def velocity_ell(q1, q2):
fig = plt.figure()
ax = fig.add_subplot(111)
plt.axis('equal')
ax.set_xlim([-2.5, 3.5])
ax.set_ylim([-2.5, 3.5])
# load some test data for demonstration and plot a wireframe
ax.plot([0, 1], [0, 0], [0, 0], c='blue')
ax.plot([0, 0], [0, 1], [0, 0], c='green')
link_1, = ax.plot([0, 0], [1, 0], lw=6, c='blue')
link_2, = ax.plot([1, 2], [0, 0], lw=6, c='green')
el = Ellipse((2, 0), 1, 0, angle=90, linewidth=3, fill=False)
ax.add_patch(el)
l1 = 1
l2 = 1
point1 = np.array([l1 * np.cos(q1), l1 * np.sin(q1)])
point2 = np.array(
[point1[0] + l2 * np.cos(q2 + q1), point1[1] + l2 * np.sin(q2 + q1)])
link_1.set_data([0, point1[0]], [0, point1[1]])
link_2.set_data([point1[0], point2[0]], [point1[1], point2[1]])
Jac = np.array(
[[-l1 * np.sin(q1) - l2 * np.sin(q1 + q2), -l2 * np.sin(q1 + q2)],
[l1 * np.cos(q1) + l2 * np.cos(q1 + q2), l2 * np.cos(q1 + q2)]])
A = np.matmul(Jac, np.transpose(Jac))
w, v = np.linalg.eig(A)
el.center = (point2[0], point2[1])
el.height = np.sqrt(min(w))
el.width = np.sqrt(max(w))
el.angle = np.degrees(np.arccos(v[1][0]))
plt.show()
cL = Ellipse(tuple(model_iris.model[0:2]),
2 * model_iris.model[2],
2 * model_iris.model[3],
np.rad2deg(model_iris.model[4]))
cE.set_facecolor('None')
cE.set_edgecolor((1.0, 0.0, 0.0))
cL.set_facecolor('None')
cL.set_edgecolor((0.0, 1.0, 0.0))
cI = plts.imshow(I)
cM = plts.imshow(mask_woSkin, alpha=0.5)
plts.add_patch(cE)
plts.add_patch(cL)
plt.show()
plt.pause(.01)
else:
cE.center = tuple(pupil_loc)
cE.angle = np.rad2deg(model_pupil.model[4])
cE.width = 2 * model_pupil.model[2]
cE.height = 2 * model_pupil.model[3]
cL.center = tuple(model_iris.model[0:2])
cL.width = 2 * model_iris.model[2]
cL.height = 2 * model_iris.model[3]
cL.angle = np.rad2deg(model_iris.model[-1])
cI.set_data(I)
cM.set_data(mask_woSkin)
mypause(0.01)
i = i + 1
print('{} images: {}'.format(dirCond, i))
# Stack data
Data['Images'] = np.stack(Data['Images'], axis=0)
def animate_multiple_predictions_and_goal_likelihood(img, x, y, nUsedData,
nGoals, goalsLikelihood,
predictedXYVec, varXYVec,
toFile):
observedX = x[0:nUsedData]
observedY = y[0:nUsedData]
fig, ax = plt.subplots()
plt.gca().set_axis_off()
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
# Plot the observed data
lineObs, = ax.plot(observedX, observedY, lw=3, color='c')
# Show the image
ax.imshow(img)
plt.tight_layout()
# Likelihoods
maxLikelihood = max(goalsLikelihood)
maxLW = 3.0
# For all potential goals
ells = []
pls = []
#ax.set_aspect('equal')
for i in range(nGoals):
lw = max((goalsLikelihood[i] / maxLikelihood) * maxLW, 1)
e = Ellipse([0, 0], 0, 0)
e.set_fill(0)
if (predictedXYVec[i].shape[0] == 0):
l, = ax.plot([0], [0], lw=0, color='b')
e.set_lw(0)
else:
l, = ax.plot(predictedXYVec[i][0, 0],
predictedXYVec[i][0, 1],
lw=lw,
color='b')
## Ellipse center
xy = [predictedXYVec[i][0, 0], predictedXYVec[i][0, 1]]
vx = varXYVec[i][0, 0, 0]
vy = varXYVec[i][1, 0, 0]
e.center = xy
e.width = 6.0 * math.sqrt(math.fabs(vx))
e.height = 6.0 * math.sqrt(math.fabs(vy))
e.set_edgecolor('b')
e.set_lw(lw)
ax.add_patch(e)
