def analyze_contrast(gr_name, graph_axes, iceberg_slice=np.s_[:,:], set_name=None):
gr_full = cv2.imread(gr_name, cv2.IMREAD_GRAYSCALE)
gr = cv2.resize(gr_full, (0,0), fx=1/5, fy=1/5, interpolation=cv2.INTER_AREA)
warped_images = np.empty((11),dtype=object)
adjs = np.arange(-1,1.2,.2)
ace_obj = HistogramWarpingACE(no_bits=8, tau=0.01, stretch_factor=1, debug=True, plot_histograms=False)
v_k, a_k = ace_obj.compute_vk_and_ak(gr)
for i, adj in enumerate(adjs):
print(i,adj)
outputs = ace_obj.compute_bk_and_dk(v_k, a_k, adjustment_factor=adj)
warped_images[i], Tx = ace_obj.transform_image(*outputs, gr)
fig,axes = plt.subplots(2,5, sharex=True, sharey=True)
[axi.set_axis_off() for axi in axes.ravel()]
warped_images_for_plot = np.delete(warped_images,5).reshape(2,5)
adjs_display = np.delete(adjs,5)
for adj, ax, img in zip(adjs_display, axes.ravel(), warped_images_for_plot.ravel()):
ax.imshow(img, cmap='gray', vmin=0, vmax=255)
ax.set_title("Adj factor = {:.2f}".format(adj))
fig.subplots_adjust(left=0.05, bottom=0.1, right=0.99, top=.9, wspace=0.01, hspace=0.01)
fig.suptitle(set_name)
show_plot()
full_img_gcfs = np.zeros(warped_images.shape)
iceberg_slice_gcfs = np.zeros(warped_images.shape)
for i, wimg in enumerate(warped_images):
full_img_gcfs[i] = compute_global_contrast_factor(wimg)
iceberg_slice_gcfs[i] = compute_global_contrast_factor(wimg[iceberg_slice])
graph_axes.plot(adjs, full_img_gcfs, '.-',label=set_name+" Full Image" )
graph_axes.plot(adjs, iceberg_slice_gcfs, '.-',label=set_name+" Iceberg Slice" )
graph_axes.legend()
fig2,axes2 = plt.subplots(2,5, sharex=True, sharey=True)
fig2.suptitle(set_name)
for adj, ax, img in zip(adjs_display, axes2.ravel(), warped_images_for_plot.ravel()):
plot_harris_eig_vals(img, ax)
#plot_harris_eig_vals(img, ax)
ax.set_title("Adj factor = {:.2f}".format(adj))
ax.set(adjustable='box', aspect='equal')
ax.set_xlim(ax.set_ylim(0,None))
def analyze_contrast(gr_name, graph_axes, iceberg_slice=np.s_[:,:], set_name=None):
gr_full = cv2.imread(gr_name, cv2.IMREAD_GRAYSCALE)
gr = cv2.resize(gr_full, (0,0), fx=1/5, fy=1/5, interpolation=cv2.INTER_AREA)
adjs = np.arange(0,-1.2,-.2)
sfs = np.arange(-1.0,0.2,.2)
sfs_g, adj_g = np.meshgrid(sfs, adjs)
ace_obj = HistogramWarpingACE(no_bits=8, tau=0.01, lam=5, adjustment_factor=-1.0, stretch_factor=-1.0,
min_stretch_bits=4, downsample_for_kde=True,debug=False, plot_histograms=False)
v_k, a_k = ace_obj.compute_vk_and_ak(gr)
warped_images = np.empty(adj_g.shape,dtype=object)
fig,axes = plt.subplots(*adj_g.shape, sharex=True, sharey=True)
for axi in axes.ravel():
axi.get_xaxis().set_ticks ([])
axi.get_yaxis().set_ticks ([])
axi.spines['left'].set_visible(False)
axi.spines['right'].set_visible(False)
axi.spines['bottom'].set_visible(False)
axi.spines['top'].set_visible(False)
for (i,j),adj in np.ndenumerate(adj_g):
print(i,adj)
outputs = ace_obj.compute_bk_and_dk(v_k, a_k, adjustment_factor=adj, stretch_factor=sfs_g[i,j])
warped_images[i,j], Tx = ace_obj.transform_image(*outputs, gr)
axes[i,j].imshow(warped_images[i,j], cmap='gray', vmin=0, vmax=255)
#ax.set_title("Adj factor = {:.2f}".format(adj))
for i, sf in enumerate(sfs):
axes[-1,i].set_xlabel('Stretch: {:.2f}'.format(sf))
for j, adj in enumerate(adjs):
axes[j,0].set_ylabel('Adj: {:.2f}'.format(adj))
fig.subplots_adjust(left=0.025, bottom=0.025, right=0.99, top=.9, wspace=0.00, hspace=0.00)
fig.suptitle(set_name)
show_plot()
'''
plot_histograms=False)
v_k, a_k = ace_obj.compute_vk_and_ak(gr)
adjs = np.arange(0, -1.05, -.05)
contrast_estimates = np.zeros((len(adjs), len(contrast_estimators)))
contrast_estimates_slice = np.zeros((len(adjs), len(contrast_estimators)))
warped_images = np.empty(adjs.shape, dtype=object)
for i, adj in enumerate(adjs):
print(i, adj)
outputs = ace_obj.compute_bk_and_dk(v_k,
a_k,
adjustment_factor=adj,
stretch_factor=adj)
warped_images[i], Tx = ace_obj.transform_image(*outputs, gr)
contrast_estimates[i, :] = np.array(
[ce(warped_images[i]) for nm, ce in contrast_estimators.items()])
contrast_estimates_slice[i, :] = np.array([
ce(warped_images[i][gr_slice])
for nm, ce in contrast_estimators.items()
])
fig_imgs, axes_imgs = plt.subplots(*calculate_best_screen_packing(
len(adjs), img_resolution=gr.shape),
sharex=True,
sharey=True)
fig_imgs.suptitle(gr_name)
[ax.set_axis_off() for ax in axes_imgs.ravel()]
fig_imgs.subplots_adjust(left=0.01,
bottom=0.1,