🎨 更新图像处理逻辑，优化边缘检测功能
- 修改 yellow_track_demo.py 中的输入和输出路径，确保使用最新的图像文件 - 在 detect_track.py 中改进掩码处理，添加形态学操作以提升掩码质量 - 优化轮廓检测逻辑，增加边缘检测和直线拟合的步骤，确保检测到的线段更准确 - 更新了相关的观察输出信息，便于调试和分析
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@ -44,8 +44,8 @@ def process_image(image_path, save_dir=None, show_steps=False):
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def main():
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parser = argparse.ArgumentParser(description='黄色赛道检测演示程序')
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parser.add_argument('--input', type=str, default='res/path/image_20250513_162556.png', help='输入图像或视频的路径')
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parser.add_argument('--output', type=str, default='res/path/test/result_image_20250513_162556.png', help='输出结果的保存路径')
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parser.add_argument('--input', type=str, default='res/path/image_20250514_024313.png', help='输入图像或视频的路径')
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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')
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parser.add_argument('--type', type=str, choices=['image', 'video'], help='输入类型，不指定会自动检测')
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parser.add_argument('--show', default=True, action='store_true', help='显示处理步骤')
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@ -1,7 +1,5 @@
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import cv2
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import numpy as np
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from sklearn.cluster import KMeans
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from sklearn.metrics import silhouette_score
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from sklearn import linear_model
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def detect_horizontal_track_edge(image, observe=False, delay=1000):
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@ -58,21 +56,15 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
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# 创建黄色的掩码
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 添加形态学操作以改善掩码
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kernel = np.ones((3, 3), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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if observe:
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print("步骤2: 创建黄色掩码")
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cv2.imshow("黄色掩码", mask)
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cv2.waitKey(delay)
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# 使用形态学操作改善掩码质量
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kernel = np.ones((5, 5), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # 闭操作填充小空洞
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # 开操作移除小噪点
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if observe:
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print("步骤2.1: 形态学处理后的掩码")
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cv2.imshow("处理后的掩码", mask)
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cv2.waitKey(delay)
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# 应用掩码，只保留黄色部分
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yellow_only = cv2.bitwise_and(img, img, mask=mask)
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@ -81,190 +73,181 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
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cv2.imshow("只保留黄色", yellow_only)
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cv2.waitKey(delay)
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# 查找轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 裁剪掩码到搜索区域
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search_mask = mask[top_bound:bottom_bound, left_bound:right_bound]
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# 找到掩码在搜索区域中最底部的非零点位置
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bottom_points = []
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non_zero_cols = np.where(np.any(search_mask, axis=0))[0]
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# 寻找每列的最底部点
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for col in non_zero_cols:
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col_points = np.where(search_mask[:, col] > 0)[0]
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if len(col_points) > 0:
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bottom_row = np.max(col_points)
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bottom_points.append((left_bound + col, top_bound + bottom_row))
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if len(bottom_points) < 3:
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# 如果找不到足够的底部点，使用canny+霍夫变换
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edges = cv2.Canny(mask, 50, 150, apertureSize=3)
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# 如果没有找到轮廓，返回None
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if not contours:
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if observe:
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print("未找到轮廓")
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return None, None
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print("步骤3.1: 边缘检测")
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cv2.imshow("边缘检测", edges)
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cv2.waitKey(delay)
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if observe:
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print(f"步骤4: 找到 {len(contours)} 个轮廓")
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contour_img = img.copy()
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cv2.drawContours(contour_img, contours, -1, (0, 255, 0), 2)
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cv2.imshow("所有轮廓", contour_img)
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cv2.waitKey(delay)
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# 使用霍夫变换检测直线 - 调低阈值以检测短线段
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lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30,
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minLineLength=width*0.1, maxLineGap=30)
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# 筛选可能属于横向赛道的轮廓
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horizontal_contours = []
