From e08c8b15a987e32556d7f420018e470a1c6f46b2 Mon Sep 17 00:00:00 2001 From: Havoc <2993167370@qq.com> Date: Mon, 19 May 2025 20:52:38 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E9=BB=84=E8=89=B2=E8=B5=9B?= =?UTF-8?q?=E9=81=93=E6=A3=80=E6=B5=8B=E6=BC=94=E7=A4=BA=E7=A8=8B=E5=BA=8F?= =?UTF-8?q?=E7=9A=84=E8=BE=93=E5=85=A5=E8=B7=AF=E5=BE=84=E4=B8=BA=E6=B5=8B?= =?UTF-8?q?=E8=AF=95=E5=9B=BE=E5=83=8F=EF=BC=8C=E5=B9=B6=E5=9C=A8=E8=BE=B9?= =?UTF-8?q?=E7=BC=98=E6=A3=80=E6=B5=8B=E5=8A=9F=E8=83=BD=E4=B8=AD=E5=A2=9E?= =?UTF-8?q?=E5=8A=A0=E4=BA=86=E5=AF=B9=E4=B8=8A=E4=B8=8B=E8=BE=B9=E7=BC=98?= =?UTF-8?q?=E7=BA=BF=E7=9A=84=E5=A4=84=E7=90=86=E9=80=BB=E8=BE=91=EF=BC=8C?= =?UTF-8?q?=E4=BC=98=E5=8C=96=E4=BA=86=E6=88=AA=E6=96=AD=E7=BA=BF=E7=9A=84?= =?UTF-8?q?=E4=BF=AE=E6=AD=A3=E6=9C=BA=E5=88=B6=EF=BC=8C=E6=8F=90=E5=8D=87?= =?UTF-8?q?=E4=BA=86=E6=A3=80=E6=B5=8B=E7=9A=84=E5=87=86=E7=A1=AE=E6=80=A7?= =?UTF-8?q?=E5=92=8C=E7=A8=B3=E5=AE=9A=E6=80=A7=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- logs/robot_2025-05-19.log | 57 ++++ res/path/test-1.jpg | Bin 0 -> 116663 bytes test/task-path-track/yellow_track_demo.py | 2 +- utils/detect_track.py | 356 ++++++++++++++-------- 4 files changed, 288 insertions(+), 127 deletions(-) create mode 100644 logs/robot_2025-05-19.log create mode 100644 res/path/test-1.jpg diff --git a/logs/robot_2025-05-19.log b/logs/robot_2025-05-19.log new file mode 100644 index 0000000..41c9546 --- /dev/null +++ b/logs/robot_2025-05-19.log @@ -0,0 +1,57 @@ +2025-05-19 20:41:09 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_204109_537087.jpg +2025-05-19 20:41:09 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_204109_537087', 'edge_point': (397, 816), 'distance_to_center': -563, 'slope': 0.0, 'distance_to_bottom': 264.0, 'intersection_point': (960, 816), 'is_truncated': False} +2025-05-19 20:44:06 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-19 20:44:07 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-19 20:44:08 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-19 20:44:09 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-19 20:44:10 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-19 20:44:11 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 15 条直线 +2025-05-19 20:44:12 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 13 条水平线 +2025-05-19 20:44:13 | DEBUG | utils.log_helper - 👁️ 步骤7: 找到边缘点 (397, 816) +2025-05-19 20:44:14 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-19 20:44:15 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_204415_945888.jpg +2025-05-19 20:44:15 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_204415_945888', 'edge_point': (397, 816), 'distance_to_center': -563, 'slope': 0.0, 'distance_to_bottom': 264.0, 'intersection_point': (960, 816), 'is_truncated': False} +2025-05-19 20:44:57 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-19 20:44:58 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-19 20:44:59 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-19 20:45:00 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-19 20:45:01 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-19 20:45:02 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 10 条直线 +2025-05-19 20:45:03 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 8 条水平线 +2025-05-19 20:45:04 | DEBUG | utils.log_helper - 👁️ 步骤7: 找到边缘点 (831, 971) +2025-05-19 20:45:05 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-19 20:45:06 