def getSeenSPs(labels, myGaze, method=None):
"""
Finds coordinate of centroid and label index of region whose center of mass is closest to gaze-point:
"""
print("Getting label index of seen parts...")
nLabels = np.max(labels[0])
nFrames = len(labels)
seenCentroids = np.zeros((nLabels, nFrames))
seenLabel = np.zeros(nFrames)
mask = np.zeros(labels[0].shape)
thisCentroid = []
for i in range(nFrames): #iterate over frames
distsToCentroid = []
#for j in range(nLabels+1): #iterate over labels
for j in range(nLabels): #iterate over labels
#print i,j,nLabels,nFrames
mask = (labels[i] == j)
thisCentroid = ndimage.measurements.center_of_mass(mask)
ci, cj = gaze.gazeCoord2Pixel(myGaze[i, 3], myGaze[i, 4],
mask.shape[1], mask.shape[0])
distsToCentroid.append(
np.linalg.norm(np.array([ci, cj]) - thisCentroid))
seenCentroids[np.argmin(distsToCentroid), i] = 1
#seenLabel[i] = labels[i][np.argmin(distsToCentroid)]
seenLabel[i] = np.argmin(distsToCentroid)
return (seenCentroids, seenLabel)
def getMeanColorInCircle(img, gazeCoord, gaze_radius, normCst=1):
ci, cj = gaze.gazeCoord2Pixel(gazeCoord[0], gazeCoord[1], img.shape[1],
img.shape[0])
this_pix, _ = getValuesInCircle(img, (cj, ci), gaze_radius)
this_mean = np.mean(this_pix, axis=0) / np.tile(normCst, 3)
return this_mean, this_pix
def getCircleIdx(gazeCoord, shape, radius):
ci, cj = gaze.gazeCoord2Pixel(gazeCoord[1], gazeCoord[0], shape[1],
shape[0])
center = (ci, cj)
y, x = np.ogrid[0:shape[0], 0:shape[1]]
mask = (x - center[1])**2 + (y - center[0])**2 <= radius * radius
return np.where(mask)
def getSeenSPsInRadius(labels, myGaze, gazeRadius):
nLabels = np.max(labels[0])
nFrames = myGaze.shape[0]
seenCentroids = np.zeros((nLabels, nFrames))
mask = np.zeros(labels[0].shape)
thisCentroid = []
for i in range(nFrames): #iterate over labels
for j in range(nLabels + 1): #iterate over frames
mask = (labels[i] == j)
thisCentroid = ndimage.measurements.center_of_mass(mask)
ci, cj = gaze.gazeCoord2Pixel(myGaze[i, 3], myGaze[i, 4],
mask.shape[1], mask.shape[0])
if np.linalg.norm(np.array([ci, cj]) - thisCentroid) <= gazeRadius:
seenCentroids[j, i] = 1
return seenCentroids
def getSeenSPsInRect(labels, myGaze, w_norm, h_norm, center, frameIndSource,
frameIndTarget):
seenParts = []
nLabels = np.max(labels[0])
w = w_norm * labels.shape[1]
h = h_norm * labels.shape[0]
mask = np.zeros(labels[0].shape)
ci, cj = gaze.gazeCoord2Pixel(myGaze[frameIndSource,
3], myGaze[frameIndSource, 4],
mask.shape[1], mask.shape[0])
mask[int(ci - h / 2):int(ci + h / 2), int(cj - w / 2):int(cj + w / 2)] = 1
for j in range(nLabels + 1): #iterate over labels
thisLabelMask = (labels[frameIndTarget] == j)
thisCentroid = ndimage.measurements.center_of_mass(thisLabelMask)
if mask[thisCentroid[0], thisCentroid[1]]:
seenParts.append(j)
return seenParts
def getSeenSPsIn(labels, myGaze):
"""
Finds coordinate of centroid and label index of region with gaze-point in
"""
print("Getting label index of seen parts...")
nFrames = np.min((labels.shape[2], myGaze.shape[0]))
seenLabel = np.zeros(nFrames)
mask = np.zeros((labels.shape[0], labels.shape[1]))
thisCentroid = []
for i in range(nFrames): #iterate over frames
#for j in range(nLabels+1): #iterate over labels
labelNums = np.unique(labels[:, :, i])
ci, cj = gaze.gazeCoord2Pixel(myGaze[i, 3], myGaze[i, 4],
mask.shape[1], mask.shape[0])
for j in range(labelNums.shape[0]): #iterate over labels
#print i,j,nFrames
mask = (labels[:, :, i] == labelNums[j])
#thisCentroid = ndimage.measurements.center_of_mass(mask)
if (mask[ci, cj]):
break
seenLabel[i] = labelNums[j]
return (seenLabel)
def make_frame(idx,
ref_idx,
frames,
labels,
label_contours,
gts,
labels_clicked,
mouse_clicks,
mode='right',
highlight_idx=None):
if (mode == 'right'):
the_idx = idx
else:
the_idx = ref_idx
shape = labels[:, :, 0].shape
colors_ = [
color_dict['red'], color_dict['green'], color_dict['blue'],
color_dict['magenta'], color_dict['white']
]
mask = np.zeros(shape, dtype=np.uint8)
img = utls.imread(frames[the_idx])
if (img.shape[2] > 3): img = img[:, :, 0:3]
if (label_contours is not None):
idx_contours = np.where(label_contours[the_idx, :, :])
