detect object in image with almost similar background - opencv

Question

I have to detect mice in a cage, input images look like following:
at the moment I am using cv.createBackgroundSubtractorMOG2() in the video stream to find the area containing the mice and afterwards Canny Edge detector to extract the contours of the mice.
However, this is not working that well.. the more the mice is moving the better, but I guess there could be a better approach to detect the mice.
Does anyne have a different idea how to detect the mice?
thanks in advance

Answer 1

After subtracting the background, you could use a threshold to remove noise. Try saving the subtracted image and seeing what it looks like. Here's a script I use to tweak filter parameters (run it with the subtracted image):
import cv2
import numpy as np
screenshot_path = 'screenshot.bmp'
def nothing(x):
pass
# Creating a window for later use
cv2.namedWindow('mask', cv2.WINDOW_NORMAL)
cv2.namedWindow('trackbar', cv2.WINDOW_NORMAL)
# Starting with 100's to prevent error while masking
h, s, v = 100, 100, 100
# Creating track bar
cv2.createTrackbar('h', 'trackbar', 0, 180, nothing)
cv2.createTrackbar('s', 'trackbar', 0, 255, nothing)
cv2.createTrackbar('v', 'trackbar', 164, 255, nothing)
cv2.createTrackbar('h2', 'trackbar', 120, 180, nothing)
cv2.createTrackbar('s2', 'trackbar', 12, 255, nothing)
cv2.createTrackbar('v2', 'trackbar', 253, 255, nothing)
frame = cv2.imread(screenshot_path)
# converting to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
while (1):
# get info from track bar and appy to result
h = cv2.getTrackbarPos('h', 'trackbar')
s = cv2.getTrackbarPos('s', 'trackbar')
v = cv2.getTrackbarPos('v', 'trackbar')
h2 = cv2.getTrackbarPos('h2', 'trackbar')
s2 = cv2.getTrackbarPos('s2', 'trackbar')
v2 = cv2.getTrackbarPos('v2', 'trackbar')
# Normal masking algorithm
lower = np.array([h, s, v])
upper = np.array([h2, s2, v2])
mask = cv2.inRange(hsv, lower, upper)
result = cv2.bitwise_and(frame,frame,mask = mask)
cv2.imshow('result', result)
print(h, s, v, h2, s2, v2)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
If that doesn't work, I would use an object tracker API like CSRT
# python opencv_object_tracking.py
# python opencv_object_tracking.py --video dashcam_boston.mp4 --tracker csrt
# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import argparse
import imutils
import time
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str,
help="path to input video file")
ap.add_argument("-t", "--tracker", type=str, default="kcf",
help="OpenCV object tracker type")
args = vars(ap.parse_args())
# extract the OpenCV version info
(major, minor) = cv2.__version__.split(".")[:2]
# if we are using OpenCV 3.2 OR BEFORE, we can use a special factory
# function to create our object tracker
if int(major) == 3 and int(minor) < 3:
tracker = cv2.Tracker_create(args["tracker"].upper())
# otherwise, for OpenCV 3.3 OR NEWER, we need to explicity call the
# approrpiate object tracker constructor:
else:
# initialize a dictionary that maps strings to their corresponding
# OpenCV object tracker implementations
OPENCV_OBJECT_TRACKERS = {
"csrt": cv2.TrackerCSRT_create,
"kcf": cv2.TrackerKCF_create,
"boosting": cv2.TrackerBoosting_create,
"mil": cv2.TrackerMIL_create,
"tld": cv2.TrackerTLD_create,
"medianflow": cv2.TrackerMedianFlow_create,
"mosse": cv2.TrackerMOSSE_create
}
# grab the appropriate object tracker using our dictionary of
# OpenCV object tracker objects
tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
# initialize the bounding box coordinates of the object we are going
# to track
initBB = None
# if a video path was not supplied, grab the reference to the web cam
if not args.get("video", False):
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(1.0)
# otherwise, grab a reference to the video file
else:
vs = cv2.VideoCapture(args["video"])
# initialize the FPS throughput estimator
fps = None
# loop over frames from the video stream
while True:
# grab the current frame, then handle if we are using a
# VideoStream or VideoCapture object
frame = vs.read()
frame = frame[1] if args.get("video", False) else frame
# check to see if we have reached the end of the stream
if frame is None:
break
# resize the frame (so we can process it faster) and grab the
# frame dimensions
frame = imutils.resize(frame, width=500)
(H, W) = frame.shape[:2]
# check to see if we are currently tracking an object
if initBB is not None:
# grab the new bounding box coordinates of the object
(success, box) = tracker.update(frame)
# check to see if the tracking was a success
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
# update the FPS counter
fps.update()
fps.stop()
# initialize the set of information we'll be displaying on
# the frame
info = [
("Tracker", args["tracker"]),
("Success", "Yes" if success else "No"),
("FPS", "{:.2f}".format(fps.fps())),
]
# loop over the info tuples and draw them on our frame
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 's' key is selected, we are going to "select" a bounding
# box to track
if key == ord("s"):
# select the bounding box of the object we want to track (make
# sure you press ENTER or SPACE after selecting the ROI)
initBB = cv2.selectROI("Frame", frame, fromCenter=False,
showCrosshair=True)
# start OpenCV object tracker using the supplied bounding box
# coordinates, then start the FPS throughput estimator as well
tracker.init(frame, initBB)
fps = FPS().start()
# if the `q` key was pressed, break from the loop
elif key == ord("q"):
break
# if we are using a webcam, release the pointer
if not args.get("video", False):
vs.stop()
# otherwise, release the file pointer
else:
vs.release()
# close all windows
cv2.destroyAllWindows()