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_204506_674753.jpg +2025-05-19 20:45:06 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_204506_674753', 'edge_point': (831, 971), 'distance_to_center': -129, 'slope': -0.07004830917874397, 'distance_to_bottom': 118.036231884058, 'intersection_point': (960, 961), 'is_truncated': False} +2025-05-19 20:45:37 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-19 20:45:39 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-19 20:45:40 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-19 20:45:41 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-19 20:45:42 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-19 20:45:43 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 17 条直线 +2025-05-19 20:45:44 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 12 条水平线 +2025-05-19 20:45:45 | DEBUG | utils.log_helper - 👁️ 步骤7: 找到边缘点 (923, 849) +2025-05-19 20:45:46 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-19 20:45:47 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_204547_188654.jpg +2025-05-19 20:45:47 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_204547_188654', 'edge_point': (923, 849), 'distance_to_center': -37, 'slope': 0.0, 'distance_to_bottom': 231.0, 'intersection_point': (960, 849), 'is_truncated': False} +2025-05-19 20:47:53 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-19 20:47:55 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-19 20:47:56 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-19 20:47:57 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-19 20:47:58 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-19 20:47:59 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 17 条直线 +2025-05-19 20:48:00 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 12 条水平线 +2025-05-19 20:48:01 | DEBUG | utils.log_helper - 👁️ 步骤7: 找到边缘点 (923, 849) +2025-05-19 20:48:02 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-19 20:48:03 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_204803_276122.jpg +2025-05-19 20:48:03 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_204803_276122', 'edge_point': (923, 849), 'distance_to_center': -37, 'slope': 0.0, 'distance_to_bottom': 231.0, 'intersection_point': (960, 849), 'is_truncated': False} +2025-05-19 20:50:32 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-19 20:50:33 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-19 20:50:34 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-19 20:50:35 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-19 20:50:36 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-19 20:50:37 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 25 条直线 +2025-05-19 20:50:38 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 18 条水平线 +2025-05-19 20:50:39 | DEBUG | utils.log_helper - 👁️ 步骤7: 找到边缘点 (92, 1077) +2025-05-19 20:50:40 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-19 20:50:41 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250519_205041_583040.jpg +2025-05-19 20:50:41 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250519_205041_583040', 'edge_point': (92, 1077), 'distance_to_center': -868, 'slope': -0.07008086253369272, 'distance_to_bottom': 63.83018867924534, 'intersection_point': (960, 1016), 'is_truncated': False} diff --git a/res/path/test-1.jpg b/res/path/test-1.jpg new file mode 100644 index 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z_)>A@N3?kU@&8R+^#5Pq@M%&w{!Px{;6yn7X8K!