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Commit before my laptop burns

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Gasper Spagnolo 2022-11-20 16:58:55 +01:00
parent f57399666d
commit 647ff20f61
2 changed files with 126 additions and 111 deletions
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126
assignment3/solution.py
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@ -10,9 +10,9 @@ import os
############################################## ##############################################
def ex1(): def ex1():
#one_a() one_a()
#one_b() one_b()
#one_c() one_c()
one_e() one_e()
def one_a() -> None: def one_a() -> None:
@ -153,11 +153,11 @@ def one_e():
""" """
hello hello
""" """
museum = uz_image.imread('./images/museum.jpg', uz_image.ImageType.float64) #museum = uz_image.imread('./images/museum.jpg', uz_image.ImageType.float64)
#uz_image.get_image_bins_gradient_magnitude_and_angles(museum) #uz_image.get_image_bins_gradient_magnitude_and_angles(museum)
#ex2_naive('../assignment2/data/dataset/', 8) ex2_naive('../assignment2/data/dataset/', 8)
ex2_optimized('../assignment2/data/dataset/', 8) ex2_optimized('../assignment2/data/dataset/', 8)
@ -270,11 +270,10 @@ def ex2_optimized(directory: str, n_bins: int):
############################################ ############################################
def ex2(): def ex2():
#two_a() two_a()
two_b() two_b()
two_c() two_c()
def two_a(): def two_a():
""" """
Firstly, create a function findedges that accepts an image I, and the parameters Firstly, create a function findedges that accepts an image I, and the parameters
@ -346,9 +345,10 @@ def ex3():
#three_a() #three_a()
#three_b() #three_b()
#three_c() #three_c()
#three_d() three_d()
#three_e() three_e()
three_f() three_f()
#three_g()
def three_a(): def three_a():
""" """
@ -507,33 +507,30 @@ def three_d():
fig, axs = plt.subplots(1, 3) fig, axs = plt.subplots(1, 3)
def select_best_pairs(image_line_params: npt.NDArray[np.float64], n =10):
image_line_params = np.array(image_line_params)
# Sorts just kth element so every eleement before kth element is lower than kth element
# and every element after kth element is higher than kth element
partition = np.argpartition(image_line_params, kth=len(image_line_params) - n - 1, axis=0)[-n:]
image_line_params = image_line_params[partition.T[0]]
return image_line_params
# Plot synthetic image # Plot synthetic image
axs[0].imshow(synthetic_image, cmap='gray') axs[0].imshow(synthetic_image, cmap='gray')
neighbour_pairs = uz_image.retrieve_hough_pairs(synthetic_image, synthetic_image_hough_nonmax, np.max(synthetic_image_hough_nonmax)*0.99, N_BINS_THETA, N_BINS_RHO) neighbour_pairs = uz_image.retrieve_hough_pairs(synthetic_image, synthetic_image_hough_nonmax, np.max(synthetic_image_hough_nonmax)*0.80, N_BINS_THETA, N_BINS_RHO)
for neighbour in neighbour_pairs:
best_paris = uz_image.select_best_pairs(neighbour_pairs)
for neighbour in best_paris:
xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], synthetic_image.shape[0], synthetic_image.shape[1]) xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], synthetic_image.shape[0], synthetic_image.shape[1])
axs[0].plot(xs, ys, 'r', linewidth=0.7) axs[0].plot(xs, ys, 'r', linewidth=0.7)
# Plot oneline image # Plot oneline image
axs[1].imshow(oneline_image, cmap='gray') axs[1].imshow(oneline_image, cmap='gray')
neighbour_pairs = uz_image.retrieve_hough_pairs(oneline_image, oneline_image_hough_nonmax, np.max(oneline_image_hough_nonmax)*0.5, N_BINS_THETA, N_BINS_RHO) neighbour_pairs = uz_image.retrieve_hough_pairs(oneline_image, oneline_image_hough_nonmax, np.max(oneline_image_hough_nonmax)*0.4, N_BINS_THETA, N_BINS_RHO)
for neighbour in neighbour_pairs:
best_paris = uz_image.select_best_pairs(neighbour_pairs)
for neighbour in best_paris:
xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], oneline_image.shape[0], oneline_image.shape[1]) xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], oneline_image.shape[0], oneline_image.shape[1])
axs[1].plot(xs, ys, 'r', linewidth=0.7) axs[1].plot(xs, ys, 'r', linewidth=0.7)
# Plot rectangle image # Plot rectangle image
axs[2].imshow(rectangle_image, cmap='gray') axs[2].imshow(rectangle_image, cmap='gray')
neighbour_pairs = uz_image.retrieve_hough_pairs(rectangle_image, rectangle_image_hough_nonmax, np.max(rectangle_image_hough_nonmax)*0.35, N_BINS_THETA, N_BINS_RHO) neighbour_pairs = uz_image.retrieve_hough_pairs(rectangle_image, rectangle_image_hough_nonmax, np.max(rectangle_image_hough_nonmax)*0.35, N_BINS_THETA, N_BINS_RHO)
best_paris = select_best_pairs(neighbour_pairs) best_paris = uz_image.select_best_pairs(neighbour_pairs)
for neighbour in best_paris: for neighbour in best_paris:
xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], rectangle_image.shape[0], rectangle_image.shape[1]) xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], rectangle_image.shape[0], rectangle_image.shape[1])
@ -646,37 +643,82 @@ def three_f():
the [−π/2, π/2] interval. Test the modified function on several images and compare the [−π/2, π/2] interval. Test the modified function on several images and compare
the results with the original implementation. the results with the original implementation.
