def score(testfile, correctfile):
test_pts = Annotating.read_anno(testfile)
correct_pts = Annotating.read_anno(correctfile)
net_error = 0.0
max_error = 0.0
rel_pts = []
if IGNORE_COLLAR:
if MODE == SWEATER or MODE == TEE:
check_points = (0, 1, 2, 3, 4, 8, 9, 10, 11, 12)
else:
check_points = range(len(test_pts))
else:
check_points = range(len(test_pts))
errors = []
(x_axis, y_axis) = get_axes(correct_pts)
for i in check_points: #(1,4,8,11):#range(len(test_pts)):
test_pt = test_pts[i]
correct_pt = correct_pts[i]
rel_pt = Vector2D.pt_diff(test_pt, correct_pt)
rel_pts.append(
(Vector2D.dot_prod(rel_pt,
x_axis), Vector2D.dot_prod(rel_pt, y_axis)))
error = Vector2D.pt_distance(test_pt, correct_pt)
errors.append(error)
return (lst_avg(errors), rel_pts)
def sampled(line,NUM_SAMPLES):
pts = []
(start,end) = Vector2D.end_points(line)
dist = Vector2D.pt_distance(start,end)
for i in range(NUM_SAMPLES):
pt = Vector2D.extrapolate(line, dist * i / float(NUM_SAMPLES))
pts.append(pt)
return pts
def score(testfile,correctfile):
test_pts = Annotating.read_anno(testfile)
correct_pts = Annotating.read_anno(correctfile)
net_error = 0.0
max_error = 0.0
rel_pts = []
if IGNORE_COLLAR:
if MODE == SWEATER or MODE == TEE:
check_points = (0,1,2,3,4,8,9,10,11,12)
else:
check_points = range(len(test_pts))
else:
check_points = range(len(test_pts))
errors = []
(x_axis,y_axis) = get_axes(correct_pts)
for i in check_points:#(1,4,8,11):#range(len(test_pts)):
test_pt = test_pts[i]
correct_pt = correct_pts[i]
rel_pt = Vector2D.pt_diff(test_pt,correct_pt)
rel_pts.append((Vector2D.dot_prod(rel_pt,x_axis),Vector2D.dot_prod(rel_pt,y_axis)))
error = Vector2D.pt_distance(test_pt,correct_pt)
errors.append(error)
return (lst_avg(errors),rel_pts)
def fit(self,model,contour,img_annotated=None,img=None):
assert not model.illegal()
if not img_annotated:
xs = [x for (x,y) in contour]
ys = [y for (x,y) in contour]
width = max(xs) - min(xs)
height = max(ys) - min(ys)
cv.Set(img_annotated,cv.CV_RGB(255,255,255))
model.set_image(cv.CloneImage(img_annotated))
shape_contour = contour
if SHOW_CONTOURS:
cv.DrawContours(img_annotated,shape_contour,cv.CV_RGB(255,0,0),cv.CV_RGB(255,0,0),0,1,8,(0,0))
if self.INITIALIZE:
self.printout("INITIALIZING")
(real_center,real_top,real_theta,real_scale) = get_principle_info(shape_contour)
if SHOW_UNSCALED_MODEL:
model.draw_to_image(img_annotated,cv.CV_RGB(0,0,255))
model_contour = model.vertices_dense(constant_length=False,density=30,includeFoldLine = False)
(model_center,model_top,model_theta,model_scale) = get_principle_info(model_contour)
displ = displacement(model_center,real_center)
#Drawing
if SHOW_INIT_PTS:
top_img = cv.CloneImage(img_annotated)
cv.DrawContours(top_img,shape_contour,cv.CV_RGB(255,0,0),cv.CV_RGB(255,0,0),0,1,8,(0,0))
model.draw_contour(top_img,cv.CV_RGB(0,0,255),2)
draw_pt(top_img,real_top,cv.CV_RGB(255,0,0))
draw_pt(top_img,real_center,cv.CV_RGB(255,0,0))
draw_pt(top_img,model_top,cv.CV_RGB(0,0,255))
draw_pt(top_img,model_center,cv.CV_RGB(0,0,255))
cv.NamedWindow("Top")
cv.ShowImage("Top",top_img)
cv.WaitKey()
angle = model_theta - real_theta
#if self.ORIENT_OPT:
# angle = 0
scale = real_scale/float(model_scale)
if scale < 0.25:
scale = 1
model_trans = translate_poly(model.polygon_vertices(),displ)
model_rot = rotate_poly(model_trans,-1*angle,real_center)
model_scaled = scale_poly(model_rot,scale,real_center)
""" DEBUG
print "/**************Test****************/"
A = [ (int(pt[0]),int(pt[1])) for pt in model_trans]
