def add_brush_category_to_final(model_name):
final_data = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json")
on_surface = [
'BLOB', 'CLAY', 'CLAY_STRIPS', 'CREASE', 'DRAW', 'FLATTEN', 'INFLATE',
'LAYER', 'PINCH', 'SCRAPE', 'SMOOTH'
]
global_brush = ['GRAB', 'SNAKE_HOOK']
mask_brush = ['MASK']
for step in final_data:
print("%s / %d" % (step, len(final_data) - 1), )
if final_data[step]["brush_data"]["brush_type"] in on_surface:
final_data[step]["brush_data"]["brush_category"] = "ON_SURFACE"
elif final_data[step]["brush_data"]["brush_type"] in global_brush:
final_data[step]["brush_data"]["brush_category"] = "GLOBAL"
elif final_data[step]["brush_data"]["brush_type"] in mask_brush:
final_data[step]["brush_data"]["brush_category"] = "MASK"
common.save_json(
final_data,
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json",
compressed=False)
def add_brush_type_to_final(model_name):
final_data = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json")
brush_type_data = common.load_json("../steps/" + model_name[0] +
"/brush_type_new.json")
for step in final_data:
print("%s / %d" % (step, len(final_data) - 1), )
final_data[step]["brush_data"]["brush_type"] = brush_type_data[step]
common.save_json(
final_data,
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json",
compressed=False)
def final_data_flattening(model_name):
'''
Saves a flattened version of the brush data, duplicating the data for each point of the polyline.
'''
final_data = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data.json")
flattened_data = []
print("Flattening model %s" % model_name[0])
for step in final_data:
print("%s / %d" % (step, len(final_data) - 1), )
step_data = final_data[step]["brush_data"]
if step_data["valid"]:
for idx, point in enumerate(step_data["paths"][0]):
single_data = {
"step": int(step),
"projected_size": step_data["size"][0][0],
"unprojected_size": step_data["size"][0][1],
"position": point,
"pressure": step_data["pressure"][0][idx],
"mode": step_data["mode"][0],
}
flattened_data.append(single_data)
save_path = "/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/flattened/" + model_name[
0] + "/"
common.make_dirs(save_path)
common.save_json(flattened_data,
save_path + "flattened_data.json",
compressed=False)
def add_brush_2d_pos_to_final(model_name):
final_data = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_2.json")
brush_2d_pos_data = common.load_json("../steps/" + model_name[0] +
"/brush_2d_pos.json")
for step in final_data:
print("%s / %d" % (step, len(final_data) - 1), ),
if step in brush_2d_pos_data:
final_data[step]["brush_data"]["brush_2d_pos"] = brush_2d_pos_data[
step]["path"]
final_data[step]["brush_data"]["lenght_2d"] = brush_2d_pos_data[
step]["lenght"]
elif final_data[step]["brush_data"]["valid"]:
print("############# ERROR #############")
common.save_json(
final_data,
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json",
compressed=False)
def generate_script(self, diff_no):
# .: diff_no = 10 obj_1 = 9 obj_2 = 10
bbox_diag = {
"alien": 4.88411,
"elder": 18.1198,
"elf": 5.62413,
"engineer": 2.03795,
"explorer": 17.2768,
"fighter": 2.0678,
"gargoyle": 10.5912,
"gorilla": 12.3074,
"man": 4.38392,
"merman": 4.90317,
"monster": 6.87775,
"ogre": 20.0907,
"sage": 4.91853
}
#open mesh data
md = common.load_json("../steps/" + self.model_name +
"/mesh_data.json")
