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estimate_homography.py
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estimate_homography.py
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import cv2
import numpy as np
import matplotlib.pyplot as plt
import os
def calculate_homography(in_pts, out_pts):
"""
in_pts = H*out_pts
:param in_pts: correspond to src
:param out_pts:
:return:
"""
if isinstance(in_pts, list):
in_pts = np.array(in_pts)
if isinstance(out_pts, list):
out_pts = np.array(out_pts)
mat_A, mat_b = build_sys_equations(in_pts, out_pts)
H = np.matmul(np.linalg.pinv(mat_A), mat_b)
# print(mat_b)
#
# print(np.matmul(mat_A, H))
H = np.reshape(np.hstack((H,1)), (3,3))
return H
def convert_to_homogenous_crd(inp, axis=1):
if isinstance(inp, list):
inp = np.array(inp)
r, c = inp.shape
if axis == 1:
out = np.concatenate((inp, np.ones((r, 1))), axis=axis)
else:
out = np.concatenate((inp, np.ones((1, c))), axis=axis)
return out
def get_pixel_coord(mask):
"""
Function to get x, y coordinates of white pixels in mask as homogenous coordinates
:param mask:
:return:
"""
y, x = np.where(mask)
pts = np.concatenate((x[:,np.newaxis], y[:, np.newaxis], np.ones((x.size, 1))), axis=1) # rows of [x1, y1, 1]
print(pts)
return pts
def fit_image_in_target_space(img_src, img_dst, mask, H, offset=np.array([0, 0, 0])):
"""
:param img_src: source image
:param img_dst: destination image
:param mask: mask corresponding to dest image
:param H: pts_in_src_img = H * pts_in_dst_img
:param offset: np array [x_offset, y_offset, 0]. Offset 0,0 in mask to this value
:return:
"""
pts = get_pixel_coord(mask) # rows of [x1, y1, 1]
pts = pts + offset
out_src = np.matmul(H, pts.T) # out_src has cols of [x1, y1, z1]
out_src = out_src/out_src[-1,:]
# print(out_src[-1,:])
# print(out_src[:,0])
# Return only x, y non-homogenous coordinates
out_src = out_src[0:2, :] # corresponds to pixels in img_src
out_src = out_src.T # rows of [x1, y1]
# Convert pts to out_src convention
pts = pts[:, 0:2].astype(np.int64) # Corresponds to pixel locs in img_dst, rows of [x1,y1]
h, w, _ = img_src.shape
get_pixel_val(img_dst, img_src, pts, out_src, offset)
# # x -> c y -> r
# for i in range(out_src.shape[0]):
# if (0 <= out_src[i,0] < w-1) and (0 <= out_src[i,1] < h-1):
# img_dst[pts[i,1], pts[i,0], :] = get_pixel_val(out_src[i], img_src)
return img_dst
def get_pixel_val(img_dst, img_src, pts, out_src, offset):
"""
:param img_dst:
:param pts: pts for img_dst rows of [x1, y1]
:param out_src: rows of [x1, y1], corresponding pts in src img after homography on dst points
:return:
"""
h, w, _ = img_src.shape
tl = np.floor(out_src[:, ::-1]).astype(np.int64) # reverse cols to get row, col notation
br = np.ceil(out_src[:, ::-1]).astype(np.int64)
pts = pts - offset[:2]
r_lzero = np.where(~np.logical_or(np.any(tl < 0, axis=1), np.any(br < 0, axis=1)))
pts = pts[r_lzero[0], :]
out_src = out_src[r_lzero[0], :]
tl = tl[r_lzero[0], :]
br = br[r_lzero[0], :]
r_fl = np.where(~np.logical_or(tl[:, 0] >= h-1, tl[:, 1] >= w-1))
pts = pts[r_fl[0], :]
out_src = out_src[r_fl[0], :]
tl = tl[r_fl[0], :]
br = br[r_fl[0], :]
r_ce = np.where(~np.logical_or(br[:, 0] >= h-1, br[:, 1] >= w-1))
pts = pts[r_ce[0], :]
out_src = out_src[r_ce[0], :]
tl = tl[r_ce[0], :]
br = br[r_ce[0], :]
print(pts.shape)
print(out_src.shape)
print(tl.shape)
print(br.shape)
tr = np.concatenate((tl[:, 0:1], br[:, 1:2]), axis=1)
bl = np.concatenate((br[:, 0:1], tl[:, 1:2]), axis=1)
weight = np.zeros((out_src.shape[0], 4))
weight[:, 0] = np.linalg.norm(tl-out_src[:, ::-1], axis=1)
weight[:, 1] = np.linalg.norm(tr-out_src[:, ::-1], axis=1)
weight[:, 2] = np.linalg.norm(bl-out_src[:, ::-1], axis=1)
weight[:, 3] = np.linalg.norm(br - out_src[:, ::-1], axis=1)
weight[np.all(weight == 0, axis=1)] = 1 # For entries where they exactly overlap
weight = 1/weight
# pts = pts - offset[:2]
img_dst[pts[:,1], pts[:,0], :] = (img_src[tl[:,0], tl[:,1], :] * weight[:, 0:1] + \
img_src[tr[:,0], tr[:,1], :] * weight[:, 1:2] + \
img_src[bl[:,0], bl[:,1], :] * weight[:, 2:3] + \
img_src[br[:,0], br[:,1], :] * weight[:, 3:4])/ np.sum(weight, axis=1, keepdims=True)
return img_dst
def build_sys_equations(in_pts, out_pts):
"""
:param in_pts: nparray [[x1, y1], [x2, y2], ...]