""" """
rectangle_image = uz_image.imread_gray('./images/rectangle.png', uz_image.ImageType.float64) SIGMA = 1
THETA = 0.02
T_LOW = 0.04
T_HIGH = 0.16
image_with_edges_n, derivative_magnitude_n, gradient_angles_n, hough_image_n, hough_image_nms_n, pairs_n, best_pairs_n = uz_image.find_lines_in_image_naive( img = cv2.imread('images/rectangle.png')
rectangle_image
)
image_with_edges_i, derivative_magnitude_i, gradient_angles_i, hough_image_i, hough_image_nms_i, pairs_i, best_pairs_i = uz_image.find_lines_in_image_improved( # Get gradient magntude and gradient angle
rectangle_image
) t_lower = 70
t_upper = 200
edge_detected_image = cv2.Canny(img, t_lower, t_upper)
gm, ga = uz_image.gradient_magnitude(img, 1)
print('Edge detected:', edge_detected_image.shape)
# Transform image into hough space
image_transformed_into_hough_space = uz_image.hough_find_lines_i(edge_detected_image, ga, gm, 360, 360, 0.2)
print('Hough lines drawn:', image_transformed_into_hough_space.shape)
hugh_pairs = uz_image.retrieve_hough_pairs(img, image_transformed_into_hough_space, np.max(image_transformed_into_hough_space) * 0.2, 360, 360)
best_pairs = hugh_pairs
fig, axs = plt.subplots(2, 2) fig, axs = plt.subplots(2, 2)
axs[0, 0].imshow(hough_image_n) axs[0, 0].imshow(edge_detected_image)
axs[0, 0].set(title='normal') axs[0, 0].set(title='normal')
axs[0, 1].imshow(hough_image_i) axs[0, 1].imshow(image_transformed_into_hough_space)
axs[0, 1].set(title='normal') axs[0, 1].set(title='normal')
axs[1, 0].imshow(rectangle_image, cmap='gray') axs[1, 0].imshow(edge_detected_image, cmap='gray')
for param in best_pairs_n: for param in best_pairs:
xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1]) xs, ys = uz_image.get_line_to_plot(param[0], param[1], img.shape[0], img.shape[1])
axs[1, 0].plot(xs, ys, 'r', linewidth=0.7) axs[1, 0].plot(xs, ys, 'r', linewidth=0.7)
axs[1,1].imshow(rectangle_image, cmap='gray') axs[1, 1].imshow(image_transformed_into_hough_space)
for param in best_pairs_i:
xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1]) #axs[1,1].imshow(rectangle_image, cmap='gray')
axs[1, 1].plot(xs, ys, 'r', linewidth=0.7) #for param in best_pairs_i:
# xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1])
# axs[1, 1].plot(xs, ys, 'r', linewidth=0.7)
#plt.show()
plt.show() plt.show()
def three_g():
"""
F (5 points) Implement a Hough transform that detects circles of a fixed radius.
You can test the algorithm on image eclipse.jpg. Try using a radius somewhere
between 45 and 50 pixels.
