更新液面diff代码

muju_cls/main.py

@@ -0,0 +1,120 @@
+import os
+import cv2
+from rknnlite.api import RKNNLite
+
+# classify_single_image, StableClassJudge, CLASS_NAMES 已在 muju_cls_rknn 中定义
+from muju_cls_rknn import classify_single_image, StableClassJudge, CLASS_NAMES
+
+
+def run_stable_classification_loop(
+    model_path,
+    roi_file,
+    image_source,
+    stable_frames=3,
+    display_scale=0.5,      # 显示缩放比例（0.5 = 显示为原来 50%）
+    show_window=False        # 是否显示窗口
+):
+    """
+    image_source: cv2.VideoCapture 对象
+    """
+
+    judge = StableClassJudge(
+        stable_frames=stable_frames,
+        ignore_class=2  # 忽略“有遮挡”类别参与稳定判断
+    )
+
+    cap = image_source
+    if not hasattr(cap, "read"):
+        raise TypeError("image_source 必须是 cv2.VideoCapture 实例")
+
+    # 可选：创建可缩放窗口
+    if show_window:
+        cv2.namedWindow("RTSP Stream - Press 'q' to quit", cv2.WINDOW_NORMAL)
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("无法读取视频帧（可能是流断开或结束）")
+            break
+
+        # 上下左右翻转
+        frame = cv2.flip(frame, -1)
+
+        # ---------------------------
+        # 单帧推理
+        # ---------------------------
+        result = classify_single_image(frame, model_path, roi_file)
+
+        class_id = result["class_id"]
+        class_name = result["class"]
+        score = result["score"]
+
+        print(f"[FRAME] {class_name} | conf={score:.3f}")
+
+        # ---------------------------
+        # 稳定判断
+        # ---------------------------
+        stable_class_id = judge.update(class_id)
+
+        if stable_class_id is not None:
+            print(f"\n稳定输出: {CLASS_NAMES[stable_class_id]}\n")
+
+        # ---------------------------
+        # 显示画面（缩小窗口）
+        # ---------------------------
+        if show_window:
+            h, w = frame.shape[:2]
+            display_frame = cv2.resize(
+                frame,
+                (int(w * display_scale), int(h * display_scale)),
+                interpolation=cv2.INTER_AREA
+            )
+
+            cv2.imshow("RTSP Stream - Press 'q' to quit", display_frame)
+
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    # ---------------------------
+    # 配置参数
+    # ---------------------------
+    MODEL_PATH = "muju_cls.rknn"
+    ROI_FILE = "./roi_coordinates/muju_roi.txt"
+    RTSP_URL = "rtsp://admin:XJ123456@192.168.250.61:554/streaming/channels/101"
+
+    STABLE_FRAMES = 3
+    DISPLAY_SCALE = 0.5     # 显示窗口缩放比例
+    SHOW_WINDOW = False      # 部署时改成 False，测试的时候打开
+
+    # ---------------------------
+    # 打开 RTSP 视频流
+    # ---------------------------
+    print(f"正在连接 RTSP 流: {RTSP_URL}")
+    cap = cv2.VideoCapture(RTSP_URL)
+
+    # 降低 RTSP 延迟（部分摄像头支持）
+    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+
+    if not cap.isOpened():
+        print("无法打开 RTSP 流，请检查网络、账号密码或 URL")
+        exit(1)
+
+    print("RTSP 流连接成功，开始推理...")
+
+    # ---------------------------
+    # 启动稳定分类循环三帧稳定判断
+    # ---------------------------
+    run_stable_classification_loop(
+        model_path=MODEL_PATH,
+        roi_file=ROI_FILE,
+        image_source=cap,
+        stable_frames=STABLE_FRAMES,
+        display_scale=DISPLAY_SCALE,
+        show_window=SHOW_WINDOW
+    )
+

