def example_04_find_homography_blend_multi_band(detector='SIFT', matchtype='knn'):
folder_input = 'images/ex_keypoints/'
img_left = cv2.imread(folder_input + 'left.jpg')
img_right = cv2.imread(folder_input + 'rght.jpg')
folder_output = 'images/output/'
output_left = folder_output + 'left_out.jpg'
output_rght = folder_output + 'rght_out.jpg'
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
points_left, des_left = tools_alg_match.get_keypoints_desc(img_left,detector)
points_rght, des_rght = tools_alg_match.get_keypoints_desc(img_right,detector)
homography = tools_calibrate.get_homography_by_keypoints_desc(points_left, des_left, points_rght, des_rght)
result_left, result_right= tools_calibrate.get_stitched_images_using_homography(img_left, img_right, homography)
result_image = tools_image.blend_multi_band(result_left,result_right)
cv2.imwrite(output_left, result_image)
homography = tools_calibrate.get_homography_by_keypoints_desc(points_rght, des_rght, points_left, des_left)
result_right, result_left= tools_calibrate.get_stitched_images_using_homography(img_right, img_left, homography)
result_image = tools_image.blend_multi_band(result_left,result_right)
cv2.imwrite(output_rght, result_image)
return
def align_two_images_translation(img1,
img2,
detector='SIFT',
matchtype='knn',
borderMode=cv2.BORDER_REPLICATE,
background_color=(0, 255, 255)):
points1, des1 = tools_alg_match.get_keypoints_desc(img1, detector)
points2, des2 = tools_alg_match.get_keypoints_desc(img2, detector)
coord1, coord2, distance = tools_alg_match.get_matches_from_keypoints_desc(
points1, des1, points2, des2, matchtype)
translation = None
if coord1 is not None and coord1.size >= 8:
translation = get_transform_by_keypoints(coord1, coord2)
if translation is None or math.isnan(translation[0, 0]):
return img1, img2, 0
#T = numpy.eye(3,dtype=numpy.float32)
#T[0:2,0:2] = translation[0:2,0:2]
#angle = math.fabs(rotationMatrixToEulerAngles(T)[2])
#if translation[0, 2] >= 0.10*img1.shape[0] or translation[1, 2] >= 0.10*img1.shape[1]:# or angle>0.05:
# return img1, img2, 0
else:
return img1, img2, 0
if borderMode == cv2.BORDER_REPLICATE:
result_image1, result_image2 = get_stitched_images_using_translation(
img1,
img2,
translation,
borderMode=cv2.BORDER_REPLICATE,
keep_shape=True)
else:
result_image1a, result_image2 = get_stitched_images_using_translation(
img1,
img2,
translation,
borderMode=cv2.BORDER_CONSTANT,
background_color=(0, 255, 0),
keep_shape=True)
result_image1b, result_image2 = get_stitched_images_using_translation(
img1,
img2,
translation,
borderMode=cv2.BORDER_CONSTANT,
background_color=(255, 0, 255),
keep_shape=True)
result_image1 = result_image1b.copy()
idx2 = numpy.all(result_image1a != result_image1b, axis=-1)
result_image1[idx2] = background_color
q = ((cv2.matchTemplate(
result_image1, result_image2, method=cv2.TM_CCOEFF_NORMED)[0, 0]) +
1) * 128
return result_image1, result_image2, int(q)
def example_find_matches_for_homography(detector='SIFT', matchtype='knn'):
folder_input = './images/ex_keypoints/'
img1 = cv2.imread(folder_input + 'left.jpg')
img2 = cv2.imread(folder_input + 'rght.jpg')
img1_gray_rgb = tools_image.desaturate(img1)
img2_gray_rgb = tools_image.desaturate(img2)
folder_output = './images/output/'
if not os.path.exists(folder_output):
os.makedirs(folder_output)
#else:
# tools_IO.remove_files(folder_output)
points1, des1 = tools_alg_match.get_keypoints_desc(img1, detector)
points2, des2 = tools_alg_match.get_keypoints_desc(img2, detector)
match1, match2, distance = tools_alg_match.get_matches_from_keypoints_desc(points1, des1, points2, des2, matchtype)