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if lines is None or len(lines) == 0:
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if observe:
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print("未检测到直线")
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return None, None
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for contour in contours:
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# 计算轮廓的边界框
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x, y, w, h = cv2.boundingRect(contour)
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# 计算轮廓的宽高比
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aspect_ratio = float(w) / max(h, 1)
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# 在搜索区域内且宽高比大于1（更宽而非更高）的轮廓更可能是横向线段
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if (left_bound <= x + w // 2 <= right_bound and
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top_bound <= y + h // 2 <= bottom_bound and
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aspect_ratio > 1.0):
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horizontal_contours.append(contour)
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if not horizontal_contours:
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if observe:
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print("未找到符合条件的横向轮廓")
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print(f"步骤4: 检测到 {len(lines)} 条直线")
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lines_img = img.copy()
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for line in lines:
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x1, y1, x2, y2 = line[0]
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cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.imshow("检测到的直线", lines_img)
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cv2.waitKey(delay)
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# 如果没有找到符合条件的横向轮廓，尝试使用所有在搜索区域内的轮廓
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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if (left_bound <= x + w // 2 <= right_bound and
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top_bound <= y + h // 2 <= bottom_bound):
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horizontal_contours.append(contour)
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# 筛选水平线，但放宽斜率条件
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horizontal_lines = []
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for line in lines:
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x1, y1, x2, y2 = line[0]
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# 计算斜率 (避免除零错误)
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if abs(x2 - x1) < 5: # 几乎垂直的线
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continue
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slope = (y2 - y1) / (x2 - x1)
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# 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度
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if abs(slope) < 0.3:
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# 确保线在搜索区域内
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if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or
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(left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)):
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# 计算线的中点y坐标
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mid_y = (y1 + y2) / 2
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line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
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# 保存线段、其y坐标和长度
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horizontal_lines.append((line[0], mid_y, slope, line_length))
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if not horizontal_lines:
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if observe:
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print("未检测到水平线")
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return None, None
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if not horizontal_contours:
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if observe:
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print("在搜索区域内未找到任何轮廓")
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return None, None
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print(f"步骤4.1: 找到 {len(horizontal_lines)} 条水平线")
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h_lines_img = img.copy()
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for line_info in horizontal_lines:
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line, _, slope, _ = line_info
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x1, y1, x2, y2 = line
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cv2.line(h_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
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# 显示斜率
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cv2.putText(h_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
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cv2.imshow("水平线", h_lines_img)
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cv2.waitKey(delay)
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if observe:
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print(f"步骤4.1: 找到 {len(horizontal_contours)} 个可能的横向轮廓")
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horizontal_img = img.copy()
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cv2.drawContours(horizontal_img, horizontal_contours, -1, (0, 255, 0), 2)
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cv2.imshow("横向轮廓", horizontal_img)
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cv2.waitKey(delay)
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# 按y坐标排序 (从大到小，底部的线排在前面)
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horizontal_lines.sort(key=lambda x: x[1], reverse=True)
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# 收集所有可能的横向轮廓点
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all_horizontal_points = []
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for contour in horizontal_contours:
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for point in contour:
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x, y = point[0]
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if (left_bound <= x <= right_bound and