muju_cls/muju_cls_rknn.py

@@ -0,0 +1,282 @@
+import os
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+from collections import deque
+
+class StableClassJudge:
+    """
+    连续三帧稳定判决器：
+    - class0 / class1 连续 3 帧 -> 输出
+    - class2 -> 清空计数，重新统计
+    """
+
+    def __init__(self, stable_frames=3, ignore_class=2):
+        self.stable_frames = stable_frames
+        self.ignore_class = ignore_class
+        self.buffer = deque(maxlen=stable_frames)
+
+    def reset(self):
+        self.buffer.clear()
+
+    def update(self, class_id):
+        """
+        输入单帧分类结果
+        返回：
+          - None：尚未稳定
+          - class_id：稳定输出结果
+        """
+
+        # 遇到 class2，直接清空重新计数
+        if class_id == self.ignore_class:
+            self.reset()
+            return None
+
+        self.buffer.append(class_id)
+
+        # 缓冲未满
+        if len(self.buffer) < self.stable_frames:
+            return None
+
+        # 三帧完全一致
+        if len(set(self.buffer)) == 1:
+            stable_class = self.buffer[0]
+            self.reset()   # 输出一次后重新计数（防止重复触发）
+            return stable_class
+
+        return None
+
+# ---------------------------
+# 三分类映射，模具车1是小的，模具车2是大的
+# ---------------------------
+CLASS_NAMES = {
+    0: "模具车1",
+    1: "模具车2",
+    2: "有遮挡"
+}
+
+# ---------------------------
+# RKNN 全局实例（只加载一次）
+# ---------------------------
+_global_rknn = None
+
+
+def init_rknn_model(model_path):
+    global _global_rknn
+    if _global_rknn is not None:
+        return _global_rknn
+
+    rknn = RKNNLite(verbose=False)
+    ret = rknn.load_rknn(model_path)
+    if ret != 0:
+        raise RuntimeError(f"Load RKNN failed: {ret}")
+
+    ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+    if ret != 0:
+        raise RuntimeError(f"Init runtime failed: {ret}")
+
+    _global_rknn = rknn
+    print(f"[INFO] RKNN 模型加载成功: {model_path}")
+    return rknn
+
+
+# ---------------------------
+# 预处理
+# ---------------------------
+def letterbox(image, new_size=640, color=(114, 114, 114)):
+    h, w = image.shape[:2]
+    scale = min(new_size / h, new_size / w)
+    nh, nw = int(h * scale), int(w * scale)
+    resized = cv2.resize(image, (nw, nh))
+    new_img = np.full((new_size, new_size, 3), color, dtype=np.uint8)
+    top = (new_size - nh) // 2
+    left = (new_size - nw) // 2
+    new_img[top:top + nh, left:left + nw] = resized
+    return new_img
+
+
+def resize_stretch(image, size=640):
+    return cv2.resize(image, (size, size))
+
+
+def preprocess_image_for_rknn(
+        img,
+        size=640,
+        resize_mode="stretch",
+        to_rgb=True,
+        normalize=False,
+        layout="NHWC"
+):
+    if resize_mode == "letterbox":
+        img_box = letterbox(img, new_size=size)
+    else:
+        img_box = resize_stretch(img, size=size)
+
+    if to_rgb:
+        img_box = cv2.cvtColor(img_box, cv2.COLOR_BGR2RGB)
+
+    img_f = img_box.astype(np.float32)
+
+    if normalize:
+        img_f /= 255.0
+
+    if layout == "NHWC":
+        out = np.expand_dims(img_f, axis=0)
+    else:
+        out = np.expand_dims(np.transpose(img_f, (2, 0, 1)), axis=0)
+
+    return np.ascontiguousarray(out)
+
+
+# ---------------------------
+# 单次 RKNN 推理（三分类）
+# ---------------------------
+def rknn_classify_preprocessed(input_tensor, model_path):
+    rknn = init_rknn_model(model_path)
+
+    input_tensor = np.ascontiguousarray(input_tensor.astype(np.float32))
+    outs = rknn.inference([input_tensor])
