Para trackear a la culebra

motion.py

import cv2
import argparse
import os
import glob
import ipdb
import numpy as np
from matplotlib import pyplot as plt


def last_img():
    return max(glob.iglob('2015*.jpg'), key=os.path.getctime)



parser = argparse.ArgumentParser(description='Detect motion')
#parser.add_argument('-b','--base_img', type=unicode,default='base.jpg', help='A clean image of the place without animals')
parser.add_argument('-n','--now_img',  type=unicode,default=last_img(), help='An image of the place right now')
parser.add_argument('-s','--save', dest='save',action='store_true', help='Save a diff file')
args = parser.parse_args()


def get_banner(last_img):
    img = cv2.imread(last_img)
    return img[465:]

def get_imgs(last_img):
    file_list = sorted(glob.iglob('2015*.jpg'), key=os.path.getctime)
    i = file_list.index(last_img)
    tbefore = cv2.imread(file_list[i-2],cv2.COLOR_RGB2GRAY)[:465]
    tnow = cv2.imread(file_list[i-1],cv2.COLOR_RGB2GRAY)[:465]
    tafter = cv2.imread(file_list[i],cv2.COLOR_RGB2GRAY)[:465]
    return tbefore,tnow,tafter


def diffImg(tbefore,tnow,tafter):
    d1 = cv2.absdiff(tbefore,tafter)
    d2 = cv2.absdiff(tbefore, tnow)
    return cv2.bitwise_and(d1, d2)




tbefore,tnow,tafter = get_imgs(args.now_img)
diff = diffImg(tbefore,tnow,tafter)

#ipdb.set_trace()


diff = np.vstack((diff,get_banner(args.now_img)))
if args.save:
    cv2.imwrite("diff_" + args.now_img, diff)
else:
    for i,img in enumerate([tbefore,tnow,tafter,diff]):
        if i == 0: title = "T-1"
        if i == 1: title = "T now"
        if i == 2: title = "T+1"
        if i == 3: title = "Diff"
        plt.subplot(2,2,i+1).set_title(title)
        plt.imshow(img)
    #plt.imshow(diff)
    plt.show()