vertices_no = int(md[str(diff_no)]['vertices_no'])
file_content = "<!DOCTYPE FilterScript>\n" + \
"<FilterScript>\n" + \
"<filter name=\"Hausdorff Distance\">\n" + \
"<Param type=\"RichMesh\" name=\"SampledMesh\" value=\"1\" />\n" + \
"<Param type=\"RichMesh\" name=\"TargetMesh\" value=\"0\" />\n" + \
"<Param type=\"RichBool\" name=\"SaveSample\" value=\"true\" />\n" + \
"<Param type=\"RichBool\" name=\"SampleVert\" value=\"true\" />\n" + \
"<Param type=\"RichBool\" name=\"SampleEdge\" value=\"true\" />\n" + \
"<Param type=\"RichBool\" name=\"SampleFauxEdge\" value=\"false\"/>\n" + \
"<Param type=\"RichBool\" name=\"SampleFace\" value=\"true\" />\n" + \
"<Param type=\"RichInt\" name=\"SampleNum\" value=\"" + str(int(vertices_no * self.sample_perc)) + "\" />\n" + \
"<Param type=\"RichAbsPerc\" name=\"MaxDist\" value=\"" + str((bbox_diag[self.model_name] / 100.0) * 5.0) + "\" min=\"0\" max=\"" + str(bbox_diag[self.model_name]) + "\"/>\n" + \
"</filter>\n" + \
"<filter name=\"Select None\">\n" + \
"<Param type=\"RichBool\" name=\"allFaces\" value=\"true\" />\n" + \
"<Param type=\"RichBool\" name=\"allVerts\" value=\"true\" />\n" + \
"</filter>\n" + \
"<filter name=\"Change the current layer\">\n" + \
"<Param type=\"RichMesh\" name=\"mesh\" value=\"1\" />\n" + \
"</filter>\n" + \
"</FilterScript>\n"
script_fh = open(self.script_path, 'w')
script_fh.write(file_content)
script_fh.close()
def load_data(filename, is_brush=True):
final_data = common.load_json(filename)
id = 0
loaded_data = [None, ] * len(final_data)
for step in final_data:
step_data = final_data[step]
try:
loaded_data[int(step_data["step_number"])] = Data(id,
int(step_data["step_number"]),
"b" if is_brush else "d",
step_data["brush_data" if is_brush else "diff_data"])
id += 1
except TypeError:
continue
loaded_data = [el for el in loaded_data if el]
return loaded_data
def add_brush_2d_angle_to_final(model_name):
final_data = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json")
for step in final_data:
print("%s / %d" % (step, len(final_data) - 1), )
if final_data[step]["brush_data"]["valid"]:
final_data[step]["brush_data"]["brush_2d_angles"] = get_2d_angles(
final_data[step]["brush_data"])
final_data[step]["brush_data"][
"brush_2d_angles_filtered"] = get_2d_angles(
final_data[step]["brush_data"], True)
common.save_json(
final_data,
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name[0] + "/final_data_3.json",
compressed=False)
def visualize(self, show=False, save_image=True, prefix=""):
load_dir = "/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/clustering/" + self.model_name + "/"
fh = open(load_dir + prefix + "_centroid_pos", 'rb')
centroid_pos = np.load(fh)
fh.close()
fh = open(load_dir + prefix + "_labels", 'rb')
labels = np.load(fh)
fh.close()
fh = open(load_dir + prefix + "_raw_data", 'rb')
raw_data = np.load(fh)
fh.close()
cluster_timings = common.load_json(load_dir + prefix + "_times.json")
noise = []
for obj in self.data:
noise.append(obj.time)
max_time = -1
for c in cluster_timings:
for el in cluster_timings[c]:
max_time = max(max_time, el)
if el in noise:
noise.remove(el)
for el in noise:
max_time = max(max_time, el)
fig2 = plt.figure(figsize=(22, 10), facecolor="white")
cols = int(math.sqrt(len(cluster_timings) + 1))
rows = ((len(cluster_timings) + 1)// cols) + 1
for c_idx in cluster_timings:
cl_time = np.array(cluster_timings[c_idx])