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top_bound <= y <= bottom_bound):
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all_horizontal_points.append((x, y))
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# 取最靠近底部且足够长的线作为横向赛道线
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selected_line = None
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selected_slope = 0
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for line_info in horizontal_lines:
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line, _, slope, length = line_info
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if length > width * 0.1: # 确保线足够长
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selected_line = line
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selected_slope = slope
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break
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if not all_horizontal_points:
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if observe:
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print("在搜索区域内未找到有效点")
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return None, None
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if selected_line is None and horizontal_lines:
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# 如果没有足够长的线，就取最靠近底部的线
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selected_line = horizontal_lines[0][0]
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selected_slope = horizontal_lines[0][2]
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# 按y值对点进行分组（针对不同的水平线段）
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# 使用聚类方法将点按y值分组
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y_values = np.array([p[1] for p in all_horizontal_points])
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y_values = y_values.reshape(-1, 1) # 转换为列向量
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if selected_line is None:
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if observe:
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print("无法选择合适的线段")
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return None, None
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# 如果点较少，直接按y值简单分组
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if len(y_values) < 10:
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# 简单分组：通过y值差异判断是否属于同一水平线
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y_groups = []
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current_group = [all_horizontal_points[0]]
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current_y = all_horizontal_points[0][1]
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for i in range(1, len(all_horizontal_points)):
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point = all_horizontal_points[i]
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if abs(point[1] - current_y) < 10: # 如果y值接近当前组的y值
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current_group.append(point)
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else:
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y_groups.append(current_group)
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current_group = [point]
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current_y = point[1]
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if current_group:
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y_groups.append(current_group)
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x1, y1, x2, y2 = selected_line
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else:
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# 使用K-means聚类按y值将点分为不同组
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max_clusters = min(5, len(y_values) // 2) # 最多5个聚类或点数的一半
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# 尝试不同数量的聚类，找到最佳分组
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best_score = -1
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best_labels = None
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for n_clusters in range(1, max_clusters + 1):
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kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=0).fit(y_values)
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score = silhouette_score(y_values, kmeans.labels_) if n_clusters > 1 else 0
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if score > best_score:
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best_score = score
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best_labels = kmeans.labels_
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# 根据聚类结果分组
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y_groups = [[] for _ in range(max(best_labels) + 1)]
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for i, point in enumerate(all_horizontal_points):
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group_idx = best_labels[i]
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y_groups[group_idx].append(point)
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if observe:
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clusters_img = img.copy()
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colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0), (255, 255, 0), (0, 255, 255)]
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for i, group in enumerate(y_groups):
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color = colors[i % len(colors)]
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for point in group:
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cv2.circle(clusters_img, point, 3, color, -1)
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cv2.imshow("按Y值分组的点", clusters_img)
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cv2.waitKey(delay)
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# 为每个组计算平均y值
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avg_y_values = []
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for group in y_groups:
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avg_y = sum(p[1] for p in group) / len(group)
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avg_y_values.append((avg_y, group))
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# 按平均y值降序排序（越大的y值越靠近底部，也就是越靠近相机）
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avg_y_values.sort(reverse=True)
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# 从y值最大的组开始分析，找到符合横向赛道特征的组
