def __init__(self, class_names, weight_path, input_shape):
self.class_names = class_names
self.num_classes = len(class_names)
self.input_shape = input_shape
self.model = SSD(self.input_shape, num_classes=self.num_classes)
self.model.load_weights(weight_path)
self.model._make_predict_function()
self.bbox_util = BBoxUtility(self.num_classes)
#self.timer = Timer(1, self.timer_callback)
#self.current_time = 0
# self.current_fps = 0
#self.exec_time = None
#self.prev_extra_time = None
#self.extra_time = None
# self.fps_time_slot = list()
#self.is_finish = False
#self.fps_start = False
#self.fps_start_time = 0
# Create unique and somewhat visually distinguishable bright
# colors for the different classes.
self.class_colors = []
for i in range(0, self.num_classes):
# This can probably be written in a more elegant manner
hue = 255 * i / self.num_classes
col = np.zeros((1, 1, 3)).astype("uint8")
col[0][0][0] = hue
col[0][0][1] = 128 # Saturation
col[0][0][2] = 255 # Value
cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR)
col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]),
int(cvcol[0][0][2]))
self.class_colors.append(col)
def __init__(self, class_names, model, input_shape):
self.class_names = class_names
self.num_classes = len(class_names)
self.model = model
self.input_shape = input_shape
self.bbox_util = BBoxUtility(self.num_classes)
# Create unique and somewhat visually distinguishable bright
# colors for the different classes.
self.class_colors = []
for i in range(0, self.num_classes):
# This can probably be written in a more elegant manner
hue = 255 * i / self.num_classes
col = np.zeros((1, 1, 3)).astype("uint8")
col[0][0][0] = hue
col[0][0][1] = 128 # Saturation
col[0][0][2] = 255 # Value
cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR)
col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]),
int(cvcol[0][0][2]))
self.class_colors.append(col)