def experiment_yen_threshold(image=None):
images = image_generator()
if image is not None:
img_path = os.path.join(BASE_DIR, "data/imgs/")
fn = os.path.join(img_path, image)
im = load_images([fn])[0].astype("int32")
images = itertools.chain([(fn, im)], images)
for fn, im in images:
imgs = [im]
titles = ["Original"]
print("computing yen threshold")
yen = threshold_yen(im)
channels = threshold_by_channel(im, yen)
titles.append("Yen Thresholded")
imgs.append(channels[0])
for cp in [175, 200, 225, 250]:
titles.append("Cutpoint %d" % cp)
print("cutting")
rtn = np.zeros(im.shape)
rtn[np.average(im, axis=2, weights=[0.7, 0.1, 0.2]) > cp, 0] = 255
for i in range(1, 3):
rtn[:, :, i] = rtn[:, :, 0]
imgs.append(rtn)
print("plotting")
plot_images(imgs, titles=titles, suptitle=fn.split("/")[-1])
def segmentation(retain=3, n_segments=20, compactness=20, sigma=0):
images = image_generator()
for im in images:
retained_segments = segment_image_slic(
image, retain=retain, n_segments=n_segments, compactness=compactness, sigma=sigma
)
plot_images([image, retained_segments], ["Original", "Segmented"])
def main(csv_path=os.path.join(BASE_DIR, 'data/train.csv'),
img_path=os.path.join(BASE_DIR, 'data/imgs/')):
whale_index = pd.read_csv(csv_path)
image_file_names = lookup_whale_images(whale_index, whale_id='whale_78785')
image_arrays = load_images(image_file_names)
image_file = image_file_names[0]
image = image_arrays[0]
resized_array = resize_images(image_arrays)
transformed_array = transform_images(resized_array)
t_im_1 = transformed_array[0].reshape((3, 300, 300)).transpose(1, 2, 0)
t_im_2 = transformed_array[1].reshape((3, 300, 300)).transpose(1, 2, 0)
plot_images([t_im_1, t_im_2],['transformed image 1','transformed image 2'])
def experiment_thresholding(votes_min=3):
from skimage.filters import threshold_otsu
from skimage.filters import threshold_li
from skimage.filters import threshold_yen
from skimage.filters import threshold_adaptive
from scipy.ndimage import median_filter
images = image_generator()
for fn, im in images:
print("inspecting image: ", fn)
print("computing otsu threshold")
otsu = threshold_otsu(im)
otsu_ch1 = np.zeros(im.shape)
otsu_ch2 = np.zeros(im.shape)
otsu_ch3 = np.zeros(im.shape)
otsu_ch1[im[:, :, 0] > otsu, 0] = 255
otsu_ch2[im[:, :, 1] > otsu, 1] = 255
otsu_ch3[im[:, :, 2] > otsu, 2] = 255
otsu_ch1 = smallest_partition(otsu_ch1, 0)
otsu_ch2 = smallest_partition(otsu_ch2, 1)
otsu_ch3 = smallest_partition(otsu_ch3, 2)
print("computing yen threshold")
yen = threshold_yen(im)
yen_ch1 = np.zeros(im.shape)
yen_ch2 = np.zeros(im.shape)
yen_ch3 = np.zeros(im.shape)
yen_ch1[im[:, :, 0] > yen, 0] = 255
yen_ch2[im[:, :, 1] > yen, 1] = 255
yen_ch3[im[:, :, 2] > yen, 2] = 255
yen_ch1 = smallest_partition(yen_ch1, 0)
yen_ch2 = smallest_partition(yen_ch2, 1)
yen_ch3 = smallest_partition(yen_ch3, 2)
print("computing li threshold")
li = threshold_li(im)
li_ch1 = np.zeros(im.shape)
li_ch2 = np.zeros(im.shape)
li_ch3 = np.zeros(im.shape)
li_ch1[im[:, :, 0] > li, 0] = 255
li_ch2[im[:, :, 1] > li, 1] = 255
li_ch3[im[:, :, 2] > li, 2] = 255
li_ch1 = smallest_partition(li_ch1, 0)
li_ch2 = smallest_partition(li_ch2, 1)
li_ch3 = smallest_partition(li_ch3, 2)
print("computing average threshold")
av_ch1 = np.zeros(im.shape)
av_ch2 = np.zeros(im.shape)
av_ch3 = np.zeros(im.shape)
votes1 = otsu_ch1 + yen_ch1 + li_ch1
votes1 = otsu_ch1 + yen_ch1 + li_ch1
votes2 = otsu_ch2 + yen_ch2 + li_ch2
votes3 = otsu_ch3 + yen_ch3 + li_ch3
av_ch1[votes1[:, :, 0] >= (255 * votes_min), 0] = 255
av_ch2[votes2[:, :, 1] >= (255 * votes_min), 1] = 255
av_ch3[votes3[:, :, 2] >= (255 * votes_min), 2] = 255
thresholded_images = [
otsu_ch1,
otsu_ch2,
otsu_ch3,
yen_ch1,
yen_ch2,
yen_ch3,
li_ch1,
li_ch2,
li_ch3,
av_ch1,
av_ch2,
av_ch3,
]
print("filtering out specks")
for idx, im in enumerate(thresholded_images):
thresholded_images[idx] = median_filter(im, size=3)
titles = [
"Channel 1 Otsu",
"Channel 2 Otsu",
"Channel 3 Otsu",
"Channel 1 Yen",
"Channel 2 Yen",
"Channel 3 Yen",
"Channel 1 Li",
"Channel 2 Li",
"Channel 3 Li",
"Channel 1 Avg",
"Channel 2 Avg",
"Channel 3 Avg",
]
print("plotting")
plot_images(thresholded_images, titles=titles, suptitle=fn.split("/")[-1])
