def dct_blur_segmentation(in_dir, block_size, blur_kernel=None):
"""
todo: + make num_planes_to_keep a parameter, optimize over it
+ make multi-scale: incorporate info from multiple block sizes; see if it leads to performance improvement
+ make diff_blurmaps threshold a parameter; optimize over it to see if it leads to performance improvement
"""
if isinstance(block_size, int):
block_size_tuple = (block_size, block_size)
else:
block_size_tuple = block_size
if isinstance(blur_kernel, int):
blur_kernel = (blur_kernel, blur_kernel)
files = list(sorted(os.listdir(in_dir)))
filenames = list(sorted([os.path.join(in_dir, f) for f in files]))
img0 = lm(cv2.imread(filenames[0]))
if blur_kernel:
img0 = cv2.blur(img0, blur_kernel)
mask_zeros = np.zeros((int(img0.shape[0] / 3) + 12, img0.shape[1]))
mask_ones = np.ones(
(img0.shape[0] - 12 - int(img0.shape[0] / 3), img0.shape[1]))
mask = np.vstack((mask_zeros, mask_ones)).astype(int)
# img0 = slice_bit_planes(img=img0, num_planes_to_keep=5)
blurmap0 = stretch_histogram(
dct_motion_blur_detection(img=img0, block_size=block_size))
for j, f in tqdm(enumerate(filenames[1:150])):
img1 = lm(cv2.imread(f))
if blur_kernel:
img1 = cv2.blur(img1, blur_kernel)
img_stacked = np.dstack((img1, img1, img1))
# img1 = slice_bit_planes(img=img1, num_planes_to_keep=5)
blurmap1 = stretch_histogram(
dct_motion_blur_detection(img=img1, block_size=block_size))
diff_blurmaps = blurmap1 - blurmap0
diff_blurmaps = np.where(diff_blurmaps > 20, 255, 0)
diff_blurmaps = closing(diff_blurmaps, selem=np.ones((21, 21)))
# diff_blurmaps = closing(diff_blurmaps, selem=np.ones((15,15)))
# diff_blurmaps = dilation(diff_blurmaps, selem=np.ones((11,11)))
# diff_blurmaps = closing(diff_blurmaps, selem=np.ones((11,11)))
# diff_blurmaps = closing(diff_blurmaps, selem=np.ones((17,17)))
diff_blurmaps = opening(diff_blurmaps, selem=np.ones((19, 19)))
# diff_blurmaps = dilation(diff_blurmaps, selem=np.ones((11,11)))
diff_blurmaps = closing(diff_blurmaps, selem=np.ones((27, 27)))
diff_blurmaps *= mask
diff_blurmaps = np.dstack(
(diff_blurmaps, np.zeros(diff_blurmaps.shape),
np.zeros(diff_blurmaps.shape)))
# diff_blurmaps = np.where(diff_blurmaps > 0, 255, img_stacked)
# mask_diff = np.where(diff_blurmaps > 0, 255, 0)
cv2.imwrite('../output_data/final_systems/dct_blur/' + files[j + 1],
diff_blurmaps)
cv2.imwrite(
'../output_data/final_systems/masks/dct_blur/' + files[j + 1],
mask_diff)
img0, blurmap0 = img1, blurmap1
def main(img_file='../input_data/tunnels/tunnel_1.png', out_dir='../output_data/frequency_domain/original_tunnel/'):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
img = cv2.imread(img_file)
# this method will automatically convert `img` to grayscale. set param `make_gray` = `False` if you don't want this
dft_img = to_frequency_domain(img)
dft_shift = np.fft.fftshift(dft_img)
magnitude = 20 * np.log(np.abs(dft_shift.real) + 1)
phase = dft_shift.imag
#dims = int(img.shape[0] / 8), int(img.shape[1] / 8)
dims = 5
if isinstance(dims, int):
dims = (dims, dims)
start_x = int((dft_img.shape[0] - dims[0]) / 2)
start_y = int((dft_img.shape[1] - dims[1]) / 2)
# highpass filtering
highpass_kernel = construct_highpass_filter(dims, dft_img.shape, start_x, start_y)
highpass_fft_filtered = dft_shift * highpass_kernel
highpass_fft_filtered_magnitude = highpass_fft_filtered.real
highpass_fft_filtered_phase = highpass_fft_filtered.imag
