Object Tracking

파이썬+OPENCV로 객체 추적을 확인해 보기 위해서 자료를 찾아보았다.

- 기본적으로 MeanShift를 사용해서 물체를 추적하는것으로 보인다.(CamShift로 개선하여 사용한다)

 

dsbook.tistory.com/180

import cv2
import numpy as np

capture = cv2.VideoCapture("TestVideo.mp4")

histogram = None

terminal = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 15, 0.5)

while capture.isOpened():
    ret, frame = capture.read()
    if not ret:
        break
    draw = frame.copy()
    
    if histogram is not None:
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv], [0], histogram, [0,180], 1)
        ret, (x,y,w,h) = cv2.CamShift(dst, (x,y,w,h), terminal)
        cv2.rectangle(draw,(x,y), (x+w, y+h), (0,255,0), 2)
        result = np.hstack((draw, cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)))
    else :
        cv2.putText(draw, "Target", (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 1, cv2.LINE_AA)
        result = draw
    
    cv2.imshow("CamShift Tracking", result)
    key = cv2.waitKey(10) & 0xff
    if key == 27:
        break
    elif key == ord(' '):
        x,y,w,h = cv2.selectROI("CamShift Tracking", frame, False)
        if w and h :
            roi = frame[y:y+h, x:x+w]
            roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
            histogram = cv2.calcHist([roi], [0], None, [180], [0,180])
            cv2.normalize(histogram, histogram, 0, 255, cv2.NORM_MINMAX)
        else:
            histogram = None

capture.release()
cv2.destroyAllWindows()

 

s-engineer.tistory.com/15

import cv2
import numpy as np
 
roi = None
drag_start = None
mouse_status = 0
tracking_start = False
 
def onMouse(event, x, y, flags, param = None):
    global roi
    global drag_start
    global mouse_status
    global tracking_start
    
    if event == cv2.EVENT_LBUTTONDOWN:
        drag_start = (x, y)
        roi = (0, 0, 0, 0) # ROI를 재설정하는 경우를 위한 초기화
        tracking_start = False
    elif event == cv2.EVENT_MOUSEMOVE:
        if flags == cv2.EVENT_FLAG_LBUTTON:
            xmin = min(x, drag_start[0])
            ymin = min(y, drag_start[1])
            xmax = max(x, drag_start[0])
            ymax = max(y, drag_start[1])
            roi = (xmin, ymin, xmax, ymax)
            mouse_status = 1
    elif event == cv2.EVENT_LBUTTONUP:
        mouse_status = 2
 
cv2.namedWindow('meanShift tracking')
cv2.setMouseCallback('meanShift tracking', onMouse)
 
cap = cv2.VideoCapture('TestVideo.mp4')
if(not cap.isOpened()):
    print('Error opening video')
height, width = (int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
roi_mask = np.zeros((height, width), dtype = np.uint8)
term_crit = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 10, 1)
 
while True:
    ret, meanframe = cap.read()
    if not ret:
        break
    
    camframe = meanframe.copy()
    hsv = cv2.cvtColor(meanframe, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, (0., 60., 32.), (180., 255., 255.))
    # Checks if array elements lie between the elements of two other arrays.
 
    if mouse_status == 1:
        x1, y1, x2, y2 = roi
        cv2.rectangle(meanframe, (x1, y1), (x2, y2), (255, 0, 0), 2)
 
    if mouse_status == 2:
        print('Initializing...', end = ' ')
        mouse_status = 0
        x1, y1, x2, y2 = roi
        if (np.abs(x1 - x2) < 10) or (np.abs(y1 - y2) < 10):
            print('failed. Too small ROI. (Width: %d, Height: %d)' %(np.abs(x1 - x2), np.abs(y1 - y2)))
            continue
 
        mask_roi = mask[y1:y2, x1:x2]
        hsv_roi = hsv[y1:y2, x1:x2]
 
        hist_roi = cv2.calcHist([hsv_roi], [0], mask_roi, [16], [0, 180])
        cv2.normalize(hist_roi, hist_roi, 0, 255, cv2.NORM_MINMAX)
        track_window1 = (x1, y1, x2 - x1, y2 - y1)
        track_window2 = (x1, y1, x2 - x1, y2 - y1)
        tracking_start = True
        print('Done.')
 
    if tracking_start:
        backP = cv2.calcBackProject([hsv], [0], hist_roi, [0, 180], 1)
        # Calculates the back projection of a histogram.
        backP &= mask
        cv2.imshow('backP', backP)
 
        ret, track_window1 = cv2.meanShift(backP, track_window1, term_crit)
        # Finds an object on a back projection image.
        x, y, w, h = track_window1
        cv2.rectangle(meanframe, (x, y), (x + w, y + h), (0, 0, 255), 2)
 
        track_box, track_window2 = cv2.CamShift(backP, track_window2, term_crit)
        # Finds an object center, size, and orientation.
        x, y, w, h = track_window2
        cv2.rectangle(camframe, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.ellipse(camframe, track_box, (0, 255, 255), 2)
        pts = cv2.boxPoints(track_box) # Finds the four vertices of a rotated rect.
        pts = np.int0(pts)
        dst = cv2.polylines(camframe, [pts], True, (0, 0, 255), 2)
 
    cv2.imshow('meanShift tracking', meanframe)
    cv2.imshow('CamShift tracking', camframe)
    key = cv2.waitKey(25)
    if key == 27:
        break
 
if cap.isOpened():
    cap.release()
cv2.destroyAllWindows()

 

 

찾은코드

gist.github.com/pknowledge/70ea4e0aa30f728eb2a7235edd41b99a

import numpy as np
import cv2 as cv
cap = cv.VideoCapture('TestVideo.mp4')
# take first frame of the video
ret, frame = cap.read()
# setup initial location of window
x, y, width, height = 880, 185, 50, 50
track_window = (x, y ,width, height)
# set up the ROI for tracking
roi = frame[y:y+height, x : x+width]
hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255)))
roi_hist = cv.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv.normalize(roi_hist, roi_hist, 0, 255,cv.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1)
cv.imshow('roi',roi)
while(1):
    ret, frame = cap.read()
    if ret == True:

        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
        # apply meanshift to get the new location
        ret, track_window = cv.meanShift(dst, track_window, term_crit)
        # Draw it on image
        x,y,w,h = track_window
        final_image = cv.rectangle(frame, (x,y), (x+w, y+h), 255, 3)

        cv.imshow('dst', dst)
        cv.imshow('final_image',final_image)
        k = cv.waitKey(30) & 0xff
        if k == 27:
            break
    else:
        break

 

우선적으로 검색한 자료

deep-learning-study.tistory.com/275

[파이썬 OpenCV] 영상의 객체 추적 - 평균 이동(Mean Shift) 방법 - cv2.meanShift, cv2.calcBackProject

 

MeanShift

darkpgmr.tistory.com/64

[영상추적#1] Mean Shift 추적

 

히스토그램 역투영

vision0814.tistory.com/70

히스토그램