def single_person(model_features, input_features, normalise=True):
(input_features_copy, model_features_copy) = prepocessing.handle_undetected_points(input_features, model_features)
if (normalise):
model_features_copy = normalising.feature_scaling(model_features_copy)
input_features_copy = normalising.feature_scaling(input_features_copy)
(model_face, model_torso, model_legs) = prepocessing.split_in_face_legs_torso(model_features_copy)
(input_face, input_torso, input_legs) = prepocessing.split_in_face_legs_torso(input_features_copy)
(input_transformed_face, transformation_matrix_face) = affine_transformation.find_transformation(model_face, input_face)
(input_transformed_torso, transformation_matrix_torso) = affine_transformation.find_transformation(model_torso, input_torso)
(input_transformed_legs, transformation_matrix_legs) = affine_transformation.find_transformation(model_legs, input_legs)
input_transformation = prepocessing.unsplit(input_transformed_face, input_transformed_torso, input_transformed_legs)
if(not normalise):
result = MatchResult(None,
error_score=0,
input_transformation=input_transformation)
return result
max_euclidean_error_face = pose_comparison.max_euclidean_distance(model_face, input_transformed_face)
max_euclidean_error_torso = pose_comparison.max_euclidean_distance(model_torso, input_transformed_torso)
max_euclidean_error_legs = pose_comparison.max_euclidean_distance(model_legs, input_transformed_legs)
max_euclidean_error_shoulders = pose_comparison.max_euclidean_distance_shoulders(model_torso, input_transformed_torso)
######### THE THRESHOLDS #######
eucl_dis_tresh_torso = 0.11 #0.065 of 0.11 ??
rotation_tresh_torso = 40
eucl_dis_tresh_legs = 0.055
rotation_tresh_legs = 40
eucld_dis_shoulders_tresh = 0.063
################################
result_torso, torso_value = pose_comparison.decide_torso_shoulders_incl(max_euclidean_error_torso, transformation_matrix_torso,
eucl_dis_tresh_torso, rotation_tresh_torso,
max_euclidean_error_shoulders, eucld_dis_shoulders_tresh)
result_legs = pose_comparison.decide_legs(max_euclidean_error_legs, transformation_matrix_legs,
eucl_dis_tresh_legs, rotation_tresh_legs)
#TODO: construct a solid score algorithm
error_score = (max_euclidean_error_torso + max_euclidean_error_legs)/2.0
result = MatchResult((result_torso and result_legs),
error_score=error_score,
input_transformation=input_transformation)
return result, torso_value
def single_person(model_features, input_features, normalise=True):
# Filter the undetected features and mirror them in the other pose
(input_features_copy,
model_features_copy) = prepocessing.handle_undetected_points(
input_features, model_features)
if (normalise):
model_features_copy = normalising.feature_scaling(model_features_copy)
input_features_copy = normalising.feature_scaling(input_features_copy)
#Split features in three parts
(model_face, model_torso,
model_legs) = prepocessing.split_in_face_legs_torso(model_features_copy)
(input_face, input_torso,
input_legs) = prepocessing.split_in_face_legs_torso(input_features_copy)
# Zoek transformatie om input af te beelden op model
# Returnt transformatie matrix + afbeelding/image van input op model
(input_transformed_face,
transformation_matrix_face) = affine_transformation.find_transformation(
model_face, input_face)
(input_transformed_torso,
transformation_matrix_torso) = affine_transformation.find_transformation(
model_torso, input_torso)
(input_transformed_legs,
transformation_matrix_legs) = affine_transformation.find_transformation(
model_legs, input_legs)
# Wrapped the transformed input in one whole pose
input_transformation = prepocessing.unsplit(input_transformed_face,
input_transformed_torso,
input_transformed_legs)
# In case of no normalisation, return here (ex; plotting)
# Without normalisation the thresholds don't say anything
# -> so comparison is useless
if (not normalise):
result = MatchResult(None,
error_score=0,
input_transformation=input_transformation)
return result
max_euclidean_error_face = pose_comparison.max_euclidean_distance(
model_face, input_transformed_face)
max_euclidean_error_torso = pose_comparison.max_euclidean_distance(
model_torso, input_transformed_torso)
max_euclidean_error_legs = pose_comparison.max_euclidean_distance(
model_legs, input_transformed_legs)
max_euclidean_error_shoulders = pose_comparison.max_euclidean_distance_shoulders(
model_torso, input_transformed_torso)
######### THE THRESHOLDS #######
eucl_dis_tresh_torso = 0.11 #0.065 of 0.11 ??