highpass_filtered = np.fft.ifft2(highpass_fft_filtered)
# get magnitude
highpass_filtered = np.abs(highpass_filtered.real)
print(highpass_filtered)
# lowpass filtering
lowpass_kernel = construct_lowpass_filter(dims, dft_img.shape, start_x, start_y)
lowpass_fft_filtered = dft_shift * lowpass_kernel
lowpass_fft_filtered_magnitude = lowpass_fft_filtered.real
lowpass_fft_filtered_phase = lowpass_fft_filtered.imag
lowpass_filtered = np.fft.ifft2(lowpass_fft_filtered)
# get magnitude
lowpass_filtered = np.abs(lowpass_filtered.real)
print(lowpass_filtered)
cv2.imwrite(os.path.join(out_dir, 'highpass_fft_filtered_magnitude.png'), np.abs(highpass_fft_filtered_magnitude))
cv2.imwrite(os.path.join(out_dir, 'lowpass_fft_filtered_magnitude.png'), np.abs(lowpass_fft_filtered_magnitude))
cv2.imwrite(os.path.join(out_dir, 'highpass_fft_filtered_phase.png'), np.abs(highpass_fft_filtered_phase))
cv2.imwrite(os.path.join(out_dir, 'lowpass_fft_filtered_phase.png'), np.abs(lowpass_fft_filtered_phase))
cv2.imwrite(os.path.join(out_dir, 'lowpass_filter.png'), stretch_histogram(lowpass_kernel))
cv2.imwrite(os.path.join(out_dir, 'highpass_filter.png'), stretch_histogram(highpass_kernel))
cv2.imwrite(os.path.join(out_dir, 'magnitude.png'), magnitude)
cv2.imwrite(os.path.join(out_dir, 'phase.png'), phase)
cv2.imwrite(os.path.join(out_dir, 'highpass_filtered.png'), stretch_histogram(highpass_filtered))
cv2.imwrite(os.path.join(out_dir, 'lowpass_filtered.png'), stretch_histogram(lowpass_filtered))
def _prepare_frame_segment(seed_img,
threshold=20,
filename=None,
img=None,
blur=None,
as_numeric=True,
stretched=True):
if filename:
img = lm(cv2.imread(filename))
elif img is not None:
if len(img.shape) == 3:
img = lm(img)
else:
raise Exception(
'Must specify either a path to the image file or a numpy-array image; got neither'
)
if blur:
if isinstance(blur, int):
blur = (blur, blur)
img = cv2.blur(img, blur)
#seed_img = cv2.blur(seed_img, blur)
img = slice_bit_planes(img, 4)
seed_img = slice_bit_planes(seed_img, 4)
diff_img = np.abs(img - seed_img)
segmented = diff_img > threshold
if as_numeric:
segmented = segmented.astype(int)
if stretched:
segmented = stretch_histogram(segmented)
return segmented
def stretch_histograms(dir='../output_data/denoised/'):
for f in os.listdir(dir):
file_name = os.path.join(dir, f)
print(file_name)
img = cv2.imread(file_name)
img = color_to_gray_operations.luminosity_method(img)
stretched = histogram_processing.stretch_histogram(img)
cv2.imwrite('../output_data/denoised_stretched/' + f, stretched)
def test_abstract_painting():
out_dir = '../output_data/abstract_painting_nopadding/'
img = lm(cv2.imread(config['image_paths']['original']))
start_num = 1
for t in tqdm(range(11, 151, 2)):
#padded_counter = pad_int(t, 3)
thresholded = threshold_niblack(img, t)
segmented = (img < thresholded).astype(int)
segmented = stretch_histogram(segmented)
cv2.imwrite(os.path.join(out_dir, str(start_num) + '.jpg'), segmented)
start_num += 1
def get_binary_segmentation(img, threshold, numeric=True, stretch=True):
segmented = img >= threshold
if numeric:
segmented = segmented.astype(int)
if stretch:
segmented = stretch_histogram(segmented)
print(segmented)
return segmented
def bi_gamma_correction(img_file, thresh=180, lower_g=1.0, higher_g=1.0):
img = luminosity_method(cv2.imread(img_file))
#img = gaussian(img, sigma=2)
new_img = np.where(img < thresh, img, 2 * (img / np.log(img + 1)))
