import pyrealsense2 as rs
import numpy as np
import cv2
import supervision as sv
from ultralytics import YOLO
import time

# 啟動 RealSense 相機
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
pipeline.start(config)
# profile = config.resolve(pipeline)
# # Declare sensor object and set options
# depth_sensor = profile.get_device().first_depth_sensor()
# depth_sensor.set_option(rs.option.visual_preset, 5) # 5 is short range, 3 is low ambient light

# 初始化 YOLO 模型
model = YOLO('yolov8n-seg.pt')
byte_tracker = sv.ByteTrack()

align_to = rs.stream.color
align = rs.align(align_to)

try:
    while True:
        # 讀取 RealSense 相機影像
        frames = pipeline.wait_for_frames()
        aligned_frames = align.process(frames)
        depth_frame = aligned_frames.get_depth_frame()
        color_frame = aligned_frames.get_color_frame()

        if not depth_frame or not color_frame:
            continue

        depth_image = np.asanyarray(depth_frame.get_data())
        color_image = np.asanyarray(color_frame.get_data())
        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_WINTER)

        # 進行物體檢測
        result = model(color_image)[0]
        detections = sv.Detections.from_ultralytics(result)
        detections_tracker = byte_tracker.update_with_detections(detections=detections)

        polygon_annotator = sv.PolygonAnnotator()
        annotated_frame = polygon_annotator.annotate(
            scene=color_image.copy(), detections=detections)

        mask_annotator = sv.MaskAnnotator()
        annotated_frame = mask_annotator.annotate(
            scene=annotated_frame, detections=detections
        )

        labels = [
            f"#{tracker_id} {result.names[class_id]} {confidence:.2f}"
            for class_id, tracker_id, confidence
            in zip(detections_tracker.class_id, detections_tracker.tracker_id, detections_tracker.confidence)
        ]

        label_annotator = sv.LabelAnnotator()
        annotated_frame = label_annotator.annotate(
            scene=annotated_frame,
            detections=detections_tracker,
            labels=labels
        )

        if detections:
            position = sv.Position.CENTER_OF_MASS
            xy = detections.get_anchors_coordinates(anchor=position)
            #print('postion',xy)
            for detection_idx in range(len(detections)):
                #print(xy)
                center = (int(xy[detection_idx,0]), int(xy[detection_idx,1]))
                cv2.circle(annotated_frame, center,radius=3, color=(255,255,0), thickness=-1)
                #print(center[0],center[1])

                # 在畫面上顯示距離信息和檢測結果
                text_depth = "Depth value at ({x},{y}): {:.2f} meters".format(
                    depth_frame.get_distance(int(xy[detection_idx,0]), int(xy[detection_idx,1])),x = int(xy[detection_idx,0]),y=int(xy[detection_idx,1]))
                annotated_frame = cv2.putText(annotated_frame, text_depth, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                              (0, 0, 255), 2)
                print("距離:",depth_frame.get_distance(int(xy[detection_idx,0]), int(xy[detection_idx,1])))

                #annotated_frame = cv2.circle(annotated_frame, (320, 240), 4, (0, 0, 255), -1)

        annotated_frame = np.hstack((annotated_frame, depth_colormap))

        # 顯示畫面
        cv2.imshow('RealSense', annotated_frame)

        key = cv2.waitKey(1)
        if key & 0xFF == ord('q') or key == 27:
            cv2.destroyAllWindows()
            break

finally:
    pipeline.stop()