B = [ (int(pt[0]),int(pt[1])) for pt in model_rot]
C = [ (int(pt[0]),int(pt[1])) for pt in model_scaled]
cv.NamedWindow("Debug window")
im = cv.CloneImage(img_annotated)
model.draw_contour(im,cv.CV_RGB(100,100,100),2)
cv.DrawContours(im,shape_contour,cv.CV_RGB(255,0,0),cv.CV_RGB(255,0,0),0,1,8,(0,0))
draw_pt(im,real_top,cv.CV_RGB(255,0,0))
draw_pt(im,real_center,cv.CV_RGB(255,0,0))
draw_pt(im,model_top,cv.CV_RGB(100,100,100))
draw_pt(im,model_center,cv.CV_RGB(100,100,100))
cv.PolyLine(im,[A],1,cv.CV_RGB(255,0,0),1)
cv.ShowImage("Debug window",im)
cv.WaitKey()
cv.PolyLine(im,[B],1,cv.CV_RGB(0,255,0),1)
cv.ShowImage("Debug window",im)
cv.WaitKey()
cv.PolyLine(im,[C],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Debug window",im)
cv.WaitKey()
cv.DestroyWindow("Debug window")
print "/************EndOfTest*************/"
self.exitIfKeyPressed("c");
#"""
#(model_center,model_top,model_theta,model_scale) = get_principle_info(model_scaled)
#Do the same to the actual model
# translate model
model.translate(displ,True)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Translate model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[model.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Translate model",im)
cv.WaitKey()
cv.DestroyWindow("Translate model")
print "/************EndOfTest*************/"
#"""
#rotate model
if self.ROTATE:
model.rotate(-1*angle,real_center,True)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Rotate model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[model.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Rotate model",im)
cv.WaitKey()
cv.DestroyWindow("Rotate model")
print "/************EndOfTest*************/"
#"""
#scale model
model.scale(scale,real_center,True)
if SHOW_SCALED_MODEL:
model.draw_to_image(img_annotated,cv.CV_RGB(0,0,255))
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Scale model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[model.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Scale model",im)
cv.WaitKey()
cv.DestroyWindow("Scale model")
print "/************EndOfTest*************/"
#"""
self.printout("Energy is: %f"%model.score(shape_contour))
self.printout("Shape contour has %d points"%(len(shape_contour)))
sparse_shape_contour = make_sparse(shape_contour,1000)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Sparse_shape_contour model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[sparse_shape_contour],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Sparse_shape_contour model",im)
cv.WaitKey()
cv.DestroyWindow("Sparse_shape_contour model")
print "/************EndOfTest*************/"
#"""
#Optimize
# Orientation phase
if self.ORIENT_OPT:
self.printout("ORIENTATION OPTIMIZATION")
init_model = Models.Orient_Model(model,pi/2)
orient_model_finished = self.black_box_opt(model=init_model,contour=shape_contour,num_iters = self.num_iters,delta=init_model.preferred_delta(),epsilon = 0.01,mode="orient",image=img)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Orientation phase: final model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[orient_model_finished.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Orientation phase: final model",im)
cv.WaitKey()
cv.DestroyWindow("Orientation phase: final model")
print "/************EndOfTest*************/"
#"""
model_oriented = orient_model_finished.transformed_model()
else:
model_oriented = model
# Symmetric phase
if self.SYMM_OPT:
self.printout("SYMMETRIC OPTIMIZATION")
