def part_3a_2():
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
yos_img_03 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_03.jpg'), 0) / 255.
levels = 4
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02, levels)
yos_img_03_g_pyr = ps4.gaussian_pyramid(yos_img_03, levels)
level_id = 0
k_size = 51
k_type = "gaussian"
sigma = 3
u, v = ps4.optic_flow_lk(yos_img_02_g_pyr[level_id],
yos_img_03_g_pyr[level_id], k_size, k_type, sigma)
u, v = scale_u_and_v(u, v, level_id, yos_img_03_g_pyr)
interpolation = cv2.INTER_CUBIC
border_mode = cv2.BORDER_REFLECT101
yos_img_03_warped = ps4.warp(yos_img_03, u, v, interpolation, border_mode)
diff_yos_img = yos_img_02 - yos_img_03_warped
cv2.imwrite(os.path.join(output_dir, "ps4-3-a-2.png"),
ps4.normalize_and_scale(diff_yos_img))
def part_3a_2():
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
yos_img_03 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_03.jpg'), 0) / 255.
levels = 1 # Define the number of pyramid levels
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02, levels)
yos_img_03_g_pyr = ps4.gaussian_pyramid(yos_img_03, levels)
level_id = 3 # TODO: Select the level number (or id) you wish to use
k_size = 3 # TODO: Select a kernel size
k_type = "uniform" # TODO: Select a kernel type
sigma = 1 # TODO: Select a sigma value if you are using a gaussian kernel
u, v = ps4.optic_flow_lk(yos_img_02_g_pyr[level_id],
yos_img_03_g_pyr[level_id], k_size, k_type, sigma)
u, v = scale_u_and_v(u, v, level_id, yos_img_03_g_pyr)
interpolation = cv2.INTER_CUBIC # You may try different values
border_mode = cv2.BORDER_REFLECT101 # You may try different values
yos_img_03_warped = ps4.warp(yos_img_03, u, v, interpolation, border_mode)
diff_yos_img = yos_img_02 - yos_img_03_warped
cv2.imwrite(os.path.join(output_dir, "ps4-3-a-2.png"),
ps4.normalize_and_scale(diff_yos_img))
def part_3a_1():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_01.jpg'), 0) / 255.
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
levels = 2 # Define the number of pyramid levels
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01, levels)
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02, levels)
level_id = 1
k_size = 32
k_type = "uniform"
sigma = 0
u, v = ps4.optic_flow_lk(yos_img_01_g_pyr[level_id],
yos_img_02_g_pyr[level_id], k_size, k_type, sigma)
u, v = scale_u_and_v(u, v, level_id, yos_img_02_g_pyr)
interpolation = cv2.INTER_CUBIC # You may try different values
border_mode = cv2.BORDER_REFLECT101 # You may try different values
yos_img_02_warped = ps4.warp(yos_img_02, u, v, interpolation, border_mode)
diff_yos_img = yos_img_01 - yos_img_02_warped
cv2.imwrite(os.path.join(output_dir, "ps4-3-a-1.png"),
ps4.normalize_and_scale(diff_yos_img))
def part_3a_1():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_01.jpg'), 0) / 255.
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
levels = 4 # Define the number of pyramid levels
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01, levels)
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02, levels)
# k_size = "kSize"
# window = "Params"
# cv2.namedWindow(window)
# cv2.createTrackbar(k_size, window, 1, 100, nothing)
# while 1:
# k = cv2.waitKey(1) & 0xFF
# if k == 27:
# break
#
# level_id = 0 # TODO: Select the level number (or id) you wish to use
# # k_size = 11 # TODO: Select a kernel size
# k_size = cv2.getTrackbarPos('kSize', 'Params')
# k_type = 'uniform' # TODO: Select a kernel type
# sigma = 0 # TODO: Select a sigma value if you are using a gaussian kernel
# u, v = ps4.optic_flow_lk(yos_img_01_g_pyr[level_id],
# yos_img_02_g_pyr[level_id],
# k_size, k_type, sigma)
#
# u, v = scale_u_and_v(u, v, level_id, yos_img_02_g_pyr)
#
# interpolation = cv2.INTER_CUBIC # You may try different values
# border_mode = cv2.BORDER_REFLECT101 # You may try different values
# yos_img_02_warped = ps4.warp(yos_img_02, u, v, interpolation, border_mode)
#
# diff_yos_img_01_02 = yos_img_01 - yos_img_02_warped
#
# cv2.imshow("Params", diff_yos_img_01_02)
level_id = 1 # TODO: Select the level number (or id) you wish to use
k_size = 23 # TODO: Select a kernel size
k_type = 'uniform' # TODO: Select a kernel type
sigma = 0 # TODO: Select a sigma value if you are using a gaussian kernel
u, v = ps4.optic_flow_lk(yos_img_01_g_pyr[level_id],
yos_img_02_g_pyr[level_id], k_size, k_type, sigma)
u, v = scale_u_and_v(u, v, level_id, yos_img_02_g_pyr)
interpolation = cv2.INTER_CUBIC # You may try different values
border_mode = cv2.BORDER_REFLECT101 # You may try different values
yos_img_02_warped = ps4.warp(yos_img_02, u, v, interpolation, border_mode)
diff_yos_img_01_02 = yos_img_01 - yos_img_02_warped
cv2.imwrite(os.path.join(output_dir, "ps4-3-a-1.png"),
ps4.normalize_and_scale(diff_yos_img_01_02))
def part_3():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_03.jpg'), 0) / 255.