hist, bins = histogram_processing.compute_image_histogram(img)
new_img = histogram_processing.stretch_histogram(new_img)
histogram_processing.plot_histogram(
img, out_file='../output_data/test_output/tunnel_hist_orig.png')
histogram_processing.plot_histogram(
new_img,
out_file='../output_data/test_output/tunnel_hist_equalized.png')
return new_img
f2 = (luminosity_method(f2)).astype(int)
#f2 = exposure.adjust_gamma(f1, 1)
cv2.imwrite(
'/Users/adamcatto/SRC/dippy/input_data/motion/gray_second_frame.png', f2)
#cv2.imwrite('/Users/adamcatto/SRC/dippy/output_data/decomposition/motion/subtract.png', f2-f1)
#print(f1)
detector = MovingObjectDetector([f1, f2])
bg = detector.model_background(2, max_frames=2)
zipped_bit_planes = list(zip(*bg))
compared = [cv2.bitwise_xor(x[0], x[1]) for x in zipped_bit_planes]
compared = compared[1]
cv2.imwrite(
'/Users/adamcatto/SRC/dippy/output_data/decomposition/motion/bitwise_xor.png',
stretch_histogram(compared))
def compute_nms(ns, compared):
"""
ns: neighborhood_size
"""
nms = np.zeros(compared.shape)
#print(nms.shape)
for _ in range(5):
for i in range(ns, compared.shape[0] - ns):
for j in range(ns, compared.shape[1] - ns):
if compared[i, j] > 0:
neighborhood = [
1 if compared[i + x, j + y] > 0 else 0
def combined_edge_dct_blur_segmentation(in_dir,
block_size,
num_frames,
num_prev_frames,
blur_kernel=None):
if isinstance(block_size, int):
block_size_tuple = (block_size, block_size)
else:
block_size_tuple = block_size
if isinstance(blur_kernel, int):
blur_kernel = (blur_kernel, blur_kernel)
files = list(sorted(os.listdir(in_dir)))
filenames = list(sorted([os.path.join(in_dir, f) for f in files]))
img0 = lm(cv2.imread(filenames[0]))
if blur_kernel:
img0 = cv2.blur(img0, blur_kernel)
mask_zeros = np.zeros((int(img0.shape[0] / 3) + 12, img0.shape[1]))
mask_ones = np.ones(
(img0.shape[0] - 12 - int(img0.shape[0] / 3), img0.shape[1]))
mask = np.vstack((mask_zeros, mask_ones)).astype(int)
blurmap0 = stretch_histogram(
dct_motion_blur_detection(img=img0, block_size=block_size))
sobel_edges0 = sobel(img0)
sobel_edges0 = np.where(sobel_edges0 > 10, 255, 0)
for j, f in tqdm(enumerate(filenames[num_prev_frames:num_frames])):
img1 = lm(cv2.imread(f))
if blur_kernel:
img1 = cv2.blur(img1, blur_kernel)
img_stacked = np.dstack((img1, img1, img1))
blurmap1 = stretch_histogram(
dct_motion_blur_detection(img=img1, block_size=block_size))
diff_blurmaps = blurmap1 - blurmap0
diff_blurmaps = np.where(diff_blurmaps > 30, 255, 0)
# edge map component
sobel_edges1 = sobel(img1)
sobel_edges1 = np.where(sobel_edges1 > 10, 255, 0)
sobel_diff = sobel_edges1 - sobel_edges0
# combine
diff = np.clip(diff_blurmaps + sobel_diff, 0, 255)
diff = closing(diff, selem=np.ones((13, 13)))
diff = opening(diff, selem=np.ones((19, 19)))
diff = dilation(diff, selem=np.ones((21, 21)))
diff *= mask
diff = np.dstack((diff, np.zeros(diff.shape), np.zeros(diff.shape)))
diff = np.where(diff > 0, 255, img_stacked)
mask_diff = np.where(diff > 0, 255, 0)
cv2.imwrite('../output_data/final_systems/combined/' + files[j + 1],
diff)
cv2.imwrite(
'../output_data/final_systems/masks/combined/' + files[j + 1],
mask_diff)
img0, blurmap0, sobel_edge0 = img1, blurmap1, sobel_edges1
def write_thresholds(img, out_dir):
img = lm(img)
for t in tqdm(range(256)):
segmented = img <= t
segmented = stretch_histogram(segmented.astype(int))
cv2.imwrite(os.path.join(out_dir, str(t) + '.jpg'), segmented)