homography = tools_calibrate.get_homography_by_keypoints_desc(points1, des1, points2, des2, matchtype)
if homography is None:
return
match2_cand = cv2.perspectiveTransform(match1.reshape(-1, 1, 2).astype(numpy.float32), homography)
hit = numpy.zeros(match1.shape[0])
for i in range(0, match1.shape[0]):
if numpy.linalg.norm(match2_cand[i] - match2[i]) <= 5:
hit[i] = 1
img1_gray_rgb = tools_draw_numpy.draw_circle(img1_gray_rgb, int(match1[i][1]), int(match1[i][0]), 3,[32, 192, 0])
img2_gray_rgb = tools_draw_numpy.draw_circle(img2_gray_rgb, int(match2[i][1]), int(match2[i][0]), 3,[32, 192, 0])
else:
img1_gray_rgb = tools_draw_numpy.draw_circle(img1_gray_rgb, int(match1[i][1]), int(match1[i][0]), 3,[0, 64, 255])
img2_gray_rgb = tools_draw_numpy.draw_circle(img2_gray_rgb, int(match2[i][1]), int(match2[i][0]), 3,[0, 64, 255])
img2_gray_rgb, img1_gray_rgb = tools_calibrate.get_stitched_images_using_homography(img2_gray_rgb, img1_gray_rgb,homography)
fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(hspace=0.01)
roc_auc = 0
if hit.size >= 2:
fpr, tpr, thresholds = metrics.roc_curve(hit, -distance)
roc_auc = auc(fpr, tpr)
filename_out = folder_output + ('_ROC_%s_%s_auc_%1.3f.png' % (detector, matchtype, roc_auc))
tools_plot.plot_tp_fp(plt.subplot(1, 1, 1), fig, tpr, fpr, roc_auc)
plt.savefig(filename_out)
cv2.imwrite(folder_output + ('%s_%s_auc_%1.3f_left_matches.png' % (detector, matchtype, roc_auc)), img1_gray_rgb)
cv2.imwrite(folder_output + ('%s_%s_auc_%1.3f_rght_matches.png' % (detector, matchtype, roc_auc)), img2_gray_rgb)
return
def example_03_find_homography_by_keypoints(detector='SIFT', matchtype='knn'):
folder_input = 'images/ex_keypoints/'
img1 = cv2.imread(folder_input + 'left.jpg')
img2 = cv2.imread(folder_input + 'rght.jpg')
img1_gray_rgb = tools_image.desaturate(img1)
img2_gray_rgb = tools_image.desaturate(img2)
folder_output = 'images/output/'
output_filename1 = folder_output + 'left_transformed_homography.png'
output_filename2 = folder_output + 'rght_transformed_homography.png'
output_filename = folder_output + 'blended_homography.png'
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
points1, des1 = tools_alg_match.get_keypoints_desc(img1, detector)
points2, des2 = tools_alg_match.get_keypoints_desc(img2, detector)
homography = tools_calibrate.get_homography_by_keypoints_desc(
points1, des1, points2, des2, matchtype)
match1, match2, distance = tools_alg_match.get_matches_from_keypoints_desc(
points1, des1, points2, des2, matchtype)
for each in match1:
img1_gray_rgb = tools_draw_numpy.draw_circle(img1_gray_rgb,
int(each[1]),
int(each[0]), 3,
[0, 0, 255])
for each in match2:
img2_gray_rgb = tools_draw_numpy.draw_circle(img2_gray_rgb,
int(each[1]),
int(each[0]), 3,
[255, 255, 0])
result_image1, result_image2 = tools_calibrate.get_stitched_images_using_homography(
img1_gray_rgb,
img2_gray_rgb,
homography,
background_color=(255, 255, 255))
result_image = tools_image.blend_avg(result_image1,
result_image2,
background_color=(255, 255, 255))
# result_image = tools_calibrate.blend_multi_band(result_image1, result_image2)
cv2.imwrite(output_filename1, result_image1)
cv2.imwrite(output_filename2, result_image2)
cv2.imwrite(output_filename, result_image)
return
def demo_stereo_03_keypoints():
folder_input = 'images/ex_stereo/'
folder_output = 'images/output/'
filenameL = '1L.png'
filenameR = '1R.png'
disp_v1, disp_v2, disp_h1, disp_h2 = 0, 1, -70, -10
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
imgL = cv2.imread(folder_input + filenameL)
imgR = cv2.imread(folder_input + filenameR)
imgL_gray_rgb = tools_image.desaturate(imgL)
imgR_gray_rgb = tools_image.desaturate(imgR)
points1, des1 = tools_alg_match.get_keypoints_desc(imgL)
