def canny_without_blur():
tag = "canny without blur"
data_points[tag] = []
for _ in range(MEASUREMENTS):
img = pgu.img_to_surface(IMG_PATH)
data_points[tag].append(
measure_execution_time(lambda: canny(img))[0])
def canny_with_blur():
# with 3x3 gaussian kernel
tag = "canny with 3x3 gaussian blur"
data_points[tag] = []
for _ in range(MEASUREMENTS):
img = pgu.img_to_surface(IMG_PATH)
data_points[tag].append(
measure_execution_time(lambda: canny(img, GAUSSIAN[3]))[0])
def benchmark_lab3():
"""
img to greyscale
canny without blur
canny with blur
"""
source_img = pgu.img_to_surface(IMG_PATH)
data_points = {
"greyscale":
measure_execution_time(lambda: eu.surf_to_greyscale(source_img),
MEASUREMENTS)
}
def canny(image, kernel=None):
image = eu.surf_to_greyscale(image)
if kernel is not None:
image = transform_by_kernel(image, kernel)
canny_edge_detection(image, SOBEL_SUM)
def canny_without_blur():
tag = "canny without blur"
data_points[tag] = []
for _ in range(MEASUREMENTS):
img = pgu.img_to_surface(IMG_PATH)
data_points[tag].append(
measure_execution_time(lambda: canny(img))[0])
def canny_with_blur():
# with 3x3 gaussian kernel
tag = "canny with 3x3 gaussian blur"
data_points[tag] = []
for _ in range(MEASUREMENTS):
img = pgu.img_to_surface(IMG_PATH)
data_points[tag].append(
measure_execution_time(lambda: canny(img, GAUSSIAN[3]))[0])
canny_without_blur()
canny_with_blur()
print_measurements("Pygame", data_points)
def benchmark_lab2():
"""
transform by kernel:
- avg 1x1 3x3 5x5
- gaussian 1x1 3x3 5x5
"""
data_points = {}
source_img = pgu.img_to_surface(IMG_PATH)
for kernel_size in AVERAGES:
data_points["average " + str(kernel_size)] = measure_execution_time(
lambda: transform_by_kernel(source_img, AVERAGES[kernel_size]),
MEASUREMENTS)
for kernel_size in GAUSSIAN:
data_points["gaussian " + str(kernel_size)] = measure_execution_time(
lambda: transform_by_kernel(source_img, GAUSSIAN[kernel_size]),
MEASUREMENTS)
print_measurements("Pygame", data_points)
def transform_by_kernel(surf, kernel):
"""
:param surf: surface to be transformed
:param kernel: kernel to be used for convolution
:return: new surface
"""
old_array = pygame.surfarray.pixels3d(surf)
new_array = pygame.surfarray.array3d(surf)
kernel_array, kernel_sum = kernel
radius = len(kernel_array) // 2
width, height, _ = old_array.shape
for x in range(0, width):
for y in range(0, height):
new_array[x,
y] = bu.transform_pixel(old_array, x, y, radius, width,
height, kernel_array, kernel_sum)
del old_array
return pygame.surfarray.make_surface(new_array)
if __name__ == "__main__":
surface = pgu.img_to_surface(path=IMG_PATH)
kernel = bu.avg_kernel(size=3)
pgu.display_surface(transform_by_kernel(surface, kernel))