89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import os
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import cv2
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#from rknnlite.api import RKNNLite
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import time
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# classify_single_image, StableClassJudge, CLASS_NAMES 已在 muju_cls_rknn 中定义
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from .muju_cls_rknn import classify_single_image, StableClassJudge, CLASS_NAMES
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# 获取当前文件所在目录的绝对路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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def run_stable_classification_loop():
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"""
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image_source: cv2.VideoCapture 对象
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"""
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_ret=None
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# 使用相对于当前文件的绝对路径
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model_path = os.path.join(current_dir, "muju_cls.rknn")
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roi_file = os.path.join(current_dir, "roi_coordinates", "muju_roi.txt")
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RTSP_URL = "rtsp://admin:XJ123456@192.168.250.61:554/streaming/channels/101"
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stable_frames=3
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print(f"正在连接 RTSP 流: {RTSP_URL}")
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cap =None
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try:
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cap = cv2.VideoCapture(RTSP_URL)
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# 降低 RTSP 延迟（部分摄像头支持）
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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if not cap.isOpened():
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print("无法打开 RTSP 流，请检查网络、账号密码或 URL")
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return None
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print("RTSP 流连接成功，开始推理...")
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judge = StableClassJudge(
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stable_frames=stable_frames,
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ignore_class=2 # 忽略“有遮挡”类别参与稳定判断
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)
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if not hasattr(cap, "read"):
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raise TypeError("image_source 必须是 cv2.VideoCapture 实例")
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_max_count=10
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while True:
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_max_count=_max_count-1
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ret, frame = cap.read()
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if not ret:
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print("无法读取视频帧（可能是流断开或结束）")
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continue
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# 上下左右翻转
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frame = cv2.flip(frame, -1)
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# ---------------------------
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# 单帧推理
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# ---------------------------
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result = classify_single_image(frame, model_path, roi_file)
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class_id = result["class_id"]
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class_name = result["class"]
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score = result["score"]
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print(f"[FRAME] {class_name} | conf={score:.3f}")
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if score>0.8:
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# ---------------------------
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# 稳定判断
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# ---------------------------
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stable_class_id = judge.update(class_id)
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if stable_class_id is not None:
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_ret=CLASS_NAMES[stable_class_id]
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if _ret is None:
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print("-------当前类别为：为空，继续等待稳定------")
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continue
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if _ret=="模具车1" or _ret=="模具车2":
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break
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print(f"当前类别为：{_ret}，继续等待稳定...")
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else:
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print("当前类别为：为空，继续等待稳定...")
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time.sleep(0.1)
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finally:
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if cap is not None:
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cap.release()
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return _ret
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