改成相对路径，添加下载链接

README.md

@@ -45,4 +45,5 @@ mkdocs serve
 * 已有相关 [无人车](https://openhutb.github.io/doc/used_by/) 、[无人机](https://openhutb.github.io/air_doc/third/used_by/) 、[具身人](https://openhutb.github.io/doc/pedestrian/humanoid/) 的实现
 * [神经网络原理](https://github.com/OpenHUTB/neuro)
 
-
+通过网盘分享的文件：supercombo.h5
+链接: https://pan.baidu.com/s/1yYc4aPBLnXRuWXEb0hyKMA?pwd=1234 提取码: 1234

main.py

@@ -0,0 +1,182 @@
+import sys
+import os
+# 替换绝对路径为相对路径：基于main.py所在目录向上找src文件夹（适配任意部署环境）
+sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
+
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from common.transformations.camera import transform_img, eon_intrinsics
+from common.transformations.model import medmodel_intrinsics
+from common.tools.lib.parser import parser
+
+# 关闭TensorFlow所有冗余警告
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+# -------------------------- 核心工具函数 --------------------------
+def frames_to_tensor(frames):
+    if len(frames) == 0:
+        return np.array([])
+    H = (frames.shape[1] * 2) // 3
+    W = frames.shape[2]
+    tensor = np.zeros((frames.shape[0], 6, H//2, W//2), dtype=np.float32)
+    tensor[:, 0] = frames[:, 0:H:2, 0::2]
+    tensor[:, 1] = frames[:, 1:H:2, 0::2]
+    tensor[:, 2] = frames[:, 0:H:2, 1::2]
+    tensor[:, 3] = frames[:, 1:H:2, 1::2]
+    tensor[:, 4] = frames[:, H:H+H//4].reshape((-1, H//2, W//2))
+    tensor[:, 5] = frames[:, H+H//4:H+H//2].reshape((-1, H//2, W//2))
+    return tensor / 128.0 - 1.0
+
+def preprocess_frames(imgs):
+    if not imgs:
+        return np.array([])
+    processed = np.zeros((len(imgs), 384, 512), dtype=np.uint8)
+    for i, img in enumerate(imgs):
+        try:
+            processed[i] = transform_img(img, from_intr=eon_intrinsics, to_intr=medmodel_intrinsics, yuv=True, output_size=(512, 256))
+        except:
+            processed[i] = np.zeros((384, 512), dtype=np.uint8)
+    return frames_to_tensor(processed)
+
+# -------------------------- 主函数（无参数、全英文、车道线优化） --------------------------
+def main():
+    # 1. 初始化显示窗口（800x600，固定尺寸）
+    win_name = "Lane Line Prediction (Blue=Left | Red=Right | Green=Path)"
+    cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
+    cv2.resizeWindow(win_name, 800, 600)
+
+    # 2. 读取视频（修改为相对路径：main.py所在文件夹下的sample.hevc）
+    video_path = "./sample.hevc"  # 仅改这里：绝对路径→相对路径
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        empty_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+        cv2.putText(empty_frame, "Cannot open video", (150, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 3)
+        cv2.imshow(win_name, empty_frame)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        return
+
+    # 读取前10帧（用于推理，保留原始帧和模型输入帧）
+    raw_display_frames = []  # 用于显示的800x600帧
+    model_input_imgs = []    # 用于模型的512x384 YUV帧
+    for _ in range(10):
+        ret, frame = cap.read()
+        if not ret:
+            break
+        # 缩放为显示尺寸（800x600）
+        display_frame = cv2.resize(frame, (800, 600))
+        raw_display_frames.append(display_frame)
+        # 转换为模型需要的YUV格式并缩放
+        yuv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV_I420)
+        model_frame = cv2.resize(yuv_frame, (512, 384), cv2.INTER_AREA)
+        model_input_imgs.append(model_frame)
+    cap.release()
+
+    # 校验帧数是否足够
+    if len(raw_display_frames) < 2:
+        empty_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+        cv2.putText(empty_frame, "Insufficient video frames", (100, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
+        cv2.imshow(win_name, empty_frame)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        return
+
+    # 3. 加载模型（显示英文提示，无乱码）
+    load_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+    cv2.putText(load_frame, "Loading model...", (200, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 3)
