[Feat] instance segmentation 추론 결과 확인

[Jinsun-Lee]

def parse_hypothesis(self, results: Results) -> List[Dict]:
        hypothesis_list = []

        results = self.yolo.predict(source=cv_image, verbose=False, stream=False, conf=self.threshold, device=self.device)
        results: Results = results[0].cpu()

        #result_plot = results[0].plot() # 추론 결과 전부 표시
        #cv2.imshow("inference result", result_plot)

        if results.boxes:
            hypothesis = self.parse_hypothesis(results) # id, class name, score
            boxes = self.parse_boxes(results)           # box x, y, size

            color = self._class_to_color[label]
            label = aux_msg.class_name

            #masking_inner = self.masking_result(copy_img, aux_msg.mask.data, color) # 영역 내부를 색칠 

            if label == 'lane2':
                color = PINK
                result_img = self.masking_result(black_img, aux_msg.mask.data, color) # 영역 외부는 검정

        #masking_inner = self.is_empty_img(cv_image, masking_inner) # 영역 내부를 색칠 
        masking_outer = self.is_empty_img(cv_image, result_img)

        detections_msg.header = msg.header
        self.pub_img.publish(self.cv_bridge.cv2_to_imgmsg(masking_outer, encoding=msg.encoding))
        self.pub_info.publish(detections_msg)

        #cv2.imshow('inner_fill', masking_inner) # 영역 내부를 색칠 
        cv2.imshow('outer_black', masking_outer)
        cv2.waitKey(1)

def main(args=None):
    rclpy.init(args=args)

https://github.com/SKKUAutoLab/ROS2-Based-Autonomous-Driving-SW-Camp/commit/ade756a8b9a226da33cedbd686cff98d2ed617fd#diff-dcbf6960b5e93b2244addaa4518c521a3f910593a79460a0cdd19919ab381d73