print("%d%d%d" % (rows, cols, int(c_idx)))
ax = fig2.add_subplot(rows, cols, int(c_idx))
n, bin, patches = ax.hist(cl_time,
bins=30,
range=(0, max_time),
color=plt.cm.spectral(float(int(c_idx)) / (len(cluster_timings) + 1)))
plt.title("C%s [%d, m=%.2f, v=%.2f]" % (c_idx,
len(cl_time),
np.mean(cl_time),
np.std(cl_time))
)
ax = fig2.add_subplot(rows, cols, len(cluster_timings) + 1)
n, bin, patches = ax.hist(noise,
bins=30,
range=(0, max_time),
color=plt.cm.spectral(1.0))
fig2.tight_layout()
if save_image:
root_images = "/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/results/"
common.make_dirs(root_images)
plt.savefig(root_images + self.model_name + "_st-dbscan-" + prefix + "_times.png")
fig = plt.figure(figsize=(22, 10), facecolor="white")
plot_clustering_scatter(centroid_pos, labels, raw_data,
title="[%s] st-dbscan" % self.model_name,
fig=fig,
subplot_idx=111,
model_name=self.model_name,
save_image=save_image,
prefix=prefix)
if show:
plt.show()
"artist": "jw"
},
"ogre": {
"technique": "subd",
"artist": "jw"
},
"sage": {
"technique": "subd",
"artist": "jw"
}
}
brush_types = {}
for model_name in data:
mesh_data = common.load_json(data_dir + model_name + "/mesh_data.json")
max_step_num = str(len(mesh_data.keys()) - 1)
data[model_name]["faces_no"] = mesh_data[max_step_num]["faces_no"]
final_data = common.load_json(final_data_dir + model_name +
"/final_data_3.json")
brush_no = 0
for step in final_data:
if final_data[step]["brush_data"]["valid"]:
brush_no += final_data[step]["brush_data"]["brush_number"]
if final_data[step]["brush_data"]["brush_type"] in brush_types:
brush_types[final_data[step]["brush_data"]["brush_type"]] += 1
else:
brush_types[final_data[step]["brush_data"]["brush_type"]] = 1
data[model_name]["brush_no"] = brush_no
ax.scatter(X_red[:, force_idx[0]],
-1.0 * X_red[:, force_idx[2]],
X_red[:, force_idx[1]],
c=labels,
cmap=plt.cm.cool,
s=c * 1000)
ax.view_init(elev=0, azim=-90)
plt.axis('equal')
if title is not None:
plt.title(title, size=17)
# Loading brush data
model_name = "monster"
json_array = common.load_json("../steps/" + model_name +
"/feature_vector.json")
modes = {}
for idx_l in range(len(json_array)):
for idx_e in range(len(json_array[idx_l])):
if idx_e != 2:
json_array[idx_l][idx_e] = float(json_array[idx_l][idx_e])
elif idx_e == 2:
if json_array[idx_l][idx_e] in modes:
json_array[idx_l][idx_e] = modes[json_array[idx_l][idx_e]]
else:
print("Adding %d for %s" %
(len(modes), json_array[idx_l][idx_e]))
modes[json_array[idx_l][idx_e]] = len(modes)
json_array[idx_l][idx_e] = modes[json_array[idx_l][idx_e]]
labels = []
k = 0
for step_idx in range(len(final_data)):
if final_data[str(step_idx)]["valid"]:
for i in range(final_data[str(step_idx)]["brush_data"]["brush_number"]):
if time_serialized:
ret_list.append([step_idx, final_data[str(step_idx)]["brush_data"][attribute][i]])
else:
ret_list.append(final_data[str(step_idx)]["brush_data"][attribute][i])
labels.append(k)
k += 1
return ret_list, labels
# Loading brush data
model_name = "ogre"
json_array = common.load_json("../steps/" + model_name + "/final_data.json")
centroid, centroid_label = filter_brush_attribute(json_array, "centroids", False)
centroid_dist = []
for point_idx in range(1, len(centroid)):
dist = np.linalg.norm(np.array(centroid[point_idx]) - np.array(centroid[point_idx - 1]))
centroid_dist.append(dist)