ells.append(e)
pls.append(l)
v = [0, img.shape[1], img.shape[0], 0]
plt.axis(v)
def animate(i):
for j in range(nGoals):
if (predictedXYVec[j].shape[0] == 0):
continue
predictedN = predictedXYVec[j].shape[0]
if (i < predictedN):
p = [predictedXYVec[j][i, 0], predictedXYVec[j][i, 1]]
vx = varXYVec[j][0, i, i]
vy = varXYVec[j][1, i, i]
pls[j].set_data(predictedXYVec[j][0:i, 0],
predictedXYVec[j][0:i, 1])
ells[j].center = p
ells[j].width = 6.0 * math.sqrt(math.fabs(vx))
ells[j].height = 6.0 * math.sqrt(math.fabs(vy))
anim = FuncAnimation(fig, animate, frames=100, interval=100)
if toFile:
anim.save('test-%d.mp4' % i)
else:
plt.show()
return
def process(args, ds_num, fName):
warnings.filterwarnings("error")
PATH_IMAGES = os.path.join(PATH_DIR, fName, 'synthetic')
PATH_MASK_SKIN = os.path.join(PATH_DIR, fName, 'mask-withskin')
PATH_MASK_NOSKIN = os.path.join(PATH_DIR, fName,
'mask-withoutskin-noglasses')
imList = glob.glob(os.path.join(PATH_IMAGES, '*.tif'))
if not noDisp:
fig, plts = plt.subplots(1, 1)
Data, keydict = generateEmptyStorage(
name='riteyes-{}'.format(args.which_riteyes_set),
subset='riteyes-{}_{}'.format(args.which_riteyes_set, fName))
ds_name = 'riteyes-{}_{}_{}'.format(args.which_riteyes_set, fName, ds_num)
fr_num = 0
for ele in imList:
imName_withext = os.path.split(ele)[1]
imName = os.path.splitext(ele)[0]
path2im = os.path.join(PATH_IMAGES, imName_withext)
path2mask = os.path.join(PATH_MASK_SKIN, imName_withext)
path2mask_woskin = os.path.join(PATH_MASK_NOSKIN, imName_withext)
try:
I = np.asarray(Image.open(path2im).convert('L'))
maskIm = np.asarray(Image.open(path2mask).convert('RGB'))
maskIm_woskin = np.asarray(
Image.open(path2mask_woskin).convert('RGB'))
I = cv2.resize(I, (640, 480), interpolation=cv2.INTER_CUBIC)
maskIm = cv2.resize(maskIm, (640, 480),
interpolation=cv2.INTER_NEAREST)
maskIm_woskin = cv2.resize(maskIm_woskin, (640, 480),
interpolation=cv2.INTER_NEAREST)
maskIm, maskIm_woskin = quantizeMask(maskIm, maskIm_woskin)
except:
print('Corrupt data found in {}.'.format(ele))
import pdb
pdb.set_trace()
pupilPts, irisPts = getValidPoints(maskIm_woskin)
# Pupil ellipse fit
model_pupil = ransac(pupilPts, ElliFit, 15, 40, 5e-3, 15).loop()
pupil_fit_error = my_ellipse(model_pupil.model).verify(pupilPts)
r, c = np.where(maskIm_woskin == 2)
pupil_loc = model_pupil.model[:
2] if pupil_fit_error < 0.05 else np.stack(
[np.mean(c), np.mean(r)], axis=0)
# Iris ellipse fit
model_iris = ransac(irisPts, ElliFit, 15, 40, 5e-3, 15).loop()
iris_fit_error = my_ellipse(model_iris.model).verify(irisPts)
if (pupil_fit_error > 0.05) | (iris_fit_error > 0.05):
print('Skipping: {}'.format(imName))
continue
Data['Info'].append(imName)
Data['Masks'].append(maskIm)
Data['Images'].append(I)
Data['pupil_loc'].append(pupil_loc)
Data['subject_id'].append(str(fName))
Data['Masks_noSkin'].append(maskIm_woskin)
keydict['archive'].append(ds_name)
keydict['pupil_loc'].append(pupil_loc)
keydict['resolution'].append(I.shape)
keydict['subject_id'].append(str(fName))
Data['Fits']['pupil'].append(model_pupil.model)
