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 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 main(args):
#Properly set phases
orient_opt = 'O' in args.phases
symm_opt = 'S' in args.phases
asymm_opt = 'A' in args.phases
fine_tuning_opt = 'F' in args.phases
#Properly set background colors
if args.background_color == 'GREEN':
background = thresholding.GREEN_BG
elif args.background_color == 'WHITE':
background = thresholding.WHITE_BG
elif args.background_color == 'BLACK':
background = thresholding.BLACK_BG
else:
raise Exception("Invalid background color: %s"%args.background_color)
#Translate silent/verbose
silent = not args.verbose
#Translate output- directory and prefix
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_image)
if not os.path.exists(directory):
print "Making dir %s"%directory
os.system("mkdir -p %s"%directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix = os.path.splitext(os.path.basename(args.input_image))[0]+"_fit"
#Load model and image
models = [pickle.load(open(modelpath)) for modelpath in args.models]
image_raw = cv.LoadImage(args.input_image,cv.CV_LOAD_IMAGE_COLOR)
best_model = None
best_nearest_pts = None
best_fitted_model = None
best_image_out = None
best_index = 0
best_score = 100
scores = []
for i,model in enumerate(models):
print "On model %d of %d"%(i+1,len(models))
#Create an image to output
image_out = cv.CloneImage(image_raw)
#Use the thresholding module to get the contour out
shape_contour = thresholding.get_contour(image_raw,bg_mode=background,filter_pr2=False,crop_rect=None)
fitter = shape_fitting.ShapeFitter( ORIENT_OPT=orient_opt, SYMM_OPT=symm_opt,
ASYMM_OPT=asymm_opt, FINE_TUNE=fine_tuning_opt,
SILENT=silent, SHOW=args.show_graphics,
num_iters=args.num_iters )
(nearest_pts, final_model, fitted_model) = fitter.fit(model,shape_contour,image_out,image_raw)
final_model.set_image(cv.CloneImage(image_raw))
score = final_model.score(shape_contour,image_raw)
final_model.set_image(None)
scores.append(score)
if not best_model or score <= best_score:
best_score = score
best_model = model
best_nearest_pts = nearest_pts
best_fitted_model = fitted_model
best_image_out = image_out
best_index = i
final_model = best_model
nearest_pts = best_nearest_pts
fitted_model = best_fitted_model
image_out = best_image_out
#Optionally save the nearest points in a .anno file, for comparison with my own annotations
full_prefix = directory + '/' + prefix
if args.save_anno:
anno_path = full_prefix+".anno"
print "Saving anno to " + anno_path
Annotating.write_anno(nearest_pts,anno_path)
#Optionally save the model, for reuse later
if args.save_model:
#Remove the image to make pickling possible
final_model.set_image(None)
save_model_path = full_prefix+".pickle"
print "Saving model to " + save_model_path
model_dest = open(save_model_path,'w')
pickle.dump(final_model,model_dest)
model_dest.close()
#Optionally save scores
if args.save_scores:
savepath = full_prefix+".scores"
print "Saving scores to " + savepath
savefile = open(savepath,'w')
for i in range(len(scores)):
savefile.write("%s\t%f\n"%(args.models[i],scores[i]))
savefile.close()
#Optionally save the classification
if args.save_classification:
savepath = full_prefix+".classif"
print "Saving classification to " + savepath
savefile = open(savepath,'w')
savefile.write(models[best_index].name()+"\n")
savefile.close()
#Optionally save the image
if args.save_image:
savepath = full_prefix+".png"
print "Saving annotated image to " + savepath
cv.SaveImage(savepath,image_out)
return
else:
cv.NamedWindow("Result")
cv.ShowImage("Result",image_out)
cv.WaitKey()
return
def main(args):
#Properly set phases
orient_opt = 'O' in args.phases
symm_opt = 'S' in args.phases
asymm_opt = 'A' in args.phases
fine_tuning_opt = 'F' in args.phases
#Properly set background colors
if args.background_color == 'GREEN':
background = thresholding.GREEN_BG
elif args.background_color == 'WHITE':
background = thresholding.WHITE_BG
elif args.background_color == 'BLACK':
background = thresholding.BLACK_BG
else:
raise Exception("Invalid background color: %s" % args.background_color)
#Translate silent/verbose
silent = not args.verbose
#Translate output- directory and prefix
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_image)
if not os.path.exists(directory):
print "Making dir %s" % directory
os.system("mkdir -p %s" % directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix = os.path.splitext(os.path.basename(
args.input_image))[0] + "_fit"
#Load model and image
models = [pickle.load(open(modelpath)) for modelpath in args.models]
image_raw = cv.LoadImage(args.input_image, cv.CV_LOAD_IMAGE_COLOR)
best_model = None
best_nearest_pts = None
best_fitted_model = None
best_image_out = None
best_index = 0
best_score = 100
scores = []
for i, model in enumerate(models):
print "On model %d of %d" % (i + 1, len(models))
#Create an image to output
image_out = cv.CloneImage(image_raw)
#Use the thresholding module to get the contour out
shape_contour = thresholding.get_contour(image_raw,
bg_mode=background,
filter_pr2=False,
crop_rect=None)
fitter = shape_fitting.ShapeFitter(ORIENT_OPT=orient_opt,
SYMM_OPT=symm_opt,
ASYMM_OPT=asymm_opt,
FINE_TUNE=fine_tuning_opt,
SILENT=silent,
SHOW=args.show_graphics,
num_iters=args.num_iters)
(nearest_pts, final_model,
fitted_model) = fitter.fit(model, shape_contour, image_out, image_raw)
final_model.set_image(cv.CloneImage(image_raw))
score = final_model.score(shape_contour, image_raw)
final_model.set_image(None)
scores.append(score)
if not best_model or score <= best_score:
best_score = score
best_model = model
best_nearest_pts = nearest_pts
best_fitted_model = fitted_model
best_image_out = image_out
best_index = i
final_model = best_model
nearest_pts = best_nearest_pts
fitted_model = best_fitted_model
image_out = best_image_out
#Optionally save the nearest points in a .anno file, for comparison with my own annotations
full_prefix = directory + '/' + prefix
if args.save_anno:
anno_path = full_prefix + ".anno"
print "Saving anno to " + anno_path
Annotating.write_anno(nearest_pts, anno_path)
#Optionally save the model, for reuse later
if args.save_model:
#Remove the image to make pickling possible
final_model.set_image(None)
save_model_path = full_prefix + ".pickle"
print "Saving model to " + save_model_path
model_dest = open(save_model_path, 'w')
pickle.dump(final_model, model_dest)
model_dest.close()
#Optionally save scores
if args.save_scores:
savepath = full_prefix + ".scores"
print "Saving scores to " + savepath
savefile = open(savepath, 'w')
for i in range(len(scores)):
savefile.write("%s\t%f\n" % (args.models[i], scores[i]))
savefile.close()
#Optionally save the classification
if args.save_classification:
savepath = full_prefix + ".classif"
print "Saving classification to " + savepath
savefile = open(savepath, 'w')
savefile.write(models[best_index].name() + "\n")
savefile.close()
#Optionally save the image
if args.save_image:
savepath = full_prefix + ".png"
print "Saving annotated image to " + savepath
cv.SaveImage(savepath, image_out)
return
else:
cv.NamedWindow("Result")
cv.ShowImage("Result", image_out)
cv.WaitKey()
return
def writeAnno(self):
Annotating.write_anno(self.pts, self.anno_path)
def writeAnno(self):
Annotating.write_anno(self.pts,self.anno_path)