for attr_name in ["lenghts", "size", "unp_size", "centroid_dist"]:
if attr_name == "size" or attr_name == "unp_size":
attr_data, labels = filter_brush_attribute(json_array, "size", False)
elif attr_name == "centroid_dist":
attr_data, labels = centroid_dist, centroid_label
else:
attr_data, labels = filter_brush_attribute(json_array, attr_name, False)
model_names = [
"alien", "elder", "elf", "engineer", "explorer", "fighter", "gargoyle",
"gorilla", "man", "merman", "monster", "ogre", "sage"
]
on_surface = [
'BLOB', 'CLAY', 'CLAY_STRIPS', 'CREASE', 'DRAW', 'FLATTEN', 'INFLATE',
'LAYER', 'PINCH', 'SCRAPE', 'SMOOTH', 'MASK'
]
global_brush = ['GRAB', 'SNAKE_HOOK']
for model_name in model_names:
print("Analyzing model " + model_name)
fd_json = common.load_json(brush_dir + model_name + "/final_data_3.json")
final_list = []
final_list_flipped = []
for k in range(len(fd_json)):
print(str(k) + "/" + str(len(fd_json)), )
bd_json = fd_json[str(k)]["brush_data"]
if bd_json["valid"]:
points = bd_json["paths"][0]
flattened_points = []
flattened_points_flipped = []
flattened_indices = []
for idx, p in enumerate(points):
flattened_points += p
flattened_points_flipped += [-p[0], p[1], p[2]]
if idx < len(points) - 1:
models = [
["alien", 2216],
["elder", 3119],
["elf", 4307],
["engineer", 987],
["explorer", 1858],
["fighter", 1608],
["gargoyle", 1058],
["gorilla", 2719],
["man", 1580],
["merman", 2619],
["monster", 967],
["ogre", 1720],
["sage", 2136]
]
'''
for model_name, max_step in models:
print("saving data for " + model_name)
bd = BrushData(model_name, max_step)
type_json = {}
for k in range(max_step + 1):
type_json[str(k)] = bd.load_brush_type(k)
common.save_json(type_json, "../steps/" + model_name + "/brush_type_new.json")
'''
for model_name, step in models:
bt = common.load_json("../steps/" + model_name + "/brush_type_new.json")
bd = BrushData(model_name, step)
type = bd.load_brush_type(step)
bt[str(step)] = type
common.save_json(bt, "../steps/" + model_name + "/brush_type_new.json")
"sage": {
0: [-1, 0],
80: [0, 1],
1267: [2, 3],
1525: [3, 4],
1798: [4, 3],
1802: [3, 4],
558: [1, 2]
},
"man": {
0: [-1, 0],
16: [3, 2],
658: [2, 3],
1174: [3, 4],
9: [0, 1],
10: [1, 2],
11: [2, 3]
}
}
for model_name in subd_steps:
bd = common.load_json(
"/Users/christian/Desktop/Ph.D./sculptAnalysis_final_data/complete/" +
model_name + "/final_data_3.json")
for step in subd_steps[model_name]:
for k in [-1, 0, 1]:
if str(step + k) in bd:
print(model_name, step + k,
bd[str(step + k)]["distance_data"]["distance_mean"])
print()
print("--------")
def brush_flattening(model_names):
'''
Saves a flattened version of the brush data (for Weka analysis and such)
'''
feature_vectors = {}
for model_name in model_names:
brush_data = common.load_json("../steps/" + model_name[0] +
"/brush_data.json")
feature_vectors[model_name[0]] = []
for step_idx in brush_data:
print("Model %s | Step %s" % (model_name[0], step_idx))
data = brush_data[str(step_idx)]
if data["valid"]:
sizes = float(data["size"][0])
unp_sizes = float(data["size"][1])
modes = data["mode"]
b_number = data["brush_number"]
path_lenghts = 0
for i in range(b_number + 1):
path_lenghts = float(data["lenghts"][i])
path_centroids = data["centroids"][i]
obb_center = [None, None, None]
obb_center[0] = data["obboxes"][i]["bbox_center"][0]
obb_center[1] = data["obboxes"][i]["bbox_center"][1]
