_x = upper_left_x + x
# is front of object outside the monitired boundary? Then write date, time and speed on image
# and save it
if ((x <= 2) and (direction == RIGHT_TO_LEFT)) \
or ((x+w >= monitored_width - 2) \
and (direction == LEFT_TO_RIGHT)):
if (last_mph > MIN_SPEED): # save the image
# timestamp the image
cv2.putText(image, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, image.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
# write the speed: first get the size of the text
size, base = cv2.getTextSize( "%.0f mph" % last_mph, cv2.FONT_HERSHEY_SIMPLEX, 2, 3)
# then center it horizontally on the image
cntr_x = int((IMAGEWIDTH - size[0]) / 2)
cv2.putText(image, "%.0f mph" % last_mph,
(cntr_x , int(IMAGEHEIGHT * 0.2)), cv2.FONT_HERSHEY_SIMPLEX, 2.00, (0, 255, 0), 3)
# and save the image to disk
imageFilename = "car_at_" + datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpg"
# use the following image file name if you want to be able to sort the images by speed
#imageFilename = "car_at_%02.0f" % last_mph + "_" + datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpg"
cv2.imwrite(imageFilename,image)
if SAVE_CSV:
cap_time = datetime.datetime.now()
record_speed(cap_time.strftime("%Y.%m.%d")+','+cap_time.strftime('%A')+','+\
cap_time.strftime('%H%M')+','+("%.0f" % last_mph) + ','+imageFilename)
state = SAVING
# if the object hasn't reached the end of the monitored area, just remember the speed
# and its last position
last_mph = mph
last_x = x
else:
if state != WAITING:
state = WAITING
direction = UNKNOWN
text_on_image = 'No Car Detected'
print(text_on_image)
# only update image and wait for a keypress when waiting for a car
# This is required since waitkey slows processing.
if (state == WAITING):
# draw the text and timestamp on the frame
cv2.putText(image, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, image.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
cv2.putText(image, "Road Status: {}".format(text_on_image), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX,0.35, (0, 0, 255), 1)
if SHOW_BOUNDS:
#define the monitored area right and left boundary
cv2.line(image,(upper_left_x,upper_left_y),(upper_left_x,lower_right_y),(0, 255, 0))
cv2.line(image,(lower_right_x,upper_left_y),(lower_right_x,lower_right_y),(0, 255, 0))
# show the frame and check for a keypress
if SHOW_IMAGE:
prompt_on_image(prompt)
cv2.imshow("Speed Camera", image)
# Adjust the base_image as lighting changes through the day
if state == WAITING:
last_x = 0
cv2.accumulateWeighted(gray, base_image, 0.25)
state=WAITING;
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the loop and terminate processing
if key == ord("q"):
break
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# cleanup the camera and close any open windows
cv2.destroyAllWindows()