def targets(self):
targets = []
for i, rivi in enumerate(self.rivit):
if rivi["Target"] != '' and rivi["Cq"] != 'NaN':
found = False
for target in targets:
if target.compare(rivi["Target"], self.tiedostot[i]) is True:
target.add_str_cq(rivi["Cq"])
found = True
if found is False:
t = TargetData(rivi["Target"], self.tiedostot[i], self.tiedoston_numero(self.tiedostot[i]))
t.add_str_cq(rivi["Cq"])
targets.append(t)
return targets
def __init__(self, networkTable):
"""initializes all values to presets or None if need to be set
"""
self.targetData = TargetData()
self.networkTable = networkTable
self.__resize_image_width = 320.0
self.__resize_image_height = 240.0
self.__resize_image_interpolation = cv2.INTER_CUBIC
self.resize_image_output = None
self.__hsl_threshold_input = self.resize_image_output
self.__hsl_threshold_hue = [51.798561151079134, 93.99317406143345]
self.__hsl_threshold_saturation = [71.08812949640287, 255.0]
self.__hsl_threshold_luminance = [36.690647482014384, 255.0]
self.hsl_threshold_output = None
self.__find_contours_input = self.hsl_threshold_output
self.__find_contours_external_only = True
self.find_contours_output = None
self.__filter_contours_contours = self.find_contours_output
self.__filter_contours_min_area = 20.0
self.__filter_contours_min_perimeter = 0.0
self.__filter_contours_min_width = 0.0
self.__filter_contours_max_width = 1000.0
self.__filter_contours_min_height = 0.0
self.__filter_contours_max_height = 1000.0
self.__filter_contours_solidity = [0, 100]
self.__filter_contours_max_vertices = 1000000.0
self.__filter_contours_min_vertices = 0.0
self.__filter_contours_min_ratio = 0.0
self.__filter_contours_max_ratio = 1000.0
self.filter_contours_output = None
self.lastCenterX = float('NaN')
self.lastCenterY = float('NaN')
self.lastDistance_inches = float('NaN')
self.lastCenter_deg = float('NaN')
def group_ct_mean(self, bees):
target_means = {}
for bee in bees:
targets = self.ct_means(bee.targets())
for target in targets:
if target.ct_mean != 0:
if target.name in target_means:
target_means[target.name].add_cq(target.ct_mean)
else:
t = TargetData(target.name)
t.add_cq(target.ct_mean)
target_means[target.name] = t
for target in target_means:
t = target_means[target]
t.ct_mean = self.count_mean(t.cq_list)
t.standard_deviation = self.count_standard_deviation(t.cq_list)
return target_means