points2, des2 = tools_alg_match.get_keypoints_desc(imgR)
match1, match2, distance = tools_alg_match.get_matches_from_keypoints_desc(
points1, des1, points2, des2)
idx = []
for i in range(0, match1.shape[0]):
row1, col1 = match1[i, 1], match1[i, 0]
row2, col2 = match2[i, 1], match2[i, 0]
if (col2 - col1 >= disp_h1) and (col2 - col1 < disp_h2) and (
row2 - row1 >= disp_v1) and (row2 - row1 < disp_v2):
idx.append(i)
match1, match2, distance = match1[idx], match2[idx], distance[idx]
for i in range(0, match1.shape[0]):
r = int(255 * numpy.random.rand())
color = cv2.cvtColor(
numpy.array([r, 255, 225], dtype=numpy.uint8).reshape(1, 1, 3),
cv2.COLOR_HSV2BGR)
imgL_gray_rgb = tools_draw_numpy.draw_circle(imgL_gray_rgb, match1[i,
1],
match1[i, 0], 4, color)
imgR_gray_rgb = tools_draw_numpy.draw_circle(imgR_gray_rgb, match2[i,
1],
match2[i, 0], 4, color)
cv2.imwrite(folder_output + 'matches03_L.png', imgL_gray_rgb)
cv2.imwrite(folder_output + 'matches03_R.png', imgR_gray_rgb)
return
def demo_stereo_04_keypoints_limited():
folder_input = 'images/ex_stereo/'
folder_output = 'images/output/'
filenameL = '1L.png'
filenameR = '1R.png'
disp_v1, disp_v2, disp_h1, disp_h2 = 0, 1, -70, -10
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
imgL = cv2.imread(folder_input + filenameL)
imgR = cv2.imread(folder_input + filenameR)
imgL_gray_rgb = tools_image.desaturate(imgL)
imgR_gray_rgb = tools_image.desaturate(imgR)
points1, des1 = tools_alg_match.get_keypoints_desc(imgL)
points2, des2 = tools_alg_match.get_keypoints_desc(imgR)
match1, match2, distance = tools_alg_match.get_matches_from_desc_limit_by_disp(
points1, des1, points2, des2, disp_v1, disp_v2, disp_h1, disp_h2,
'ccc')
R = 4
for i in range(0, match1.shape[0]):
r = int(255 * numpy.random.rand())
color = cv2.cvtColor(
numpy.array([r, 255, 225], dtype=numpy.uint8).reshape(1, 1, 3),
cv2.COLOR_HSV2BGR)
imgL_gray_rgb = tools_draw_numpy.draw_circle(imgL_gray_rgb, match1[i,
1],
match1[i, 0], R, color)
imgR_gray_rgb = tools_draw_numpy.draw_circle(imgR_gray_rgb, match2[i,
1],
match2[i, 0], R, color)
cv2.imwrite(folder_output + 'matches03_L.png', imgL_gray_rgb)
cv2.imwrite(folder_output + 'matches03_R.png', imgR_gray_rgb)
return
def example_ORB(filename_in, filename_out, R=2): img = cv2.imread(filename_in) gray_rgb = tools_image.desaturate(img) points, desc = tools_alg_match.get_keypoints_desc(img, detector='ORB') for each in points: gray_rgb = tools_draw_numpy.draw_circle(gray_rgb, each[1], each[0], R, [0, 0, 255]) cv2.imwrite(filename_out, gray_rgb) return
def align_two_images_homography(img1, img2, detector='SIFT', matchtype='knn'):
img1_gray_rgb = tools_image.desaturate(img1)
img2_gray_rgb = tools_image.desaturate(img2)
points1, des1 = tools_alg_match.get_keypoints_desc(img1, detector)
points2, des2 = tools_alg_match.get_keypoints_desc(img2, detector)
match1, match2, distance = tools_alg_match.get_matches_from_keypoints_desc(
points1, des1, points2, des2, matchtype)
homography = None
if match1.size != 0:
homography = get_homography_by_keypoints_desc(points1, des1, points2,
des2, matchtype)
if homography is None:
return img1, img2
else:
return img1, img2
for each in match1:
cv2.circle(img1_gray_rgb, (int(each[0]), int(each[1])),
3, [0, 0, 255],
thickness=-1)
for each in match2:
cv2.circle(img2_gray_rgb, (int(each[0]), int(each[1])),
3, [255, 255, 0],
thickness=-1)
result_image1, result_image2 = get_stitched_images_using_homography(
img2_gray_rgb,
img1_gray_rgb,
homography,
borderMode=cv2.BORDER_REPLICATE,
background_color=(255, 255, 255))
q = cv2.matchTemplate(result_image1,
result_image2,
method=cv2.TM_CCOEFF_NORMED)[0, 0]