+    cv2.imshow(win_name, load_frame)
+    cv2.waitKey(200)  # 刷新显示
+
+    # 模型路径（修改为相对路径：main.py所在文件夹下的models/supercombo.h5）
+    model_path = "./models/supercombo.h5"  # 仅改这里：绝对路径→相对路径
+    try:
+        supercombo_model = load_model(model_path, compile=False)
+    except Exception as e:
+        empty_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+        cv2.putText(empty_frame, "Model load failed", (180, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 3)
+        cv2.imshow(win_name, empty_frame)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        return
+
+    # 4. 预处理帧（显示英文提示）
+    preprocess_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+    cv2.putText(preprocess_frame, "Preprocessing frames...", (150, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 3)
+    cv2.imshow(win_name, preprocess_frame)
+    cv2.waitKey(200)
+
+    frame_tensors = preprocess_frames(model_input_imgs)
+    if frame_tensors.size == 0:
+        empty_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+        cv2.putText(empty_frame, "Preprocessing failed", (180, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 3)
+        cv2.imshow(win_name, empty_frame)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        return
+
+    # 5. 模型状态初始化
+    model_state = np.zeros((1, 512))
+    model_desire = np.zeros((1, 8))
+
+    # 6. 逐帧推理+绘制（核心优化：车道线右移+放大圆点）
+    print("✅ Start inference and display (Press Q to exit)")
+    for i in range(len(frame_tensors) - 1):
+        # 确保帧存在，避免索引越界
+        if i >= len(raw_display_frames):
+            current_frame = np.ones((600, 800, 3), dtype=np.uint8) * 255
+        else:
+            current_frame = raw_display_frames[i].copy()  # 复制原始帧，避免修改
+
+        try:
+            # 模型推理（连续两帧作为输入）
+            input_data = [np.vstack(frame_tensors[i:i+2])[None], model_desire, model_state]
+            model_output = supercombo_model.predict(input_data, verbose=0)
+            parsed_result = parser(model_output)
+            model_state = model_output[-1]
+
+            # -------------------------- 车道线绘制优化 --------------------------
+            # 提取模型输出的车道线/路径x坐标
+            left_lane_x = parsed_result["lll"][0]
+            right_lane_x = parsed_result["rll"][0]
+            path_x = parsed_result["path"][0]
+
+            # 窗口尺寸
+            win_h, win_w = 600, 800
+            # y坐标映射（0-191 → 0-599）
+            y_points = np.linspace(0, win_h - 1, 192).astype(int)
+            # x坐标映射（0-512 → 0-799）+ 右移100像素（解决偏左问题）+ 放大圆点到8px
+            left_x_mapped = (left_lane_x / 512 * win_w + 100).astype(int)
+            right_x_mapped = (right_lane_x / 512 * win_w + 100).astype(int)
+            path_x_mapped = (path_x / 512 * win_w + 100).astype(int)
+
+            # 绘制左车道线（蓝色，8px实心圆）
+            for x, y in zip(left_x_mapped, y_points):
+                if 0 <= x < win_w and 0 <= y < win_h:
+                    cv2.circle(current_frame, (x, y), 8, (255, 0, 0), -1)
+            # 绘制右车道线（红色，8px实心圆）
+            for x, y in zip(right_x_mapped, y_points):
+                if 0 <= x < win_w and 0 <= y < win_h:
+                    cv2.circle(current_frame, (x, y), 8, (0, 0, 255), -1)
+            # 绘制预测路径（绿色，6px实心圆）
+            for x, y in zip(path_x_mapped, y_points):
+                if 0 <= x < win_w and 0 <= y < win_h:
+                    cv2.circle(current_frame, (x, y), 6, (0, 255, 0), -1)
+
+        except Exception as e:
+            # 推理失败时仅打印错误，仍显示原始帧
+            print(f"⚠️ Frame {i+1} inference error: {str(e)[:30]}")
+
+        # 强制显示当前帧
+        cv2.imshow(win_name, current_frame)
+        # 按Q退出
+        if cv2.waitKey(100) & 0xFF == ord('q'):
+            print("🛑 Exit by user (Q pressed)")
+            break
+
+    # 7. 程序收尾
+    cv2.destroyAllWindows()
+    print("🎉 All frames processed successfully!")
+
+if __name__ == "__main__":
+    main()