L38_camshift_object_tracking.py

import cv2, numpy as np

# CAMshift tracking algorithm:
# Continously Adaptive Meanshift
cap = cv2.VideoCapture('slow_traffic.mp4')

ret, frame = cap.read()

x, y, width, height = 300, 200, 100, 50  # calcolato "a mano"
track_window = (x, y, width, height)

ROI = frame[y:y+height, x:x+width]
hsv_ROI = cv2.cvtColor(ROI, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_ROI, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
ROI_hist = cv2.calcHist([hsv_ROI], [0], mask, [180], [0, 180])

cv2.normalize(ROI_hist, ROI_hist, 0, 255, cv2.NORM_MINMAX)

term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)

while cap.isOpened():
    ret, frame = cap.read()
    if ret == True:
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv], [0], ROI_hist, [0, 180], 1)
        # apply the Camshift to get the new location
        ret, track_window = cv2.CamShift(dst, track_window, term_crit)
        #print(ret)
        pts = cv2.boxPoints(ret)
        #print(pts) 
        pts = np.int0(pts)
        final_img = cv2.polylines(frame, [pts], True, (255,0,0), 2)
        #x, y, w, h = track_window
        #final_img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 3)
        cv2.imshow('Final Image', final_img)
        cv2.imshow('Dst', dst)

        #cv2.imshow('Video', frame)
        if cv2.waitKey(30) & 0xFF == 27:
            break
    else:
        break

cap.release()
cv2.destroyAllWindows()