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selected_group = None
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selected_slope = 0
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for avg_y, group in avg_y_values:
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# 计算该组点的斜率
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if len(group) < 2:
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continue
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x_coords = np.array([p[0] for p in group])
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y_coords = np.array([p[1] for p in group])
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if np.std(x_coords) <= 0:
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continue
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slope, _ = np.polyfit(x_coords, y_coords, 1)
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# 判断该组是否可能是横向赛道
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# 横向赛道的斜率应该比较小（接近水平）
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if abs(slope) < 0.5: # 允许一定的倾斜
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selected_group = group
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selected_slope = slope
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break
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# 如果没有找到符合条件的组，使用y值最大的组
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if selected_group is None and avg_y_values:
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selected_group = avg_y_values[0][1]
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# 重新计算斜率
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if len(selected_group) >= 2:
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x_coords = np.array([p[0] for p in selected_group])
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y_coords = np.array([p[1] for p in selected_group])
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if np.std(x_coords) > 0:
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selected_slope, _ = np.polyfit(x_coords, y_coords, 1)
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if selected_group is None:
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# 使用底部点拟合直线
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if observe:
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print("未能找到有效的横向赛道线")
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return None, None
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bottom_points_img = img.copy()
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for point in bottom_points:
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cv2.circle(bottom_points_img, point, 3, (0, 255, 0), -1)
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cv2.imshow("底部边缘点", bottom_points_img)
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cv2.waitKey(delay)
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# 找出选定组中y值最大的点（最靠近相机的点）
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bottom_edge_point = max(selected_group, key=lambda p: p[1])
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# 使用RANSAC拟合直线以去除异常值
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x_points = np.array([p[0] for p in bottom_points]).reshape(-1, 1)
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y_points = np.array([p[1] for p in bottom_points])
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# 如果点过少或分布不够宽，返回None
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if len(bottom_points) < 3 or np.max(x_points) - np.min(x_points) < width * 0.1:
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if observe:
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print("底部点太少或分布不够宽")
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return None, None
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ransac = linear_model.RANSACRegressor(residual_threshold=5.0)
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ransac.fit(x_points, y_points)
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# 获取拟合参数
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selected_slope = ransac.estimator_.coef_[0]
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intercept = ransac.estimator_.intercept_
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# 检查斜率是否在合理范围内
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if abs(selected_slope) > 0.3:
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if observe:
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print(f"拟合斜率过大: {selected_slope:.4f}")
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return None, None
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# 使用拟合的直线参数计算线段端点
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x1 = left_bound
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y1 = int(selected_slope * x1 + intercept)
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x2 = right_bound
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y2 = int(selected_slope * x2 + intercept)
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if observe:
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fitted_line_img = img.copy()
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cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
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cv2.imshow("拟合线段", fitted_line_img)
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cv2.waitKey(delay)
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# 确保x1 < x2
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if x1 > x2:
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x1, x2 = x2, x1
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y1, y2 = y2, y1
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# 找到线上y值最大的点作为边缘点(最靠近相机的点)
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if y1 > y2:
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bottom_edge_point = (x1, y1)
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else:
|
||||
bottom_edge_point = (x2, y2)
|
||||
|
||||
# 获取线上的更多点
|
||||
selected_points = []
|
||||
step = 5 # 每5个像素取一个点
|
||||
for x in range(max(left_bound, int(min(x1, x2))), min(right_bound, int(max(x1, x2)) + 1), step):
|
||||
y = int(selected_slope * (x - x1) + y1)
|
||||
if top_bound <= y <= bottom_bound:
|
||||
selected_points.append((x, y))
|
||||
|
||||
if observe:
|
||||
print(f"步骤5: 找到边缘点 {bottom_edge_point}")
|
||||
edge_img = img.copy()
|
