def count_droplets_doh(img):
stats = ImageStats(img)
s = stretch_composite_histogram(img, stats)
blobs = doh(img)
if DEBUG:
make_circles_fig(s, blobs).show()
return len(blobs)
def count_droplets_log(img):
stats = ImageStats(img)
s = stretch_composite_histogram(img, stats)
blobs = log(s)
if DEBUG:
make_circles_fig(s, blobs, title=f"log {len(blobs)}").show()
return len(blobs)
def main():
start = time.monotonic()
image_pth = Path(os.path.dirname(
os.path.realpath(__file__))) / Path("../simulation/screenshot.png")
img_orig = imread(image_pth, as_gray=True)
filtered_img = gaussian(img_orig, sigma=1)
s2 = stretch_composite_histogram(filtered_img)
blobs = blob_dog(s2,
max_sigma=10,
min_sigma=1,
threshold=0.001,
overlap=0.5,
sigma_ratio=1.01)
print("dog time ", time.monotonic() - start)
blobs[:, 2] = blobs[:, 2] * sqrt(2)
print(len(blobs))
def preprocess_times():
image_pth = Path(os.path.dirname(os.path.realpath(__file__))) / Path(
"../simulation/screenshot.png"
)
# image_pth = Path(os.path.dirname(os.path.realpath(__file__))) / Path(
# "../data/RawData/R109_60deg_6-8-25_OHP-LS000252.T000.D000.P000.H000.PLIF1.TIF"
# )
img_cpu = io.imread(image_pth)
stretch_times = []
gaussian_times = []
img_as_float_times = []
torch_times = []
for i in range(REPEATS):
START = time.monotonic()
img = img_as_float(img_cpu)
END = time.monotonic()
img_as_float_times.append(END - START)
START = time.monotonic()
img = stretch_composite_histogram(img)
END = time.monotonic()
stretch_times.append(END - START)
START = time.monotonic()
img = gaussian(img, sigma=1)
END = time.monotonic()
gaussian_times.append(END - START)
START = time.monotonic()
img = torch.from_numpy(img).float()
END = time.monotonic()
torch_times.append(END - START)
print(f"img_as_float times {np.mean(img_as_float_times)}")
print(f"stretch times {np.mean(stretch_times)}")
print(f"gaussian times {np.mean(gaussian_times)}")
print(f"torch times {np.mean(torch_times)}")
print()
stretch_times = []
gaussian_times = []
img_as_float_times = []
torch_times = []
for i in range(REPEATS):
START = time.monotonic()
img = stretch_composite_histogram(img_cpu)
END = time.monotonic()
stretch_times.append(END - START)
START = time.monotonic()
img = img_as_float(img)
END = time.monotonic()
img_as_float_times.append(END - START)
START = time.monotonic()
img = gaussian(img, sigma=1)
END = time.monotonic()
gaussian_times.append(END - START)
START = time.monotonic()
img = torch.from_numpy(img).float()
END = time.monotonic()
torch_times.append(END - START)
print(f"stretch times {np.mean(stretch_times)}")
print(f"gaussian times {np.mean(gaussian_times)}")
print(f"img_as_float times {np.mean(img_as_float_times)}")
print(f"torch times {np.mean(torch_times)}")
def transform(self, img):
img = img_as_float(img)
img = gaussian(img, sigma=1)
img = stretch_composite_histogram(img)
img = torch.from_numpy(img).float()
return img
def test_dog(img_orig, show_fig=True):
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8),
(ax9, ax10)) = plt.subplots(nrows=5, ncols=2, figsize=(24, 48))
fig.tight_layout()
ax1.axis("off")
# ax2.axis("off")
ax3.axis("off")
ax4.axis("off")
ax5.axis("off")
ax6.axis("off")
ax7.axis("off")
ax8.axis("off")
# ax9.axis("off")
# ax10.axis("off")
im_fft = np.fft.fftshift(fftpack.fft2(img_orig))
power = np.abs(im_fft) / (np.sum(np.abs(im_fft)**2) / im_fft.size)
ax1.imshow(power, norm=LogNorm())
ax1.set_title("log power")
threshed_power = power > 1e-5
power_vals = np.hstack(
[np.argwhere(threshed_power), power[threshed_power, np.newaxis]])
pca = decomposition.PCA(n_components=2)
pca.fit_transform(power_vals)
center = list(pca.mean_[:2])[::-1]
for length, vector in zip(pca.explained_variance_, pca.components_):
length = np.sqrt(length) * 10
print(length)
v = vector[:2][::-1] * length
draw_vector(center, center + v, ax1)
ax2.hist(np.log(power.ravel()), bins=256, density=True)
ax2.set_title("power distribution")
filtered_img = gaussian(img_orig, sigma=1)
log_img = np.log(filtered_img)
print(log_img.min(), log_img.max())
ax3.imshow(log_img, cmap="gray")
ax3.set_title("log scaled")
blobs = blob_dog(log_img,
max_sigma=10,
min_sigma=5,
threshold=1,
overlap=0.8)
blobs[:, 2] = blobs[:, 2] * sqrt(2)
ax4.imshow(log_img, cmap="gray")
ax4.set_title(f"log blobs {len(blobs)}")
for y, x, r in blobs:
c = plt.Circle((x, y), r, color="red", linewidth=0.5, fill=False)
ax4.add_patch(c)
s2 = stretch_composite_histogram(filtered_img)
ax5.imshow(s2, cmap="gray")
ax5.set_title("constrast stretched")
#
# blobs = blob_dog(s2, max_sigma=10, min_sigma=1, threshold=0.02, overlap=0.8)
# blobs[:, 2] = blobs[:, 2] * sqrt(2)
# ax6.imshow(s2, cmap="gray")
# ax6.set_title(f"contrast stretched blobs {len(blobs)}")
# for y, x, r in blobs:
# c = plt.Circle((x, y), r, color="red", linewidth=0.5, fill=False)
# ax6.add_patch(c)
# ax10.hist([r for _, _, r in blobs], bins=256, density=True)
# ax10.set_title("constrast stretched bubble distribution")
# denoised = denoise(s2)
# ax7.imshow(denoised, cmap="gray")
# ax7.set_title("n2n denoised")
# blobs = blob_dog(denoised, max_sigma=10, min_sigma=5, threshold=0.02, overlap=0.5)
# blobs[:, 2] = blobs[:, 2] * sqrt(2)
# ax8.imshow(denoised, cmap="gray")
# ax8.set_title(f"denoised blobs {len(blobs)}")
# for y, x, r in blobs:
# c = plt.Circle((x, y), r, color="red", linewidth=0.5, fill=False)
# ax8.add_patch(c)
#
# print([r for _, _, r in blobs])
# ax9.hist([r for _, _, r in blobs], bins=256, density=True)
# ax9.set_title("denoised bubble distribution")
if show_fig:
fig.show()
else:
fig.savefig(f"processed_images/{img_fp}.png", dpi=96)