obb_center[2] = data["obboxes"][i]["bbox_center"][2]
obb_dimensions = [None, None, None]
obb_dimensions[0] = data["obboxes"][i]["bbox_ext"][0]
obb_dimensions[1] = data["obboxes"][i]["bbox_ext"][1]
obb_dimensions[2] = data["obboxes"][i]["bbox_ext"][2]
break
pressure_mean = data["pressure_mean"]
pressure_variance = data["pressure_variance"]
pressure_skewness = data["pressure_skewness"]
pressure_curtosis = data["pressure_curtosis"]
path_mean = data["path_mean"]
path_variance = data["path_variance"]
path_skewness = data["path_skewness"]
path_curtosis = data["path_curtosis"]
feature_vectors[model_name[0]].append([
sizes, unp_sizes, modes[0], path_lenghts,
path_centroids[0], path_centroids[1], path_centroids[2],
obb_center[0], obb_center[1], obb_center[2],
obb_dimensions[0], obb_dimensions[1], obb_dimensions[2],
pressure_mean, pressure_variance, pressure_skewness,
pressure_curtosis, path_mean, path_variance, path_skewness,
path_curtosis,
int(step_idx)
])
common.save_json(feature_vectors[model_name[0]],
"../steps/" + model_name[0] + "/feature_vector.json",
compressed=False)
out = open("../steps/" + model_name[0] + "/feature_vector.csv", "w")
out.write('size,unp_size,mode,lenght,' + \
'centroid_x,centroid_y,centroid_z,' + \
'obb_cen_x,obb_cen_y,obb_cen_z,'+ \
'obb_dim_x,obb_dim_y,obb_dim_z,'+ \
'pressure_mean,pressure_variance,pressure_skewness,pressure_curtosis,'+ \
'path_mean,path_variance,path_skewness,path_curtosis,'+ \
'step\n')
for line in feature_vectors[model_name[0]]:
l = ','.join([str(el) for el in line])
out.write(l + '\n')
out.close()
def generate_final_data(model_names):
"""
Produces the final JSON, with all the data extracted from brush stroke and diff
For each step, it saves:
final_data = {
diff_data = {
"added_vertices"
"deleted_vertices"
"added_faces"
"deleted_faces"
"diff_added_centroids"
"diff_added_bbox"
"diff_deleted_centroids"
"diff_deleted_bbox"
"added_mean"
"added_variance"
"added_skewness"
"added_curtosis"
"deleted_mean"
"deleted_variance"
"deleted_skewness"
"deleted_curtosis"
}
brush_data = {
"valid"
"size"
"mode"
"brush_number"
"paths"
"centroid"
"obboxes"
"aabboxes"
"lenghts"
"pressure"
}
distance_data = {
"distance_mean"
"distance_variance"
"distance_skewness"
"distance_curtosis"
}
}
"""
for model_name in model_names:
print("Creating fina data for " + model_name[0])
final_data = {}
brush_data = common.load_json("../steps/" + model_name[0] +
"/brush_data.json")
diff_data = common.load_json("../steps/" + model_name[0] +
"/diff_plot_data.json")
distance_data = common.load_json("../steps/" + model_name[0] +
"/distance_data.json")
final_data[0] = {
"step_number": 0,
"valid": brush_data['0']["valid"],
"brush_data": sanitize_brush_data(brush_data['0']),
"diff_data": null_diff_data(),
"distance_data": null_distance_data()
}
for step_idx in range(1, len(brush_data)):
print(str(step_idx) + " ", )
final_data[step_idx] = {}
final_data[step_idx]["step_number"] = step_idx
final_data[step_idx]["valid"] = brush_data[str(step_idx)]["valid"]
final_data[step_idx]["brush_data"] = sanitize_brush_data(
brush_data[str(step_idx)])
final_data[step_idx]["diff_data"] = get_diff_data_step(
diff_data, step_idx - 1)
final_data[step_idx]["distance_data"] = get_distance_data_step(
distance_data, str(step_idx))
common.save_json(final_data,
"../final_data/" + model_name[0] + "/final_data.json",
compressed=False)