q = int((1 + q) * 128)
return result_image1, result_image2, q
def example_03_find_homography_live():
USE_CAMERA = False
USE_TRANSFORM = True
filename_out = './images/output/frame.jpg'
image_deck = cv2.imread('./images/ex_homography_live/frame2.jpg')
image_card = cv2.imread('./images/ex_homography_live/card.jpg')
image_sbst = cv2.imread('./images/ex_homography_live/card2.jpg')
image_sbst = cv2.resize(image_sbst,
(image_card.shape[1], image_card.shape[0]))
points1, des1 = tools_alg_match.get_keypoints_desc(image_card, 'SURF')
if USE_CAMERA:
capture = cv2.VideoCapture(0)
while (True):
if USE_CAMERA:
ret, image_deck = capture.read()
points2, des2 = tools_alg_match.get_keypoints_desc(image_deck, 'SURF')
if USE_TRANSFORM:
H = tools_calibrate.get_transform_by_keypoints_desc(
points1, des1, points2, des2, 'knn')
else:
H = tools_calibrate.get_homography_by_keypoints_desc(
points1, des1, points2, des2, 'knn')
if (H is not None):
if USE_TRANSFORM:
aligned1, aligned2 = tools_calibrate.get_stitched_images_using_translation(
image_sbst,
image_deck,
H,
background_color=(0, 0, 0),
keep_shape=True)
else:
aligned1, aligned2 = tools_calibrate.get_stitched_images_using_homography(
image_sbst, image_deck, H, background_color=(0, 0, 0))
#im_result = tools_image.put_layer_on_image(aligned2,aligned1,background_color=(0,0,0))
im_result = tools_image.blend_multi_band_large_small(
aligned2,
aligned1,
background_color=(0, 0, 0),
filter_size=10,
leveln_default=1)
cv2.imshow('frame', im_result)
else:
cv2.imshow('frame', image_deck)
key = cv2.waitKey(1)
if key & 0xFF == 27: break
if (key & 0xFF == 13) or (key & 0xFF == 32):
cv2.imwrite(filename_out, image_deck)
if USE_CAMERA:
capture.release()
cv2.destroyAllWindows()
return
def example_find_matches_for_frames(filename1_in,
filename2_in,
folder_output,
detector='SIFT',
matchtype='knn'):
img1 = cv2.imread(filename1_in)
img2 = cv2.imread(filename2_in)
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
points1, des1 = tools_alg_match.get_keypoints_desc(img1, detector)
points2, des2 = tools_alg_match.get_keypoints_desc(img2, detector)
match1, match2, distance = tools_alg_match.get_matches_from_keypoints_desc(
points1, des1, points2, des2, matchtype)
img1_gray_rgb = tools_image.desaturate(img1)
img2_gray_rgb = tools_image.desaturate(img2)
for m1, m2 in zip(match1, match2):
if math.sqrt((m1[1] - m2[1]) * (m1[1] - m2[1]) + (m1[0] - m2[0]) *
(m1[0] - m2[0])) > 1 and math.sqrt((m1[1] - m2[1]) *
(m1[1] - m2[1]) +
(m1[0] - m2[0]) *
(m1[0] - m2[0])) < 55:
img1_gray_rgb = tools_draw_numpy.draw_line(img1_gray_rgb,
int(m1[1]), int(m1[0]),
int(m2[1]), int(m2[0]),
[0, 0, 255])
img2_gray_rgb = tools_draw_numpy.draw_line(img2_gray_rgb,
int(m1[1]), int(m1[0]),
int(m2[1]), int(m2[0]),
[0, 0, 255])
cv2.imwrite(folder_output + 'out1.png', img1_gray_rgb)
cv2.imwrite(folder_output + 'out2.png', img2_gray_rgb)
img1_gray_rgb = tools_image.desaturate(img1)
img2_gray_rgb = tools_image.desaturate(img2)
for m1, m2 in zip(match1, match2):
if math.sqrt((m1[1] - m2[1]) * (m1[1] - m2[1]) + (m1[0] - m2[0]) *
(m1[0] - m2[0])) > 5 and math.sqrt((m1[1] - m2[1]) *
(m1[1] - m2[1]) +
(m1[0] - m2[0]) *
(m1[0] - m2[0])) < 55:
r = int(255 * numpy.random.rand())
color = cv2.cvtColor(
numpy.array([r, 255, 225], dtype=numpy.uint8).reshape(1, 1, 3),
cv2.COLOR_HSV2BGR)
img1_gray_rgb = tools_draw_numpy.draw_circle(img1_gray_rgb,
int(m1[1]),
int(m1[0]),
4,
color,
alpha_transp=0.3)
img2_gray_rgb = tools_draw_numpy.draw_circle(img2_gray_rgb,
int(m2[1]),
int(m2[0]),
4,
color,
alpha_transp=0.3)
cv2.imwrite(folder_output + 'out3.png', img1_gray_rgb)
cv2.imwrite(folder_output + 'out4.png', img2_gray_rgb)
return