*i8j}c>PT}r zn^l@=ax(Q)Y`T(GJHSm>XrGkK0B&-%vMB<%>5CRvQ}hAe@uz{3Fc{7bj1%&y&k)od zzNVDb<{(UJ<>M`2cVDl;+Z^~w$SmgUjy{krsZT!3miaOSa!D$-v~DBJ*FJM_RdCCe4F`(hw8%3Ulp{W?_AT7*zFiP;udfMYzXaVl$PkL(RLXTo%c)A|c=A-qzyb?P8KBrUL zz7jk&?+^aJpMUi=U{{z<{)Rq4MG}0-hRx+L&0zGco^w1Yt;TY79A4d$0dA~Yhmc;N zS#@d}{{$+BVhQNO%_R6N4#ix(IM&Fn@?j;&(NHQr+ literal 0 HcmV?d00001 diff --git a/test/task-path-track/yellow_track_demo.py b/test/task-path-track/yellow_track_demo.py index fd99705..278cfc0 100644 --- a/test/task-path-track/yellow_track_demo.py +++ b/test/task-path-track/yellow_track_demo.py @@ -44,7 +44,7 @@ def process_image(image_path, save_dir=None, show_steps=False): def main(): parser = argparse.ArgumentParser(description='黄色赛道检测演示程序') - parser.add_argument('--input', type=str, default='res/path/image_20250514_024313.png', help='输入图像或视频的路径') + parser.add_argument('--input', type=str, default='res/path/test-1.jpg', help='输入图像或视频的路径') parser.add_argument('--output', type=str, default='res/path/test/result_image_20250514_024313.png', help='输出结果的保存路径') parser.add_argument('--type', type=str, choices=['image', 'video'], help='输入类型，不指定会自动检测') parser.add_argument('--show', default=True, action='store_true', help='显示处理步骤') diff --git a/utils/detect_track.py b/utils/detect_track.py index 360bb6b..2a0386d 100644 --- a/utils/detect_track.py +++ b/utils/detect_track.py @@ -8,6 +8,7 @@ from utils.log_helper import get_logger, debug, info, warning, error, success def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True): """ 检测正前方横向黄色赛道的边缘，并返回y值最大的边缘点 + 优先检测下方横线，但在遇到下方线截断的情况时会考虑上边缘 参数: image: 输入图像，可以是文件路径或者已加载的图像数组 @@ -18,7 +19,7 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True edge_point: 赛道前方边缘点的坐标 (x, y) edge_info: 边缘信息字典 """ - observe = False # TSET + # observe = False # TSET # 如果输入是字符串（文件路径），则加载图像 if isinstance(image, str): img = cv2.imread(image) @@ -91,142 +92,240 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True bottom_row = np.max(col_points) bottom_points.append((left_bound + col, top_bound + bottom_row)) - if len(bottom_points) < 3: - # 如果找不到足够的底部点，使用canny+霍夫变换 - edges = cv2.Canny(mask, 50, 150, apertureSize=3) - + # 寻找每列的最顶部点（上边缘点） + top_points = [] + for col in non_zero_cols: + col_points = np.where(search_mask[:, col] > 0)[0] + if len(col_points) > 0: + top_row = np.min(col_points) + top_points.append((left_bound + col, top_bound + top_row)) + + if observe: + debug("检测底部和顶部边缘点", "处理") + edge_points_img = img.copy() + for point in bottom_points: + cv2.circle(edge_points_img, point, 3, (0, 255, 0), -1) + for point in top_points: + cv2.circle(edge_points_img, point, 3, (255, 0, 255), -1) + cv2.imshow("边缘点", edge_points_img) + cv2.waitKey(delay) + + # 边缘检测 + edges = cv2.Canny(mask, 50, 150, apertureSize=3) + + if observe: + debug("步骤4: 边缘检测", "处理") + cv2.imshow("边缘检测", edges) + cv2.waitKey(delay) + + # 使用霍夫变换检测直线 + lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, + minLineLength=width*0.1, maxLineGap=30) + + if lines is None or len(lines) == 0: if observe: - debug("步骤3.1: 边缘检测", "处理") - cv2.imshow("边缘检测", edges) - cv2.waitKey(delay) - - # 使用霍夫变换检测直线 - 调低阈值以检测短线段 - lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, - minLineLength=width*0.1, maxLineGap=30) - - if lines is None or len(lines) == 0: - if observe: - error("未检测到直线", "失败") - return None, None - - if observe: - debug(f"步骤4: 检测到 {len(lines)} 条直线", "处理") - lines_img = img.copy() - for line in lines: - x1, y1, x2, y2 = line[0] - cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2) - cv2.imshow("检测到的直线", lines_img) - cv2.waitKey(delay) - - # 筛选水平线，但放宽斜率条件 - horizontal_lines = [] + error("未检测到直线", "失败") + return None, None + + if observe: + debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理") + lines_img = img.copy() for line in lines: x1, y1, x2, y2 = line[0] - - # 计算斜率 (避免除零错误) - if abs(x2 - x1) < 5: # 几乎垂直的线 - continue - - slope = (y2 - y1) / (x2 - x1) - - # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度 - if abs(slope) < 0.3: - # 确保线在搜索区域内 - if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or - (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)): - # 计算线的中点y坐标 - mid_y = (y1 + y2) / 2 - line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) - # 保存线段、其y坐标和长度 - horizontal_lines.append((line[0], mid_y, slope, line_length)) + cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2) + cv2.imshow("检测到的直线", lines_img) + cv2.waitKey(delay) + + # 筛选水平线，但放宽斜率条件 + horizontal_lines = [] + for line in lines: + x1, y1, x2, y2 = line[0] - if not horizontal_lines: - if observe: - error("未检测到水平线", "失败") - return None, None + # 计算斜率 (避免除零错误) + if abs(x2 - x1) < 5: # 几乎垂直的线 + continue + + slope = (y2 - y1) / (x2 - x1) + # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度 + if abs(slope) < 0.3: + # 确保线在搜索区域内 + if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or + (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)): + # 计算线的中点y坐标 + mid_y = (y1 + y2) / 2 + line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) + # 保存线段、其y坐标和长度 + horizontal_lines.append((line[0], mid_y, slope, line_length)) + + if not horizontal_lines: if observe: - debug(f"步骤4.1: 找到 {len(horizontal_lines)} 条水平线", "处理") - h_lines_img = img.copy() - for line_info in horizontal_lines: - line, _, slope, _ = line_info - x1, y1, x2, y2 = line - cv2.line(h_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2) - # 显示斜率 - cv2.putText(h_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), - cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) - cv2.imshow("水平线", h_lines_img) - cv2.waitKey(delay) - + error("未检测到水平线", "失败") + return None, None + + if observe: + debug(f"步骤6: 找到 {len(horizontal_lines)} 条水平线", "处理") + h_lines_img = img.copy() + for line_info in horizontal_lines: + line, _, slope, _ = line_info + x1, y1, x2, y2 = line + cv2.line(h_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2) + # 显示斜率 + cv2.putText(h_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) + cv2.imshow("水平线", h_lines_img) + cv2.waitKey(delay) + + # 将水平线分为上边缘线和下边缘线（按y坐标排序） + bottom_line = None + top_line = None + is_truncated = False # 标记下方线是否被截断 + + if len(horizontal_lines) > 1: # 按y坐标排序 (从大到小，底部的线排在前面) horizontal_lines.sort(key=lambda x: x[1], reverse=True) - # 取最靠近底部且足够长的线作为横向赛道线 - selected_line = None - selected_slope = 0 - for line_info in horizontal_lines: - line, _, slope, length = line_info - if length > width * 0.1: # 确保线足够长 - selected_line = line - selected_slope = slope - break + # 提取最底部和次底部的线段 + bottom_line = horizontal_lines[0] - if selected_line is None and horizontal_lines: - # 如果没有足够长的线，就取最靠近底部的线 - selected_line = horizontal_lines[0][0] - selected_slope = horizontal_lines[0][2] + # 检查是否有明显的上下边缘 + y_coords = [line[1] for line in horizontal_lines] + y_diffs = [y_coords[i] - y_coords[i+1] for i in range(len(y_coords)-1)] - if selected_line is None: - if observe: - error("无法选择合适的线段", "失败") - return None, None + if len(y_diffs) > 0 and max(y_diffs) > height * 0.05: # 如果有明显的高度差 + # 找到高度差最大的位置 + split_idx = y_diffs.index(max(y_diffs)) - x1, y1, x2, y2 = selected_line + # 分别获取下边缘线和上边缘线 + bottom_line = horizontal_lines[0] # 最底部的线 + top_line = horizontal_lines[split_idx+1] # 上边缘线 + + # 检查上下两条线是否平行 - 计算斜率差异 + bottom_slope = bottom_line[2] + top_slope = top_line[2] + slope_diff = abs(bottom_slope - top_slope) + + # 计算两线的交点（如果存在） + bottom_x1, bottom_y1, bottom_x2, bottom_y2 = bottom_line[0] + top_x1, top_y1, top_x2, top_y2 = top_line[0] + + # 根据斜率差异判断是否平行 + # 如果斜率差异很小，认为基本平行 + if slope_diff > 0.05: # 斜率差异超过阈值 + # 计算两条线延长后的交点 + # 线段方程: y = mx + b + # 计算两条线的截距b + bottom_b = bottom_y1 - bottom_slope * bottom_x1 + top_b = top_y1 - top_slope * top_x1 + + # 检查交点是否在图像范围内或者附近 + # 求解 y = m1*x + b1 = m2*x + b2 + if abs(bottom_slope - top_slope) > 1e-6: # 避免除以接近0的值 + intersection_x = (top_b - bottom_b) / (bottom_slope - top_slope) + intersection_y = bottom_slope * intersection_x + bottom_b + + # 判断交点是否在图像宽度的2倍范围内 + if -width <= intersection_x <= width * 2: + is_truncated = True + if observe: + debug(f"检测到上下边缘线不平行，交点: ({intersection_x:.1f}, {intersection_y:.1f})", "分析") + debug("判断下方线被截断", "分析") + + # 显示上下边缘线及其延长线和交点 + intersect_img = img.copy() + # 画原始线段 + cv2.line(intersect_img, (bottom_x1, bottom_y1), (bottom_x2, bottom_y2), (0, 255, 0), 2) + cv2.line(intersect_img, (top_x1, top_y1), (top_x2, top_y2), (255, 0, 255), 2) + + # 延长线段以显示交点 + ext_left_x = max(0, int(intersection_x - width/2)) + ext_right_x = min(width-1, int(intersection_x + width/2)) + + # 计算延长线上的点 + bottom_ext_left_y = int(bottom_slope * ext_left_x + bottom_b) + bottom_ext_right_y = int(bottom_slope * ext_right_x + bottom_b) + top_ext_left_y = int(top_slope * ext_left_x + top_b) + top_ext_right_y = int(top_slope * ext_right_x + top_b) + + # 绘制延长线 + cv2.line(intersect_img, (ext_left_x, bottom_ext_left_y), + (ext_right_x, bottom_ext_right_y), (0, 255, 0), 1, cv2.LINE_DASHED) + cv2.line(intersect_img, (ext_left_x, top_ext_left_y), + (ext_right_x, top_ext_right_y), (255, 0, 255), 1, cv2.LINE_DASHED) + + # 标记交点 + if 0 <= intersection_x < width and 0 <= intersection_y < height: + cv2.circle(intersect_img, (int(intersection_x), int(intersection_y)), + 10, (0, 0, 255), -1) + + cv2.imshow("上下边缘线交点分析", intersect_img) + cv2.waitKey(delay) + + # 如果检测到下方线被截断，使用上边缘线来估计实际的下边缘线 + if is_truncated and top_line is not None: + if observe: + debug("使用上边缘估计真实的下边缘", "处理") + + # 获取赛道平均宽度（可以是预先测量的固定值，或根据未截断部分测量） + # 这里假设赛道宽度是固定的，可以根据实际情况调整 + track_width_pixels = height * 0.15 # 假设赛道宽度是图像高度的15% + + # 计算一个修正后的底部线段，方向与上边缘线平行，但位置下移 + corrected_bottom_slope = top_slope # 使用上边缘的斜率 + + # 计算上边缘线的方程: y = mx + b + top_b = top_y1 - top_slope * top_x1 + + # 计算修正后的下边缘线的截距，使其下移track_width_pixels距离 + # 由于是在图像坐标系，y轴向下，所以是加法 + corrected_bottom_b = top_b + track_width_pixels + + # 计算修正后的下边缘线的两个端点 + corrected_bottom_x1 = left_bound + corrected_bottom_y1 = int(corrected_bottom_slope * corrected_bottom_x1 + corrected_bottom_b) + corrected_bottom_x2 = right_bound + corrected_bottom_y2 = int(corrected_bottom_slope * corrected_bottom_x2 + corrected_bottom_b) + + # 创建修正后的底部线段 + corrected_bottom_line = ( + [corrected_bottom_x1, corrected_bottom_y1, corrected_bottom_x2, corrected_bottom_y2], + (corrected_bottom_y1 + corrected_bottom_y2) / 2, # mid_y + corrected_bottom_slope, + np.sqrt((corrected_bottom_x2-corrected_bottom_x1)**2 + (corrected_bottom_y2-corrected_bottom_y1)**2) # length + ) + + if observe: + # 显示修正后的线段 + corrected_img = img.copy() + # 原始线段 + cv2.line(corrected_img, (bottom_x1, bottom_y1), (bottom_x2, bottom_y2), (0, 255, 0), 2) + cv2.line(corrected_img, (top_x1, top_y1), (top_x2, top_y2), (255, 0, 255), 2) + # 修正后的线段 + cv2.line(corrected_img, (corrected_bottom_x1, corrected_bottom_y1), + (corrected_bottom_x2, corrected_bottom_y2), (0, 0, 255), 2) + + cv2.putText(corrected_img, "截断的底边", (bottom_x1, bottom_y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) + cv2.putText(corrected_img, "上边缘", (top_x1, top_y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) + cv2.putText(corrected_img, "修正后的底边", (corrected_bottom_x1, corrected_bottom_y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) + + cv2.imshow("修正后的边缘线", corrected_img) + cv2.waitKey(delay) + + # 使用修正后的底部线作为选定的线 + bottom_line = corrected_bottom_line else: - # 使用底部点拟合直线 - if observe: - debug("正在处理底部边缘点", "处理") - bottom_points_img = img.copy() - for point in bottom_points: - cv2.circle(bottom_points_img, point, 3, (0, 255, 0), -1) - cv2.imshow("底部边缘点", bottom_points_img) - cv2.waitKey(delay) - - # 使用RANSAC拟合直线以去除异常值 - x_points = np.array([p[0] for p in bottom_points]).reshape(-1, 1) - y_points = np.array([p[1] for p in bottom_points]) - - # 如果点过少或分布不够宽，返回None - if len(bottom_points) < 3 or np.max(x_points) - np.min(x_points) < width * 0.1: - if observe: - warning("底部点太少或分布不够宽", "警告") - return None, None - - ransac = linear_model.RANSACRegressor(residual_threshold=5.0) - ransac.fit(x_points, y_points) - - # 获取拟合参数 - selected_slope = ransac.estimator_.coef_[0] - intercept = ransac.estimator_.intercept_ - - # 检查斜率是否在合理范围内 - if abs(selected_slope) > 0.3: - if observe: - warning(f"拟合斜率过大: {selected_slope:.4f}", "警告") - return None, None - - # 使用拟合的直线参数计算线段端点 - x1 = left_bound - y1 = int(selected_slope * x1 + intercept) - x2 = right_bound - y2 = int(selected_slope * x2 + intercept) - - if observe: - debug("显示拟合线段", "处理") - fitted_line_img = img.copy() - cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2) - cv2.imshow("拟合线段", fitted_line_img) - cv2.waitKey(delay) + # 只有一条水平线 + bottom_line = horizontal_lines[0] + + # 使用底部线段作为最终选择 + selected_line = bottom_line[0] + selected_slope = bottom_line[2] + + # 提取线段端点 + x1, y1, x2, y2 = selected_line # 确保x1 < x2 if x1 > x2: @@ -248,7 +347,7 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True selected_points.append((x, y)) if observe: - debug(f"步骤5: 找到边缘点 {bottom_edge_point}", "检测") + debug(f"步骤7: 找到边缘点 {bottom_edge_point}", "检测") edge_img = img.copy() # 画线 cv2.line(edge_img, (x1, y1), (x2, y2), (0, 255, 0), 2) @@ -297,6 +396,9 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(slope_img, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + if is_truncated: + cv2.putText(slope_img, "下方线被截断(已修正)", (10, 190), + cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) if observe: debug("显示边缘斜率和中线交点", "显示") @@ -321,7 +423,8 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True "distance_to_center": distance_to_center, "slope": selected_slope, "distance_to_bottom": distance_to_bottom, - "intersection_point": intersection_point + "intersection_point": intersection_point, + "is_truncated": is_truncated } info(f"横向边缘检测结果: {log_info}", "日志") @@ -335,6 +438,7 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True "points_count": len(selected_points), # 该组中点的数量 "intersection_point": intersection_point, # 中线与横向线的交点 "distance_to_bottom": distance_to_bottom, # 交点到图像底部的距离 + "is_truncated": is_truncated, # 下方线是否被截断并修正 # "points": selected_points # 添加选定的点组 }