||||
# 绘制选定的组
|
||||
for point in selected_group:
|
||||
# 画线
|
||||
cv2.line(edge_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
# 绘制所有点
|
||||
for point in selected_points:
|
||||
cv2.circle(edge_img, point, 3, (255, 0, 0), -1)
|
||||
# 标记边缘点
|
||||
cv2.circle(edge_img, bottom_edge_point, 10, (0, 0, 255), -1)
|
||||
@ -274,119 +257,12 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
|
||||
# 计算这个点到中线的距离
|
||||
distance_to_center = bottom_edge_point[0] - center_x
|
||||
|
||||
# 改进斜率计算，使用BFS找到同一条边缘线上的更多点
|
||||
def get_better_slope(start_point, points, max_distance=20):
|
||||
"""使用BFS算法寻找同一条边缘线上的点，并计算更准确的斜率"""
|
||||
queue = [start_point]
|
||||
visited = {start_point}
|
||||
line_points = [start_point]
|
||||
|
||||
# BFS搜索相连的点
|
||||
while queue and len(line_points) < 200: # 增加最大点数
|
||||
current = queue.pop(0)
|
||||
cx, cy = current
|
||||
|
||||
# 对所有未访问点计算距离
|
||||
for point in points:
|
||||
if point in visited:
|
||||
continue
|
||||
|
||||
px, py = point
|
||||
# 计算欧氏距离
|
||||
dist = np.sqrt((px - cx) ** 2 + (py - cy) ** 2)
|
||||
|
||||
# 如果距离在阈值内，认为是同一条线上的点
|
||||
# 降低距离阈值，使连接更精确
|
||||
if dist < max_distance:
|
||||
queue.append(point)
|
||||
visited.add(point)
|
||||
line_points.append(point)
|
||||
|
||||
# 如果找到足够多的点，计算斜率
|
||||
if len(line_points) >= 5: # 至少需要更多点来拟合
|
||||
x_coords = np.array([p[0] for p in line_points])
|
||||
y_coords = np.array([p[1] for p in line_points])
|
||||
|
||||
# 使用RANSAC算法拟合直线，更加鲁棒
|
||||
# 尝试使用RANSAC进行更鲁棒的拟合
|
||||
try:
|
||||
# 创建RANSAC对象
|
||||
ransac = linear_model.RANSACRegressor()
|
||||
X = x_coords.reshape(-1, 1)
|
||||
|
||||
# 拟合模型
|
||||
ransac.fit(X, y_coords)
|
||||
new_slope = ransac.estimator_.coef_[0]
|
||||
|
||||
# 获取内点（符合模型的点）
|
||||
inlier_mask = ransac.inlier_mask_
|
||||
inlier_points = [line_points[i] for i in range(len(line_points)) if inlier_mask[i]]
|
||||
|
||||
# 至少需要3个内点
|
||||
if len(inlier_points) >= 3:
|
||||
return new_slope, inlier_points
|
||||
except:
|
||||
# 如果RANSAC失败，回退到普通拟合
|
||||
pass
|
||||
|
||||
# 标准拟合方法作为后备
|
||||
if np.std(x_coords) > 0:
|
||||
new_slope, _ = np.polyfit(x_coords, y_coords, 1)
|
||||
return new_slope, line_points
|
||||
|
||||
return selected_slope, line_points
|
||||
|
||||
# 尝试获取更准确的斜率
|
||||
improved_slope, better_line_points = get_better_slope(bottom_edge_point, selected_group)
|
||||
|
||||
# 使用改进后的斜率
|
||||
slope = improved_slope
|
||||
|
||||
if observe:
|
||||
improved_slope_img = img.copy()
|
||||
# 画出底部边缘点
|
||||
cv2.circle(improved_slope_img, bottom_edge_point, 10, (0, 0, 255), -1)
|
||||
# 画出改进后找到的所有点
|
||||
for point in better_line_points:
|
||||
cv2.circle(improved_slope_img, point, 3, (255, 255, 0), -1)
|
||||
# 使用改进后的斜率画线
|
||||
line_length = 300
|
||||
|
||||
# 确保线条经过边缘点
|
||||
mid_x = bottom_edge_point[0]
|
||||
mid_y = bottom_edge_point[1]
|
||||
|
||||
# 计算线条起点和终点
|
||||
end_x = mid_x + line_length
|
||||
end_y = int(mid_y + improved_slope * line_length)
|
||||
start_x = mid_x - line_length
|
||||
start_y = int(mid_y - improved_slope * line_length)
|
||||
|
||||
# 绘制线条
|
||||
cv2.line(improved_slope_img, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
|
||||
|
||||
# 添加文本显示信息
|
||||
cv2.putText(improved_slope_img, f"原始斜率: {selected_slope:.4f}", (10, 150),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
|
||||
cv2.putText(improved_slope_img, f"改进斜率: {improved_slope:.4f}", (10, 190),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
|
||||
cv2.putText(improved_slope_img, f"找到点数: {len(better_line_points)}", (10, 230),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
|
||||
|
||||
# 显示所有原始点和改进算法选择的点之间的比较
|
||||
cv2.putText(improved_slope_img, f"原始点数: {len(selected_group)}", (10, 270),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
|
||||
|
||||
cv2.imshow("改进的斜率计算", improved_slope_img)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 计算中线与检测到的横向线的交点
|
||||
# 横向线方程: y = slope * (x - edge_x) + edge_y
|
||||
# 横向线方程: y = slope * (x - x1) + y1
|
||||
# 中线方程: x = center_x
|
||||
# 解这个方程组得到交点坐标
|
||||
edge_x, edge_y = bottom_edge_point
|
||||
intersection_x = center_x
|
||||
intersection_y = slope * (center_x - edge_x) + edge_y
|
||||
intersection_y = selected_slope * (center_x - x1) + y1
|
||||
intersection_point = (int(intersection_x), int(intersection_y))
|
||||
|
||||
# 计算交点到图像底部的距离（以像素为单位）
|
||||
@ -394,30 +270,19 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
|
||||
|
||||
if observe:
|
||||
slope_img = img.copy()
|
||||
# 画出底部边缘点
|
||||
# 画出检测到的线
|
||||
cv2.line(slope_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
# 标记边缘点
|
||||
cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1)
|
||||
# 画出选定组中的所有点
|
||||
for point in selected_group:
|
||||
cv2.circle(slope_img, point, 3, (255, 0, 0), -1)
|
||||
# 使用斜率画一条线来表示边缘方向
|
||||
line_length = 200
|
||||
end_x = bottom_edge_point[0] + line_length
|
||||
end_y = int(bottom_edge_point[1] + slope * line_length)
|
||||
start_x = bottom_edge_point[0] - line_length
|
||||
start_y = int(bottom_edge_point[1] - slope * line_length)
|
||||
cv2.line(slope_img, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
|
||||
|
||||
# 画出中线
|
||||
cv2.line(slope_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)
|
||||
|
||||
# 标记中线与横向线的交点 (高亮显示)
|
||||
# 标记中线与横向线的交点
|
||||
cv2.circle(slope_img, intersection_point, 12, (255, 0, 255), -1)
|
||||
cv2.circle(slope_img, intersection_point, 5, (255, 255, 255), -1)
|
||||
|
||||
# 画出交点到底部的距离线
|
||||
cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2)
|
||||
|
||||
cv2.putText(slope_img, f"Slope: {slope:.4f}", (10, 30),
|
||||
cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
cv2.putText(slope_img, f"Distance to center: {distance_to_center}px", (10, 70),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
@ -434,12 +299,12 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
|
||||
"x": bottom_edge_point[0],
|
||||
"y": bottom_edge_point[1],
|
||||
"distance_to_center": distance_to_center,
|
||||
"slope": slope,
|
||||
"is_horizontal": abs(slope) < 0.05, # 判断边缘是否接近水平
|
||||
"points_count": len(selected_group), # 该组中点的数量
|
||||
"slope": selected_slope,
|
||||
"is_horizontal": abs(selected_slope) < 0.05, # 判断边缘是否接近水平
|
||||
"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
|
||||
"points": selected_group # 添加选定的点组
|
||||
"points": selected_points # 添加选定的点组
|
||||
}
|
||||
|
||||
return bottom_edge_point, edge_info
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user