import cv2
import numpy as np
import random
import math
from scipy import linalg

no_of_images = 0
t1_y = 10
t2_y = 10
for t1_x in range(30,315,1000):  #30
    for top_size in range(200,355,1000):
        t2_x = t1_x + top_size
        b1_x = t1_x
        b2_x = t2_x
        for b_y in range(205,385,1000): #205,305
            b1_y = b_y
            b2_y = b_y
            no_of_images+=1
            image_name="road"
            location1 = "/path/to/image/"+image_name+".jpg"
            # Read source image.
                im_src = cv2.imread(location1)
                # Four corners in source image
                pts_src = np.array([[float(t2_x), float(t2_y)], [float(b2_x), float(b2_y)], [float(b1_x), float(b1_y)], [float(t1_x), float(t1_y)]])
            location2 = "/path/to/dest/image/after homography.jpg"
                im_dst = cv2.imread(location2)
                # Four corners in destination image.
            for x in range(0,500,1):
                pts_dst = np.array([[500.0, 168.0],[640.0, 358.0],[0.0, 358.0],[500.0-x, 168.0]]) #150

                # Calculate Homography
                h, status = cv2.findHomography(pts_src, pts_dst)
                print "h = ", h

                # Warp source image to destination based on homography
                im_out = cv2.warpPerspective(im_src, h, (im_dst.shape[1],im_dst.shape[0]))

[[  2.46712622e+00  -5.07091356e-03  -7.29742493e+01]
 [  3.54718152e-16   5.63521116e-01   1.60487917e+02]
 [  1.34873822e-18  -1.11718564e-03   1.00000000e+00]]