def split_label5_contours(self):
"splits contours, not used anymore "
for contour_instance in self.contour_instances:
if contour_instance.label == '4' or contour_instance.label == '5':
contour_instance_new = contour.Contour()
contour_instance_new.contour_id = 'clone'
contour_instance_new.rectangle_height = contour_instance.rectangle_height
contour_instance_new.rectangle_width = contour_instance.rectangle_width
contour_instance_new.centroid_x = contour_instance.centroid_x
contour_instance_new.centroid_y = contour_instance.centroid_y
contour_instance_new.contour = contour_instance.contour
if contour_instance_new.rectangle_width > contour_instance_new.rectangle_height:
contour_instance_new.centroid_x = int(contour_instance_new.centroid_x \
+ contour_instance_new.rectangle_width*0.25)
contour_instance.centroid_x = int(contour_instance.centroid_x \
- contour_instance.rectangle_width*0.25)
elif contour_instance_new.rectangle_width < contour_instance_new.rectangle_height:
contour_instance_new.centroid_y = int(contour_instance_new.centroid_y \
+ contour_instance_new.rectangle_height*0.25)
contour_instance.centroid_y = int(contour_instance.centroid_y \
+ contour_instance.rectangle_height*0.25)
self.contour_instances.append(contour_instance_new)
self.contour_instance_count = len(self.contour_instances)
self.logger.debug("contour_instance_count after splitting label 5 contours: %s"\
,self.contour_instance_count)
return self.contour_instances
def initiate_filter_calculate_contour_instances(self,crop_window_up, crop_window_down\
, trained_model):
"performs all needed preparations of contours. Do not use in main_generate_training_data"
# initiate_contour_instances(self):
contour_index = 0
contour_instances_temp = []
for contour_instance in range(self.contour_count):
contour_instance = contour.Contour()
contour_instance.contour_id = self.frame_id + '-' + str(
contour_index).rjust(10, '0')
contour_instance.contour = self.contours[contour_index]
contour_index += 1
# filter_contour_instances
contour_instance.area = cv2.contourArea(contour_instance.contour)
moment = cv2.moments(contour_instance.contour)
try:
contour_instance.centroid_x = int(moment['m10'] /
moment['m00'])
contour_instance.centroid_y = int(moment['m01'] /
moment['m00'])
except ZeroDivisionError:
continue
if contour_instance.centroid_y > crop_window_up \
and contour_instance.centroid_y < crop_window_down :
self.logger.debug("contour_instance with id %s has area %s and is appended to "\
"filtered list" ,contour_instance.contour_id, contour_instance.area)
#calculate properties
contour_instance.calculate_properties()
contour_instance.calculate_area_white_gray(
self.frame_backgroundsubsstractor)
#predict
observation = [contour_instance.get_observation()]
observation = pd.DataFrame(observation)
observation = observation[['area'\
,'area_white'\
,'area_grey'\
,'centroid_x'\
,'centroid_y'\
,'contour_arc_length'\
,'hull_area'\
,'poly_area'\
,'rectangle_width'\
,'rectangle_height'\
,'angle' ]]
contour_instance.label = trained_model.predict(observation)[0]
self.logger.debug('predicted contour label of contour_id %s with label %s'\
, contour_instance.contour_id, contour_instance.label)
if contour_instance.label == '1' or contour_instance.label == '2' \
or contour_instance.label == '4' or contour_instance.label == '5':
self.logger.debug("contour_instance with contour_id %s has label 1, 2 or 5 "\
"and is appended to filtered list", contour_instance.contour_id)
contour_instances_temp.append(contour_instance)
self.contour_instances = contour_instances_temp
return self.contour_instances
def set_sweep_angle(self, angle):
if self.span < 0.01:
print "Must set wing.span value before sweep angle"
return
tip_offset = self.span * math.tan(math.radians(angle))
self.sweep = contour.Contour()
self.sweep.top.append((0.0, 0.0))
self.sweep.top.append((self.span, tip_offset))
def initiate_contour_instances(self):
"only used in main_generate_training_data processing of the contour"
contour_index = 0
for contour_instance in range(self.contour_count):
contour_instance = contour.Contour()
contour_instance.contour_id = self.frame_id + '-' + str(
contour_index).rjust(10, '0')
contour_instance.contour = self.contours[contour_index]
self.contour_instances.append(contour_instance)
contour_index += 1
self.contour_instance_count = len(self.contour_instances)
self.logger.debug("contour_instance_count after initating: %s",
self.contour_instance_count)
def iceDiagnosticPlotFactory(plot_type,env):
"""Create and return an object of the requested type.
"""
plot_set = {}
if plot_type == "PLOT_CONT":
for seas in ('jfm', 'amj', 'jas', 'ond', 'ann'):
plot_set['contour_'+seas] = contour.Contour(seas,env)
plot_set['iceSat_iceThickness'] = iceSat.IceSat_iceThickness(env)
elif plot_type == "PLOT_CONT_DIFF":
for seas in ('jfm', 'amj', 'jas', 'ond', 'ann'):
plot_set['contour_diff'+seas] = contourDiff.ContourDiff(seas,env)
plot_set['IceSat_iceThicknessDiff'] = iceSatDiff.IceSat_iceThicknessDiff(env)
elif plot_type == "PLOT_VECT":
plot_set['vector'] = vector.Vector(env)
elif plot_type == "PLOT_VECT_DIFF":
plot_set['vector_diff'] = vectorDiff.VectorDiff(env)
elif plot_type == "PLOT_LINE":
plot_set['web_hem_avg'] = web_hem_avg.Web_Hem_Avg(env)
plot_set['web_hem_clim'] = web_hem_clim.Web_Hem_Clim(env)
elif plot_type == "PLOT_LINE_DIFF":
plot_set['web_hem_avgDiff'] = web_hem_avgDiff.Web_Hem_AvgDiff(env)
plot_set['web_hem_climDiff'] = web_hem_climDiff.Web_Hem_ClimDiff(env)
elif plot_type == "PLOT_REGIONS":
for reg_num in range(0,16):
plot_set['web_reg_avg_'+str(reg_num)] = web_reg_avg.Web_Reg_Avg(reg_num, env)
plot_set['web_reg_clim'] = web_reg_clim.Web_Reg_Clim(env)
elif plot_type == "PLOT_REGIONS_DIFF":
for reg_num in range(0,16):
plot_set['web_reg_avgDiff_'+str(reg_num)] = web_reg_avgDiff.Web_Reg_AvgDiff(reg_num, env)
plot_set['web_reg_climDiff'] = web_reg_climDiff.Web_Reg_ClimDiff(env)
else:
raise UnknownPlotType("WARNING: Unknown plot type requested: '{0}'".format(plot_type))
return plot_set
import contour print(dir(contour)) #print(contour.cos_func(1.0)) cont1 = contour.Contour() cont2 = contour.Contour() pt = [1.0, 0.0, 0.0] cont1.add_control_point(pt)