new_model_symm = self.black_box_opt(model=model_oriented,contour=shape_contour,num_iters = self.num_iters,delta=model.preferred_delta(),epsilon = 0.01,mode="symm",image=img)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Symmetric phase: final model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[new_model_symm.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Symmetric phase: final model",im)
cv.WaitKey()
cv.DestroyWindow("Symmetric phase: final model")
print "/************EndOfTest*************/"
#"""
else:
new_model_symm = model_oriented
if SHOW_SYMM_MODEL:
new_model_symm.draw_to_image(img=img_annotated,color=cv.CV_RGB(0,255,0))
# Asymmetric phase
model=new_model_symm.make_asymm()
if self.HIGH_EXPLORATION:
exp_factor = 3.0
else:
exp_factor = 1.5
if self.ASYMM_OPT:
self.printout("ASYMMETRIC OPTIMIZATION")
new_model_asymm = self.black_box_opt(model=model,contour=shape_contour,num_iters=self.num_iters,delta=model.preferred_delta(),exploration_factor=exp_factor,fine_tune=False,mode="asymm",image=img)
""" DEBUG
print "/**************Test****************/"
cv.NamedWindow("Asymmetric phase: final model")
im = cv.CloneImage(img_annotated)
cv.PolyLine(im,[new_model_symm.polygon_vertices_int()],1,cv.CV_RGB(0,0,255),1)
cv.ShowImage("Asymmetric phase: final model",im)
cv.WaitKey()
cv.DestroyWindow("Asymmetric phase: final model")
print "/************EndOfTest*************/"
#"""
else:
new_model_asymm = model
#Final tune phase
if self.FINE_TUNE:
self.printout("FINAL TUNE")
#tunable_model = model_oriented.make_tunable()
tunable_model = new_model_asymm.make_tunable()
final_model = self.black_box_opt(model=tunable_model,contour=shape_contour,num_iters=self.num_iters,delta=5.0,exploration_factor=1.5,fine_tune=False,image=img)
final_model = final_model.final()
else:
final_model = new_model_asymm
final_model.draw_to_image(img=img_annotated,color=cv.CV_RGB(255,0,255))
# Find nearest points
nearest_pts = []
for vert in final_model.polygon_vertices():
nearest_pt = min(shape_contour,key=lambda pt: Vector2D.pt_distance(pt,vert))
cv.Circle(img_annotated,nearest_pt,5,cv.CV_RGB(255,255,255),3)
nearest_pts.append(nearest_pt)
fitted_model = Models.Point_Model_Contour_Only_Asymm(*nearest_pts)
if SHOW_FITTED:
fitted_model.draw_to_image(img=img_annotated,color=cv.CV_RGB(0,255,255))
return (nearest_pts,final_model,fitted_model)
def fit(self,model,contour,img_annotated=None,img=None):
assert not model.illegal()
if not img_annotated:
xs = [x for (x,y) in contour]
ys = [y for (x,y) in contour]
width = max(xs) - min(xs)
height = max(ys) - min(ys)
cv.Set(img_annotated,cv.CV_RGB(255,255,255))
model.set_image(cv.CloneImage(img_annotated))
shape_contour = contour
if SHOW_CONTOURS:
cv.DrawContours(img_annotated,shape_contour,cv.CV_RGB(255,0,0),cv.CV_RGB(255,0,0),0,1,8,(0,0))
if self.INITIALIZE:
self.printout("INITIALIZING")
(real_center,real_top,real_theta,real_scale) = get_principle_info(shape_contour)
if SHOW_UNSCALED_MODEL:
model.draw_to_image(img_annotated,cv.CV_RGB(0,0,255))
model_contour = model.vertices_dense(constant_length=False,density=30)
(model_center,model_top,model_theta,model_scale) = get_principle_info(model_contour)
displ = displacement(model_center,real_center)
#Drawing
if SHOW_INIT_PTS:
top_img = cv.CloneImage(img_annotated)
cv.DrawContours(top_img,shape_contour,cv.CV_RGB(255,0,0),cv.CV_RGB(255,0,0),0,1,8,(0,0))
model.draw_contour(top_img,cv.CV_RGB(0,0,255),2)
draw_pt(top_img,real_top,cv.CV_RGB(255,0,0))
draw_pt(top_img,real_center,cv.CV_RGB(255,0,0))
draw_pt(top_img,model_top,cv.CV_RGB(0,0,255))
