def experiement_clahe_and_entropy(file=None):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
claheizer = cv2.createCLAHE()
for fn, im in images:
image_arrays = []
titles = []
# plot original image
titles.append("Original")
image_arrays.append(im)
# clahe
clahe = cv2.cvtColor(im.astype("uint8"), cv2.COLOR_RGB2Lab)
tmp = clahe.copy()
tmp[:, :, 0] = claheizer.apply(clahe[:, :, 0])
clahe = cv2.cvtColor(tmp.astype("uint8"), cv2.COLOR_Lab2RGB)
titles.append("CLAHE")
image_arrays.append(clahe)
tmp = cv2.cvtColor(clahe, cv2.COLOR_RGB2Lab)
ent = skimage.filters.rank.entropy(tmp[:, :, 0], skimage.morphology.disk(5))
titles.append("Entropy L")
image_arrays.append(ent)
# plot
subplot_images(image_arrays, titles=titles, show_plot=True, suptitle=fn.split("/")[-1])
def experiment_yen_mask_rescale(file=None):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
claheizer = cv2.createCLAHE()
for fn, im in images:
image_arrays = []
titles = []
# plot original image
titles.append("Original")
image_arrays.append(im)
# thresholded
yen = threshold_yen(im)
thresholded = threshold_by_channel(im, yen)[0]
titles.append("Yen Thresholded")
image_arrays.append(thresholded)
# masked and equalized
masked = im.copy()
masked[thresholded <= 0] = 0
titles.append("Masked")
image_arrays.append(masked)
# equalize
from skimage.util import img_as_ubyte
equalized = masked.copy()
for chan in range(1, 3):
img = masked[:, :, chan].astype("uint8")
rng = (0, 175) if chan == 1 else (175, 255)
rescaled = exposure.rescale_intensity(img, in_range=rng)
equalized[:, :, chan] = rescaled
break
titles.append("Equalied")
image_arrays.append(equalized)
# plot
subplot_images(image_arrays, titles=titles, show_plot=True, suptitle=fn.split("/")[-1])
def experiement_segment_crop_gabor(file=None, dilation_iterations=40, num_regions=4):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
gabor_filters = build_gabor_filters()
for fn, im in images:
image_arrays = []
titles = []
# plot original image
titles.append("Original Image")
image_arrays.append(im)
# segmented image
segmented = segment_image(im, n_segments=20, compactness=20, sigma=2)
titles.append("Segmented Image")
image_arrays.append(segmented)
# re-segment image
brightest_colors = extract_brightest_colors(segmented, thresh=3.5)
bright_regions = select_colors(segmented, brightest_colors)
resegmented = segmented.copy()
resegmented[bright_regions <= 0] = 0
resegmented = segment_image(resegmented, n_segments=5, compactness=30, sigma=2)
titles.append("Re-Segmented Image")
image_arrays.append(resegmented)
colors = list_colors_exclude_black(segmented)[[0, 4, 8, 12, 16]]
rgb_crops = []
rgb_labels = []
crop_titles = []
for color in list(colors):
mask = select_colors(segmented, [color]) > 0
x, y = ndimage.measurements.center_of_mass((mask[:, :, 0] > 0).astype(int))
xmin = int(max(x - 50, 0))
xmax = int(min(x + 50, im.shape[0] - 1))
ymin = int(max(y - 50, 0))
ymax = int(min(y + 50, im.shape[1] - 1))
rgb_crops.append(im[xmin:xmax, ymin:ymax, :].copy())
rgb_labels.append(str(color))
crop_titles.append("Crop for Color " + str(color))
subplot_images(rgb_crops, titles=crop_titles, suptitle="Crops")
for crop, color in zip(rgb_crops, rgb_labels):
rgb_gabor = apply_gabor_filters(crop, gabor_filters)
subplot_images(rgb_gabor, suptitle="RGB Gabor Filters for Color " + color)
