def dehaze_from_fp(fp):
with PIL.Image.open(fp) as img:
img.load()
img = np.array(img)/255
# remove background, assuming retinal fundus image
background = get_background(img)
img[background] = 1
return dehaze(img)
def illuminate_dehaze(img):
"""
Perform illumination correction to remove shadows followed by dehazing.
Correctly remove background
Return a tuple of dicts. The first dict is output of illumination
correction. Second dict is output from dehazing.
"""
# compute a background mask to clean up noise from the guided filter
background = get_background(img)
img[background] = 1
d = illumination_correction(img)
# reset the background
d['radiance'][background] = 1/255
d2 = dehaze(d['radiance'])
d['background'] = background
return d, d2
(I).astype('float32'),
sh_blur_radius,
sh_blur_guided_eps)
# kernel = np.outer(*([sp.stats.norm.pdf(np.linspace(-1, 1, sh_blur_radius), 0, .7)]*2))
# A2 = sp.signal.fftconvolve(I/t1, reshape_A(kernel))
return (I - A2) / t + A2
dset = IDRiD('./data/IDRiD_segmentation')
img, labels = dset['IDRiD_25']
# he = labels['HE']
# ma = labels['MA']
# ex = labels['EX']
# se = labels['SE']
# od = labels['OD']
# set background pure black.
bg = util.get_background(img)
img[bg] = 0
best = illuminate_sharpen(img)
illum = methods.illuminate_dcp(I, focus_region=~bg)
plt.figure(1)
plt.imshow(sh(illum))
plt.figure(2)
plt.imshow(sh(Jc2, .2))
# best = competing_methods.sharpen(Jc2, focus_region=~bg)
import sys
sys.exit()
plt.figure()
plt.imshow(img)
plt.figure()
plt.imshow(best)
clip = 30
enhanced_img = clahe(I, clipLimit=clip, tileGridSize=gridsize, colorspace=cv2.COLOR_RGB2LAB)
axs[ax_idx].imshow(enhanced_img)
axs[ax_idx].set_title(f"Grid Size: {clip}", fontsize=20)
axs[ax_idx].axis('off')
f.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0.1)
f.savefig(save_fp, bbox_inches='tight')
s = time.time()
from ietk.methods.sharpen_img import sharpen
from ietk.util import get_background
from skimage.color import rgb2lab, lab2rgb
save_fp = f'{ns.save_fig_dir}/qualitative-{img_idx}-sharpen-nogf.png'
f, axs = plt.subplots(1, 5, num=3, figsize=(4*5, 4))
bg = get_background(I)
I[bg] = 0
# for ax_idx, t in enumerate([.5, .3, .15, .1, .05]):
for ax_idx, t in enumerate([.1, .01, .005, .002, .001,]):
print('sharpen l', t)
labI = rgb2lab(I)
assert labI.shape[-1] == 3
labI[:,:,0] = sharpen(labI[:,:,0], t=t, blur_guided_eps=.1, use_guidedfilter=False)
enhanced_img = lab2rgb(labI)
axs[ax_idx].imshow(enhanced_img)
axs[ax_idx].set_title(f"t: {t}", fontsize=20)
axs[ax_idx].axis('off')
f.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0.1)
f.savefig(save_fp, bbox_inches='tight')
print(time.time() - s)