draw_pt(top_img,model_center,cv.CV_RGB(0,0,255))
cv.NamedWindow("Top")
cv.ShowImage("Top",top_img)
cv.WaitKey()
angle = model_theta - real_theta
if self.ORIENT_OPT:
angle = 0
scale = real_scale/float(model_scale)
if scale < 0.25:
scale = 1
model_trans = translate_poly(model.polygon_vertices(),displ)
model_rot = rotate_poly(model_trans,-1*angle,real_center)
model_scaled = scale_poly(model_rot,scale,real_center)
#(model_center,model_top,model_theta,model_scale) = get_principle_info(model_scaled)
#Do the same to the actual model
model.translate(displ)
if self.ROTATE:
model.rotate(-1*angle,real_center)
model.scale(scale,real_center)
if SHOW_SCALED_MODEL:
model.draw_to_image(img_annotated,cv.CV_RGB(0,0,255))
self.printout("Energy is: %f"%model.score(shape_contour))
self.printout("Shape contour has %d points"%(len(shape_contour)))
sparse_shape_contour = make_sparse(shape_contour,1000)
#Optimize
if self.ORIENT_OPT:
init_model = Models.Orient_Model(model,pi/2)
orient_model_finished = self.black_box_opt(model=init_model,contour=shape_contour,num_iters = self.num_iters,delta=init_model.preferred_delta(),epsilon = 0.01,mode="orient",image=img)
model_oriented = orient_model_finished.transformed_model()
else:
model_oriented = model
if self.SYMM_OPT:
self.printout("SYMMETRIC OPTIMIZATION")
new_model_symm = self.black_box_opt(model=model_oriented,contour=shape_contour,num_iters = self.num_iters,delta=model.preferred_delta(),epsilon = 0.01,mode="symm",image=img)
else:
new_model_symm = model_oriented
if SHOW_SYMM_MODEL:
new_model_symm.draw_to_image(img=img_annotated,color=cv.CV_RGB(0,255,0))
model=new_model_symm.make_asymm()
if self.HIGH_EXPLORATION:
exp_factor = 3.0
else:
exp_factor = 1.5
if self.ASYMM_OPT:
new_model_asymm = self.black_box_opt(model=model,contour=shape_contour,num_iters=self.num_iters,delta=model.preferred_delta(),exploration_factor=exp_factor,fine_tune=False,mode="asymm",image=img)
else:
new_model_asymm = model
if self.FINE_TUNE:
#tunable_model = model_oriented.make_tunable()
tunable_model = new_model_asymm.make_tunable()
final_model = self.black_box_opt(model=tunable_model,contour=shape_contour,num_iters=self.num_iters,delta=5.0,exploration_factor=1.5,fine_tune=False,image=img)
final_model = final_model.final()
else:
final_model = new_model_asymm
final_model.draw_to_image(img=img_annotated,color=cv.CV_RGB(255,0,255))
nearest_pts = []
for vert in final_model.polygon_vertices():
nearest_pt = min(shape_contour,key=lambda pt: Vector2D.pt_distance(pt,vert))
cv.Circle(img_annotated,nearest_pt,5,cv.CV_RGB(255,255,255),3)
nearest_pts.append(nearest_pt)
fitted_model = Models.Point_Model_Contour_Only_Asymm(*nearest_pts)
#fitted_model = final_model
if SHOW_FITTED:
fitted_model.draw_to_image(img=img_annotated,color=cv.CV_RGB(0,255,255))
return (nearest_pts,final_model,fitted_model)
def fit(self, model, contour, img_annotated=None, img=None):
assert not model.illegal()
if not img_annotated:
xs = [x for (x, y) in contour]
ys = [y for (x, y) in contour]
width = max(xs) - min(xs)
height = max(ys) - min(ys)
cv.Set(img_annotated, cv.CV_RGB(255, 255, 255))
model.set_image(cv.CloneImage(img_annotated))
shape_contour = contour
if SHOW_CONTOURS:
cv.DrawContours(img_annotated, shape_contour, cv.CV_RGB(255, 0, 0),
cv.CV_RGB(255, 0, 0), 0, 1, 8, (0, 0))
if self.INITIALIZE:
self.printout("INITIALIZING")
(real_center, real_top, real_theta,
real_scale) = get_principle_info(shape_contour)
if SHOW_UNSCALED_MODEL:
model.draw_to_image(img_annotated, cv.CV_RGB(0, 0, 255))