subplot_images(image_arrays, titles=titles, suptitle=fn.split("/")[-1], show_plot=True)
def experiment_yen_distort_reds(file=None):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
for fn, im in images:
image_arrays = []
titles = []
# plot original image
titles.append("Original")
image_arrays.append(im)
yen = threshold_yen(im)
thresholded = threshold_by_channel(im, yen)[0]
titles.append("Yen Thresholded")
image_arrays.append(thresholded)
# equalize
equalized = np.zeros_like(im)
for chan in range(3):
equalized[:, :, chan] = (exposure.equalize_hist(im[:, :, chan]) * 255).astype("uint8")
titles.append("Equalized")
image_arrays.append(equalized)
# experiments point to a reasonable threshold of 10 or 20
thresh = 140 # TUNE: 160
reds_ycbcr = extract_brightest_reds(equalized, thresh=thresh, verbose=False)
titles.append("Reds Thresholded YCBCR %d" % thresh)
image_arrays.append(reds_ycbcr)
filled_ycbcr = reds_ycbcr.copy()
mask = ndimage.binary_fill_holes((reds_ycbcr[:, :, 0] > 0))
# mask = ndimage.binary_fill_holes((np.amax(reds_ycbcr, axis=2) > 0))
filled_ycbcr[mask, :] = np.array([255, 0, 0])
titles.append("Filled YCBCR Reds")
image_arrays.append(filled_ycbcr)
smoothed_ycbcr = reds_ycbcr.copy()
smoothed_ycbcr[:, :, 0] = binary_closing(smoothed_ycbcr[:, :, 0] > 0, iterations=3) * 255
mask = np.amax(smoothed_ycbcr, axis=2) > 0
structure = calculate_binary_opening_structure(mask, weight=1, hollow=False)
mask = binary_opening(mask, structure=structure, iterations=1)
smoothed_ycbcr[mask <= 0, :] = 0
titles.append("Smoothed YCBCR Reds")
image_arrays.append(smoothed_ycbcr)
# plot
subplot_images(image_arrays, titles=titles, show_plot=True, suptitle=fn.split("/")[-1])
continue
# segmented image
segmented = segment_image(smoothed_ycbcr, n_segments=3, compactness=100, sigma=2)
titles.append("Segmented")
image_arrays.append(segmented)
titles.append("Least Gray Bright Region")
image_arrays.append(bright)
d, m, b = extract_colors_grays(segmented, n_grays=6)
grays = select_colors(segmented, [d, m, b])
titles.append("Gray Regions of Varied Intensity")
image_arrays.append(grays)
quantized = quantize_colors(
segmented,
n_colors=3,
n_samples=1000,
max_iter=300,
n_init=10,
n_jobs=1,
random_state=SEED,
verbose=True,
split=False,
)
titles.append("Quantized Image %d Colors" % 3)
image_arrays.append(quantized)
quantized = quantize_colors(
segmented,
n_colors=4,
n_samples=1000,
max_iter=300,
n_init=10,
n_jobs=1,
random_state=SEED,
verbose=True,
split=False,
)
titles.append("Quantized Image %d Colors" % 4)
image_arrays.append(quantized)
quantized = quantize_colors(
segmented,
n_colors=5,
n_samples=1000,
max_iter=300,
n_init=10,
n_jobs=1,
random_state=SEED,
verbose=True,
split=False,
)
titles.append("Quantized Image %d Colors" % 5)
image_arrays.append(quantized)
subplot_images(image_arrays, titles=titles, show_plot=True, suptitle=fn.split("/")[-1])
def experiment_yen_distort_reds_entropy_blues(file=None):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
for fn, im in images:
image_arrays = []
titles = []
# plot original image
titles.append("Original")
image_arrays.append(im)
# threshold
yen = threshold_yen(im)
thresholded = threshold_by_channel(im, yen)[0]
titles.append("Yen Thresholded")
image_arrays.append(thresholded)
# distort
equalized = np.zeros_like(im, dtype="uint8")
