def cpu_run_times(fn):
image_pth = Path(os.path.dirname(os.path.realpath(__file__))) / Path(
"../simulation/screenshot.png"
)
screenshot = SimulPLIF(img_path=image_pth, num_repeats=1)
with open(f"{fn}.csv", "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(["n_bin", "max_sigma", "time"])
for i in range(REPEATS):
for max_sigma in range(min_sigma, mx_sigma + 1):
for n_bin in range(min_bin, max_bin + 1):
START = time.monotonic()
cpu_blob_dog(
screenshot[0],
min_sigma=min_sigma,
max_sigma=max_sigma,
overlap=0.9,
threshold=0.012,
sigma_bins=n_bin,
prune=False,
)
res = [n_bin, max_sigma, time.monotonic() - START]
writer.writerow(res)
print(res)
def cpu_accuracy():
glob_str = (str(
Path(os.path.dirname(os.path.realpath(__file__))) /
Path("../simulation/test_data/")) + "/*.png")
print(glob_str)
for image_pth in glob.glob(glob_str):
screenshot = SimulPLIF(img_path=image_pth,
num_repeats=1,
load_truth=False)
blobs = cpu_blob_dog(
screenshot[0],
min_sigma=min_sigma,
max_sigma=mx_sigma,
overlap=0.5,
threshold=0.1,
sigma_bins=max_bin,
prune=False,
)
blobs[:, 2] = blobs[:, 2] * np.sqrt(2)
# make_circles_fig(screenshot[0].numpy(), blobs).show()
# break
fn = str(
Path(os.path.dirname(os.path.realpath(__file__))) /
f"accuracy_results/cpu/{os.path.basename(image_pth)}.res")
np.savetxt(fn, blobs)
print(fn)