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person_tracking.py
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person_tracking.py
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import numpy as np
import cv2
import time
import imutils
import datetime
import argparse
#import spectral as spc
from matplotlib import pyplot as plt
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())
#pts = deque(maxlen=args["buffer"])
person_cascade = cv2.CascadeClassifier('haarcascade_fullbody.xml')
# face_cascade = cv2.CascadeClassifier('haarcascade_face.xml')
# if the video argument is None, then we are reading from webcam
if args.get("video", None) is None:
camera = cv2.VideoCapture(0)
time.sleep(0.25)
# otherwise, we are reading from a video file
else:
# time.sleep(0.10)
camera = cv2.VideoCapture(args["video"])
firstFrame = None
xpoints = np.array([[]], np.int32)
ypoints = np.array([[]], np.int32)
numObjects = 0
# loop over the frames of the video
while True:
# grab the current frame and initialize the occupied/unoccupied
# text
time.sleep(0.03)
peopleCount = 0
carCount = 0
(grabbed, frame) = camera.read()
# frame = frame[0:500, 0:500]
text = "Unoccupied"
# if the frame could not be grabbed, then we have reached the end
# of the video
if not grabbed:
break
# Limit based on number of objects
blurLimit = 0
# if (numObjects>20):
# blurLimit = 18
# elif (numObjects>14):
# blurLimit = 6
# else:
# blurLimit = 0
# resize the frame, convert it to grayscale, and blur it
frame = cv2.resize(frame, (500,375))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5 + blurLimit, 5 + blurLimit), 0)
#print frame.shape
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
continue
# compute the absolute difference between the current frame and
# first frame
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# firstFrame = gray
# dilate the thresholded image to fill in 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)
numObjects = len(cnts)
# # (cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
# cv2.CHAIN_APPROX_SIMPLE)
index = 0
# loop over the contours
for c in cnts:
if (len(cnts)>20):
limit = 350
elif (len(cnts)>16):
limit = 90
else:
limit = 15
# if the contour is too small, ignore it
if cv2.contourArea(c) < limit:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
# Checks for height and width ratio to detect people
if ((w*8)/5 <= h) or (cv2.contourArea(c) < 50):
cv2.rectangle(frame, (x, y), (x + w, y + h), (255,0, 0), 2)
peopleCount += 1
else:
carCount += 1
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
xpoints = np.append(xpoints, x)
ypoints = np.append(ypoints, y)
for coord in xrange(0,len(xpoints)):
if (x != xpoints[coord] or y != ypoints[coord]):
if ((w*8)/5 <= h) or (cv2.contourArea(c) < 50):
cv2.rectangle(frame, (x, y), (x + w, y + h), (255,0, 0), 2)
#peopleCount += 1
else:
#carCount += 1
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if (index == 20):
index = 0
xpoints = np.array([[]], np.int32)
ypoints = np.array([[]], np.int32)
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
people = person_cascade.detectMultiScale(roi_gray)
text = "Occupied"
# identify the object
# cv2.putText(frame, "Object".format(text), (x,y),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
# for (ex,ey,ew,eh) in people:
# cv2.putText(frame, (xpoints[i], ypoints[i]), 1, (0, 255, 0), thickness=1, lineType=8, shift=0)
index+=1
# draw the text and timestamp on the frame
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# Display people and car count
cv2.putText(frame, "People: {}".format(peopleCount), (250, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0, 0), 2)
cv2.putText(frame, "Objects: {}".format(carCount), (350, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
pts1 = np.float32([(170,56),(284,87),(169,173),(286,183)])
pts2 = np.float32([(0,337),(0,291),(0,337),(0,291)])
# show the frame and record if the user presses a key
# hostage = frame[0:200, 0:300]
cv2.imshow("webcam feed", frame)
# cv2.imshow("webcam feed hostage view", hostage)
cv2.imshow("Thresh", thresh)
cv2.imshow("Frame Delta", frameDelta)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
userAsk = "none"
while str(userAsk) != "3":
userAsk = input("To get more info about cars type 1, to get more infor about people type 2, to close the program type 3 \n")
if str(userAsk) == "1":
print "Fetching details about cars..."
elif str(userAsk) == "2":
print "Fetching details about people..."
elif str(userAsk) == "3":
print "Exiting..."
else:
print "This is not a possible command"
#print userAsk
camera.release()
cv2.destroyAllWindows()