Data['Fits']['iris'].append(model_iris.model)
keydict['Fits']['pupil'].append(model_pupil.model)
keydict['Fits']['iris'].append(model_iris.model)
fr_num += 1
if not noDisp:
if fr_num == 1:
cE = Ellipse(tuple(pupil_loc),
2 * model_pupil.model[2],
2 * model_pupil.model[3],
angle=np.rad2deg(model_pupil.model[-1]))
cL = Ellipse(tuple(model_iris.model[0:2]),
2 * model_iris.model[2], 2 * model_iris.model[3],
np.rad2deg(model_iris.model[4]))
cE.set_facecolor('None')
cE.set_edgecolor((1.0, 0.0, 0.0))
cL.set_facecolor('None')
cL.set_edgecolor((0.0, 1.0, 0.0))
cI = plts.imshow(I)
cM = plts.imshow(maskIm, alpha=0.5)
cX = plts.scatter(pupil_loc[0], pupil_loc[1])
plts.add_patch(cE)
plts.add_patch(cL)
plt.show()
plt.pause(.01)
else:
cE.center = tuple(pupil_loc)
cE.angle = np.rad2deg(model_pupil.model[-1])
cE.width = 2 * model_pupil.model[2]
cE.height = 2 * model_pupil.model[3]
cL.center = tuple(model_iris.model[0:2])
cL.width = 2 * model_iris.model[2]
cL.height = 2 * model_iris.model[3]
cL.angle = np.rad2deg(model_iris.model[-1])
newLoc = np.array([pupil_loc[0], pupil_loc[1]])
cI.set_data(I)
cM.set_data(maskIm)
cX.set_offsets(newLoc)
mypause(0.01)
keydict['resolution'] = np.stack(keydict['resolution'], axis=0)
keydict['subject_id'] = np.stack(keydict['subject_id'], axis=0)
keydict['archive'] = np.stack(keydict['archive'], axis=0)
keydict['pupil_loc'] = np.stack(keydict['pupil_loc'], axis=0)
keydict['Fits']['pupil'] = np.stack(keydict['Fits']['pupil'], axis=0)
keydict['Fits']['iris'] = np.stack(keydict['Fits']['iris'], axis=0)
Data['subject_id'] = np.stack(Data['subject_id'], axis=0)
Data['pupil_loc'] = np.stack(Data['pupil_loc'], axis=0)
Data['Images'] = np.stack(Data['Images'], axis=0)
Data['Masks'] = np.stack(Data['Masks'], axis=0)
Data['Masks_noSkin'] = np.stack(Data['Masks_noSkin'], axis=0)
Data['Fits']['pupil'] = np.stack(Data['Fits']['pupil'], axis=0)
Data['Fits']['iris'] = np.stack(Data['Fits']['iris'], axis=0)
warnings.filterwarnings("ignore")
# Save data
dd.io.save(os.path.join(PATH_DS, str(ds_name) + '.h5'), Data)
scio.savemat(os.path.join(PATH_MASTER,
str(ds_name) + '.mat'),
keydict,
appendmat=True)
ds_num = ds_num + 1
if not args.noDisp:
if Image_counter == 0:
cI = plts.imshow(I_cropped, cmap='gray')
cE = Ellipse(tuple(irisFit.model[:2]),
2 * irisFit.model[2],
2 * irisFit.model[3],
angle=np.rad2deg(irisFit.model[-1]))
cE.set_facecolor('None')
cE.set_edgecolor((0.0, 1.0, 0.0))
cM = plts.imshow(mask, alpha=0.3)
plts.add_patch(cE)
plt.show()
plt.pause(.01)
else:
cE.center = tuple(irisFit.model[:2])
cE.angle = np.rad2deg(irisFit.model[-1])
cE.width = 2 * irisFit.model[2]
cE.height = 2 * irisFit.model[3]
cI.set_data(I_cropped)
cM.set_data(mask)
mypause(0.01)
Image_counter = Image_counter + 1
Data['Images'] = np.stack(Data['Images'], axis=0)
Data['Masks'] = np.stack(Data['Masks'], axis=0)
Data['Masks_noSkin'] = np.stack(Data['Masks_noSkin'], axis=0)
Data['pupil_loc'] = np.stack(Data['pupil_loc'], axis=0)
keydict['pupil_loc'] = np.stack(keydict['pupil_loc'], axis=0)
keydict['resolution'] = np.stack(keydict['resolution'], axis=0)