rotation_tresh_torso = 40
eucl_dis_tresh_legs = 0.055
rotation_tresh_legs = 40
eucld_dis_shoulders_tresh = 0.063
################################
result_torso = pose_comparison.decide_torso_shoulders_incl(
max_euclidean_error_torso, transformation_matrix_torso,
eucl_dis_tresh_torso, rotation_tresh_torso,
max_euclidean_error_shoulders, eucld_dis_shoulders_tresh)
result_legs = pose_comparison.decide_legs(max_euclidean_error_legs,
transformation_matrix_legs,
eucl_dis_tresh_legs,
rotation_tresh_legs)
#TODO: construct a solid score algorithm
error_score = (max_euclidean_error_torso + max_euclidean_error_legs) / 2.0
result = MatchResult((result_torso and result_legs),
error_score=error_score,
input_transformation=input_transformation)
return result
def plot_single_person(
model_features,
input_features,
model_image_name,
input_image_name,
input_title="input",
model_title="model",
transformation_title="transformed input -incl. split()"):
# Filter the undetected features and mirror them in the other pose
(input_features_copy,
model_features_copy) = prepocessing.handle_undetected_points(
input_features, model_features)
# plot vars
markersize = 3
#Load images
# model_image = plt.imread(model_image_name)
# input_image = plt.imread(input_image_name)
# Split features in three parts
(model_face, model_torso,
model_legs) = prepocessing.split_in_face_legs_torso(model_features_copy)
(input_face, input_torso,
input_legs) = prepocessing.split_in_face_legs_torso(input_features_copy)
# Zoek transformatie om input af te beelden op model
# Returnt transformatie matrix + afbeelding/image van input op model
(input_transformed_face,
transformation_matrix_face) = affine_transformation.find_transformation(
model_face, input_face)
(input_transformed_torso,
transformation_matrix_torso) = affine_transformation.find_transformation(
model_torso, input_torso)
(input_transformed_legs,
transformation_matrix_legs) = affine_transformation.find_transformation(
model_legs, input_legs)
whole_input_transform = prepocessing.unsplit(input_transformed_face,
input_transformed_torso,
input_transformed_legs)
model_image = plt.imread(
model_image_name
) #png.read_png_int(model_image_name) #plt.imread(model_image_name)
input_image = plt.imread(
input_image_name
) #png.read_png_int(input_image_name) #plt.imread(input_image_name)
model_image = draw_humans.draw_humans(model_image, model_features,
True) # plt.imread(model_image_name)
input_image = draw_humans.draw_humans(input_image, input_features,
True) # plt.imread(input_image_name)
input_trans_image = draw_humans.draw_square(plt.imread(model_image_name),
model_features)
input_trans_image = draw_humans.draw_humans(
input_trans_image, whole_input_transform, True
) # plt.imread(input_image_name) png.read_png_int(model_image_name)
f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(14, 6))
implot = ax1.imshow(model_image)
plt.axis('off')
#ax1.set_title(model_image_name + ' (model)')
ax1.set_title(model_title)
ax1.axis('off')
# ax1.plot(*zip(*model_features_copy), marker='o', color='magenta', ls='', label='model', ms=markersize) # ms = markersize
# red_patch = mpatches.Patch(color='magenta', label='model')
# ax1.legend(handles=[red_patch])
#ax2.set_title(input_image_name + ' (input)')
ax2.set_title(input_title)
ax2.axis('off')
ax2.imshow(input_image)
# ax2.plot(*zip(*input_features_copy), marker='o', color='r', ls='', ms=markersize)
# ax2.legend(handles=[mpatches.Patch(color='red', label='input')])
ax3.set_title(transformation_title)
ax3.axis('off')
ax3.imshow(input_trans_image)
# ax3.plot(*zip(*model_features_copy), marker='o', color='magenta', ls='', label='model', ms=markersize) # ms = markersize
# ax3.plot(*zip(*whole_input_transform), marker='o', color='b', ls='', ms=markersize)
# ax3.legend(handles=[mpatches.Patch(color='blue', label='transformed input'), mpatches.Patch(color='magenta', label='model')])
plot_name = model_image_name.split("/")[-1] + "_" + input_image_name.split(
"/")[-1]
plt.savefig(plot_name + '.png', bbox_inches='tight')
plt.show(block=False)