img[idx_contours[0], idx_contours[1], :] = (255, 255, 255)
l_ = (utls.imread(gts[the_idx]) > 0)[..., 0].astype(np.uint8)
l_idx = np.where(l_)
mask[l_idx[0], l_idx[1]] = 1
#Draw set of tracked labels
if (mode == 'right'):
labels_to_draw = [l[-1] for l in labels_clicked if (l[1] == idx)]
else:
print('labels_clicked: ' + str(labels_clicked))
print('idx: ' + str(idx))
labels_to_draw = [l[-1] for l in labels_clicked if (l[0] == ref_idx)]
print('labels_to_draw: ' + str(labels_to_draw))
if (len(labels_to_draw) > 0):
#print(self.curr_idx)
for i in range(len(labels_to_draw)):
this_label = labels_to_draw[i]
mask_tmp = labels[:, :, the_idx] == this_label
l_ = mask_tmp.astype(np.uint8)
l_idx = np.where(l_)
mask[l_idx[0], l_idx[1]] = 2
if (mode == 'right'):
mouse_clicks_to_draw = [r for r in mouse_clicks if (r[1] == idx)]
#print(mouse_clicks_to_draw)
else:
mouse_clicks_to_draw = [r for r in mouse_clicks if (r[0] == ref_idx)]
#mouse_clicks_to_draw = mouse_clicks
if (len(mouse_clicks_to_draw) > 0):
for i in range(len(mouse_clicks_to_draw)):
x = mouse_clicks_to_draw[i][-2]
y = mouse_clicks_to_draw[i][-1]
g_i, g_j = gaze.gazeCoord2Pixel(x, y, mask.shape[1], mask.shape[0])
rr, cc = circle(g_i, g_j, radius=7)
mask[rr, cc] = 3
#mask = np.zeros(shape, dtype=np.uint8)
#if(highlight_idx is not None):
# mouse_clicks_to_draw = [r for r in mouse_clicks if(r[0] in highlight_idx)]
# for i in range(len(mouse_clicks_to_draw)):
# x = mouse_clicks_to_draw[i][3]
# y = mouse_clicks_to_draw[i][4]
# g_i, g_j = gaze.gazeCoord2Pixel(x,
# y,
# mask.shape[1],
# mask.shape[0])
# rr, cc = circle(g_i, g_j, radius=7)
# mask[rr, cc] = 4
img = color.label2rgb(mask, img, alpha=.2, bg_label=0, colors=colors_)
img = np.rot90(img)[::-1, :, :]
return img
def calc_training_patches(self, save=False):
ps = self.conf.patch_size
my_gaze = self.conf.myGaze_fg
scale_factor = self.conf.scale_factor
seen_patches = list()
unseen_patches = list()
selem = morphology.square(5)
print('Getting seen patches')
with progressbar.ProgressBar(maxval=len(self.conf.frameFileNames)) as bar:
for i in range(len(self.conf.frameFileNames)):
bar.update(i)
img = utls.imread(self.conf.frameFileNames[i])
img = (color.rgb2gray(img)*255).astype(np.uint8)
img = median(img, selem=selem) # Uses square of size 3
ci_seen, cj_seen = gaze.gazeCoord2Pixel(
my_gaze[i, 3], my_gaze[i, 4], img.shape[1], img.shape[0])
i_, j_ = self.get_centers_negatives(ci_seen, cj_seen, ps,
self.conf.overlap_ratio,
img.shape)
img_padded = pad(img,((ps,ps),),mode='symmetric')
seen_patch = img_padded[int(ci_seen + ps/2):int(ci_seen + 3*ps/2),int(cj_seen + ps/2):int(cj_seen + 3*ps/2)]
seen_patch_mean = np.mean(seen_patch)
seen_patch_std = np.std(seen_patch)
if(seen_patch_std == 0): seen_patch_std = 1
seen_patch = (seen_patch - seen_patch_mean) / seen_patch_std
seen_patch = rescale(
seen_patch,
scale=scale_factor,
order=1,
mode='reflect',
preserve_range=True)
seen_patches.append((i,
ci_seen,
cj_seen,
seen_patch.ravel()))
for k in range(i_.shape[0]):
unseen_patch = img[int(i_[k]- ps / 2):int(i_[k] + ps / 2), int(
j_[k]- ps / 2):int(j_[k]+ ps / 2)]
unseen_patch_mean = np.mean(unseen_patch)
unseen_patch_std = np.std(unseen_patch)
if(unseen_patch_std == 0): unseen_patch_std = 1
unseen_patch = (unseen_patch - unseen_patch_mean) / unseen_patch_std
unseen_patch = rescale(
unseen_patch,
scale=scale_factor,
order=1,
mode='reflect',
preserve_range=True)
unseen_patches.append((i,
i_[k],
j_[k],
unseen_patch.ravel()))
if(save):
seen_patches_df = pd.DataFrame(seen_patches,
columns=['frame',
'c_i',
'c_j',
'patch'])
unseen_patches_df = pd.DataFrame(unseen_patches,
columns=['frame',
'c_i',
'c_j',
'patch'])
save_path = os.path.join(self.conf.dataOutDir,
'vilar')
if(not os.path.exists(save_path)):
os.mkdir(save_path)
seen_patches_df.to_pickle(os.path.join(save_path,
'vilar_seen_patches_df.p'))
unseen_patches_df.to_pickle(
os.path.join(save_path,
'vilar_unseen_patches_df.p'))
return True
def getSeenColorsInCircle(img, gazeCoord, gaze_radius, standardization=None):
ci, cj = gaze.gazeCoord2Pixel(gazeCoord[i, 3], gazeCoord[i, 4],
imgShape[1], imgShape[0])
this_ = getValuesInCircle(img, (cj, ci), gaze_radius)
