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