"""
circle_image = uz_image.imread('images/eclipse.jpg', uz_image.ImageType.uint8)
#img = cv2.imread('images/rectangle.png')
t_lower = 100
t_upper = 150
edge_detected_image = cv2.Canny(circle_image, t_lower, t_upper)
hugh_transformed_circle = uz_image.hough_transform_a_circle(edge_detected_image, 45, 50, 0.2)
fig, axs = plt.subplots(1, 2)
axs[0].imshow(circle_image, cmap='gray')
axs[0].set(title='Original')
axs[1].imshow(edge_detected_image, cmap='gray')
axs[1].set(title='Edges')
plt.show()
for i in range(hugh_transformed_circle.shape[2]):
plt.imshow(hugh_transformed_circle[:, :, i])
plt.show()
# ######## # # ######## #
@ -684,9 +726,9 @@ def three_f():
# ######## # # ######## #
def main(): def main():
ex1() #ex1() # everything K
#ex2() #ex2() # everything OK
#ex3() ex3()
if __name__ == '__main__': if __name__ == '__main__':
main() main()

111
assignment3/uz_framework/image.py
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@ -241,7 +241,13 @@ def get_image_bins_ND(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint
return hist / np.sum(hist) return hist / np.sum(hist)
def get_image_bins_gradient_magnitude_and_angles(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> npt.NDArray[np.float64]: def get_image_bins_gradient_magnitude_and_angles(image: Union[npt.NDArray[np.float64],
npt.NDArray[np.uint8]]) -> npt.NDArray[np.float64]:
"""
Accepts: image,
Returns: 1D histogram of image using gradient magnitude and gradient angles
Works OK on many dimensions
"""
WIDTH = image.shape[0] WIDTH = image.shape[0]
HEIGHT = image.shape[1] HEIGHT = image.shape[1]
WIDTH_8 = WIDTH // 8 WIDTH_8 = WIDTH // 8
@ -720,6 +726,30 @@ def hough_transform_a_point(x: int, y: int, n_bins: int) -> npt.NDArray[np.float
accumlator[int(r), i] += 1 accumlator[int(r), i] += 1
return accumlator return accumlator
def hough_transform_a_circle(edged_image: Union[npt.NDArray[np.float64] , npt.NDArray[np.uint8]],
r_start: int, r_end: int, treshold: float) -> npt.NDArray[np.float64]:
"""
Accepts: image, r_start, r_end
Returns: hough space
"""
image = edged_image.copy()
image[image < treshold] = 0
accumlator = np.zeros((edged_image.shape[0], edged_image.shape[1], r_end - r_start))
indices = np.argwhere(image)
sine_value = np.sin(np.linspace(0, np.pi, 360))
cosine_value = np.cos(np.linspace(0, np.pi, 360))
# Loop through all nonzero pixels above treshold
for i in tqdm(range(len(indices)), desc='Hough transform'):
for r in range(0, r_end - r_start):
x, y = indices[i]
for svcv in range(sine_value.shape[0]):
a = x - r * cosine_value[svcv]
b = y - r * sine_value[svcv]
accumlator[int(a), int(b), r ] += 1
return accumlator
def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]],
@ -758,38 +788,37 @@ def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]
return accumulator return accumulator
def hough_find_lines_i(image_with_lines: npt.NDArray[np.float64], gradient_angles: npt.NDArray[np.float64], def hough_find_lines_i(image_with_lines: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], gradient_angles: npt.NDArray[np.float64],
gradient_magnitude: npt.NDArray[np.float64], gradient_magnitude: npt.NDArray[np.float64],
n_bins_theta: int, n_bins_rho: int, treshold: float) -> npt.NDArray[np.uint64]: n_bins_theta: int, n_bins_rho: int, treshold: float) -> Union[npt.NDArray[np.uint64], npt.NDArray[np.float64]]:
"""" """"
Accepts: bw image with lines, n_bins_theta, n_bins_rho, treshold Accepts: bw image with lines, n_bins_theta, n_bins_rho, treshold
Returns: image points above treshold transformed into hough space Returns: image points above treshold transformed into hough space
""" """
image = image_with_lines.copy() image = image_with_lines.copy()
#image[image < treshold] = 0 image[image < treshold] = 0
theta_values = np.linspace(-np.pi/2, np.pi/2, n_bins_theta) theta_values = np.linspace(-np.pi/2, np.pi/2, n_bins_theta)
D = np.sqrt(image.shape[0]**2 + image.shape[1]**2) D = np.sqrt(image.shape[0]**2 + image.shape[1]**2)
rho_values = np.linspace(-D, D, n_bins_rho) rho_values = np.linspace(-D, D, n_bins_rho)
accumulator = np.zeros((n_bins_rho, n_bins_theta), dtype=np.uint64) accumulator = np.zeros((n_bins_rho, n_bins_theta), dtype=np.float64)
cos_precalculated = np.cos(theta_values) cos_precalculated = np.cos(theta_values)