for chan in range(3):
equalized[:, :, chan] = (exposure.equalize_hist(im[:, :, chan]) * 255).astype("uint8")
# entropy
ent = skimage.filters.rank.entropy(equalized[:, :, 0], skimage.morphology.disk(3))
titles.append("Entropy")
image_arrays.append(ent)
# thresholded
thresh = 1.5
blues = np.zeros_like(im, dtype="uint8")
blues[ent > thresh, :] = 0
blues[ent <= thresh] = 255
titles.append("Entropy Blues Thresholded %d" % thresh)
image_arrays.append(blues)
# reds
thresh = 160
reds = extract_brightest_reds(equalized, thresh=thresh, verbose=False)
titles.append("Reds Thresholded YCBCR %d" % thresh)
image_arrays.append(reds)
# union
union = np.zeros_like(im)
union[thresholded > 0] = 255
union[reds > 0] = 255
union[blues > 0] = 255
titles.append("Union")
image_arrays.append(union)
for name, color_image in zip(["yen", "blues", "reds", "union"], [thresholded, blues, reds, union]):
binary_image = color_image[:, :, 0] > 0
structure = calculate_binary_opening_structure(binary_image)
binary_image = binary_opening(binary_image, structure=structure)
binary_image = binary_dilation(binary_image, iterations=3)
if name == "yen":
regions, labels = extract_largest_regions(binary_image, num_regions=2)
else:
regions, labels = extract_largest_regions(binary_image, num_regions=3)
mask = convex_hull_mask(regions > 0, mask=True)
print(mask.shape)
crop = np.zeros_like(im)
crop[mask > 0, :] = im[mask > 0, :]
titles.append(name.title() + "Region")
image_arrays.append(crop)
# plot
subplot_images(
image_arrays,
titles=titles,
# show_plot=True,
save_plot=True,
suptitle="crop-" + fn.split("/")[-1],
)
def experiment_yen_reds_fit_region(file=None):
images = image_generator()
if file is not None:
images = add_image_to_image_generator(images, file)
for fn, im in images:
print(fn)
image_arrays = []
titles = []
# plot original image
titles.append("Original")
image_arrays.append(im)
# threshold
yen = threshold_yen(im)
thresholded = threshold_by_channel(im, yen)[0]
titles.append("Yen Thresholded")
image_arrays.append(thresholded)
# reds
equalized = np.zeros_like(im, dtype="uint8")
for chan in range(3):
equalized[:, :, chan] = (exposure.equalize_hist(im[:, :, chan]) * 255).astype("uint8")
# reds
thresh = 160
reds = extract_brightest_reds(equalized, thresh=thresh, verbose=False)
titles.append("Reds Thresholded YCBCR %d" % thresh)
image_arrays.append(reds)
# encode
encoded = np.zeros_like(im[:, :, 0], dtype="uint8") * -1
encoded[thresholded[:, :, 0] > 0] = 2
encoded[np.amax(reds, axis=2) > 0] = 1
encoded = encoded.astype("uint8")
titles.append("Encoded")
image_arrays.append(encoded)
print("fitting reference frame")
best_loc, best_val = None, 0
for x in range(100, 1000, 100):
print("x = ", x)
for y in range(3 * x // 2, 1500, 100):
print("y = ", y)
for h in range(y // 3 - 1, y, y // 3):
for z in range(x // 3 - 1, x, x // 3):
for i in range(1, 4):
theta = i * np.pi / 3
reference = draw_reference_frame(x, y, z, h, theta)
res = cv2.matchTemplate(encoded, reference, cv2.TM_SQDIFF)
_, max_val, _, max_loc = cv2.minMaxLoc(res)
if max_val > best_val:
best_val = max_val
best_loc = max_loc
print(x, y, z, h, np.degrees(theta))
print(max_val, best_loc)
print("\n")
# plot
subplot_images(
image_arrays,
titles=titles,
# show_plot=True,
save_plot=True,
suptitle="crop-" + fn.split("/")[-1],
)