+
+    pred = outs[0].reshape(-1).astype(float)  # shape = (3,)
+    class_id = int(np.argmax(pred))
+
+    return class_id, pred
+
+# ---------------------------
+# ROI
+# ---------------------------
+def load_single_roi(txt_path):
+    if not os.path.exists(txt_path):
+        raise RuntimeError(f"ROI 文件不存在: {txt_path}")
+
+    with open(txt_path) as f:
+        for line in f:
+            s = line.strip()
+            if not s:
+                continue
+            x, y, w, h = map(int, s.split(','))
+            return (x, y, w, h)
+
+    raise RuntimeError("ROI 文件为空")
+
+
+def crop_and_return_roi(img, roi):
+    x, y, w, h = roi
+    h_img, w_img = img.shape[:2]
+
+    if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
+        raise RuntimeError(f"ROI 超出图像范围: {roi}")
+
+    return img[y:y + h, x:x + w]
+
+
+# ---------------------------
+# 单张图片推理（三分类）
+# ---------------------------
+def classify_single_image(
+        frame,
+        model_path,
+        roi_file,
+        size=640,
+        resize_mode="stretch",
+        to_rgb=True,
+        normalize=False,
+        layout="NHWC"
+):
+    if frame is None:
+        raise FileNotFoundError("输入帧为空")
+
+    roi = load_single_roi(roi_file)
+    roi_img = crop_and_return_roi(frame, roi)
+
+    input_tensor = preprocess_image_for_rknn(
+        roi_img,
+        size=size,
+        resize_mode=resize_mode,
+        to_rgb=to_rgb,
+        normalize=normalize,
+        layout=layout
+    )
+
+    class_id, probs = rknn_classify_preprocessed(input_tensor, model_path)
+    class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")
+
+    return {
+        "class_id": class_id,
+        "class": class_name,
+        "score": round(float(probs[class_id]), 4),
+        "raw": probs.tolist()
+    }
+
+
+
+# ---------------------------
+# 示例调用
+# ---------------------------
+if __name__ == "__main__":
+    model_path = "muju_cls.rknn"
+    roi_file = "./roi_coordinates/muju_roi.txt"
+    image_path = "./test_image/test.png"
+
+    frame = cv2.imread(image_path)
+    if frame is None:
+        raise FileNotFoundError(f"无法读取图片: {image_path}")
+
+    result = classify_single_image(frame, model_path, roi_file)
+    print("[RESULT]", result)
+
+# ---------------------------
+# 示例判断逻辑
+'''
+import cv2
+from muju_cls_rknn import classify_single_image,StableClassJudge,CLASS_NAMES
+
+def run_stable_classification_loop(
+        model_path,
+        roi_file,
+        image_source,
+        stable_frames=3
+):
+    """
+    image_source:
+      - cv2.VideoCapture
+    """
+    judge = StableClassJudge(
+        stable_frames=stable_frames,
+        ignore_class=2   # 有遮挡
+    )
+    
+    cap = image_source
+    if not hasattr(cap, "read"):
+        raise TypeError("image_source 必须是 cv2.VideoCapture")
+
+    while True:
+        ret, frame = cap.read()
+        # 上下左右翻转
+        frame = cv2.flip(frame, -1)
+        
+        if not ret:
+            print("读取帧失败，退出")
+            break
+
+        result = classify_single_image(frame, model_path, roi_file)
+
+        class_id = result["class_id"]
+        class_name = result["class"]
+        score = result["score"]
+
+        print(f"[FRAME] {class_name}  conf={score}")
+
+        stable = judge.update(class_id)
+
+        if stable is not None:
+            print(f"\n稳定输出: {CLASS_NAMES[stable]} \n")
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+'''
+# ---------------------------