sin_precalculated = np.sin(theta_values) sin_precalculated = np.sin(theta_values)
indices = np.argwhere(image) indices = np.argwhere(image)
# Loop through all nonzero pixels above treshold # Loop through all nonzero pixels above treshold
for i in tqdm(range(len(indices)), desc='Hough transform'): for i in tqdm(range(len(indices)), desc='Hough transform'):
y, x= indices[i] y, x = indices[i]
angle = (np.mod(gradient_angles[y, x] + np.pi/2 , np.pi)) - np.pi/2
theta = np.digitize(gradient_angles[y, x] / 2, theta_values) -1
rho = np.round(x* cos_precalculated[theta] + y* sin_precalculated[theta]).astype(np.int64) theta = np.digitize(angle, theta_values) -1
rho = np.round(x* cos_precalculated[theta] + y* sin_precalculated[theta]).astype(np.float64)
binned_rho = np.digitize(rho, rho_values) - 1 # cuz digitize is returning bin number + 1 binned_rho = np.digitize(rho, rho_values) - 1 # cuz digitize is returning bin number + 1
# Add to accumulator # Add to accumulator
print(gradient_magnitude[y, x]) accumulator[binned_rho, theta] += gradient_magnitude[y, x]
accumulator[binned_rho, theta] += 1
return accumulator return accumulator
@ -818,7 +847,6 @@ def nonmaxima_suppression_box(image: npt.NDArray[np.uint64]) -> npt.NDArray[np.u
break break
return image return image
def retrieve_hough_pairs(original_image: npt.NDArray[np.float64], hough_image: npt.NDArray[np.uint64], def retrieve_hough_pairs(original_image: npt.NDArray[np.float64], hough_image: npt.NDArray[np.uint64],
treshold: int, n_bins_theta: int, n_bins_rho: int) -> list[tuple[int, int]]: treshold: int, n_bins_theta: int, n_bins_rho: int) -> list[tuple[int, int]]:
""" """
@ -856,61 +884,6 @@ def select_best_pairs(image_line_params, n =10):
partition = np.argpartition(image_line_params, kth=len(image_line_params) - n - 1, axis=0)[-n:] partition = np.argpartition(image_line_params, kth=len(image_line_params) - n - 1, axis=0)[-n:]
image_line_params = image_line_params[partition.T[0]] image_line_params = image_line_params[partition.T[0]]
return image_line_params return image_line_params
def find_lines_in_image_naive(image: npt.NDArray[np.float64], SIGMA=1, THETA=0.02, T_LOW=0.04,
T_HIGH=0.16, N_BINS_THETA=360, N_BINS_RHO=360, TRESHOLD=0.2):
"""
Aplies all methods to transform image into hough space and find lines
"""
image = image.copy()
# First step: apply canny edge detector
image_with_edges = find_edges_canny(image, SIGMA, THETA, T_LOW, T_HIGH)
# Second step: Retrieve gradient angles
derivative_magnitude, gradient_angles = gradient_magnitude(image, SIGMA)
# Third step: Transform image into hough space
hough_image = hough_find_lines(image_with_edges, int(N_BINS_THETA), int(N_BINS_RHO), TRESHOLD)
# Fourth step: Apply nonmaxima suppression
hough_image_nms = nonmaxima_suppression_box(hough_image)
# Fifth step: Retrieve sigma and theta pairs
pairs = retrieve_hough_pairs(image, hough_image_nms, np.max(hough_image_nms) *0.5, int(N_BINS_THETA), int(N_BINS_RHO))
# Sixth step: select best pairs
best_pairs = select_best_pairs(pairs, 10)
return image_with_edges, derivative_magnitude, gradient_angles, hough_image, hough_image_nms, pairs, best_pairs
def find_lines_in_image_improved(image: npt.NDArray[np.float64], SIGMA=1, THETA=0.02, T_LOW=0.04, T_HIGH=0.16, N_BINS_THETA=360, N_BINS_RHO=360, TRESHOLD=0.2):
"""
Aplies all methods to transform image into hough space and find lines
"""
image = image.copy()
# First step: apply canny edge detector
image_with_edges = find_edges_canny(image, SIGMA, THETA, T_LOW, T_HIGH)
# Second step: Retrieve gradient angles
derivative_magnitude, gradient_angles = gradient_magnitude(image, SIGMA)
# Third step: Transform image into hough space
hough_image = hough_find_lines_i(image_with_edges, gradient_angles, derivative_magnitude, N_BINS_THETA, N_BINS_RHO, TRESHOLD)
# Fourth step: Apply nonmaxima suppression
hough_image_nms = nonmaxima_suppression_box(hough_image)
# Fifth step: Retrieve sigma and theta pairs
pairs = retrieve_hough_pairs(image, hough_image_nms, 0, N_BINS_THETA, N_BINS_RHO)
# Sixth step: select best pairs
#best_pairs = select_best_pairs(pairs, 10)
best_pairs = pairs
return image_with_edges, derivative_magnitude, gradient_angles, hough_image, hough_image_nms, pairs, best_pairs
def get_line_to_plot(rho, theta, h, w): def get_line_to_plot(rho, theta, h, w):
""" """