model_contour = model.vertices_dense(constant_length=False,
density=30)
(model_center, model_top, model_theta,
model_scale) = get_principle_info(model_contour)
displ = displacement(model_center, real_center)
#Drawing
if SHOW_INIT_PTS:
top_img = cv.CloneImage(img_annotated)
cv.DrawContours(top_img, shape_contour, cv.CV_RGB(255, 0, 0),
cv.CV_RGB(255, 0, 0), 0, 1, 8, (0, 0))
model.draw_contour(top_img, cv.CV_RGB(0, 0, 255), 2)
draw_pt(top_img, real_top, cv.CV_RGB(255, 0, 0))
draw_pt(top_img, real_center, cv.CV_RGB(255, 0, 0))
draw_pt(top_img, model_top, cv.CV_RGB(0, 0, 255))
draw_pt(top_img, model_center, cv.CV_RGB(0, 0, 255))
cv.NamedWindow("Top")
cv.ShowImage("Top", top_img)
cv.WaitKey()
angle = model_theta - real_theta
if self.ORIENT_OPT:
angle = 0
scale = real_scale / float(model_scale)
if scale < 0.25:
scale = 1
model_trans = translate_poly(model.polygon_vertices(), displ)
model_rot = rotate_poly(model_trans, -1 * angle, real_center)
model_scaled = scale_poly(model_rot, scale, real_center)
#(model_center,model_top,model_theta,model_scale) = get_principle_info(model_scaled)
#Do the same to the actual model
model.translate(displ)
if self.ROTATE:
model.rotate(-1 * angle, real_center)
model.scale(scale, real_center)
if SHOW_SCALED_MODEL:
model.draw_to_image(img_annotated, cv.CV_RGB(0, 0, 255))
self.printout("Energy is: %f" % model.score(shape_contour))
self.printout("Shape contour has %d points" % (len(shape_contour)))
sparse_shape_contour = make_sparse(shape_contour, 1000)
#Optimize
if self.ORIENT_OPT:
init_model = Models.Orient_Model(model, pi / 2)
orient_model_finished = self.black_box_opt(
model=init_model,
contour=shape_contour,
num_iters=self.num_iters,
delta=init_model.preferred_delta(),
epsilon=0.01,
mode="orient",
image=img)
model_oriented = orient_model_finished.transformed_model()
else:
model_oriented = model
if self.SYMM_OPT:
self.printout("SYMMETRIC OPTIMIZATION")
new_model_symm = self.black_box_opt(model=model_oriented,
contour=shape_contour,
num_iters=self.num_iters,
delta=model.preferred_delta(),
epsilon=0.01,
mode="symm",
image=img)
else:
new_model_symm = model_oriented
if SHOW_SYMM_MODEL:
new_model_symm.draw_to_image(img=img_annotated,
color=cv.CV_RGB(0, 255, 0))
model = new_model_symm.make_asymm()
if self.HIGH_EXPLORATION:
exp_factor = 3.0
else:
exp_factor = 1.5
if self.ASYMM_OPT:
new_model_asymm = self.black_box_opt(model=model,
contour=shape_contour,
num_iters=self.num_iters,
delta=model.preferred_delta(),
exploration_factor=exp_factor,
fine_tune=False,
mode="asymm",
image=img)
else:
new_model_asymm = model
if self.FINE_TUNE:
#tunable_model = model_oriented.make_tunable()
tunable_model = new_model_asymm.make_tunable()
final_model = self.black_box_opt(model=tunable_model,
contour=shape_contour,
num_iters=self.num_iters,
delta=5.0,
exploration_factor=1.5,
fine_tune=False,
image=img)
final_model = final_model.final()
else:
final_model = new_model_asymm
final_model.draw_to_image(img=img_annotated,
color=cv.CV_RGB(255, 0, 255))
nearest_pts = []
for vert in final_model.polygon_vertices():
nearest_pt = min(shape_contour,
key=lambda pt: Vector2D.pt_distance(pt, vert))
cv.Circle(img_annotated, nearest_pt, 5, cv.CV_RGB(255, 255, 255),
3)
nearest_pts.append(nearest_pt)
fitted_model = Models.Point_Model_Contour_Only_Asymm(*nearest_pts)
#fitted_model = final_model
if SHOW_FITTED:
fitted_model.draw_to_image(img=img_annotated,
color=cv.CV_RGB(0, 255, 255))
return (nearest_pts, final_model, fitted_model)