levels = 10 # Define the number of pyramid levels
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01, levels)
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02, levels)
# Create a window
cv2.namedWindow('image')
diff_yos_img = np.zeros(yos_img_01.shape)
# create trackbars for color change
cv2.createTrackbar('level_id', 'image', 1, levels - 1, nothing)
cv2.createTrackbar('k_size', 'image', 3, 70, nothing)
cv2.createTrackbar('k_type', 'image', 0, 1, nothing)
cv2.createTrackbar('sigma', 'image', 1, 50, nothing)
while (1):
cv2.imshow('image', diff_yos_img)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
# Optional: smooth the images if LK doesn't work well on raw images
level_id = cv2.getTrackbarPos('level_id', 'image')
k_size = cv2.getTrackbarPos('k_size', 'image')
k_type = 'gaussian' if cv2.getTrackbarPos('k_type',
'image') == 1 else 'uniform'
sigma = cv2.getTrackbarPos('sigma', 'image')
u, v = ps4.optic_flow_lk(yos_img_01_g_pyr[level_id],
yos_img_02_g_pyr[level_id], k_size, k_type,
sigma)
u, v = experiment.scale_u_and_v(u, v, level_id, yos_img_02_g_pyr)
interpolation = cv2.INTER_CUBIC # You may try different values
border_mode = cv2.BORDER_REFLECT101 # You may try different values
yos_img_02_warped = ps4.warp(yos_img_02, u, v, interpolation,
border_mode)
diff_yos_img = ps4.normalize_and_scale(yos_img_01 -
yos_img_02_warped).astype(
np.uint8)
cv2.destroyAllWindows()
print level_id, k_size, k_type, sigma
def test_gaussian_pyramid(self):
input_imgs = [
'test_gauss1_pyr.npy', 'test_gauss2_pyr.npy', 'test_gauss3_pyr.npy'
]
ref_imgs = [
'test_gauss1_pyr_ref.npy', 'test_gauss2_pyr_ref.npy',
'test_gauss3_pyr_ref.npy'
]
levels = [4, 2, 4]
for i in range(3):
f1 = input_imgs[i]
f2 = ref_imgs[i]
l = levels[i]
test_array = np.load(INPUT_DIR + f1)
g_pyr = ps4.gaussian_pyramid(test_array.copy(), levels=l)
g_pyr_ref = np.load(INPUT_DIR + f2)
for l in range(len(g_pyr)):
correct = np.allclose(g_pyr[l], g_pyr_ref[l], atol=0.1)
error_msg = "Value at level {} does not match the answer." \
"".format(l)
self.assertTrue(correct, error_msg)
def part_2():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_01.jpg'), 0) / 255.
# 2a
levels = 4
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01, levels)
yos_img_01_g_pyr_img = ps4.create_combined_img(yos_img_01_g_pyr)
cv2.imshow('combined', yos_img_01_g_pyr_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite("output/ps4-2-a-1.png", yos_img_01_g_pyr_img)
# 2b
yos_img_01_l_pyr = ps4.laplacian_pyramid(yos_img_01_g_pyr)
yos_img_01_l_pyr_img = ps4.create_combined_img(yos_img_01_l_pyr)
cv2.imshow('combined', yos_img_01_l_pyr_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite("output/ps4-2-b-1.png", yos_img_01_l_pyr_img)
def part_3a_1():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_01.jpg'), 0) / 255.
yos_img_02 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_02.jpg'), 0) / 255.
k = 11
sigma = 7
yos_img_01g = cv2.GaussianBlur(yos_img_01,
ksize=(k, k),
sigmaX=sigma,
sigmaY=sigma)
yos_img_02g = cv2.GaussianBlur(yos_img_02,
ksize=(k, k),
sigmaX=sigma,
sigmaY=sigma)
levels = 5 # Define the number of pyramid levels
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01g, levels)
yos_img_02_g_pyr = ps4.gaussian_pyramid(yos_img_02g, levels)
level_id = 0 # TODO: Select the level number (or id) you wish to use
k_size = 20 # TODO: Select a kernel size
k_type = "uniform" # TODO: Select a kernel type
sigma = 10 # TODO: Select a sigma value if you are using a gaussian kernel
u, v = ps4.optic_flow_lk(yos_img_01_g_pyr[level_id],
yos_img_02_g_pyr[level_id], k_size, k_type, sigma)
u, v = scale_u_and_v(u, v, level_id, yos_img_02_g_pyr)
interpolation = cv2.INTER_CUBIC # You may try different values
border_mode = cv2.BORDER_REFLECT101 # You may try different values
yos_img_02_warped = ps4.warp(yos_img_02, u, v, interpolation, border_mode)
diff_yos_img_01_02 = yos_img_01 - yos_img_02_warped
cv2.imwrite(os.path.join(output_dir, "ps4-3-a-1.png"),
ps4.normalize_and_scale(diff_yos_img_01_02))
def part_2():
yos_img_01 = cv2.imread(
os.path.join(input_dir, 'DataSeq1', 'yos_img_01.jpg'), 0) / 255.
# 2a
levels = 4
yos_img_01_g_pyr = ps4.gaussian_pyramid(yos_img_01, levels)
yos_img_01_g_pyr_img = ps4.create_combined_img(yos_img_01_g_pyr)
cv2.imwrite(os.path.join(output_dir, "ps4-2-a-1.png"),
yos_img_01_g_pyr_img)
# 2b
yos_img_01_l_pyr = ps4.laplacian_pyramid(yos_img_01_g_pyr)
yos_img_01_l_pyr_img = ps4.create_combined_img(yos_img_01_l_pyr)
cv2.imwrite(os.path.join(output_dir, "ps4-2-b-1.png"),
yos_img_01_l_pyr_img)