:param out_pts: nparray [[x1, y1], [x2, y2], ...]
:param include_perp_bisector:
:return:
"""
mat_A = np.zeros((np.size(in_pts), 8))
mat_b = in_pts.ravel()
i = 0
for x, y in out_pts:
# x row
mat_A[i][0:3] = [x, y, 1]
mat_A[i][-2:] = [-x*mat_b[i], -y*mat_b[i]]
# y row
mat_A[i+1][-5:] = [x, y, 1, -x*mat_b[i+1], -y*mat_b[i+1]]
# row counter
i = i+2
return mat_A, mat_b
def get_perp_bisectors(in_pts, out_pts):
perp_in = np.array([in_pts[-1] + in_pts[0],
in_pts[0] + in_pts[1],
in_pts[1] + in_pts[2],
in_pts[2] + in_pts[3]])
perp_out = np.array([out_pts[-1] + out_pts[0],
out_pts[0] + out_pts[1],
out_pts[1] + out_pts[2],
out_pts[2] + out_pts[3]])
in_pts = np.concatenate((in_pts, perp_in / 2), axis=0)
out_pts = np.concatenate((out_pts, perp_out / 2), axis=0)
in_pts = in_pts.astype(np.int64)
out_pts = out_pts.astype(np.int64)
return in_pts, out_pts
def run_main(img_src_path, img_dst_path, out_pts, include_perp=False, save_fig='result.jpg'):
"""
Fit img_src into img_dst. in_pts are in img_src, out_pts are in img_dst (clkwise starting from top left)
:param img_1_path:
:param img_2_path:
:param in_pts: pts chosen in source image
:param out_pts: corresponding pts chosen in destination image
:return:
"""
fldr, fname = os.path.split(img_src_path)
_, fname = os.path.split(img_dst_path)
res_dir = os.path.join(fldr, 'results')
if not os.path.exists(res_dir):
os.makedirs(res_dir)
img_src = cv2.cvtColor(cv2.imread(img_src_path), cv2.COLOR_BGR2RGB)
img_dst = cv2.cvtColor(cv2.imread(img_dst_path), cv2.COLOR_BGR2RGB)
if isinstance(out_pts, list):
out_pts = np.array(out_pts)
h, w, _ = np.shape(img_src)
in_pts = np.array([[0, 0], [w, 0], [w, h], [0, h]])
# Get mask
mask = np.zeros(img_dst.shape[0:2], dtype=np.uint8)
cv2.fillConvexPoly(mask, out_pts, 255)
plot_req_images(img_src, img_dst, mask, os.path.join(res_dir, 'visualize_' + fname))
if include_perp:
in_pts, out_pts = get_perp_bisectors(in_pts, out_pts)
H = calculate_homography(in_pts, out_pts)
## Check if homography correctly calculated
print('-------')
t_one = np.ones((in_pts.shape[0],1))
t_out_pts = np.concatenate((out_pts, t_one), axis=1)
print('-------')
x = np.matmul(H, t_out_pts.T)
x = x/x[-1,:]
print(x)
print('-------')
print(in_pts.T)
print(cv2.findHomography(out_pts, in_pts))
print('-------')
print(H)
out = fit_image_in_target_space(img_src, img_dst, mask, H)
plt.figure()
plt.imshow(out)
plt.axis('off')
plt.savefig(os.path.join(res_dir, 'result_' + fname))
plt.show()
def plot_req_images(img_src, img_dst, mask, figName):
plt.figure()
plt.suptitle("To fit src_img to dest_img mask region")
plt.subplot(2, 2, 1)
plt.title("Source_image")
plt.imshow(img_src)
plt.axis('off')
plt.subplot(2, 2, 2)
plt.title("Dest_image")
plt.imshow(img_dst)
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(mask, cmap='gray', vmin=0, vmax=255)
plt.title("Mask")
plt.axis('off')
plt.subplot(2, 2, 4)
plt.imshow(cv2.bitwise_and(img_dst, img_dst, mask=~mask))
plt.title("Destination region in image")
plt.axis('off')
plt.savefig(figName)
plt.show()
if __name__ == "__main__":
img_src_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/Jackie.jpg'
img_dst_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/1.jpg'
out_pts = [[1518, 181], [2948, 731], [2997, 2046], [1490, 2227]] #PQSR
run_main(img_src_path, img_dst_path, out_pts, False)
######################
img_src_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/Jackie.jpg'
img_dst_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/2.jpg'
out_pts = [[1331, 335], [3014, 621], [3030, 1892], [1309, 2007]] # PQSR
run_main(img_src_path, img_dst_path, out_pts, False)
######################
img_src_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/Jackie.jpg'
img_dst_path = '/Users/aartighatkesar/Documents/homography_estimation/input_imgs/3.jpg'
out_pts = [[929, 737], [2799, 390], [2849, 2222], [907, 2079]] # PQSR
run_main(img_src_path, img_dst_path, out_pts, False)