muju_cls/roi_coordinates/muju_roi.txt

@@ -0,0 +1 @@
+2,880,385,200

muju_cls/test_imagesave.py

@@ -0,0 +1,275 @@
+import os
+import cv2
+import time
+import numpy as np
+from datetime import datetime
+from collections import deque
+from rknnlite.api import RKNNLite
+
+# =====================================================
+# 稳定判决器
+# =====================================================
+class StableClassJudge:
+    """
+    连续 N 帧稳定判决：
+    - class0 / class1 连续 N 帧 -> 输出
+    - class2 -> 清空计数
+    """
+
+    def __init__(self, stable_frames=3, ignore_class=2):
+        self.stable_frames = stable_frames
+        self.ignore_class = ignore_class
+        self.buffer = deque(maxlen=stable_frames)
+
+    def reset(self):
+        self.buffer.clear()
+
+    def update(self, class_id):
+        if class_id == self.ignore_class:
+            self.reset()
+            return None
+
+        self.buffer.append(class_id)
+
+        if len(self.buffer) < self.stable_frames:
+            return None
+
+        if len(set(self.buffer)) == 1:
+            stable = self.buffer[0]
+            self.reset()
+            return stable
+
+        return None
+
+
+# =====================================================
+# 类别定义
+# =====================================================
+CLASS_NAMES = {
+    0: "模具车1",
+    1: "模具车2",
+    2: "有遮挡"
+}
+
+
+# =====================================================
+# RKNN 全局实例
+# =====================================================
+_global_rknn = None
+
+
+def init_rknn_model(model_path):
+    global _global_rknn
+    if _global_rknn is not None:
+        return _global_rknn
+
+    rknn = RKNNLite(verbose=False)
+
+    ret = rknn.load_rknn(model_path)
+    if ret != 0:
+        raise RuntimeError(f"Load RKNN failed: {ret}")
+
+    ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+    if ret != 0:
+        raise RuntimeError(f"Init runtime failed: {ret}")
+
+    _global_rknn = rknn
+    print(f"[INFO] RKNN 模型加载成功: {model_path}")
+    return rknn
+
+
+# =====================================================
+# 图像预处理
+# =====================================================
+def letterbox(image, new_size=640, color=(114, 114, 114)):
+    h, w = image.shape[:2]
+    scale = min(new_size / h, new_size / w)
+    nh, nw = int(h * scale), int(w * scale)
+
+    resized = cv2.resize(image, (nw, nh))
+    canvas = np.full((new_size, new_size, 3), color, dtype=np.uint8)
+
+    top = (new_size - nh) // 2
+    left = (new_size - nw) // 2
+    canvas[top:top + nh, left:left + nw] = resized
+    return canvas
+
+
+def resize_stretch(image, size=640):
+    return cv2.resize(image, (size, size))
+
+
+def preprocess_image_for_rknn(
+        img,
+        size=640,
+        resize_mode="stretch",
+        to_rgb=True,
+        normalize=False,
+        layout="NHWC"
+):
+    if resize_mode == "letterbox":
+        img = letterbox(img, size)
+    else:
+        img = resize_stretch(img, size)
+
+    if to_rgb:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    img = img.astype(np.float32)
+
+    if normalize:
+        img /= 255.0
+
+    if layout == "NHWC":
+        img = np.expand_dims(img, axis=0)
+    else:
+        img = np.expand_dims(np.transpose(img, (2, 0, 1)), axis=0)
+
+    return np.ascontiguousarray(img)
+
+
+# =====================================================
+# RKNN 单次推理
+# =====================================================
+def rknn_classify_preprocessed(input_tensor, model_path):
+    rknn = init_rknn_model(model_path)
+    outs = rknn.inference([input_tensor])
+    probs = outs[0].reshape(-1).astype(float)
+    class_id = int(np.argmax(probs))
+    return class_id, probs
+
+
+# =====================================================
+# ROI 处理
+# =====================================================
+def load_single_roi(txt_path):
+    if not os.path.exists(txt_path):
+        raise RuntimeError(f"ROI 文件不存在: {txt_path}")
+
+    with open(txt_path) as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            x, y, w, h = map(int, line.split(","))
+            return (x, y, w, h)
+
+    raise RuntimeError("ROI 文件为空")
+
+
+def crop_and_return_roi(img, roi):
+    x, y, w, h = roi
+    H, W = img.shape[:2]
+
+    if x < 0 or y < 0 or x + w > W or y + h > H:
+        raise RuntimeError(f"ROI 超出图像范围: {roi}")
+
+    return img[y:y + h, x:x + w]
+
+
+# =====================================================
+# 单帧分类
+# =====================================================
+def classify_single_image(frame, model_path, roi_file):
+    roi = load_single_roi(roi_file)
+    roi_img = crop_and_return_roi(frame, roi)
+
+    input_tensor = preprocess_image_for_rknn(
+        roi_img,
+        size=640,
+        resize_mode="stretch",
+        to_rgb=True,
+        normalize=False,
+        layout="NHWC"
+    )
+
+    class_id, probs = rknn_classify_preprocessed(input_tensor, model_path)
+
+    return {
+        "class_id": class_id,
+        "class": CLASS_NAMES[class_id],
+        "score": round(float(probs[class_id]), 4),
+        "raw": probs.tolist()
+    }
+
+
+# =====================================================
+# RTSP 推理 + 保存分类结果
+# =====================================================
+def run_rtsp_classification_and_save(
+        model_path,
+        roi_file,
+        rtsp_url,
+        save_root="clsimg",
+        stable_frames=3,
+        save_mode="all"  # all / stable
+):
+    for cid in CLASS_NAMES.keys():
+        os.makedirs(os.path.join(save_root, f"class{cid}"), exist_ok=True)
+
+    cap = cv2.VideoCapture(rtsp_url)
+    if not cap.isOpened():
+        raise RuntimeError(f"无法打开 RTSP: {rtsp_url}")
+
+    judge = StableClassJudge(stable_frames=stable_frames, ignore_class=2)
+
+    print("[INFO] RTSP 推理开始")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("[WARN] RTSP 读帧失败")
+            time.sleep(0.1)
+            continue
+
+        frame = cv2.flip(frame, -1)
+
+        result = classify_single_image(frame, model_path, roi_file)
+        class_id = result["class_id"]
+        score = result["score"]
+
+        print(f"[FRAME] {result['class']}  conf={score}")
+
+        stable = judge.update(class_id)
+
+        save_flag = False
+        save_class = class_id
+
+        if save_mode == "all":
+            save_flag = True
+        elif save_mode == "stable" and stable is not None:
+            save_flag = True
+            save_class = stable
+
+        if save_flag:
+            ts = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+            filename = f"{ts}_conf{score:.2f}.jpg"
+            save_dir = os.path.join(save_root, f"class{save_class}")
+            cv2.imwrite(os.path.join(save_dir, filename), frame)
+            print(f"[SAVE] class{save_class}/{filename}")
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+# =====================================================
+# main
+# =====================================================
+if __name__ == "__main__":
+    model_path = "muju_cls.rknn"
+    roi_file = "./roi_coordinates/muju_roi.txt"
+
+    rtsp_url = "rtsp://admin:XJ123456@192.168.250.61:554/streaming/channels/101"
+
+    run_rtsp_classification_and_save(
+        model_path=model_path,
+        roi_file=roi_file,
+        rtsp_url=rtsp_url,
+        save_root="clsimg",
+        stable_frames=3,
+        save_mode="all"     # 改成 "stable" 只存稳定结果
+    )
+